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Decoding the neurological and genetic underpinnings of chronic pain

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Decoding the neurological and genetic underpinnings of chronic pain

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Content Decoding the Neurological and Genetic Underpinnings of Chronic Pain
by
Ravi R. Bhatt
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(NEUROSCIENCE)
December 2024
Copyright 2025 Ravi R. Bhatt

Dedication
To my family and loved ones.
ii

Acknowledgements
First and foremost, I would like to thank my mentor over the last four years, Dr. Neda Jahanshad. I would
not have reached this point without your unwavering support and the countless opportunities you provided,
including the chance to learn an entirely new field of research. Your guidance, expertise, and encouragement
have been instrumental in shaping my academic journey. Thank you for believing in my potential and for
fostering an environment that allowed me to thrive. I look forward to our future research endeavors. I
would also like to thank my past and current mentors, Drs. Paul M. Thompson, Lonnie K. Zeltzer, Paul M.
Zeltzer, Emeran A. Mayer, Arpana Church, Julian Koenig, DeWayne P. Williams, and Julian F. Thayer for all
playing a key role in shaping my research career from diverse perspectives and who I am as a person today.
I would also like to express my heartfelt gratitude to my family for their unwavering love and support
throughout my career. Your encouragement and belief in me have been the foundation of my success, and I
would not be where I am today without you. I would also like to thank my lab-mates Alyssa, Elizabeth,
Shayan, Iyad, Shruti, and Ankush for all of your camaraderie and support. Lastly, I extend my deepest
gratitude to Dr. Cynthia L. Boyle. Over the years, you have offered me a fresh perspective on both science
and the world at large. Your wisdom and guidance have inspired me to approach my research with rigorous
critical thinking and open-mindedness, while also teaching me valuable skills for effectively interacting
and collaborating with others. The principles you taught me will remain with me throughout my life.
Love,
Ravi
iii

Table of Contents
Dedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv
Chapter 1: Toward the Understanding of the Neurobiology of Chronic Pain . . . . . . . . . . . . . 1
1.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 The Global Burden of Chronic Pain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.3 The Heterogeneity of Chronic Pain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.4 Anatomic and Functional Pain Pathways of the Central Nervous System . . . . . . . . . . 7
1.4.1 Brain Networks involved in Chronic Pain . . . . . . . . . . . . . . . . . . . . . . . 7
1.4.1.1 Central-autonomic network . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.4.1.2 Salience Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.4.1.3 Default-Mode Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.4.1.4 Sensorimotor Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.4.1.5 Reward-motivation learning network . . . . . . . . . . . . . . . . . . . . 13
1.4.1.6 Central executive network . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.4.2 Central Sensitization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.4.3 Brain biomarkers of pain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
1.5 The Genetics of Chronic Pain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
1.6 Development and Sex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.6.1 The developing brain: a primer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.6.2 The developing brain during childhood and adolescence and its role in pain . . . . 22
1.7 Towards a Systems Biology Approach to Chronic Pain . . . . . . . . . . . . . . . . . . . . . 24
1.7.1 The Challenge of Measuring Pain . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
1.7.2 A multi-omics approach to chronic pain . . . . . . . . . . . . . . . . . . . . . . . . 25
1.7.2.1 The brain-gut-microbiome system . . . . . . . . . . . . . . . . . . . . . . 25
1.7.2.2 A multi-omics understanding of irritable bowel syndrome . . . . . . . . 27
Chapter 2: Mapping Brain Structure Variability in Chronic Pain: The Role of Widespreadness and
Pain Type and Its Mediating Relationship With Suicide Attempt . . . . . . . . . . . . . 32
2.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
iv

2.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.3 Methods and Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.3.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.3.2 Neuroimaging Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.3.3 Neuroimaging Data Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.3.4 Operational Definitions of Pain and Suicidality Variables of Interest . . . . . . . . . 37
2.3.5 Statistical Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.4.1 Participants With CP Versus Pain-Free Control Participants . . . . . . . . . . . . . 40
2.4.2 Participants With Chronic Localized Pain Versus Control Participants . . . . . . . 41
2.4.3 Participants With CP Exclusive at One Body Site Versus Control Participants . . . 41
2.4.4 CP Is Associated With History of Suicide Attempt . . . . . . . . . . . . . . . . . . 42
2.4.5 Brain Structure Mediates the Relationship Between Pain and History of Suicide
Attempt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
2.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Chapter 3: The Genetic Architecture of the Human Corpus Callosum and its Subregions . . . . . . 50
3.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.3.1 Artificial intelligence corpus callosum extraction and segmentation with SMACC . 53
3.3.1.1 Data Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.3.1.2 SMACC development and UNet training . . . . . . . . . . . . . . . . . . 54
3.3.1.3 UNet Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.3.1.4 CC shape metrics extracted with SMACC . . . . . . . . . . . . . . . . . . 56
3.3.1.5 Corpus callosum segmentation quality control (QC) with SMACC . . . . 56
3.3.1.6 SMACC vs FreeSurfer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.3.2 Study Cohorts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
3.3.2.1 U.K. Biobank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
3.3.2.2 ABCD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.3.3 GWAS meta-analysis of corpus callosum morphometry . . . . . . . . . . . . . . . . 60
3.3.3.1 Heritability and genetic correlations within and between cohorts . . . . 62
3.3.4 Gene-mapping and gene enrichment analyses . . . . . . . . . . . . . . . . . . . . . 62
3.3.5 Partitioned heritability of meta-analysis results by cell and tissue type with LDSC . 64
3.3.6 LAVA Transcriptome-Wide Association Study . . . . . . . . . . . . . . . . . . . . . 65
3.3.7 Global and local genetic correlations with cortical morphometry and Mendelian
randomization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
3.3.8 Global and local genetic correlations with neuropsychiatric conditions and
Mendelian randomization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
3.4.1 Characterization of corpus callosum shape associated loci . . . . . . . . . . . . . . 67
3.4.2 SNP heritability and genetic correlation between cohorts . . . . . . . . . . . . . . . 70
3.4.3 Gene-mapping and gene-set enrichment analyses . . . . . . . . . . . . . . . . . . . 70
3.4.4 Tissue-specific and cell-type specific expression of corpus callosum associated genes 71
3.4.5 Genetic overlap of corpus callosum and cerebral cortex architecture . . . . . . . . 75
3.4.6 Genetic overlap of corpus callosum and associated neuropsychiatric phenotypes . 79
3.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
v

Chapter 4: Integrated Neurogenetic Biomarkers Predict Chronic and Widespread Pain Development
in Children . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
4.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
4.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
4.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
4.3.1 Pain Phenotyping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
4.3.2 Biopsychosocial clinical assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
4.3.2.1 Demographics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
4.3.2.2 Pubertal status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
4.3.2.3 Developmental history . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
4.3.2.4 Sleep quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
4.3.2.5 Psychological factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
4.3.2.6 Medical history - physical comorbidities . . . . . . . . . . . . . . . . . . 97
4.3.2.7 Sociocultural factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
4.3.2.8 Neurocognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
4.3.2.9 Medications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
4.3.3 The genetic overlap of chronic widespread pain and comorbidities . . . . . . . . . 99
4.3.4 Neuroimaging Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
4.3.4.1 Brain structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
4.3.4.2 Structural connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
4.3.4.3 Resting-state functional connectivity . . . . . . . . . . . . . . . . . . . . 106
4.3.5 Multi-omics data integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
4.4.1 Characterizing pain in ABCD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
4.4.2 Evidence for substantial genetic overlap between chronic pain and commonly
observed comorbidities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
4.4.3 DIABLO shows potential neuroimaging genetic signatures for widespread pain in
children . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
4.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
4.5.1 A shared genetic architecture of chronic widespread pain . . . . . . . . . . . . . . 145
4.5.2 Towards a neuroimaging genetics signature of pain in children . . . . . . . . . . . 146
4.5.3 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
vi

List of Tables
1.1 Distinguishing features of nociceptive, neuropathic, and nociplastic pain Adapted
from (S. P. Cohen et al., 2021) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.1 Mediating Effect of Brain Regions on Relationship Between Chronic Pain and
Suicide ACME, average causal mediation effect; pFWE, familywise error–corrected p. . . 43
4.1 Feature stability from 5-fold cross validation of the DIABLO model for boys . . . . . . . . 129
4.2 Feature stability from 5-fold cross validation of the DIABLO model for girls . . . . . . . . . 139
vii

List of Figures
1.1 Brain networks involved in chronic pain Sensorimotor network: M1 primary motor
cortex, S1 primary somatosensory cortex, BG basal ganglia, THAL thalamus, posINS
posterior insula. Salience network: mPFC medial prefrontal cortex, aMCC anterior
mid-cingulate cortex, OFC orbitofrontal cortex, aINS anterior insula, Amyg amygdala.
Central executive network: dlPFC dorsolateral prefrontal cortex, AnG angular gyrus,
PrCu precuneus. Central autonomic network: mPFC medial prefrontal cortex, OFC
orbitofrontal cortex, ACC anterior cingulate cortex, aINS anterior insula, Amyg amygdala,
Brainstem brain stem. Emotion regulation network: mPFC medial prefrontal cortex, vlPFC
ventrolateral prefrontal cortex, ACC anterior cingulate cortex, Hipp hippocampus, Amyg
amygdala. Default mode network: mPFC medial prefrontal cortex, PCC posterior cingulate
cortex, IPL inferior parietal lobule, MTG middle temporal gyrus. Adapted from (Bhatt et al.,
2020). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.2 Adapted from (Kucyi & Davis, 2015). The salience network is activated to a greater degree
when the subject is focused on the painful stimulus, compared to when their mind is
"wandering." The default-mode network is active when the attention placed on a stimulus
other than pain, but suppressed when attending to pain. When the mind is "wandering" or
not attending to pain, the antinocicpetive system is more active. Individuals who have a
greater capacity of "mind wandering" exhibit greater structural connectivity and dynamic
functional connectivity between the default-mode network and periaquaductal gray. . . . 16
1.3 The contribution of axonogenesis to chronic overlapping pain conditions based on
the results from genome-wide association studies and brain imaging data A recent
study integrating neuroimaging and genetics used the UKB as the main GWAS cohort
and HUNT (population study in Norway) cohort for replication. The GWAS revealed
DCC to be the strongest candidate for multisite (widespread) chronic pain, replicated
and heritability was expressed in brain-tissues. To get a better idea of where DCC is
most strongly expressed, normalized expression values of DCC was obtained from the
Allen Brain Atlas, and maps were created using the neurosynth platform. DCC was most
expressed in the hippocampus and basal ganglia. To examine corticolimbic circuits, OD
values of the uncinate fasciculus (UF) were obtained from imaging derived phenotypes
from the UKB, and it was found that the ODI was greater as the amount of pain sites
increased. Adapted from (Diatchenko, Parisien, Jahangiri Esfahani, & Mogil, 2022). . . . . 20
viii

1.4 Adapted from (Tracey et al., 2019). Composite biomarkers and a systems biology
approach are needed to accurately improve analgesic drug development, diagnosis, patient
stratification, and treatment targeting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
1.5 The brain-gut-microbiome system. Adapted from Mayer, Ryu and Bhatt. 2023. The
brain connectome, gut connectome and gut microbiome communicate in a bidirectional
way. The response characteristics of the system are determined by vulnerability genes
interacting with different influences from the exposome. The different loops use neural,
endocrine, paracrine and immune signaling mechanisms. Perturbations (stressors) of the
different nodes of the system (brain, gut, immune, microbiota) result in non-linear effects
and alterations in response characteristics manifesting as psychiatric and/or gut symptoms.
ANS autonomic nervous system, SNS sympathetic nervous system, PBMCs peripheral
blood mononuclear cells, SCFAs short chain fatty acids, AhR aryl hydrocarbon receptor. . 28
1.6 Relevance network from the DIABLO analysis depicting the correlation between
different ’omics types. Adapted from Sarnoff and Bhatt et al. 2023. Red lines represent
positive correlations and blue lines represent negative correlations. Cutoff for the
correlations was r = 0.7. Microbiome features include (A) Paraprevotella. sp, (B) Blautia
obeum, (C) Streptococcus. sp, (D) Prevotella 9. sp, (E) Catenibacterium mitsuokai (F)
Faecalibacterium prausnitzii and (G) Bacteroides stercoris. Metabolome features include
(1) erythronate, (2) palmitoyl-linoleoyl-glycerol, (3) valine, (4) serine, (5) phenylalanine,
(6) threonine, (7) tryptophan, (8) phosphocholine, and (9) creatinine. Brain connectome
features include (1) orbitofrontal cortex (CAN) resting-state functional connectivity (rsFC),
(2) rsFC between the anterior insula (SAL) and posterior cingulate (DMN). rsFC between
the posterior insula (SMN) and subparietal sulcus (DMN). (3) rsFC between the caudate
nuclei (SMN). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.1 Significant differences in brain structure in participants reporting chronic pain,
chronic localized pain, and chronic multisite pain vs. control participants Effect
sizes (Cohen’s d) for surface area (A), cortical thickness (B), and subcortical volumes (C) of
regions that are significant between groups. Positive effect sizes indicate larger values for
chronic pain participants than control participants. Red represents greater brain metric
values, and blue represents lower brain metric values. . . . . . . . . . . . . . . . . . . . . . 40
2.2 Significant differences in brain structure in participants reporting chronic pain
across different body regions compared with control participants Effect sizes
(Cohen’s d) for surface area (A), cortical thickness (B), and subcortical volumes (C) for
regions that are significant after false discovery rate correction. Red represents greater
brain metric values, and blue represents lower brain metric values. . . . . . . . . . . . . . 42
2.3 Proportion of individuals reporting suicide attempt by chronic pain group χ
2
tests
were conducted between no chronic pain and each of the chronic pain groups. Significance
values and effect sizes are reported for each group. Hx, history. . . . . . . . . . . . . . . . 43
ix

3.1 Segment, Measure, and AutoQC the midsagittal CC (SMACC) pipeline - The
midsagittal slice from a participant registered to MNI space with 6 degrees of freedom
serves as an input to the UNet architecture used for the midsagittal corpus callosum
segmentation. The Witelson atlas was used for segmenting the CC into five different
regions. Global and subregion metrics (thickness and area-shown in green) were extracted
from the segmentation. The thickness (black arrow) is defined as the distance in the
inferior-superior direction between the top and bottom of the contour, after reorientation
to standard space, at every point along the length of the segment, then average across
the region of interest. These metrics serve as input for the ensemble machine learning
model used for labelin CC segmentations as having passed or failed quality control (QC).
Abbreviations: Montreal Neurological Institute - MNI, CC - corpus callosum, ML - Machine
Learning, KNN - K Nearest Neighbors, SVC - Support Vector Classifier. . . . . . . . . . . . 58
3.2 Regions of the midsagittal corpus callosum and associated genomic loci. An
ideogram representing loci that influence total corpus callosum area, its mean thickness,
and area and thickness of individual parcellations determined by the Witelson parcellation
scheme in a rostral-caudal gradient (1–5). All loci are significant at the Bonferroni corrected,
experiment-wide threshold of p < 6.13 x 10−9
. . . . . . . . . . . . . . . . . . . . . . . . . . 68
3.3 GWAS meta-analysis of midsagittal corpus callosum area and thickness (A) Miami
plot for SNPs (top) and genes (bottom) based on MAGMA gene analysis for total area and
total mean thickness. (B) Miami plot for SNPs (top) and genes (bottom) based on MAGMA
gene analysis for area of thickness of the CC split by the Witelson parcellation scheme
(Witelson, 1989). Significant SNPs and genes are color coded by corpus callosum traits. . . 72
3.4 Partitioned heritability, functional annotation and enrichment of gene-sets
of CC morpholog associated genetic variants (A) Significant enrichment of SNP
heritability across 53 functional categories compute by LD Score regression for area (left)
and mean thickness (right). Error bars indicate 95% confidence intervals. (B) Proportion of
GWAS SNPs in each functional category from ANNOVAR across each CC phenotype. (C)
Significant gene-sets across CC phenotypes computed via MAGMA gene-set analysis at the
Bonferroni corrected threshold of 3.23 x 10−6
. GOBP: Gene-ontology biological processes,
GOCC: Gene-Ontology Cellular Components. . . . . . . . . . . . . . . . . . . . . . . . . . 73
3.5 LAVA-TWAS analyses of corpus callosum traits with gene-expression (eQTLs) and
splicing (sQTLs). Results of local genetic correlations between CC traits and eQTLs and
sQTLs from GTEx v8 using the LAVA-TWAS framework. Associations between (A) CC
area and eQTLs, (B) CC thickness and eQTLs, (C) CC are and sQTLs, and (D) CC thickness
and sQTLs are shown via − log10 p values scaled by the direction of association (y-axis)
and chromosomal location (x-axis). All significant points are colored by tissue type and
labeled by CC trait. Significance thresholds for eQTLs (p < 2.01 × 10−6
) and sQTLs (p <
5.45 × 10−7
) were determined by Bonferroni correction. . . . . . . . . . . . . . . . . . . . . 76
x

3.6 The genetic overlap of the corpus callosum and cerebral cortex. (A) Global genetic
correlations (LDSC - rg) between CC phenotypes and cerebral cortex phenotypes. The
Bonferroni significance threshold was set at p = 6.1 × 10−5
. Surface area and cortical
thickness of significant cortical regions with each CC phenotype are displayed on brain
plots. (B) Of the significant global genetic correlations, significant Mendelian randomization
(GSMR) results are displayed, representing the effect of CC phenotypes on cortical
phenotypes free of non-genetic confounders. (C) Chord plot displaying the number of
significant bivariate local genetic correlations (LAVA) between CC and cortical phenotypes.
Underlined numbers represent the total number of genes shared with that phenotype.
(D) Volcano plots showing degree (− log10 p p-values) and direction (rg) of local genetic
correlations (LAVA) between cortical and CC phenotypes. Colors represent cortical regions
labeled on the chord plot in section C. Significant genes (Bonferroni significance threshold
was set at p = 2.18 × 10−6
) across all phenotypes are labeled. . . . . . . . . . . . . . . . . . 78
3.7 The genetic overlap of the corpus callosum and neuropsychiatric phenotypes.
(A) Global genetic correlations between CC traits and neuropsychiatric phenotypes.
The Bonferroni significance threshold was set at p = 0.0019. Of the significant global
genetic correlations, significant Mendelian randomization (GSMR) results ar displayed,
representing the effect of CC phenotypes on neuropsychiatric phenotypes free of
non-genetic confounders. (B) Volcano plots showing degree (− log10 p p-values) and
direction (rg) of local genetic correlations (LAVA) between neuropsychiatric and CC
phenotypes. Phenotypes with significant associations are colored (IQ an bipolar II disorder).
Significant genes (Bonferroni significance threshold was set at p = 2.23 x 10−6
) across
all neuropsychiatric phenotypes. AD: alzheimer’s disease, ADHD: attention deficit
hyperactivity disorder, ASD: autism spectrum disorder, BD: bipolar disorder, BD-I: bipolar
I disorder, BD-II: bipolar II disorder, COPC: chronic overlapping pain conditions, IQ:
intelligence quotient, OCD: obsessive-compulsive disorder, PTSD: post-traumatic stress
disorder, SCZ: schizophrenia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
4.1 Body maps from participants in the ABCD study, where varying numbers of body areas
can be endorsed, are harmonized into a standardized map representing 9 contiguous body
regions. This harmonized body map is designed to capture nociplastic pain with high
validity (Clauw, 2024; Hah et al., 2022). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
4.2 The ancestry of each individual in the ABCD study determined via KING using the 1000
Genomes reference panel (1000 Genomes Project Consortium et al., 2015; Manichaikul
et al., 2010) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
4.3 The endorsement of pain is shown for for individuals with local and widespread pain at
baseline in males and females. Darker shades of red represent a higher percentage of
children endorsing pain in those body regions. There were 3778 boys and 3550 girls who
did not report any pain. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
xi

4.4 Evidence for substantial genetic overlap of chronic widespread pain and commonly
observed comorbidities (a) Significant global genetic correlations are observed between
CWP and comorbid conditions. (b) Significant local genetic correlations are observed with
CWP and all comorbidities across the genome. Volcano plot shows degree (-log10 p-values)
and direction (rG) of local genetic correlations between COPC and all comorbidities (c)
Ideogram shows location of all local genetic correlations and annotated genes. . . . . . . . 113
4.5 Effect size estimates and variant level contribution to CAUSE test statistics
with chronic pain as the exposure, and co-morbid symptoms as the outcome
SNP-level effect estimates for chronic pain (horizontal axis) are plotted against estimates
for co-morbid conditions (vertical axis). SNPs with a negative (∆ELP D) which contribute
to the causal relationship are labeled. The size the points represent the significance level in
the chronic overlapping pain GWAS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
4.6 Effect size estimates and variant level contribution to CAUSE test statistics with
chronic pain as the outcome, and co-morbid symptoms as the exposure SNP-level
effect estimates for comorbid conditions (horizontal axis) are plotted against estimates
for chronic pain (vertical axis). SNPs with a negative (∆ELP D) which contribute to the
causal relationship are labeled. The size the points represent the significance level in the
respective comorbid condition GWAS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
4.7 Multi-gene-list meta-analysis of chronic overlapping pain conditions and
commonly observed comorbidities: (a) Circos plot displays the overlap of genes
between COPC and all comorbidities. Genes significant for multiple traits are colored
in dark orange, and genes unique to a trait are shown in light orange. (b) Heatmap of
enriched terms across traits in biological pathways and (c) tissue types. (d) Enrichment
network of significant genes of all traits, where the size of a slice represents the percentage
of genes under the trait that originated from the corresponding gene list. The network
shows processes relating to the postsynapse and cell morphogenesis are highly enriched
and are shared amongst all traits. (e) Selected MCODE components identified from the
combined list of 854 genes, where each node represents a protein with a pie chart encoding
its which trait it showed significance for. Complexes related to axon guidance, cell adhesion
molecule binding, chromatin remodeling, chromosome organization and G protein-coupled
glutamate receptor activity are shared between chronic overlapping pain conditions and
commonly observed comorbidities. Many genes across complexes are observed in all traits. 117
xii

4.8 Circos plots from DIABLO models integrating clinical, brain stucture, brain
function, and polygenic risk score data Circos plots represent the feature selected
via DIABLO, the mean value of each feature for each pain group, and the correlations
between features across omics types. The threshold is set at r = 0.7, to show strong
correlations. Abbreviations - COPC: chronic overlapping pain conditions, ANX: anxiety,
DEP: depression, INT: intelligence, SUI: suicide attempt, INS: insomnia, ADHD: Attentiondeficit/hyperactivity disorder, IBS: irritable bowel syndrome, affected sum: how much
affected by early life events, total bad le: total bad life events, bad affected sum: how much
affected by bad life events, bis sum: sum on behavioral inhibition scale (BIS/BAS), somatic
r score: somatic problems CBCL raw score, adrenal pds: adrenal puberty development
score, crystallized int: crystinallized intelligence, total comp agecorr nihtb: cognition total
score age corrected, language verb int: Picture vocabulary test score, internalizing r score:
internalizing raw score CBCL, 1: Brodmann area 1, 2: Brodmann area 2, 3a: Brodmann
area 3a, 3b: Primary sensory cortex, 4: Primary motor cortex, 6d: Dorsal area 6, 6v:
Ventral Area 6, 6mp: Area 6mp, 6r: Rostral area 6, 7AL: Lateral area 7A, PEF: premotor
eye field, 8bL: Area 8b lateral, TE2p: Area TE2 posterior, TE1m: Area TE1 middle, TE1p:
Area TE1 posterior, 55b: Area 55b, A4: Auditory 4 complex, TF: Area TF, V1: Primary
visual cortex, V2: Second visual area, V3: Third visual area, 10v: Brodmann area 10v, 25:
Brodmann area 25, pOFC: Posterior orbitofrontal cortex complex, 43: Brodmann Area 43,
PIT: Posterior inferotemporal complex, 13l: Brodmann area 13l, STSva: Area STSv anterior,
STSda: Area STSd anterior, STSvp: Area STSv posterior, LO1: Area lateral occipital 1,
PoI2: Posterior insular area 2, OP4: Area OP4/PV, AIP: Anterior intraparietal area, AAIC:
Anterior agranular insula complex, p24: Area posterior 24, IP2: Area intraparietal 2, p9-46v:
Area posterior 9-46v, Pft: Area PFt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
4.9 Relevance network from the DIABLO analysis depicting the correlation between
different ’omics types for boys Red lines represent positive correlations and blue lines
represent negative correlations. Cutoff for the correlations was r = 0.5. The first of two
clusters shows a positive relationship between all PRS scores and how affected individuals
are by ACEs. All PRSs are negatively associated with brain region’s thickness in the
sensorimotor network. Abbreviations are shown in Figure 4.8 . . . . . . . . . . . . . . . . 141
4.10 Relevance network from the DIABLO analysis depicting the correlation between
different ’omics types for girls Features which show high correlations with each
other are extracted from the entire DIABLO model for easy visualization. Red lines
represent positive correlations and blue lines represent negative correlations. Cutoff for the
correlations was r = 0.5. Abbreviations are shown in Figure 4.8 . . . . . . . . . . . . . . . . 142
4.11 Loading plots for the DIABLO model in boys Features which show high correlations
with each other are extracted from the entire DIABLO model for easy visualization.
Loadings depict the relative importance of each feature in discriminating the groups. Colors
represent which group has the highest or lowest mean value for that feature. Plots under
maximum contribution represent which group had the highest mean value for that feature.
Plots under minimum contribution represent which group had the lowest mean value for
that feature. Abbreviations are shown in Figure 4.8 . . . . . . . . . . . . . . . . . . . . . . 143
xiii

4.12 Loading plots for the DIABLO model in girls Loadings depict the relative importance
of each feature in discriminating the groups. Colors represent which group has the highest
or lowest mean value for that feature. Plots under maximum contribution represent which
group had the highest mean value for that feature. Plots under minimum contribution
represent which group had the lowest mean value for that feature. Abbreviations are
shown in Figure 4.8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
xiv

Abstract
Chronic pain is the leading cause of disabilty and disease burden globally. It represents a complex set of
conditions where a biopsychosocial approach is needed for a comprehensive mechanistic understanding and
treatment. This dissertation explores the multifaceted underpinnings of chronic pain through a combination
of clinical, neuroimaging, and genetic analyses, leveraging big data and cutting-edge multi-omics approaches
to uncover meaningful biomarkers and mechanisms. The first chapter provides a comprehensive overview of
chronic pain, detailing its clinical presentation, neurological pathways—centered on brain mechanisms—and
genetic foundations. This foundational work emphasizes the necessity of integrating large datasets and
multi-omics approaches to identify reliable biomarkers for chronic pain diagnosis, prognosis, and treatment.
The second chapter utilizes data from the UK Biobank to investigate structural brain differences in individuals
with chronic single-site and multisite pain, as well as investigating the differences in brain stucture in each
body site individually. This analysis identifies pain site-specific brain changes and explores their potential
role in mediating the relationship between chronic pain and suicide attempts, providing critical insights
into the neurobiological consequences of chronic pain. Chapter three employs state-of-the-art genetic
methodologies to perform a genome-wide association study (GWAS) meta-analysis on the morphometry
of the corpus callosum, a brain structure crucial for communication and pain processing. Extensive
follow-up analyses assess genetic correlations across multiple genomic resolutions and evaluate causal
relationships between corpus callosum traits and multiple neurological traits, including chronic overlapping
pain conditions through Mendelian randomization. Transcriptome-wide association studies further elucidate
xv

the patterns of gene expression and splicing, revealing key tissue-specific regulatory mechanisms. Chapter
four focuses on the Adolescent Brain Cognitive Development (ABCD) Study to investigate predictors of
chronic and widespread pain development over time in children. First, genetic architectures were examined
using Multi-Trait Analysis of GWAS (MTAG) to assess the shared genetic underpinnings of chronic pain
and related traits, including ADHD, depression, anxiety, insomnia, intelligence, neuroticism, and irritable
bowel syndrome (IBS). These MTAG-enhanced architectures were analyzed using multi-list, multi-gene
pathway analysis and protein-protein interaction networks to identify common biological mechanisms
across traits. Polygenic risk scores (PRS) were then derived using a Bayesian approach (sBayesRC), which
incorporates functional annotations to enhance predictive power. Finally, PRS scores for chronic pain and
related traits were integrated with neuroimaging (brain structure and function) and clinical data from the
ABCD cohort. This multi-omics approach was used to longitudinally predict which children are most at risk
of developing chronic and widespread pain, providing a framework for early identification and intervention.
All together, this dissertation advances our understanding of the neurological and genetic underpinnings of
chronic pain, offering novel insights into its etiology and paving the way for precision medicine approaches
to improve prevention, diagnosis, and treatment.
xvi

Chapter 1
Toward the Understanding of the Neurobiology of Chronic Pain
Portions following section include adaptations from:
Bhatt RR, Gupta A, Mayer EA, Zeltzer LK. Chronic pain in children: structural and resting-state functional
brain imaging within a developmental perspective. Pediatr Res. 2020 Dec;88(6):840-849. doi: 10.1038/s41390-
019-0689-9.
Mayer EA, Ryu HJ, Bhatt RR. The neurobiology of irritable bowel syndrome. Mol Psychiatry. 2023
Apr;28(4):1451-1465. doi: 10.1038/s41380-023-01972-w. Epub 2023 Feb 2.
Sarnoff RP*, Bhatt RR*, Osadchiy V, Dong T, Labus JS, Kilpatrick LA, Chen Z, Subramanyam V, Zhang
Y, Ellingson BM, Naliboff B, Chang L, Mayer EA, Gupta A. A multi-omic brain gut microbiome signature
differs between IBS subjects with different bowel habits. Neuropharmacology. 2023 Mar 1;225:109381. doi:
10.1016/j.neuropharm.2022.109381. Epub 2022 Dec 17. *Co-first authorship.
1.1 Abstract
1.2 The Global Burden of Chronic Pain
Chronic pain is the leading cause of disability and disease burden globally, affecting more than 20% of the
United States population (Rikard, 2023; Vos et al., 2017). Hospitalization rates of children with chronic pain
1

have increased 831% in the last decade and 200% for opioid poisonings (Coffelt et al., 2013b; Gaither et al.,
2016a). Between 5-38% of children suffer from chronic pain, 73% of whom will continue to have chronic
pain in adulthood often due to mistreatment or neglect (Dunn et al., 2011; Eccleston et al., 2021; Hassett
et al., 2013; Hotopf et al., 1998a; Huguet & Miró, 2008b; Jones et al., 2007; King et al., 2011; Perquin et al.,
2000). Nearly two-thirds of the adult population has persistent chronic pain for a year or more; almost
25 million adults in the United States suffer from high-impact chronic pain, frequently limiting work and
life activities (Nahin et al., 2023; Yong et al., 2022). Efforts have begun to provide more targeted treatment
based on better understood pain mechanisms, but an urgent need remains to develop novel biomarkers
with interventions to improve, mechanistic diagnosis, patient stratification, more targeted analgesic drug
development and treatment targeting (Tracey et al., 2019).
1.3 The Heterogeneity of Chronic Pain
The International Association for the Study of Pain (IASP) recently redefined pain as “An unpleasant
sensory and emotional experience associated with, or resembling that associated with, actual or potential
tissue damage...” (Raja et al., 2020). Differing underlying mechanisms, patient descriptors, sensory/motor
deficits, hypersensitivity, pain pattern, co-morbid conditions, and efficacy of various pharmacological
interventions underscores the heterogeneity of chronic pain presentation and etiologies (S. P. Cohen et al.,
2021). Nociceptive pain is the result of neural pathway activity from stimuli that cause or may cause
potential tissue damage. Neuropathic pain is defined as damage or disease affecting the somatosensory
nervous system (Finnerup et al., 2016); it is accompanied with sensory abnormalities such as allodynia
and numbness, greater pain intensity, and neurological abnormalities (S. P. Cohen et al., 2021). Nociplastic
pain is defined as pain resulting from altered function of pain-related sensory pathways in the central
nervous system. This pain is due to increased sensory processing, diminished inhibitory pathways, and
the development of central sensitization (Fitzcharles et al., 2021a; Harte et al., 2018; Woolf, 2011), a key
2

mechanism for conditions such as fibromyalgia, migraine, chronic pelvic pain, irritable bowel syndrome,
low back pain and temporomandibular pain, among others (Nijs, George, Clauw, Fernández-de-Las-Peñas,
et al., 2021). Despite the improvement in understanding of pharmacological and non-pharmacological
treatments for chronic pain, a combination of pharmacological therapies to improve symptoms and nonpharmacological therapies to improve dysfunction is needed in most cases for the patient to return to
normal. This in turn has been associated with normalization of brain MRI measures. (Clauw & Crofford,
2003; Erpelding et al., 2016; Gagnon et al., 2020; L. Simons et al., 2014; L. Zeltzer & Zeltzer, 2016). The
distinguishing features between nociceptive, neuropathic and nociplastic pain have been well characterized
in Table 1 from (S. P. Cohen et al., 2021). Moreover, it’s important to note that many chronic pain conditions
can be due to mechanisms from multiple types of pain. It’s estimated that up to 50% of patients with chronic
pain have mixed pain (Ibor et al., 2017).
Nociceptive Pain Neuropathic Pain Nociplastic Pain
Causes Tissue or potential tissue damage Disease or injury affecting the nervous
system
Maladaptive changes in nociceptive
processing without objective tissue or
nerve damage
Examples and
Mechanisms
Degenerative changes (e.g., disc disease,
arthritis), trauma (e.g., burns), visceral
pathology (e.g., ulcers)
Nerve compression (e.g., radiculopathy),
toxins (e.g., chemotherapy), ischemia
(e.g., diabetes)
Central sensitization, glial activation,
dysregulated stress response (e.g., fibromyalgia, IBS)
Descriptors Throbbing, aching, pressure-like Lancinating, shooting, electrical-like,
stabbing
Neuropathic-like, diffuse, gnawing,
aching
Sensory Deficits Rare, non-dermatomal Frequent (e.g., numbness, tingling) Common, non-dermatomal
Motor Deficits Pain-induced weakness possible Neurological weakness may occur if motor nerves affected
General fatigue; weakness may relate to
deconditioning
Hypersensitivity Uncommon, localized to acute injury
site
Common with allodynia or hyperalgesia Diffuse hyperalgesia; mechanical sensitivity common
Pain Pattern Proximal radiation near anatomical
structures
Distal radiation in nerve distribution Diffuse spread; not confined to anatomical patterns
Precipitating or Relieving Factors
Exacerbations less common, often
activity-related
Frequent, unpredictable exacerbations Common, often linked to psychosocial
stress
Autonomic Signs Uncommon Possible color, temperature changes,
swelling, sweating
Autonomic hyperactivity common in
diffuse pain (e.g., fibromyalgia)
Accompanying
Symptoms
Higher rates of anxiety and depression
than controls
Cognitive impairment, insomnia, hypertension common
Cognitive symptoms, insomnia, fatigue,
GI complaints frequent
Concomitant Conditions
High comorbidity with mental health
issues, insomnia, obesity
Common comorbidities include cardiovascular and autoimmune diseases (e.g.,
lupus)
High co-prevalence with chronic pain,
psychiatric conditions
Effective NonOpioid Pharmacological Treatments
NSAIDs, muscle relaxants, SNRIs, TCAs,
nerve growth inhibitors
TCAs, SNRIs, gabapentinoids, capsaicin
and lidocaine patches
TCAs, SNRIs, gabapentinoids, ketamine
infusions
Table 1.1: Distinguishing features of nociceptive, neuropathic, and nociplastic pain Adapted from
(S. P. Cohen et al., 2021)
3

Nociplastic pain is considered to be the predominant pain mechanism in conditions including, but
not limited to, fibromyalgia, urologic chronic pelvic pain syndrome (UCPPS), irritable bowel syndrome
(IBS), chronic low back pain (cLBP), and migraines (Kaplan et al., 2024; Maixner et al., 2016). Patients with
these centralized chronic pain conditions regularly present with associated , but non-pain CNS-mediated
comorbid conditions, including fatigue, sleep disturbances, multisensory hypersensitivity, and mood/anxiety
disorders (Fitzcharles et al., 2021a; Kaplan et al., 2024), all showing heterogeneous individual differences in
manifestation. In fact, the myriad of COPC symptoms often studied in isolation are actually part of two
distinct constructs of GSS (generalized sensory sensitivity) and SPACE (sleep, pain, affect, cognition and
energy) (Schrepf et al., 2018). GSS is characterized by an increased tendency to experience, perceive, and
report heightened sensitivity to external stimuli across various sensory modalities, along with a somatic
awareness of bodily sensations and widespread pain or tenderness (hyperalgesia/allodynia) in multiple
regions of the body. SPACE represents a cluster of interrelated constitutional symptoms that frequently
become disrupted simultaneously and have been observed in primary care settings, patients with cancer,
and individuals with other chronic diseases (L. L. Davis et al., 2016; Schrepf et al., 2018).
Treating nociplastic pain with traditional analgesic pharmacological approaches such as muscle relaxants, NSAIDs, acetaminophen and opioids is not as effective as for nociceptive pain (S. P. Cohen et al.,
2021). The use of opioids is being increasingly discouraged for those with nociplastic pain due to reduced
efficacy over time, hyperalgesia (due to the blocking of the production of endorphins), and disrupted sleep
architecture (Fitzcharles et al., 2021a). Instead, medications aimed at the central nervous system such as
gabapentinoids (e.g., pregabalin) and SSRI and SNRIs (e.g. milnacipran, duloxetine) show a greater promise
at treating centralized pain (Clauw, 2014). Geo-location is important. Increased levels of glutamate in
the PCC and greater connectivity between the PCC and the anterior/posterior insula have been shown
to normalize after treatment of pregabalin via inhibition of glutamate release in the brain and spinal cord
(Harris et al., 2013). Patients who respond to duloxetine (SNRI) have been shown to have greater functional
4

connectivity in the frontal pole, insula, middle and inferior frontal gyrus, pre- and postcentral gyrus,
supramarginal gyrus and lateral temporal cortex, enhanced by serotonin and noradrenaline receptors (D.
Martins et al., 2022). Given this research, future CNS aimed pharmacological neuroimaging studies focusing
on longitudinal changes in responders are needed. Cannabis and psilocybin are emerging therapies for
chronic pain aimed at the CNS, but well-designed brain imaging mechanisms have not been conducted
(Elman et al., 2022; Weizman et al., 2018). However, one study looking at the effects of THC on chronic pain
has shown a reduction in functional connectivity between the ACC/dlPFC and sensorimotor cortex was
correlated with improvement in pain symptoms in chronic radicular neuropathic pain. This suggests that
THC may reduce pain via disconnection between affective/cognitive control regions and the sensorimotor
network (Weizman et al., 2018).
Non-pharmacological neuromodulatory approaches for chronic pain have been researched for various
chronic pain conditions and are thought alter patient’s neuroexcitability by promoting neuroplasticity via
synaptic plasticity and ion channel modifications (Knotkova et al., 2019, 2021). A recent review characterized
the efficacy of various neuromodulatory procedures (Knotkova et al., 2021). The most direct approaches for
the central nervous system aimed at treating nociplastic pain conditions includes motor cortex stimulation,
deep brain stimulation, repetitive transcranial magnetic stimulation, direct cranial stimulation, and spinal
cord stimulation.
Motor cortex stimulation is thought to provide pain relief by stimulating parallel fibers involved in
top down control of pain perception and stimulating areas of the brain modulating the emotional aspects
of pain (Garcia-Larrea & Peyron, 2007; Sacco et al., 2014; Strafella et al., 2003). Systematic reviews and
meta-analyses have also shown a significant increase in the post treatment quality of life (QOL) in patients
across chronic pain conditions including trigeminal neuralgia, CRPS, and phantom pain (Knotkova et al.,
2021; Lima & Fregni, 2008)); a trial is now underway for chronic pelvic pain (J. Kutch, 2021). Deep brain
stimulation (DBS) was initially developed to treat chronic pain, but was not approved due to a lack of
5

convincing results (Gardner, 2013; Knotkova et al., 2021). Only patients with severe symptoms who are
unresponsive to less invasive treatments are suggested to receive DBS. There is continued interest in its
use for cluster headaches, however the optimal target is disputed (Akram et al., 2016; Fontaine et al., 2010;
Nowacki et al., 2020). Repetitive (extra) trans-cranial magnetic stimulation (rTMS) is delivered to specific
targets of the brain via a generation of a powerful magnetic field modulating neuronal excitability. The
motor cortex and dlPFC have been shown to be efficacious stimulation sites and are approved as a treatment
in Europe for fibromyalgia, neuropathic pain and headaches/migraines (Lefaucheur et al., 2014; Leung et al.,
2020). Transcranial direct cranial stimulation (tDCS) uses a battery powered device to deliver a low intensity
electrical current to the scalp and can be very focal. Its main mechanism is to alter changes in neuronal
excitability and is thought to directly treat central sensitization, but known to affect cortical nodes that are
distant from the stimulation site and multiple sessions are needed to get meaningful outcomes (Knotkova
et al., 2019; Wagner et al., 2007; Woods et al., 2016). Stimulating the anode results in neuronal excitation
and stimulating the cathode results in neuronal inhibition by interacting with many neurotransmitters
including GABA, serotonin, dopamine and acetylcholine (Nitsche & Paulus, 2000). Evidence shows that it
may help up for up to 6 weeks in chronic pain in general, but there is conflicting evidence for neuropathic
pain and headaches (Alwardat et al., 2020; O’Connell et al., 2018).
The understanding of individual differences in pain sensitivity via biological predisposition and acquired
mechanisms from the environment that set off priming mechanisms is crucial for improving chronic pain
and associated developmental and health outcomes (Mogil, 2021). A biological framework of shared
neurobiological mechanisms involving the differential susceptibility of the stress-response (Boyce, 2016a;
L. Miller, 2000; L. Zeltzer et al., 1997), sensory over-responsivity (Carpenter et al., 2019; Kaplan et al., 2022;
Schwarzlose et al., 2022), brain network reorganization (Bhatt et al., 2020; Martucci & MacKey, 2018; Mayer,
Labus, et al., 2015a; Mercer Lindsay et al., 2021a; Zhuo, 2016), and central sensitization (Clark et al., 2019a;
Harte et al., 2018; Nijs, George, Clauw, Fernández-de-Las-Peñas, et al., 2021; Walker et al., 2012; L. Zeltzer
6

et al., 1997) warrant further investigation into individualized neurobiological biomarkers able to predict
these overlapping phenotypes with high specificity and sensitivity. Detailed cellular and neurochemical
circuits describing nociceptive pathways from the periphery to the brain have been previously described.
This includes synaptic plasticity in the periphery and spinal cord (Kuner & Flor, 2017), the neuroimmune
interface (Grace et al., 2014; Hakim et al., 2024), the gut microbiome (Watson, 2024).
1.4 Anatomic and Functional Pain Pathways of the Central Nervous
System
1.4.1 Brain Networks involved in Chronic Pain
The culmination of neuroimaging studies has provided a seminal understanding of altered brain regions
and networks (Figure 1.1). Brain structure, function, immune activation, and neurochemical processes have
been observed in children and adults who develop chronic pain (Bhatt, Haddad, et al., 2024; Bhatt et al., 2020;
Fitzcharles et al., 2021a; Kaplan et al., 2022; Martucci & MacKey, 2018; Mercer Lindsay et al., 2021a). It’s now
accepted that multiple intrinsic brain networks and modulatory control systems work together to produce
the experience and maintenance of chronic pain (Canavero & Bonicalzi, 2015b; Fitzcharles et al., 2021a;
Martucci & MacKey, 2018; Mercer Lindsay et al., 2021a). Studying the relationship with associated non-pain
comorbidities would provide comprehensive mechanistic insight into the development and treatment of
chronic pain.
1.4.1.1 Central-autonomic network
The most primitive network regulating visceromotor, neuroendocrine, pain, and behavioral responses
essential for survival is the central autonomic network (Benarroch, 1993a). Afferents project through
the spinal cord and eventually arrive at the main homeostatic processing sites in the brainstem/central
7

Figure 1.1: Brain networks involved in chronic pain Sensorimotor network: M1 primary motor cortex,
S1 primary somatosensory cortex, BG basal ganglia, THAL thalamus, posINS posterior insula. Salience
network: mPFC medial prefrontal cortex, aMCC anterior mid-cingulate cortex, OFC orbitofrontal cortex,
aINS anterior insula, Amyg amygdala. Central executive network: dlPFC dorsolateral prefrontal cortex,
AnG angular gyrus, PrCu precuneus. Central autonomic network: mPFC medial prefrontal cortex, OFC
orbitofrontal cortex, ACC anterior cingulate cortex, aINS anterior insula, Amyg amygdala, Brainstem brain
stem. Emotion regulation network: mPFC medial prefrontal cortex, vlPFC ventrolateral prefrontal cortex,
ACC anterior cingulate cortex, Hipp hippocampus, Amyg amygdala. Default mode network: mPFC medial
prefrontal cortex, PCC posterior cingulate cortex, IPL inferior parietal lobule, MTG middle temporal gyrus.
Adapted from (Bhatt et al., 2020).
8

autonomic network (including hypothalamus, amygdala and PAG), and higher cortical processing and
modulatory regions (Lamotte et al., 2021). Early in pain MRI research, the part of the central-autonomic
network studied respective to pain was the descending pain modulatory system (DPNS). The DPNS regulates
the amount of nociceptive processing coming from the dorsal horn and spinal cord, with the periaqueductal
grey (PAG), dorsal reticular nucleus (DRt), locus coeruleus (LC) and rostral ventromedial medulla (RVM)
being key brainstem structures involved (Bhatt, Zeltzer, et al., 2019; Denk et al., 2014; Hubbard et al., 2011;
Labus et al., 2013; Llorca-Torralba et al., 2016; Suárez-Pereira et al., 2022; B. K. Taylor & Westlund, 2017).
The DPNS is involved in the bidirectional control of nociception such that pain can be alleviated through
antinociceptive processing or exacerbate pain through greater nociceptive processing (M. C. Lee et al., 2008).
Other cortical/subcortical structures involved in the DPNS and known to be part of the central-autonomic
network includes the amygdala, hypothalamus, rACC, and dlPFC (Denk et al., 2014), which have projections
to the brainstem and are the nodes by which cognitive and emotional factors/processing interact with
nociceptive signaling to produce the pain experience (Eippert et al., 2009; Geuter & Büchel, 2013).
Two of the main neurotransmitters released by the DPNS are noradrenaline and serotonin (5-HT).
The small cluster of 20,000 neurons in the human LC is the main source of noradrenaline in the brain
and has projections to the thalamus, ACC, hippocampus, hypothalamus, amygdala, basal ganglia, motor
structures (motor cortex and cerebellum) and medial prefrontal cortex (mPFC) (Poe et al., 2020). There is
a growing body of research about the role of the LC in the transition from acute to chronic pain and the
maintenance of chronic pain (I. Martins et al., 2015; Suárez-Pereira et al., 2022; B. K. Taylor & Westlund, 2017).
Traditionally speaking, 5-HT is known to act on pain inhibitory alpha-2 adrenoreceptors in the brain and
spinal circuits, and pharmacological approaches such as antidepressants and clonidine are meant to mimic
these nonandrogenic analgesic systems (Dworkin et al., 2007; B. K. Taylor & Westlund, 2017). Constant
noxious input is known to deregulate the LC circuitry such that pain inhibitory alpha-2 adrenoreceptors
are diminished and pain facilitative alpha-1 adrenoreceptors are more greatly expressed, contributing
9

to pain hypersensitivity through the LC-Drt pathway (Camarena-Delgado et al., 2022; I. Martins et al.,
2015; Suárez-Pereira et al., 2022; B. K. Taylor & Westlund, 2017). Preliminary fMRI data in patients with
sickle-cell disease (a model disease where chronic vaso-occlusive pain crises can lead to chronic pain) have
shown greater resting-state connectivity between the LC and dlPFC, homologous to the observed greater
connectivity between the LC-mPFC in rodents (Dalley et al., 2004; Labus et al., 2013; B. K. Taylor & Westlund,
2017). Moreover, this has given insight as to why antidepressants inhibiting noradrenaline reuptake may
lose efficiency over time and enhance pain processing in the brain, counterintuitive to previous beliefs
via the LC projections to the dorsal reticular formation acting on alpha-1 adrenoreceptors (Alba-Delgado
et al., 2012; I. Martins et al., 2015; Suárez-Pereira et al., 2022). This transition of an availability of specific
molecular receptors in the human brain has been inadequately studied in chronic pain but could extend
beyond noradrenaline to GABAergic, serotonergic, cholinergic, orexinergic, and oxytocinergic mechanisms
(Kuner & Kuner, 2020).
In addition to this system, other brainstem nuclei have been implicated in chronic pain (Napadow et al.,
2019), but one of the other key systems involved in pain processing is the PAG-RVM system. Extensive
research has shown the analgesic role of the PAG via projections through the RVM, which has ON (nociception), OFF (anti-nociception), and NEUTRAL classes of neurons regulating pain control (Fields, 2000;
Heinricher et al., 2009; Tracey & Dickenson, 2012), and thus, pain faciliatory pathways through the ON
cells of the PAG-SVM circuit have been proposed to underlie some chronic pain conditions in rats (Ossipov
et al., 2010; Zhuo & Gebhart, 1997). However, neuroimaging approaches of these circuits are limited mainly
due to spatial resolution (Napadow et al., 2019). Various cortical networks, mainly the salience network,
interact with the central autonomic network to regulate the stress response and behavioral arousal to guide
goal-oriented behavior (Lamotte et al., 2021).
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1.4.1.2 Salience Network
The salience network (SN) is responsible for processing behaviorally relevant salient information to
appropriate attentional, behavioral, affective, and visceral responses based on expected or experienced
external or internal perturbation of homeostasis (Lamotte et al., 2021; Mayer, Labus, et al., 2015a; Seeley,
2019). It participates in autonomic control by integrating processing of behaviorally relevant stimuli in its
core hubs, and participating in autonomic regulation via projections to the amygdala, hypothalamus and
brainstem (Cechetto, 2014). Its core hubs include the anterior insula and dorsal anterior cingulate cortex
(ACC) (Seeley, 2019; Seeley et al., 2007a). It also plays a crucial role in the dynamic “switching” between
the central-executive (exogenously driven/cognitively driven mental activity) and default-mode networks
(endogenously mediated self-referential mental activity), coordinating and adjusting physiologic/behavioral
responses to internal and environmental perturbations of homeostasis (Menon & Uddin, 2010; Toga, 2015).
The SN has also been shown to play a role in the “dynamic pain connectome” in conjunction with attentional
networks (Kucyi & Davis, 2015). The role of the salience network in acute and specifically chronic pain
has been extensively reviewed, suggesting lesions in the ACC may reduce the pain’s salience and alter the
responsive salience to pain or treatment related cues (Borsook, Edwards, et al., 2013). More recent studies
in humans suggest that greater connectivity of the salience network at baseline predicts widespread pain
development and maintenance in children and adults, as well as encoding clinical pain (Bhatt et al., 2022;
Jacobs et al., 2021; Kaplan et al., 2022; J. Kim et al., 2019; Mayer, Labus, et al., 2015a).
1.4.1.3 Default-Mode Network
The default-mode network (DMN) plays a critical role in self-awareness processing, episodic memory,
monitoring internal thoughts, external goals, and future planning (Buckner et al., 2008; Greicius et al., 2003;
Raichle et al., 2001; Whitfield-Gabrieli & Ford, 2012). The DMN’s role in pain perception acts as an opposite
manner to the SN, such that the DMN is suppressed when attention is placed on present sensory stimuli,
11

and is activated when attention is engaged with thoughts away from present sensory stimuli and engaged
in mind wandering (i.e., thoughts unrelated to the present sensory environment) (Kucyi & Davis, 2015).
Studies in chronic pain subjects have shown altered functional connectivity and topological reorganization
in various regions, consistent with DMN dysregulation (Qi et al., 2016). Overall neuroimaging research
suggests decreased activity within the DMN in patients with chronic pain conditions (Kaplan et al., 2024;
Nisticò, Rossi, D’arrigo, et al., 2022). Lower integrity of anatomical connectivity and resting-state functional
connectivity, and lower morphological integrity within the DMN (between the aMPFC and PCC) were
found to be predictive of sustained IBS symptom severity over 3–12 months (Bhatt et al., 2022). Rectal
lidocaine administration in IBS subjects has been associated with decreased pain perception and with
increased coherence within the DMN (Letzen et al., 2013), supporting an involvement of the DMN in visceral
hypersensitivity in patients with IBS.
1.4.1.4 Sensorimotor Network
The sensorimotor network (SMN) receives and interprets the sensory input and generates appropriate
motor responses. It also interacts with the salience and central-autonomic network. Neuroimaging studies
in patients with chronic pain have shown alterations of the SMN, consistent with alterations in central
processing and modulation of viscerosensory and somatosensory information (Bhatt et al., 2022; Hong et al.,
2013; Martucci & MacKey, 2018; Mayer, Gupta, et al., 2015a). The posterior insula, known for processing
interoceptive and viscerosensory information (Gehrlach et al., 2019) has reciprocal connections to the
central autonomic and salience networks (Gogolla, 2017; Lamotte et al., 2021) which converge to generate
appropriate emotional and behavioral responses to pain. Many neuroimaging studies across chronic pain
conditions have solidified the role of the sensorimotor network in chronic pain, including nociplastic pain
(Baliki et al., 2012; Becerra et al., 2014; Bhatt, Gupta, et al., 2019; Cauda et al., 2014; D. J. Kim et al., 2021;
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J. J. Kutch et al., 2017; Larkin et al., 2021; Mayer et al., 2019), and distinct differences between nociplastic
and nociceptive pain (Sandström et al., 2022).
1.4.1.5 Reward-motivation learning network
Key regions within this network include the ventral tegmental area (VTA), hippocampus, nucleus accumbens (NAcc), ventrolateral prefrontal cortex, and medial prefrontal cortex (Denk et al., 2014). Seminal
longitudinal imaging studies on chronic back pain have demonstrated that increased resting-state and
structural connectivity between the nucleus accumbens and medial prefrontal cortex predicts persistent
pain (Baliki et al., 2012; Mansour et al., 2013). These findings have spurred extensive research on VTA-NAcc
circuitry, particularly regarding its role in encoding reward and aversion and its implications in chronic
pain mechanisms (Kuner & Kuner, 2020). The NAcc receives dopaminergic input on D1 (excitatory and
promoting reward) and D2 (inhibitory and promoting aversion). Although the exact mechanisms of how
alterations in this network lead to chronic pain is unclear, a few hypotheses have been developed. A
leading one is that ongoing activity of this pathway via pain is modulated by NAcc neurons of the indirect
pathway (D2 receptors). These neurons have intrinsic hyperexcitation in a neuropathic pain rodent model,
suggesting that hyperactivity of D2-receptor expressing neurons are a key contributor. (Ren et al., 2016).
Disrupted dopaminergic activity has been observed in humans via PET, showing a greater binding potential
of D2 receptors which was associated with higher pain sensitivity in fibromyalgia and back pain patients
(Martikainen et al., 2015; Wood et al., 2007); this suggests that the greater activity of the indirect dopaminergic pathway promoting aversion may be a key therapeutic target. Corticolimbic circuitry has also shown
to play a role in the development of chronic pain longitudinally, with the genetic polymorphism in the
delta-type opioid receptor (OPRD1) gene mediating the risk to develop chronic pain by amygdala volume
in the PFC-Amygdala-NAcc circuit measured via diffusion and resting-state fMRI (Vachon-Presseau et al.,
2016).
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1.4.1.6 Central executive network
The central executive network (CEN), also referred to as the frontoparietal network, is activated during
tasks involving executive functions such as attention, working memory, planning and response selection
(Dosenbach et al., 2007; Niendam et al., 2012; J. L. Vincent et al., 2008). It consists of the lateral prefrontal
and posterior parietal cortices. It is also often co-activated with regions of the SN, as the brain attempts to
focus its limited processing capacity to only salient information via attention, working memory, planning
and response selection (Menon, 2011). Symptom focused attention and cognitive inflexibility are symptoms
mediated by the CEN which have been observed in chronic pain disorders (Mayer et al., 2023).
1.4.2 Central Sensitization
The primary mechanism for the core symptom of persistent, chronically recurring pain is thought to result
from alterations in the central processing of sensory input in the central nervous system, also referred
to as central sensitization (Fitzcharles et al., 2021a; Nijs, George, Clauw, Fernández-de-Las-Peñas, et al.,
2021). The term was originally coined to represent the specific spinal mechanisms responsible for the
amplification of nociceptive signaling involving spinal activation of the NMDA receptor (Latremoliere &
Woolf, 2009; Woolf, 1983), and is present in various chronic pain disorders such as chronic neuropathic
pain, fibromyalgia, headaches, and IBS (Fitzcharles et al., 2021a; Simrén et al., 2019; Verne et al., 2003).
Today, it is understood that spinal and supraspinal mechanisms both play key roles in the development and
maintenance of central sensitization. Based on rodent models of pain, plausible spinal mechanisms include
alterations in converging sensory input from different sites on the GI tract and body, temporal and spatial
summation, reduced endogenous dorsal horn inhibition, and glial cell activation. Based on human brain
imaging studies, supraspinal mechanisms include an altered balance between facilitatory and inhibitory
endogenous pain modulation influences, hyperconnectivity between brain networks, alterations of gray
matter architecture, elevated CSF glutamate and substance P levels, reduced GABAergic transmission,
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altered noradrenergic signaling/receptors, and glial cell activation (Fitzcharles et al., 2021a; Suárez-Pereira
et al., 2022).
1.4.3 Brain biomarkers of pain
Various "brain signatures" have been developed to attempt to capture the pattern of brain activity specific to
pain. A few models derived from brain MRI on acute pain have shown to be very influential in the literature.
For one, the Neurological Pain Signature (NPS) (Wager et al., 2013) is a signature derived from fMRI
multivariate pattern analysis in response to thermal stimuli. Regions included mainly the thalamus, anterior
and posterior insula, the secondary somatosensory cortex (SII), ACC, and PAG. The stimulus intensity
independent pain signature 1 (SIIPS1) is an updated signature with its main regions consisting of the nucleus
accumbens, lateral prefrontal and parahippocampal cortices (Woo et al., 2017). The Pain-Analgesic Network
was developed using patients taking drugs across various classes to understand their brain’s response to
pain and analgesia (Duff et al., 2015). The "dynamic pain connectome" (Kucyi & Davis, 2015) is an fMRI
signature of the interacting default-mode, salience, and descending pain modulatory networks, derived
from painful TENS stimulation on the forearm. It models how pain is modulated by attentional systems.
The model can be visualized in Figure 1.2.
The large overlap - up to a 4.27 odds ratio - between psychiatric phenotypes (primarily anxiety and
depression) and chronic pain disorders, as well as genetic overlap (Eijsbouts et al., 2021; McWilliams
et al., 2003; Mocci et al., 2023; J. Tang & Gibson, 2005), suggests central sensitization as a possible shared
pathophysiological factor (p factor) (Clark et al., 2019a; Fitzcharles et al., 2021a; M. Adams & C. Turk, 2015;
Shigetoh et al., 2019). The concept of central sensitization was introduced in psychological research in
the 1990s based on the observation that highly sensitive persons (HSPs) often share a history of early
adversity, psychological profile of introversion (“neuroticism”), and greater emotionality (Aron & Aron,
1997). Patients with chronic pain are significantly more likely to exhibit qualities of HSPs, and show
15

Figure 1.2: Adapted from (Kucyi & Davis, 2015). The salience network is activated to a greater degree
when the subject is focused on the painful stimulus, compared to when their mind is "wandering." The
default-mode network is active when the attention placed on a stimulus other than pain, but suppressed
when attending to pain. When the mind is "wandering" or not attending to pain, the antinocicpetive system
is more active. Individuals who have a greater capacity of "mind wandering" exhibit greater structural
connectivity and dynamic functional connectivity between the default-mode network and periaquaductal
gray.
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central sensitization which is expressed as general sensory hypersensitivity (Boyce, 2016a; Midenfjord
et al., 2021). The association between chronic pain disorders, psychiatric symptoms, and mechanisms of
central sensitization is likely due to the above-mentioned supraspinal alterations, including monoamine
neurotransmitter systems (i.e., serotonin, dopamine, noradrenaline), the amino acid GABA, and brain
regions underlying both pain transmission/modulation and mood disorders (Elman & Borsook, 2018;
Meerwijk et al., 2013). Striato-thalamic-frontal cortical pathways including the prefrontal cortex, amygdala,
nucleus accumbens, and thalamic nuclei are key hubs, and alterations in neuronal firing and communication
underlie sensory sensitivity and psychiatric symptoms including altered perception, arousal, cognition, and
mood (Baliki & Apkarian, 2015; Belujon & Grace, 2015; Elman & Borsook, 2018). Behaviorally, chronification
of central sensitization and negative mood states have been proposed to be in the same continuum of
aversion, such that pain motivates the avoidance of further injury, and anxiety promotes behaviors that
diminish anticipated danger (Baliki & Apkarian, 2015).
An extensive literature supports the importance of early programming by early adverse life (EAL) events
for the development of many chronic pain conditions and psychiatric syndromes (Bale et al., 2010; Osadchiy
et al., 2019; Zouikr et al., 2016). Perturbations to the developing brain play a large role in sensitizing
cortical nociceptive circuitry (Verriotis et al., 2016b). The most mechanistic study in humans shows larger
event-related potentials (ERPs) to nociceptive, but not tactical stimuli in infants exposed to many invasive,
skin-breaking, painful procedures and morphine (Slater et al., 2010). Moreover, up to 68% of children who
are exposed to early life traumatic events such as the NICU can develop chronic pain by age 10. Lower
global gray matter volumes throughout childhood (Ranger et al., 2013; van den Bosch et al., 2015) are
proportional to the quantity of pain-related stressors, painful procedures, and morphine. In addition to the
well documented changes in stress response systems (Heim & Nemeroff, 2001; Lippard & Nemeroff, 2023;
Nemeroff, 2004), the effect of early-life dietary influences on the gut microbiome and the BGM axis have
17

received increasing attention, even though a direct link with chronic pain has not been established (Coley
& Hsiao, 2021; Ratsika et al., 2021).
1.5 The Genetics of Chronic Pain
Throughout the history of chronic pain research, a central, driving question has been: “What causes chronic
pain?” This foundational inquiry has shaped and motivated much of the work in the field. However
recently, it is increasingly being recognized that the more productive question is: “Why do a few select
people develop chronic pain, while most people are able to recover normally?”. Over the past 50 years, there
has been a recurring interest in unraveling how genetic contributions to chronic pain underlie these
individual differences. With the advent of full human genome sequencing, efforts to identify “pain genes”
through candidate gene approaches initially gained momentum, especially given the high heritability of
pain observed in twin studies involving both healthy individuals (Mogil, 2021; Norbury et al., 2007) and
patients with chronic pain (Burri et al., 2018; Momi et al., 2015; Vehof et al., 2014). In rodent models,
it’s been shown what is being inherited is the susceptibility/resistance to pain, regardless of etiology
(Mogil, 2021). However, these candidate gene studies largely failed to demonstrate reproducibility, and
have been extensively reviewed (Mogil, 2012, 2021). In contrast, genome-wide association studies (GWAS)
have emerged as a more reliable and reproducible method, consistently highlighting that chronic pain is a
polygenic disease (Eijsbouts et al., 2021; Freidin et al., 2021; Hautakangas et al., 2022; Johnston et al., 2021;
Khoury et al., 2022).
Providing further insight into the genetic contribution of these individual differences, GWAS for pain
phenotypes and functional downstream analyses have uncovered various sex-specific (Freidin et al., 2021;
Johnston et al., 2021) pathophysiological mechanisms (Diatchenko, Parisien, Esfahani, & Mogil, 2022),
including biochemical pathways specific to neurogenesis and sympathetic plasticity enriched in chronic
pain, while connective tissue and bone remodeling pathways, cardiac muscle depolarization, and immune
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response via Th2-helper cells were enriched in acute pain (Bortsov et al., 2022). Over 60 pathways involved
in neural function and development have been identified specific to chronic widespread pain (Khoury et al.,
2022). A recent study integrating GWAS of chronic overlapping pain conditions and structural integrity of
the uncinate fasciculus (UF) suggested the gene DCC, which is strongly expressed in the hippocampus and
nucleus accumbens, may contribute to chronic pain via DCC-dependent axogenesis and netrin-1 receptors
in cortico-limbic circuits (Khoury et al., 2022). These findings underscore the need to differentiate between
chronic pain at a molecular level, suggesting that tailored therapeutic strategies targeting these distinct
biological mechanisms could significantly enhance pain management (Figure 1.3). Providing further insight
into the genetic contribution of these individual differences, GWAS for pain phenotypes and functional
downstream analyses have uncovered various sex-specific (Freidin et al., 2021; Johnston et al., 2021),
pathophysiological mechanisms (Diatchenko, Parisien, Esfahani, & Mogil, 2022). These findings underscore
the need to differentiate between chronic pain at a molecular level, suggesting that tailored therapeutic
strategies targeting these distinct biological mechanisms could significantly enhance pain management.
The genetic architecture of chronic pain has been shown to overlap with that of commonly observed
comorbid conditions (Mocci et al., 2023), suggesting a shared biological basis. High genetic correlations have
been identified between chronic pain and several traits, including ADHD, intelligence, anxiety, depression,
insomnia, and neuroticism (Eijsbouts et al., 2021; Friligkou et al., 2024; Mayer et al., 2023; Mocci et al.,
2023), and many common genes observed and pathways enriched are observed in mood/anxiety disorders
(Eijsbouts et al., 2021). Causal genetic liability via Mendelian randomization has also been established
between chronic pain and depression (Mocci et al., 2023), suicide attempt (Balit et al., 2024), and various
autoimmune diseases (Y. Tang et al., 2023). Although this area of research is still in its infancy, emerging
evidence suggests that shared genetic pathways may underlie the development of chronic pain and these
co-occurring conditions, offering insight into the biological complexity of chronic pain.
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Figure 1.3: The contribution of axonogenesis to chronic overlapping pain conditions based on the
results from genome-wide association studies and brain imaging data A recent study integrating
neuroimaging and genetics used the UKB as the main GWAS cohort and HUNT (population study in
Norway) cohort for replication. The GWAS revealed DCC to be the strongest candidate for multisite
(widespread) chronic pain, replicated and heritability was expressed in brain-tissues. To get a better idea
of where DCC is most strongly expressed, normalized expression values of DCC was obtained from the
Allen Brain Atlas, and maps were created using the neurosynth platform. DCC was most expressed in the
hippocampus and basal ganglia. To examine corticolimbic circuits, OD values of the uncinate fasciculus
(UF) were obtained from imaging derived phenotypes from the UKB, and it was found that the ODI was
greater as the amount of pain sites increased. Adapted from (Diatchenko, Parisien, Jahangiri Esfahani, &
Mogil, 2022).
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1.6 Development and Sex
A complex interaction between developmental factors and sex is a well-established risk factor for the onset
of chronic nociplastic pain (Kaplan et al., 2024). All nociplastic pain conditions show a significant female
preponderance, being up to twice as common in females as in males, especially after puberty (Melchior
et al., 2016; Mills et al., 2019). Gonadal hormones, particularly estrogen and testosterone, are believed
to play a role in this disparity (Craft, 2007; Gulati et al., 2023; K. Vincent & Tracey, 2008), as these sex
differences primarily emerge post-puberty, with pain responses in females varying across the menstrual
cycle (Iacovides et al., 2015; Schmitz et al., 2013). At high concentrations, estradiol exhibits anti-nociceptive
effects and can enhance the efficiency of descending pain inhibition in healthy women (Y. R. Smith et al.,
2006). However, at low concentrations, estradiol may increase susceptibility to pain (K. Vincent et al., 2013).
In a rodent models, orchiectomy (removal of testicles) in male mice result in more prolonged and widespread
pain sensitivity compared to controls, while administering testosterone to female or orchiectomized male
mice had the opposite effect, suggesting a protective role of testosterone against widespread pain (Lesnak
et al., 2020). Testosterone also appears to have anti-nociceptive effects in humans. In a 2013 study involving
adult women on oral contraceptives, lower endogenous testosterone levels were associated with a reduced
thermal pain threshold and decreased activity in the rostral ventromedial medulla (RVM), a key component
of the descending pain modulatory system (K. Vincent et al., 2013). Dysmenorrhea and a greater history of
adverse child experiences (ACE), such as physical and sexual abuse, in females are also large risk factors for
developing nociplastic pain (Bradford et al., 2012; R. Li et al., 2021).
1.6.1 The developing brain: a primer
The structure of the developing brain is a product of variable regressive (e.g., synaptic pruning) and
progressive (e.g., increased myelination) cellular processes occurring simultaneously, which correspond to
brain shrinkage and growth (Sowell et al., 2001). Between the ages of 7 and 30 years, as the brain grows
21

in an evolutionarily relevant sequence, there is a reduction in the density of GM but an increase in WM
(Sowell et al., 2001, 2003). Moreover, it has been established that trajectories that take both brain size and
age into consideration are more likely to predict functional characteristics (e.g., intellectual ability (Shaw,
Greenstein, et al., 2006) or neurodevelopmental disorders, such as autism, attention-deficit/hyperactivity
disorder, and schizophrenia (Courchesne et al., 2007; Giedd & Rapoport, 2010; Shaw, Greenstein, et al., 2006;
Shaw, Lerch, et al., 2006)) compared to absolute GM density (Giedd et al., 2008; Lenroot et al., 2007; Shaw,
Greenstein, et al., 2006).
The developing functional brain exhibits pronounced “small-world” characteristics (Menon, 2013).
This means the brain is organized into local networks that are not as communicative as more densely
connected networks found in adults. Early in development, functional hubs are largely confined to sensory
and motor regions and, with age, shift to the PCC and insula. Developmentally, the most important
networks are regarded to be the CEN, SN, and DMN. These networks are identifiable at an early age and
undergo significant change until the age of about 20 years. Deficits in these networks are often seen in
neurodevelopmental disorders and chronic pain (Bhatt et al., 2020).
1.6.2 The developing brain during childhood and adolescence and its role in pain
Neuroplasticity in infancy and childhood underlies the tremendous nociceptive brain development before
adulthood (Verriotis et al., 2016b). Neuroplasticity plays an essential role in the development of neonatal
brain circuits and GM and WM development throughout adolescence. The human brain undergoes extensive
changes in GM and RS-FC throughout development into adolescence and young adulthood (Gogtay et
al., 2004; Lebel et al., 2008; Lenroot et al., 2007; Sowell et al., 2003; Supekar et al., 2009; Uddin et al.,
2011). The effect of nociceptive stimuli on the developing, infant human brain has been studied using
electroencephalography and event-related potentials (ERPs). Larger nociceptive ERPs but not tactile ERPs
have been found in preterm infants who were exposed to many invasive, skin-breaking, painful procedures
22

and morphine (Slater et al., 2010), suggesting that pain experienced in the neonatal intensive care unit
(NICU) affects brain networks involved in pain processing but not in perception of non-painful tactile stimuli.
Up to 68.4% of children who spend time in the NICU can develop chronic pain by age 10 years, and greater
pain-related stressors, painful procedures, and morphine in the NICU are associated with lower global brain
volumes and lower GM throughout the brain during childhood (Ranger et al., 2013; van den Bosch et al.,
2015). Abnormalities in WM microstructure are associated with greater numbers of invasive procedures by
age 7 years and lower cognitive function (Vinall et al., 2014). Cortical abnormalities associated with chronic
pain can continue into adulthood and are manifest by both physical and psychological symptoms (Fearon
& Hotopf, 2001; Gupta et al., 2015; Hotopf et al., 1998b). These results suggest that the developing brain is
plastic and vulnerable and that changes in pain-processing circuits in response to nociceptive stimuli can
result in long-lasting anatomical and functional changes contributing to lifelong chronic pain.
As children age, their GM decreases and their RS-FC increases (Menon, 2013). Pain during childhood
may magnify and hasten these neurodevelopmental changes. Along these lines, the rapid neurological
changes as a consequence of GM loss during synaptic pruning and associated myelination could lead
to increased RS-FC connectivity. It’s hypothesized that this neural developmental “speed-up process”
involving microstructural processes such as the production of more myelin, selective synaptic pruning,
and the development of subcortical–cortical connectivity (Casey et al., 2019; Fair et al., 2009; Menon,
2013; Supekar et al., 2009) could be a cortical facilitator of chronic pain symptoms in children, facilitating
exacerbated communication between brain regions. In adults, a reduction of cortical GM, specifically in the
cingulate, OFC, insula, dlPFC, thalamus, and somatosensory cortex, has been associated with chronic pain
and linked to premature aging (Apkarian et al., 2004; Kuchinad et al., 2007a; Rodriguez-Raecke et al., 2009;
Schmidt-Wilcke et al., 2007; Schmidt-Wilcke et al., 2008). Longitudinal studies in adults with chronic pain,
compared to controls, have shown decreases in GM in the somatosensory cortex, motor cortex, throughout
the striatum, and in the insula (Baliki et al., 2011, 2012), a finding that is a consequence and not the cause
23

of chronic pain (Rodriguez-Raecke et al., 2009). Just as overall greater RS-FC was found in most cortical
networks in children with chronic pain, increased RS-FC has been observed in adults with chronic pain
longitudinally. Specifically, decreased GM and increased sensorimotor–mPFC functional connectivity found
in both cross-sectional and longitudinal studies predict pain chronification (Baliki et al., 2012; Hashmi et al.,
2013), and hyperconnectivity has been associated with the intensity of chronic pain (U. Lee et al., 2018).
High mPFC–nucleus accumbens (subcortical–cortical) connectivity in adults with chronic pain plays a
significant role in reorganizing and decreasing the GM in the cortex and increasing the emerging WM tracts
in pain-modulatory regions (Mansour et al., 2013).85 As the mPFC is involved in the SN, ERN, and CAN,
increased activity in mPFC in children with chronic pain can be heavily impacted by increasing activity
throughout multiple cortical networks involved in pain, thus increasing pain focus and amplifying pain
signaling (Alvarado et al., 2015; Baliki et al., 2012; Kucyi et al., 2014).
Formation of pain-associated pathologic neural networks is also influenced by genetic and epigenetic
factors (Alvarado et al., 2015; Buchheit et al., 2012; Descalzi et al., 2015). Atypical pain sensitivity and
sensory integration issues are common in children with developmental disorders (e.g., autism) (Riquelme
et al., 2018; Robertson & Simmons, 2013). These developmental brain alterations could leave children with
chronic pain vulnerable to the development or exacerbation of chronic pain and psychiatric pathologies at
later times during childhood and adolescence (Brattberg, 2004; Hassett et al., 2013; L. E. Simons et al., 2014)
and may even “set the stage” for pain problems throughout the lifespan.
1.7 Towards a Systems Biology Approach to Chronic Pain
1.7.1 The Challenge of Measuring Pain
The first hurdle to getting closer to an accurate biomarker of chronic pain is getting an accurate measurement
of pain in humans. At it’s core, pain is two dimensions of intensity (magnitude) and unpleasantness (affect).
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Clinically, patients use descriptions like stabbing, burning, throbbing, or pulsing. But much of how we
describe pain is learned (see Table 1.1), conditioned by our experiences or shaped by our language and
society or clinicians (Bourke, 2014). How we currently “measure pain” in humans falls into the following
broad categories: (1) self-reports using rating scales/descriptors/questionnaires to exogenous stimuli or
any ongoing and spontaneous pain; (2) observed measures of pain-like behavior; (3) indirect measures
of physiology/autonomic changes. Observed measures are currently subjective and may suffer from
cultural and social biases/influences, as well as a lack of sensitivity and specificity and (3) are indirect
physiology assessments and make significant assumptions when relating these physiological measures to
the underlying subjective state (Tracey et al., 2019). It’s being increasingly recognized that biomarkers of a
single modality will not be sufficient in capturing the heterogeneity of chronic pain (Figure 1.4), and that
composite biomarkers composed of behavioral/clinical data, genetics, molecular ’omics, and neuroimaging
data, using a systems biology approach, will be required to improve analgesic drug development, diagnosis,
patient stratification, and treatment targeting (Tracey et al., 2019).
1.7.2 A multi-omics approach to chronic pain
1.7.2.1 The brain-gut-microbiome system
Integrating other omics data with brain imaging holds significant potential for deepening our understanding
of molecular pathophysiology. Biomarkers representing predisposition to develop chronic pain, pain
chronification, and recovery and treatment outcomes (i.e. diagnostic, prognostic, predictive and responsive)
are being investigated in both preclinical animal models and human studies with very strong support from
the NIH HEAL Initiative and multiple NIH institutes (K. D. Davis et al., 2020). Such biomarkers should
have high interpretability, be generalizable across populations and contexts, and be easily deployable in
clinical trials or settings (K. D. Davis et al., 2020). This has historically been difficult due to small sample
sizes (Button et al., 2013), a lack of availability of clinically relevant biological features (Steingrímsdóttir
25

Figure 1.4: Adapted from (Tracey et al., 2019). Composite biomarkers and a systems biology approach are
needed to accurately improve analgesic drug development, diagnosis, patient stratification, and treatment
targeting.
26

et al., 2017), and a lack of translation in clinical studies (K. D. Davis et al., 2020). The development of
composite biomarkers using a multiomics approach from multiple assays such as brain MRI, genetics, the
microbiome, and behavior, has the potential to be efficacious for chronic pain prognosis, diagnosis, and
treatment response with high specificity and sensitivity. This approach has shown success in other fields
such as Alzheimer’s disease (L. Li et al., 2022); however no such universally accepted composite biomarker
currently exists for chronic pain (K. D. Davis et al., 2020; Tracey et al., 2019).
1.7.2.2 A multi-omics understanding of irritable bowel syndrome
Irritable bowel syndrome (IBS) is a chronic visceral pain condition for which recent research has increasingly
supported an integrative brain-gut-microbiome (BGM) model. This is one chronic pain condition for which
using a multi-omics approach has shown great results. The BGM model synthesizes extensive evidence
from studies on peripheral and central neurobiological mechanisms, brain- and gut-targeted effects of the
environment, and findings from large-scale genetic analyses that highlight links to neuronal dysfunction
in both the central nervous system (CNS) and the enteric nervous system (ENS). This systems biological
model is consistent with the frequent comorbidity of IBS with other so-called functional GI disorders, and
with other chronic pain and psychiatric disorders, in particular with anxiety (Figure 1.5) (Mayer et al., 2023).
In the case of IBS, multi-omics approaches have shown promise in uncovering subtype specific biological
pathways contributing to symptom presentation (Figure 1.6). Of note, we have shown that using resting-state
fMRI, metabolomics, the microbiome, and clinical/behavioral data, we can accurately classify constipation
predominant IBS (IBS-C), diarrhea predominant IBS (IBS-D) and healthy controls (Sarnoff et al., 2023).
Bloating and visceral sensitivity were the most important clinical variables to separate IBS from healthy
controls (HCs) in this study and thus served as clinical proxies for IBS. Compared to healthy controls, both
IBS-C and IBS-D had lower abundance of 5 bacterial taxa most important in differentiating IBS from HCs
(Paraprevotella spp., Blautia obeum, Streptococcus spp., Catenibacterium mitsuokai, and Prevotella 9 spp.).
27

Figure 1.5: The brain-gut-microbiome system. Adapted from Mayer, Ryu and Bhatt. 2023. The brain
connectome, gut connectome and gut microbiome communicate in a bidirectional way. The response
characteristics of the system are determined by vulnerability genes interacting with different influences
from the exposome. The different loops use neural, endocrine, paracrine and immune signaling mechanisms.
Perturbations (stressors) of the different nodes of the system (brain, gut, immune, microbiota) result in nonlinear effects and alterations in response characteristics manifesting as psychiatric and/or gut symptoms.
ANS autonomic nervous system, SNS sympathetic nervous system, PBMCs peripheral blood mononuclear
cells, SCFAs short chain fatty acids, AhR aryl hydrocarbon receptor.
28

This raises the possibility that dysbiosis in IBS may be largely explained by its role in visceral sensitivity
and bloating, as supported by their consistently negative relationship with these taxa in our integrated
analysis. For example, Blautia spp. and specifically Blautia obeum have been shown to be inversely related to
inflammation, obesity, and pathogenic bacteria (X. Liu et al., 2021). Therefore, our finding that Blautia obeum
was negatively associated with visceral pain and bloating may suggest an underlying inflammatory pathway,
associating the microbiome to IBS symptomatology. Prior work supports that visceral hypersensitivity
specifically may have an etiologic root in dysbiotic intestines (Chichlowski & Rudolph, 2015), where
improving the dysbiosis with, for example, a probiotic, has improved visceral hypersensitivity in animal and
human models (Parkes et al., 2008). The lower brain connectivity observed in the orbitofrontal cortex (OFC)
shown in both IBS-C and IBS-D compared to HC may implicate an impairment in pain regulatory pathways.
The OFC encodes associations between sensory stimuli and emotionally relevant internal states, interfaces
with the CAN, and regulates descending pain pathways (Mayer, Labus, et al., 2015a). Lower OFC connectivity
may thus translate to less regulatory ability to modulate descending pain pathways (from the ‘top-down’),
which is consistent with the negative association between the OFC and bloating/visceral sensitivity in our
IBS cohort. Taken together, the indirect relationship between select gut microbes/metabolites and visceral
sensitivity and bloating, coupled with the direct relationship of these microbial/metabolomic factors with
OFC connectivity alterations, suggests that interactions in the gut may mediate central autonomic and
descending pain pathways to produce symptomatology in IBS.
Brain-gut-microbiome associations between loose stool, tryptophan and phenylalanine, and key default
mode and salience regions differentiated IBS-D from IBS-C. Loose stool can serve as a proxy for IBS-D,
and hard stool for IBS-C, as these were the most important clinical variables to separate IBS-C from IBS-D
in the integrated model. Prevotella spp. emerges as the only highly important microbial variable, with
lower abundance in both IBS-C and IBS-D, but with a specifically positive association with hard stool and a
negative association with loose stool. In the metabolome, IBS-D had higher levels of multiple metabolites
29

compared to IBS-C and HCs, but tryptophan as well as phenylalanine are of particular interest. Tryptophan
has a powerful role in the gut, largely through its role in gut serotonin production and modulation. Microbes
can produce and degrade tryptophan (the precursor to serotonin) independently of the host, which affects
overall intestinal serotonin production. However, they can also modulate serotonin synthesis and activity
indirectly through producing metabolites such as SCFAs, which can promote the conversion of tryptophan
to serotonin (via tryptophan hydroxylase) and regulate serotonin transporter activity and expression
(Layunta et al., 2021). Gut serotonin increases motility, secretion, and visceral sensitivity (Mawe & Hoffman,
2013) – all hallmarks of diarrhea-predominant bowel habits. Both tryptophan and phenylalanine had a
positive association with increased brain connectivity between the SMN (posterior insula) and the DMN
(subparietal sulcus) as they related to loose stool. These brain regions are known for processing aversive
sensory stimuli from the viscera and interfacing with self-related homeostasis, respectively. Previous work
has demonstrated a positive association between gut tryptophan and DMN connectivity (Osadchiy et al.,
2020). Individuals with chronic diarrhea may have abnormal tryptophan- and phenylalanine-mediated
signaling traveling from the viscera to the posterior insula, influencing self-related thoughts, emotions
and pain perception. Together, these may produce the IBS-D phenotype. We also found BGM alterations
involving symptom duration, select microbial taxa, and the caudate differentiate IBS from HC. The results
provided further potential mechanistic insight as to how symptom duration is related to caudate nuclei
connectivity via differing relationships of Faecalibacterium prausnitzii, Bacteroides stercoris, phosphocholine,
and creatinine, but further research is needed to uncover their role. This serves as a foundation upon
which to assess potential pathophysiological and therapeutic targets in mediating bowel habit subtypes
of IBS. The association between abnormal connectivity involving central autonomic and descending pain
regulatory networks, gut microbiome/metabolite changes, and select IBS symptoms outlines a compelling
BGM signature in IBS.
30

Figure 1.6: Relevance network from the DIABLO analysis depicting the correlation between different ’omics types. Adapted from Sarnoff and Bhatt et al. 2023. Red lines represent positive correlations
and blue lines represent negative correlations. Cutoff for the correlations was r = 0.7. Microbiome features include (A) Paraprevotella. sp, (B) Blautia obeum, (C) Streptococcus. sp, (D) Prevotella 9. sp, (E)
Catenibacterium mitsuokai (F) Faecalibacterium prausnitzii and (G) Bacteroides stercoris. Metabolome
features include (1) erythronate, (2) palmitoyl-linoleoyl-glycerol, (3) valine, (4) serine, (5) phenylalanine, (6)
threonine, (7) tryptophan, (8) phosphocholine, and (9) creatinine. Brain connectome features include (1)
orbitofrontal cortex (CAN) resting-state functional connectivity (rsFC), (2) rsFC between the anterior insula
(SAL) and posterior cingulate (DMN). rsFC between the posterior insula (SMN) and subparietal sulcus
(DMN). (3) rsFC between the caudate nuclei (SMN).
31

Chapter 2
Mapping Brain Structure Variability in Chronic Pain: The Role of
Widespreadness and Pain Type and Its Mediating Relationship With
Suicide Attempt
The following section is adapted from:
Bhatt RR, Haddad E, Zhu AH, Thompson PM, Gupta A, Mayer EA, Jahanshad N. Mapping Brain Structure
Variability in Chronic Pain: The Role of Widespreadness and Pain Type and Its Mediating Relationship
With Suicide Attempt. Biol Psychiatry. 2024 Mar 1;95(5):473-481. doi: 10.1016/j.biopsych.2023.07.016
2.1 Abstract
Chronic pain affects nearly 20% of the U.S. population. It is a leading cause of disability globally and is
associated with a heightened risk for suicide. The role of the central nervous system in the perception and
maintenance of chronic pain has recently been accepted, but specific brain circuitries involved have yet
to be mapped across pain types in a large-scale study. We used data from the UK Biobank (N = 21,968)
to investigate brain structural alterations in individuals reporting chronic pain compared with pain-free
control participants and their mediating effect on history of suicide attempt. Chronic pain and, more notably,
chronic multisite pain was associated with, on average, lower surface area throughout the cortex after
32

adjusting for demographic, clinical, and neuropsychiatric confounds. Only participants with abdominal
pain showed lower subcortical volumes, including the amygdala and brainstem, and lower cerebellum
volumes. Participants with chronic headaches showed a widespread thicker cortex compared with control
participants. Mediation analyses revealed that precuneus thickness mediated the relationship of chronic
multisite pain and history of suicide attempt. Mediating effects were also identified specific to localized
pain, with the strongest effect being amygdala volume in individuals with chronic abdominal pain. Results
support a widespread effect of chronic pain on brain structure and distinct brain structures underlying
chronic musculoskeletal pain, visceral pain, and headaches. Mediation effects of regions in the extended
ventromedial prefrontal cortex subsystem suggest that exacerbated negative internal states, negative selfreferencing, and impairments in future planning may underlie suicidal behaviors in individuals with chronic
pain.
2.2 Introduction
Chronic pain (CP) affects 1 in 5 people in the United States (Kuehn, 2018) and is the leading cause of disability
and disease burden globally (Vos et al., 2017). CP is now recognized as a disease of the central nervous
system (Martucci & Mackey, 2018; Treede et al., 2019), and is often comorbid with psychopathology such as
anxiety and depression (L. E. Simons et al., 2014). Central sensitization underlies the role of the central
nervous system in amplifying normal sensory or mild nociceptive stimuli to produce an overwhelming and
sustained pain experience and highlights the key common mechanism underlying many chronic overlapping
pain conditions (Fitzcharles et al., 2021b). Currently, measures for pain include self-report, altered behavior
(e.g., avoidance and grimacing), or changes in physiology (e.g., heart or respiration rate) (Tracey, 2021).
Neuroimaging may provide a set of objective measures with mechanistic insight to help decode this
experience ultimately emerging in the brain (Tracey, 2021). The theory behind pain processing in the brain
has changed from an idea of a pain neuromatrix, which restricts the brain signature to pain sensation alone,
33

to a signature that considers various intrinsic brain networks and modulatory control systems that together
produce the experience and maintenance of CP (Canavero & Bonicalzi, 2015a; Fitzcharles et al., 2021b;
Martucci & Mackey, 2018). However, neuroimaging biomarkers reliable enough for clinical use are still
unavailable (Van Der Miesen et al., 2019).
CP is associated with an elevated risk for suicidal thoughts and behaviors (Hooley et al., 2014; Racine,
2018). The World Health Organization reports that approximately 785,000 deaths by suicide occur annually,
but these make up only 5% of suicide attempts (Turecki et al., 2019). Up to 41% of individuals reporting
CP also report suicidal ideation (Racine, 2018). Migraines, arthritis, back pain, and idiopathic pain have
each been independently associated with risk for suicidal behaviors, even after controlling for age, sex,
depression, and other coexisting conditions (Racine, 2018). A recent meta-analysis showed that people
with CP had more prevalent death wishes, suicidal ideations, intentions, attempts, and deaths compared
with people without pain (all odds ratios > 2) (Calati et al., 2015). A growing body of evidence suggests
that risk for suicidality and CP may share common neural mechanisms; there is considerable overlap in
the brain circuitry that has evolved for maintaining emotional attachment and the circuitry involved in
maintaining CP. Many of these regions have been previously implicated in the context of addiction, such
that the interaction of structures involved in sensory, reward, antireward, motivation, emotions, cognition,
and arousal neural systems regulate pain-related mood and behavior (Elman & Borsook, 2016; Elman et al.,
2013; Ren et al., 2015). This includes afferent pathways responsible for the sensory-discriminative aspects
of pain, corticolimbic structures responsible for emotional-motivational aspects, and a descending pain
modulatory system in the brainstem (Elman et al., 2013). The disruption of the normal function of stress and
reward processing structures by pain may lead to decreased ability to control suicidal urges and a greater
sensitivity to the impact of painful stimuli (Elman et al., 2013). Charting the variability in the structure and
function of the brain, especially in regions involved in the perception and emotional feeling of pain, may
34

provide mechanistic insight into how risk for CP may also be associated with increased risk for suicidal
behaviors.
We aimed to address these gaps/inconsistencies in the literature by using UK Biobank (UKB) data to
identify in vivo brain signatures of CP and assess the relationship of the CP brain circuitry with suicidal
behaviors. The UKB provides brain magnetic resonance imaging (MRI) data from thousands of participants
with consistent neuroimaging protocols (K. L. Miller, Alfaro-Almagro, Bangerter, Thomas, Yacoub, Xu,
Bartsch, Jbabdi, Sotiropoulos, Andersson, et al., 2016). This allows us to assess comorbidity across CP
phenotypes as well as CP occurring in isolation at one body site. We aimed to determine how brain
morphometry in individuals reporting CP at different body sites differs relative to individuals without pain
and if these differences mediate the relationship between CP and a history of suicide attempt.
2.3 Methods and Materials
2.3.1 Participants
Participant data were analyzed from the population-based UKB study (Alfaro-Almagro et al., 2018; K. L.
Miller, Alfaro-Almagro, Bangerter, Thomas, Yacoub, Xu, Bartsch, Jbabdi, Sotiropoulos, Andersson, et al.,
2016) through Application Number 11559. Inclusion criteria included a T1-weighted brain MRI scan at the
first imaging visit and a response to the pain question field ID 6159. Any participants with a diagnosis of a
severe mental health condition (including anxiety and depression), personality disorder, neoplasm, obesity,
or diabetes (defined via ICD-10 codes F00–F99, C00–D48, E65–E68, and E10–E14) were excluded. Individuals
classified as morbidly obese (body mass index > 40) via data field 21001 were also excluded. As many
individuals may exhibit subclinical psychological distress that does not meet diagnostic criteria for a mental
health disorder, neuroticism scores were calculated for each individual. Neuroticism scores are highly
correlated with anxiety and depression in the general population and in the UKB, where they strongly
35

mediate the relationship between anxiety or depression and functional well-being (Batty et al., 2016; Fabbri
et al., 2021; Jylhä & Isometsä, 2006). Exclusion criteria were selected to focus on potential supraspinal
nociplastic mechanisms and remove confounding sources of variance not specific to pain, including anxiety
and depression diagnoses (Fitzcharles et al., 2021b)]. Participant demographics are summarized in Table 1
and Tables S1–S13 in the Supplement of the published article.
2.3.2 Neuroimaging Acquisition
All participants completed a 31-minute neuroimaging protocol using a MAGNETOM Skyra 3T scanner
(Siemens Healthineers) and a 32-channel head coil in one of 3 image scanning locations. All structural
T1-weighted scans were acquired using the following parameters: three dimensional magnetizationprepared rapid acquisition gradient-echo; sagittal orientation; in-plane acceleration factor = 2; inversion
time/repetition time = 880/2000 ms; voxel resolution = 1 × 1 × 1 mm; acquisition matrix = 208 × 256 × 256
mm (Alfaro-Almagro et al., 2018; K. L. Miller, Alfaro-Almagro, Bangerter, Thomas, Yacoub, Xu, Bartsch,
Jbabdi, Sotiropoulos, Andersson, et al., 2016).
2.3.3 Neuroimaging Data Processing
Measures of regional cortical thickness, cortical surface area, and subcortical volume were extracted using
FreeSurfer 7.1 (Fischl, 2012a). This yielded regional cortical thickness, cortical surface area, and subcortical
volume measurements for each of the 74 bilateral regions in the Destrieux (Destrieux et al., 2010) cortical
and 11 bilateral regions plus the brainstem of the Harvard-Oxford subcortical (Frazier et al., 2005; Goldstein
et al., 2007; Makris et al., 2006) atlases. To address the atlas concordance problem (lack of consistent spatial
definitions of regions of interest across brain atlases) (Revell et al., 2022), we also conducted the analysis
with the Desikan-Killiany atlas (Desikan et al., 2006). All results corresponding to the Desikan-Killiany
atlas are presented in the Supplement of the published article.
36

2.3.4 Operational Definitions of Pain and Suicidality Variables of Interest
Participants in the UKB were asked at the time of scanning about “pain types experienced in the last month”
(field ID 6159), with possible answers being “prefer not to answer”; pain at each of 7 different body sites
(head, face, neck/shoulder, back, stomach/abdomen, hip, knee); “all over the body”; or “none of the above.”
They were then asked if the pain at the specific site had been present for 3 or more months (category ID
100048). CP was defined as having 3 or more months of pain at any of these body sites. Control participants
were defined as those who specifically reported “none of the above” in response to field ID 6159. Participants
were classified as having pain at each body site if they reported 3 or more months of pain at the said site,
even if CP was reported at other body sites. Participants having 3 or more months of pain at exclusively one
of the body sites were classified with exclusive CP at that body site. In subsequent analyses, participants
reporting CP at only one body site were classified as having chronic localized pain. Participants reporting
CP at 2 or more body sites, or responding “all over the body” were classified as having chronic multisite pain.
Analyzing these subgroups would give greater insight as to what cortical differences may be specific to CP
mediated by central sensitization, a common feature across CP diagnoses and shown in the UKB (Bortsov
et al., 2022; Khoury et al., 2021; Nijs, George, Clauw, Fernández-de-las-Peñas, et al., 2021). Participants
reporting pain, but not for 3 months, and those responding “prefer not to answer” were not included in this
analysis. Available occupation data were also obtained using field ID 22617 at the top level (9 categories).
The prevalence of chronic (localized and multisite) pain across occupation categories in participants with
CP compared with control participants is available in Figures S17–S19 of the published article.
The presence of suicide attempt was assessed either from hospital inpatient records (category 20002) of
intentional self-poisoning/self-harm (ICD-10 code X60–X84, ICD-9 code E950–E959) or from self-report
(category 146) of “ever attempted suicide” and “attempted suicide in the past year.”
37

2.3.5 Statistical Analyses
To assess if the presence of CP was associated with cortical features, baseline neuroimaging data were
compared between participants reporting CP and control participants, covarying for age, centered agesquared, sex, body mass index (data field 21001), centered age-by-sex interaction, neuroticism scores (data
fields 1920 + 1930 + 1940 + 1950 + 1960 + 1970 + 1980 + 1990 + 2000 + 2010 + 2020 + 2030 = neuroticism score),
scanner site (data field 54), socioeconomic status using the Townsend deprivation index (data field 189),
and intracranial volume (for surface area and subcortical volume, but not cortical thickness). Participants
in the CP versus healthy control analysis were matched at a 1:1 ratio and a 1:3 ratio for localized pain
conditions, also matching for age and sex using the pairmatch() function of the optmatch package in R
Hansen and Klopfer, 2006. Nociceptive pain fibers through the ascending spinothalamic-cortical tracts
mostly cross the midline and ascend contralaterally into the brain. The spinoparabrachial tract mostly
projects in a contralateral manner (60%), but also has many (40%) ipsilateral and bilateral projections (Roza
& Martinez-Padilla, 2021). While it is recognized that nociceptive information is processed contralateral
to the pain site, and many brain structures and endogenous opioid systems exhibit lateralization (Roza &
Martinez-Padilla, 2021), many higher-order brain regions (e.g., insula, posterior cingulate) regulate pain
bilaterally (Roza & Martinez-Padilla, 2021). Thus, we analyzed the current data laterally and bilaterally to
ensure maximum sensitivity. FreeSurfer metrics of brain regions were analyzed using a lateralized approach
(left and right hemispheres separately), and subsequent analyses used FreeSurfer metrics averaged across
hemispheres (Tables S14–S17 of the published article). Subsequent analyses split individuals reporting
chronic headaches, neck/shoulder pain, low back pain, abdominal pain, hip pain, knee pain, and pain-free
control participants. These participants were matched to control participants with a 3:1 control-case ratio,
accounting for age and sex. General linear models tested associations between each CP location and 478
brain features. The false discovery rate was used for multiple comparisons correction for each pain type
(i.e., across all regional cortical thickness, cortical surface area, and subcortical volume measurements).
38

To assess whether participants with CP reported greater history of suicide attempt than control participants, a χ
2
test was conducted between CP (yes/no) and history of suicide attempt (yes/no). To determine if
cortical structure mediates the relationship between CP and suicidal behavior, logistic regression mediations
were run with CP as the predictor variable, suicide attempt as the outcome variable, and individual false
discovery rate–significant cortical and subcortical features from the case/control analyses as the mediators.
Age, sex, socioeconomic status, intracranial volume, and neuroticism scores all were included as covariates.
It has been shown that a decrease in gray matter is the consequence of CP, not the cause, and is reversible
with appropriate treatment (Gwilym et al., 2010; Rodriguez-Raecke et al., 2009; Seminowicz et al., 2011),
supporting the direction of the current mediation model. All potential biological mediators were tested
simultaneously with 5000 permutations using the MultiMed package in R (Boca et al., 2013). Briefly, this is
a permutation-based method with joint correction that controls the familywise error rate, is based on the
maximal test statistic, and is able to account for the underlying correlations among mediators (Boca et al.,
2013; McDermott et al., 2018). The outcome is an S statistic and a p-value. The S statistic is the absolute
value of the product of the correlation between the independent variable and the mediator and the partial
correlation between the mediator and the outcome conditional on the independent variable. A greater S
statistic and lower p-value are indicative of a more significant mediator (Boca et al., 2013).
2.4 Results
Participants with pain were divided into those reporting chronic headaches (n = 1639, mean age = 61 years,
1149 females), neck/shoulder pain (n = 3493, mean age = 64 years, 1975 females), abdominal pain (n = 740,
mean age = 63 years, 446 females), back pain (n = 3845, mean age = 64 years, 2046 females), hip pain (n
= 2451, mean age = 65 years, 1541 females), and knee pain (n = 4551, mean age = 65 years, 2374 females).
There were 13 individuals with a history of suicide attempt from hospital inpatient records, 137 individuals
stating they had “ever attempted suicide,” and 133 individuals reporting “attempted suicide in the past year.”
39

Figure 2.1: Significant differences in brain structure in participants reporting chronic pain, chronic
localized pain, and chronic multisite pain vs. control participants Effect sizes (Cohen’s d) for surface
area (A), cortical thickness (B), and subcortical volumes (C) of regions that are significant between groups.
Positive effect sizes indicate larger values for chronic pain participants than control participants. Red
represents greater brain metric values, and blue represents lower brain metric values.
In total, 162 participants, identified from hospital inpatient records and self-report, had a history of at least
one suicide attempt. The prevalence of CP and overlap across pain locations in the UKB is depicted in
Figure S1 in the Supplement and https://brainescience.shinyapps.io/ChronicPain_UKBiobank.
2.4.1 Participants With CP Versus Pain-Free Control Participants
Participants reporting CP, irrespective of pain site, had significant effects, not restricted to any network
but globally throughout the brain, compared with control participants (Figure 2.1; Table S14). Regions of
interest throughout the cortex, except the occipital lobe, exhibited a significantly lower cortical surface
area. Regions of interest in the superior parietal cortex exhibited a greater cortical thickness. The chronic
localized pain subgroup exhibited lower surface area in the sensorimotor cortices, anterior and posterior
insula, posterior cingulate, and middle frontal gyrus. The chronic multisite pain subgroup exhibited the
strongest effects and lower surface area in the sensorimotor cortices, anterior and posterior insula, anterior
and posterior cingulate, dorsolateral prefrontal cortex (PFC), and lateral temporal lobes and greater cortical
thickness in the superior parietal lobe and orbitofrontal cortex.
40

2.4.2 Participants With Chronic Localized Pain Versus Control Participants
All results are shown in Figure 2.2 and Tables S14–S17 in the published article. Greater cortical thickness
was observed throughout the parietal cortex, occipital cortex, middle and superior frontal gyri, dorsolateral
PFC, and inferior orbital gyrus in the chronic headaches group. Differences between chronic headaches
and other pain types were statistically significant. People reporting chronic neck/shoulder pain had lower
surface area throughout the brain. They also had greater cortical thickness in the superior parietal gyrus
but lower cortical thickness in the rectus gyrus. People reporting chronic back pain had lower surface
area throughout the cortex. People reporting chronic abdominal pain had lower surface area in the middle
temporal gyrus, lingual gyrus, and parieto-occipital sulcus. Lower volumes were observed in the left
amygdala, cerebellum, and brainstem. People reporting chronic knee pain had lower surface area in the
sensorimotor cortices, anterior and posterior insula, anterior and posterior cingulate, and lateral temporal
cortices. They showed greater cortical thickness in the posterior parietal cortex and precuneus. People
reporting chronic hip pain had lower surface area throughout the majority of the cortex.
2.4.3 Participants With CP Exclusive at One Body Site Versus Control Participants
Many participants who reported CP at more than one site fell into multiple pain groups. Analyses were also
performed on the subset of individuals reporting exclusively single-site CP. Individuals with chronic exclusive abdominal pain had lower bilateral amygdala volumes, individuals with chronic exclusive headaches
had greater cortical thickness in the occipital and superior parietal lobes, and individuals with chronic
exclusive neck/shoulder pain had lower surface area in the right posterior cingulate. While many effects in
the exclusive subset were not significant after multiple comparisons correction, the direction and magnitude
of marginal effects (uncorrected p < .05) were very much in line with the larger (nonexclusive) analysis
(Tables S14–S17; Figures S7–S14 in the Supplement of the published article).
41

Figure 2.2: Significant differences in brain structure in participants reporting chronic pain across
different body regions compared with control participants Effect sizes (Cohen’s d) for surface area
(A), cortical thickness (B), and subcortical volumes (C) for regions that are significant after false discovery
rate correction. Red represents greater brain metric values, and blue represents lower brain metric values.
2.4.4 CP Is Associated With History of Suicide Attempt
The χ
2
test revealed a significant association between suicide attempt and CP (χ
2
1 = 12.68, p = 3.69 × 10−4
,
V = 0.02), localized pain (χ
2
1 = 7.24, p = 7.14 × 10−3
, V = 0.02), multisite pain (χ
2
1 = 13.26, p = 2.71 × 10−4
,
V = 0.03), headaches (χ
2
1 = 6.22, p = .01, V = 0.02), neck/shoulder pain (χ
2
1 = 18.27, p = 1.91 × 10−5
, V =
0.03), back pain (χ
2
1 = 8.80, p = 3.02 × 10−3
, V = 0.02), abdominal pain (χ
2
1 = 48.14, p = 3.97 × 10−12
, V =
0.06), hip pain (χ
2
1 = 4.31, p = .04, V = 0.02), and knee pain (χ
2
1 = 6.24, p = .01, V = 0.02) (Figure 2.3).
2.4.5 Brain Structure Mediates the Relationship Between Pain and History of Suicide
Attempt
The relationship between chronic multisite pain and suicide attempt was mediated via precuneus thickness.
The relationship between chronic neck/shoulder pain and suicide attempt was mediated via right superior
42

Figure 2.3: Proportion of individuals reporting suicide attempt by chronic pain group χ
2
tests were
conducted between no chronic pain and each of the chronic pain groups. Significance values and effect
sizes are reported for each group. Hx, history.
parietal gyrus thickness. The relationship between chronic abdominal pain and suicide attempt was
mediated via volume of the brainstem and amygdala (Table 2.1).
Pain Condition Brain Region S Statistic ACME (95% CI) pFWE
Value
Chronic Multisite Pain Precuneus thickness 0.022 0.024 (-0.01 to 0.09) .03
Abdominal Pain Brainstem 0.029 0.041 (-0.005 to 0.10) .03
Exclusive Abdominal Pain Left amygdala 0.025 0.045 (0.001 to 0.01) .04
Neck/Shoulder Pain Right superior parietal gyrus thickness 0.015 0.023 (0.003 to 0.06) .04
Table 2.1: Mediating Effect of Brain Regions on Relationship Between Chronic Pain and Suicide
ACME, average causal mediation effect; pFWE, familywise error–corrected p.
2.5 Discussion
We used in vivo brain MRI data from 10,984 adults reporting CP and 10,984 age- and sex-matched pain-free
control participants to advance our understanding of the role of the central nervous system in CP. We 1)
identified differences in brain morphometry between individuals with CP and control participants and the
widespreadness of pain; 2) identified distinct differences in brain signatures across localized pain types,
43

most notably, the distinct signature of headaches; 3) and confirmed the relationship between CP and suicide
attempt, while identifying brain structures that may mediate this relationship.
Extensive differences in cortical surface area and thickness were observed in participants with all CP
conditions versus control participants. Studies have shown lower gray matter volume throughout the brain
associated with CP, including the PFC (Shiers & Price, 2020), insula, thalamus, medial temporal lobe, anterior
and posterior cingulate, hippocampal regions, basal ganglia, sensorimotor network, brainstem, cerebellum,
and ventral diencephalon. However, larger gray matter volumes have also been observed (Apkarian et al.,
2004; Bhatt et al., 2020; Borsook, Erpelding, & Becerra, 2013; Fritz et al., 2016; Kuchinad et al., 2007b;
Martucci & Mackey, 2018; Mayer, Gupta, et al., 2015b; Rodriguez-Raecke et al., 2009; Seminowicz et al.,
2010). Our results break volume down into biologically divergent thickness and surface area components
(Panizzon et al., 2009a), helping to explain the sources of discrepancies in the literature. We show that
cortical and subcortical effects associated with CP are more extensive than previously reported, and effect
sizes are stronger in individuals with multisite pain compared with localized pain. The effects are still
present when controlling for subclinical anxiety and depression. This highlights the need for large, highly
powered studies for accurate and replicable results, with the potential for use in clinical settings (K. D. Davis
et al., 2017). When investigating CP at exclusively one body site, the lower-powered analyses did not
produce as many significant results across body sites, but greater cortical thickness in the occipital and
parietal lobes for chronic exclusive headaches, lower amygdala volumes for chronic exclusive abdominal
pain, and lower surface area in the cingulate in chronic exclusive neck/shoulder pain were consistent
findings.
Chronic back, neck/shoulder, hip, and knee pain showed similar results compared with the full sample
in terms of global cortical surface area and volume, albeit not as extensive. These results support the notion
of distinct brain signatures of chronic musculoskeletal pain, visceral pain, and headaches. Moreover, CP as
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a whole and all of the individual CP body sites were associated with suicide attempt. Chronic abdominal
pain had the greatest association with suicide attempt.
Lower surface area in regions of the default mode network (i.e., medial PFC, posterior cingulate
cortex/precuneus, lateral temporal cortex) in individuals with chronic abdominal pain supports the role
of attentional systems impacting sustained visceral pain severity (Bhatt et al., 2021; Mayer et al., 2023).
Only chronic abdominal pain showed significant effects in the amygdala, brainstem, and cerebellum. As
many brain networks have been implicated in chronic abdominal pain (Mayer et al., 2023), our highly
powered results may represent core mechanisms in the central autonomic network regardless of populationlevel individual differences (Mayer et al., 2023). This network is closely connected to the enteric nervous
system via vagal and sympathetic afferents that regulate visceromotor, neuroendocrine, pain, and behavioral
responses essential for survival (Benarroch, 1993b; Mayer et al., 2023). Brainstem function specific to visceral
pain shows greater activation compared with musculoskeletal pain and is specifically associated with anxiety
(Dunckley et al., 2005). The amygdala plays a key role in tagging emotional valence and engaging autonomic
survival responses to behaviorally relevant stimuli, (Mayer et al., 2023) and has strong reciprocal connections
to brainstem nuclei, including the dorsal vagal complex, parabrachial nucleus, periaqueductal gray, medulla,
and mesopontine reticular formation (Veinante et al., 2013). Chronic abdominal pain has been shown to
lead to dysregulation of the amygdala, facilitating the stress response and visceral pain via neurotransmitter
release from the reticular formation (Greenwood-Van Meerveld et al., 2016). Lower amygdala and brainstem
volumes have been observed across neuroimaging studies with chronic abdominal pain, and successful
pharmacologic and nonpharmacologic interventions have normalized aberrant functional connectivity
associated with symptom severity (Mayer et al., 2023; Nisticò, Rossi, D’Arrigo, et al., 2022). The mediating
effect of amygdala and brainstem volumes between chronic abdominal pain and suicide attempt may also
manifest as behaviors of self-aggression and impulsivity, which have previously been associated with lower
amygdala volumes and altered serotonergic signaling from the brainstem (Schmaal et al., 2019).
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People with chronic headaches exhibited a different brain structure pattern compared with control
participants relative to the other CP conditions. Specifically, thicker gray matter was observed throughout
the cortex. Greater cortical thickness has been observed in other studies in people reporting chronic
headaches and migraine, mainly in the primary somatosensory cortex, temporo-occipital, and middle
frontal regions (DaSilva et al., 2007; Hadjikhani, 2008; J. H. Kim et al., 2014; Maleki et al., 2012; Messina et al.,
2013; Tolner et al., 2019); however, no study has reported higher cortical thickness throughout the cortex
as currently identified. Cortical spreading depression (CSD), the current leading theory underlying the
pathophysiology of migraine (Lauritzen et al., 2010; Nyholt et al., 2017; Tolner et al., 2019), is characterized
as a neuronal and glial depolarization originating at the occipital pole and spreading anteriorly over the
lateral, medial, and ventral surfaces of the brain (Lauritzen et al., 2010; Tolner et al., 2019). A thicker cortex
could be observed where CSD occurs due to an influx of water following the influx of sodium and calcium
in neurons and sodium and potassium pumps failing to provide a sufficient outward current to balance the
inward currents of AMPA/kainate and NMDA receptor channels (Dreier, 2011; Tolner et al., 2019). A study
modeling CSD propagation in migraine patients using diffusion MRI showed that the propagation occurs
in regions overlapping with the thicker regions identified in this study, specifically the visual cortex, the
sensorimotor network, the parietal lobe, Broca’s area, and the dorsolateral PFC (Kroos et al., 2019). The
thicker occipital pole in chronic exclusive headaches suggests the originating point of CSD and should be
further investigated. These findings support the idea that chronic headaches have different biological causes
than chronic musculoskeletal and visceral pain. Neck/shoulder pain often co-occurs with and can even
aggravate headaches, and our sample showed the most overlap with chronic headaches and neck/shoulder
pain. This can be clinically observed with tenderness, tightness, and muscle contractions in myofascial
trigger points in the neck and shoulders, which leads to a pull of the dura mater and the formation of
myodural bridges (Shah & Hameed, 2020).
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The global effects of the lower surface area of CP as a whole and musculoskeletal CP (i.e., neck/shoulder,
back, hip, knee) conditions were far greater than previously observed (Bhatt et al., 2020; Martucci & Mackey,
2018; Mercer Lindsay et al., 2021b), but corroborate the involvement of multiple brain networks working
together to subserve the pain experience (Mayer, Gupta, et al., 2015b; Mayer et al., 2023; Tracey, 2021).
Thickness of the precuneus—part of the proposed extended ventral PFC subsystem with respect to suicide
(Schmaal et al., 2019)—mediated the relationship between chronic multisite pain with suicide attempt.
The right superior parietal gyrus, also a key region of the extended ventral PFC system, (Schmaal et al.,
2019) mediated the relationship between chronic neck/shoulder pain and suicide attempt. Functional
connectivity between the superior parietal gyrus and middle frontal gyrus, known as the executive control
network (Seeley et al., 2007b), is lower in suicide attempters and associated with greater suicidal ideation
and impulsivity (Cao et al., 2021; Ordaz et al., 2018). The superior parietal gyrus plays a key role in
sensorimotor integration by maintaining an internal representation of the body’s state (Wolpert et al., 1998)
and is consistently implicated in multisensory integration of pain and altered higher-level pain processes
(Martucci & Mackey, 2018). The extended ventral PFC system is implicated in exacerbating negative internal
states, negative self-referencing, and impairments in future planning, which contribute to the generation of
suicidal ideation (Schmaal et al., 2019). This system comprises regions involved in many brain networks,
including the default mode, salience, and reward networks (Mayer et al., 2023). Alterations in these regions
due to CP have the potential to increase the risk for suicidal behaviors via changes in the appraisal of
emotions, self-referential processing, and the incorporation of interoceptive cues to adjust emotional states
(Schmaal et al., 2019).
As this investigation is cross-sectional, no conclusions can be made about causal relationships between
the observed brain changes, CP, and suicide attempts. No information on the timing of suicide attempt
was provided by the UKB, which would be informative in determining causal associations between CP and
suicide attempt. Thus, the interpretation of these results should be considered as to how brain structure
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mediates the relationship between CP and history of suicide attempt. Follow-up studies should take into
account medications prescribed to patients with CP and the duration of their use, as they have the potential
to affect brain structure (Murray et al., 2021; Puiu et al., 2016; Upadhyay et al., 2010). The sample sizes for the
exclusive pain sites were also significantly lower compared with the nonexclusive pain sites. This decrease
in power is likely a reason for the difference in the significant number of regional effects between groups.
Large samples are needed when assessing brain-wide associations in neuroimaging research (Thompson
et al., 2020). Although well powered, the UKB consists of data from largely middle-aged to older British
White individuals. The replicability and generalizability of these results should be assessed in people from a
variety of age ranges and ethnicities. Moreover, no ratings of pain intensity were collected on the imaging
visits, which is a clinical marker of central sensitization and would be expected to be closely related to
differences in brain structure and quality of life (Nijs, George, Clauw, Fernández-de-las-Peñas, et al., 2021).
The source of CP in individuals is likely to be very heterogeneous and can include a combination of tissue
damage, disease or injury affecting the nervous system, and maladaptive changes in the brain that affect
nociceptive processing and modulation. This can result in mixed pain states in which different sources can
be contributing to the pain experience via different mechanisms (S. P. Cohen et al., 2021), but it is difficult
to disentangle these effects in this population-level study that is not specifically designed to study pain.
Future studies should also use other modalities in addition to gray matter morphology to better understand
mechanistic reasons underlying the brain structural differences observed in the current study.
In the largest CP sample to date to our knowledge, we report differences in brain structure compared
with control participants. CP was associated with lower surface area throughout the cortex. Individuals
with multisite CP showed stronger effects. Lower global surface area was observed in individuals with
chronic musculoskeletal pain. Lower brainstem, amygdala, and cerebellum volumes as well as lower surface
area in the default mode network were observed in individuals with visceral pain. Individuals with chronic
headaches exhibited greater cortical thickness throughout the occipital and parietal lobes, following a
48

pattern of CSD originating in the occipital lobe and spreading anteriorly across the brain. There was an
association between CP and suicide attempt regardless of body site. Regions in the extended ventral PFC
subsystem mediated the relationship between CP and suicide attempt, suggesting that exacerbated negative
internal states, self-referencing, and impairments in future planning may be key mechanisms underlying
suicidal behaviors in individuals with CP.
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Chapter 3
The Genetic Architecture of the Human Corpus Callosum and its
Subregions
The following section is adapted from:
Bhatt RR, Gadewar SP, Shetty A, Ba Gari I, Haddad E, Javid S, Ramesh A, Nourollahimoghadam E, Zhu
AH, de Leeuw C, Thompson PM, Medland SE, Jahanshad N. The Genetic Architecture of the Human Corpus
Callosum and its Subregions. bioRxiv [Preprint]. 2024 Jul 26:2024.07.22.603147. doi: 10.1101/2024.07.22.6
03147. Under Revisions at Nature Communications.
3.1 Abstract
The corpus callosum (CC) is the largest set of white matter fibers connecting the two hemispheres of the
brain. In humans, it is essential for coordinating sensorimotor responses, performing associative/executive
functions, and representing information in multiple dimensions. Understanding which genetic variants
underpin corpus callosum morphometry, and their shared influence on cortical structure and susceptibility
to neuropsychiatric disorders, can provide molecular insights into the CC’s role in mediating cortical
development and its contribution to neuropsychiatric disease. To characterize the morphometry of the
midsagittal corpus callosum, we developed a publicly available artificial intelligence based tool to extract,
parcellate, and calculate its total and regional area and thickness. Using the UK Biobank (UKB) and the
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Adolescent Brain Cognitive Development study (ABCD), we extracted measures of midsagittal corpus
callosum morphometry and performed a genome-wide association study (GWAS) meta-analysis of European
participants (combined N = 46,685). We then examined evidence for generalization to the non-European
participants of the UKB and ABCD cohorts (combined N = 7,040). Post-GWAS analyses implicate prenatal
intracellular organization and cell growth patterns, and high heritability in regions of open chromatin,
suggesting transcriptional activity regulation in early development. Results suggest programmed cell death
mediated by the immune system drives the thinning of the posterior body and isthmus. Global and local
genetic overlap, along with causal genetic liability, between the corpus callosum, cerebral cortex, and
neuropsychiatric disorders such as attention-deficit/hyperactivity and bipolar disorders were identified.
These results provide insight into variability of corpus callosum development, its genetic influence on the
cerebral cortex, and biological mechanisms related to neuropsychiatric dysfunction.
3.2 Introduction
The corpus callosum (CC) is the largest white matter tract in the human brain, facilitating higher order
functions of the cerebral cortex by allowing the two hemispheres of the brain to communicate (Fame et al.,
2011; Fenlon & Richards, 2015). This connection is essential for coordinating sensorimotor responses,
performing associative and executive functions, and representing information in multiple dimensions
(Brown et al., 1999; Paul, 2011). Most CC fibers connect corresponding left and right cortical regions of
the brain, with the organization, development of axonal elongation, and myelination of callosal fibers
being correlated with the rostro-caudal (front-to-back) distribution of functional areas (Caminiti et al.,
2013; De León Reyes et al., 2020). Regional alterations in CC shape are easily assessed with neuroimaging
studies, which have found local callosal abnormalities in complex neurodevelopmental and neuropsychiatric
disorders (De León Reyes et al., 2020; Piras et al., 2021; Unterberger et al., 2016; Vermeulen et al., 2023;
Zhao et al., 2022; L. Zhou et al., 2022), such as lower anterior volumes in autism (Valenti et al., 2020) and
51

lower posterior thickness in bipolar disorder (Videtta et al., 2023). Twin studies show up to 66% heritability
for CC area (Scamvougeras et al., 2003; Woldehawariat et al., 2014), and previous single-cohort studies of
genetic influences on CC volume and its relationship to neuropsychiatric disorders have found heritability
estimates between 22–39% (Campbell et al., 2023; S.-J. Chen et al., 2023). Yet, the interplay between genetic
variants influencing CC morphometry, the cerebral cortex, and associated neuropsychiatric disorders is not
well understood.
3D magnetic resonance imaging (MRI) provides a non-invasive approach to quantify individual variations in brain regions and connections (De León Reyes et al., 2020), including the morphology of the CC,
and how they are associated with brain-based traits and diseases. The midsagittal section of an anatomical
brain MRI scan is able to capture the entire rostro-caudal formation of the CC, which is almost always
in the field of view of 2D clinical and 3D research MRI scans alike. This 2D midsagittal representation
can be segmented to offer a lower dimensional projection of the anatomical intricacies of the CC, allowing for structural measures of CC area and thickness to be computed (Joshi et al., 2013; Luders et al.,
2010). We developed and validated a fully automated artificial intelligence based CC feature extraction
tool, Segment, Measure, and AutoQC the midsagittal CC (SMACC), which we make publicly available at
https://github.com/USC-LoBeS/smacc (Gadewar et al., 2023).
Using data from the UK Biobank (Bycroft et al., 2018) (UKB) and Adolescent Brain Cognitive Development (Volkow et al., 2018) (ABCD) studies, here we present results from a genome-wide association study
(GWAS) meta-analysis of total area and mean thickness of the CC derived using SMACC. We also present
the results for five differentiated areas based on distinguishable projections to (1) prefrontal, premotor and
supplementary motor, (2) motor, (3) somatosensory, (4) posterior parietal and superior temporal, and (5)
inferior temporal and occipital cortical brain regions (Hofer & Frahm, 2006; Witelson, 1989). These regions
are believed to represent structural-functional coherence (De León Reyes et al., 2020). We performed a
GWAS meta-analysis using two population-based cohorts, one of adolescents and another of older adults,
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to examine genetic influences on CC area and thickness (Panizzon et al., 2009b; Winkler et al., 2010). The
primary analyses were in individuals of European ancestry and the same analyses were then repeated using
the data from non-European participants to assess consistency in the magnitude and direction of effect
sizes. Downstream post-GWAS analyses investigated the enrichment of genetic association signals in tissue
types, cell types, brain regions, and biological pathways. We examined the genetic overlap at the global
and local level, using LD Score regression (LDSC) (B. K. Bulik-Sullivan et al., 2015) and Local Analysis of
Variant Association (LAVA) (Werme et al., 2022), respectively, and the causal genetic relationships between
CC phenotypes, cortical morphometry, and related neuropsychiatric conditions.
3.3 Methods
3.3.1 Artificial intelligence corpus callosum extraction and segmentation with SMACC
We developed a UNet based automated segmentation tool that segments mid CC in multiple modalities
like T1w, T2 and FLAIR, assesses the quality of the segmentation using machine learning methods on the
meaningful metrics extracted from the segmentation and is generalizable to data from various scanners
and sites. To our knowledge, there has been no published integrated pipelines for mid CC extraction with
quality control in multiple MR modalities. Existing deep learning method like DeepnCCA has been trained
to segment mid CC but only works on T2w images and has been trained on data from one scanner only, so
it might not be generalizable to data from other scanners. Other existing methods like FreeSurfer (which
was used in the previous GWASes), FastSurfer and a few UNet based methods (Brusini et al., 2022) segment
CC but do not assess the quality of the segmentations.
3.3.1.1 Data Preprocessing
All UKB participants completed a 31-minute neuroimaging protocol using a Siemens Skyra 3 Tesla scanner
and a 32-channel head coil in one of three MRI scanning locations. All 3D structural T1-weighted brain
53

scans were acquired using the following parameters: 3D MPRAGE, sagittal orientation, in-plane acceleration
factor = 2, TI/TR = 880/2000 ms, voxel resolution = 1 x 1 x 1 mm, acquisition matrix = 208 x 256 x 256
mm. All scans were pre-scan normalized using an on-scanner bias correction filter. More details of the
imaging protocols may be found in the following reference papers (Alfaro-Almagro et al., 2018; K. L. Miller,
Alfaro-Almagro, Bangerter, Thomas, Yacoub, Xu, Bartsch, Jbabdi, Sotiropoulos, Andersson, et al., 2016).
All ABCD participants completed a neuroimaging protocol in one of three scanner types at 21 different
sites (Hagler Jr et al., 2019). The Siemens Prisma had the following parameters for the T1-weighted scans:
TI/TR = 1060/2500 ms, TE = 2.88 ms, voxel resolution = 1 x 1 x 1 mm, acquisition matrix = 176 x 256 x 256,
flip angle = 8 degrees. The Philips Achieva Ingenia had a TI/TR = 1060/6.31 ms, voxel resolution = 1 x 1 x 1
mm, acquisition matrix = 225 x 256 x 256 mm and a flip angle = 8 degrees. The GE MR750 had a TI/TR =
1060/2500 ms, TE = 2 ms, voxel resolution = 1 x 1 x 1 mm, acquisition matrix = 208 x 256 x 256, and a flip
angle = 8 degrees.
All T1w MRIs were registered to MNI152 (J. Mazziotta et al., 2001; J. C. Mazziotta et al., 1995) 1mm
space with 6 degrees of freedom using FSL’s flirt (Jenkinson et al., 2002) command.
3.3.1.2 SMACC development and UNet training
Mid-sagittal T1w, T2w, and FLAIR images from UK Biobank (Bycroft et al., 2018), PING (Jernigan et al.,
2016), HCP (Essen et al., 2013), and ADNI (R. C. Petersen et al., 2010) were used for training the UNet model
for CC segmentation. Individual study scanner parameters can be found in their respective references. The
demographic information for the datasets used to create the UNet model is shown in Supplementary Table
31 in the published manuscript. Augmentation of image data is a common procedure in deep learning to
prevent model overfitting and improve model accuracy (Shorten & Khoshgoftaar, 2019). All the images
were downsampled by a factor of 2, 3, 4 and 5 along the sagittal axis and then upsampled back to original
size using MRtrix’s mrgrid command to include low resolution images in the training (J. D. Tournier et al.,
54

2019). To include lower resolution T1w images resembling older or clinical data in training, all the images
were harmonized using a fully unsupervised deep-learning framework based on a generative adversarial
network (GAN) (M. Liu et al., 2023) to a subject from the ICBM dataset (J. Mazziotta et al., 2001). The
original images in the training set already had 5-10 degrees rotation variation, so we chose to rotate images
in increments of 15 degrees to include more variety of head orientations and then resize to 256*256. Black
boxes were randomly added to the images to imitate partial agenesis cases. Supplementary Figure 1 in
the published manuscript shows some T1w augmented images that were the input training images for the
UNet model.
3.3.1.3 UNet Implementation
A Tensorflow implementation of UNet (Navab et al., 2015) was trained on 80% of the images for 250 epochs
until the difference between the intersection over union (IOU) after consecutive iterations was less than
1x10−4
. The U-Net architecture is structured with a contracting pathway and an expansive pathway. The
contracting pathway repeatedly performs two 3x3 convolutions (without padding), with each convolution
followed by a rectified linear unit (ReLU) and a 2x2 max pooling operation. At each stage in the expansive
pathway the feature map is upsampled followed by a 2x2 convolution which reduces the feature channels
by half. Then, the corresponding cropped feature map from the contracting pathway is concatenated,
and two 3x3 convolutions are applied, with each one followed by a ReLU. We used the following training
parameters: 1x10−4 learning rate and an Adam optimizer (Kingma, 2014). The rest of the data was used
for validation. The midsagittal CC (midCC) was initially segmented using image processing techniques
(Romero et al., 2019) on subjects from ADNI1 (N=1032, 54–91 years), PING (N=1178, 3–21 years), HCP
(N=963, 22–37 years) and UKB (N=190, 45–81 years). These masks were then visually verified and manually
edited by neuroanatomical experts which served as the ground truth. To evaluate the model, the area of
overlap between the predicted segmentation and the ground truth was calculated.
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3.3.1.4 CC shape metrics extracted with SMACC
SMACC provides outputs of global and regional shape metrics extracted from the corpus callosum segmentation, including area, thickness, length, perimeter and curvature. The regional shape metrics were based
on a 5 compartment version of the Witelson atlas (Hofer & Frahm, 2006; Witelson, 1989). The Witelson atlas
is composed of the (1) genu, (2) anterior midbody, (3) posterior midbody, (4) isthmus, and (5) splenium. The
metrics used for the GWAS analysis were area and mean thickness of the total CC and all of the parcellations
of the Witelson atlas. The thickness is defined as the distance in the inferior-superior direction between the
top and bottom of the contour and at every point along the length of the segment, then averaged across the
region of interest. The total area is the summation of the number of voxels with intensity value greater
than 0.5 in the segmentation.
3.3.1.5 Corpus callosum segmentation quality control (QC) with SMACC
To ensure that segmentations were of appropriate quality without having to manually assess all output
images, which eventually may scale to hundreds of thousands of scans, we included an automated quality
control (QC) assessment into SMACC. The regional and global metrics were used as inputs to the machine
learning models detailed below for automatic binary classification of segmentations as Pass or Fail. CC
segmentations from SMACC were manually assessed across multiple datasets by neuroanatomical experts.
This included data from UKB (N=12,902, aged 45–81 years), ADNI1 (N=724, aged 54–91 years), PING (N=857,
3–21 years) and HCP (N=615, 22–37 years), all of which served as the ground truth for QC model building.
All data was split 80/20 for training/testing.
Figure 3.1 gives the overview and the flow of SMACC. Several architectures including a 3-layer sequential
neural network with 42 neurons, 22 in the second layer, and 11 in the third layer; a wide & deep neural
network with 80 neurons in the first 3 layers and 40 in the last 3 layers, XGBoost classifier and an ensemble
model were tested to classify the segmentations from the UNet as pass or fail. The ensemble model consisted
56

of XGBoost, k-nearest neighbors (KNN), support vector classifier (SVC), logistic regression, and a random
forest classifier. The results from all the classifiers in the ensemble model were combined using a majority
voting classifier. All the models were compared using metrics including precision, recall, F1 score and
Area Under the Curve (AUC). Supplementary Table 34 in the published article shows the performance of
different models based on the shape metrics extracted from the CC segmentations.
3.3.1.6 SMACC vs FreeSurfer
Comparing SMACC and FreeSurfer via Dice scores with respect to manual masks: For assessing the accuracy
of the SMACC compared to the ground truth and compared to the commonly used tool FreeSurfer (Fischl,
2012b), we ran the SMACC pipeline on 30 subjects from the Hangzhou Normal University (HNU) test-retest
dataset (K. J. Gorgolewski et al., 2015; Zuo et al., 2014). Each subject in this dataset was scanned with a
full brain T1w MRI 10 times within a period of 40 days, for a total of 300 scans. All 300 scans had also
been manually segmented by a neuroanatomical expert to serve as the ground truth. Segmentations from
SMACC and FreeSurfer v7.1 were compared to manual segmentations using the Dice overlap coefficient.
The average Dice coefficient between automated CC masks from SMACC and ground truth segmentations
was 0.94 across all scans. The average Dice score between FreeSurfer CC segmentations and manual masks
was 0.82. The Dice score was consistently higher for all the subjects using SMACC. Supplementary Figure
2 and Supplementary Table 35 in the published article, show a few midCC segmentations obtained from
SMACC compared to FreeSurfer.
ICC for SMACC: To assess test-retest reliability of SMACC the intraclass correlation (ICC) scores were
calculated. Average ICC values for thickness and area of the Witelson parcellations and the total CC were
greater than 0.9 and are shown in Supplementary Figure 3 in the published article.
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Figure 3.1: Segment, Measure, and AutoQC the midsagittal CC (SMACC) pipeline - The midsagittal
slice from a participant registered to MNI space with 6 degrees of freedom serves as an input to the UNet
architecture used for the midsagittal corpus callosum segmentation. The Witelson atlas was used for
segmenting the CC into five different regions. Global and subregion metrics (thickness and area-shown in
green) were extracted from the segmentation. The thickness (black arrow) is defined as the distance in
the inferior-superior direction between the top and bottom of the contour, after reorientation to standard
space, at every point along the length of the segment, then average across the region of interest. These
metrics serve as input for the ensemble machine learning model used for labelin CC segmentations as
having passed or failed quality control (QC). Abbreviations: Montreal Neurological Institute - MNI, CC -
corpus callosum, ML - Machine Learning, KNN - K Nearest Neighbors, SVC - Support Vector Classifier.
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3.3.2 Study Cohorts
3.3.2.1 U.K. Biobank
The UK Biobank (UKB) is a large population level cohort study conducting longitudinal deep phenotyping
of around 500,000 participants in the United Kingdom (UK) aged between 40–69 at recruitment. All participants provided informed consent to participate. The North West Centre for Research Ethics Committee
(11/NW/0382) granted ethics approval for the UK Biobank study (Bycroft et al., 2018). We used genotype
data from UKB released in May 2018. The data was collected from 489,212 individuals, and 488,377 of those
individuals passed quality control checks by UKB. The genotypes were then imputed using two reference
panels: the Haplotype Reference Consortium (HRC) reference panel and a combined reference panel of the
UK10K and 1000 Genomes projects Phase 3 (1000G) panels (Bycroft et al., 2018). There were 8,422,770 SNPs
following quality control (QC) of the data which included having a genotyping call rate (SNPs missing
in individuals) of greater than 95%, removing variants with a minor allele frequency less than 0.01 (1%),
removing variants with Hardy-Weinberg equilibrium p-values less than 1e-6, and removing individuals with
greater than three standard deviations away from the mean heterozygosity rate. To determine European
ancestry in UKB, the ENIGMA MDS protocol (https://enigma.ini.usc.edu/protocols/genetics-protocols/)
was completed using 10 components. The mean and standard deviations of the first and second genetic
components of individuals who were classified as Utah residents with Northern and Western European
ancestry from the CEPH collection (CEU) from the HapMap 3 release were then calculated. Individuals in
UKB who were within a distance of 0.0101 on components 1 and 2 were classified as of European ancestry
(N = 41,979). The MDS plot of individuals included in the analysis overlaid over the HapMap 3 population
is available in Supplementary Figure 4 in the published article.
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3.3.2.2 ABCD
The Adolescent Behavioral Cognitive Development (ABCD) study is the largest study in the United States
(USA) following adolescent children starting from 9 years of age through adolescence with deep phenotyping
including neuroimaging and genotyping using the Smokescreen™ Genotyping array consisting over over
300,000 SNPs (Baurley et al., 2016; Hagler Jr et al., 2019; Uban et al., 2018). Only neuroimaging from baseline
(ages 9–10) were used. Following imputation using the ENIGMA protocol (Stein et al., 2012) with the
European 1000 Genomes Phase 3 Version 5 reference panel, phased using Eagle version 2.3 (Loh et al.,
2016), and the QC process as described in the UKB cohort, a total of 4,706 European ancestry children, and
5,683,360 SNPs were included. To determine European ancestry in ABCD, the methods described for the
UKB were completed. The MDS plot of individuals included in the analysis overlaid over the HapMap 3
population is available in Supplementary Figure 5 in the published article.
We also analyzed non-European ancestry individuals to examine the generalization of the observed
effects across ancestries. In order to accurately estimate non-European individuals using the HapMap3
reference panel, the KING software package (Manichaikul et al., 2010), which uses a well validated MDS
and support vector machine approach, was used to estimate ancestry composition in all individuals not
used in the principal GWAS (Supplementary Table 36 in the manuscript). While making sure no individuals
classified as CEU, TSI, or a combination of both, were not included, we included 636 individuals from the
UKB and 4129 individuals from ABCD.
3.3.3 GWAS meta-analysis of corpus callosum morphometry
Genome-wide association analysis (GWAS) for UKB and ABCD separately for all CC phenotypes were
completed via a linear whole-genome ridge regression model using REGENIE, allowing for the control
of genetic relatedness (Mbatchou et al., 2021). Covariates included age, sex, age*sex interaction, and the
first 10 genetic principal components. A two-step REGENIE analysis was completed with the following
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parameters. For step 1, the entire dataset was used with a block size of 1000 and leave-one-out-chromosome
validation (Mbatchou et al., 2021). Step 2 was completed with a threshold for minor allele count of 5, a
block size of 1000, and otherwise default parameters.
A meta-analysis of GWAS summary statistics of all CC derived metrics in UKB and ABCD were
conducted using METAL software and the random-metal extension (Willer et al., 2010), based on the
random-effects model. A random-effects model was chosen since the effect sizes of SNPs on the corpus
callosum has the potential to be different between the UKB and ABCD cohorts due to age. White matter
volume is known to increase through childhood and start decreasing in middle adulthood (Bethlehem
et al., 2022), which may result in different genetic effect sizes being observed. We opted to conduct a metaanalysis instead of using a two-stage discovery-replication approach because Skol et al. have shown that
this method is more powerful, despite using more stringent significance levels for multiple correction (Skol
et al., 2006), and is common practice in the literature (Demontis, Walters, Athanasiadis, Walters, Therrien,
Nielsen, Farajzadeh, Voloudakis, Bendl, Zeng, Zhang, Grove, Als, Duan, Satterstrom, Bybjerg-Grauholm,
Bækved-Hansen, Gudmundsson, Magnusson, Baldursson, Davidsdottir, Haraldsdottir, Agerbo, Hoffman,
Dalsgaard, Martin, Ribasés, Boomsma, Artigas, et al., 2023; J. J. Kim et al., 2024; Nagel et al., 2018). Percent
variance (R2) explained by each significant SNP was calculated using the approach described in (Rietveld
et al., 2013). The R2 of each variant j was calculated via:
R
2
j ≈
2pjqj · βˆ2
j
σˆ
2
y
where pj and qj are the minor and major allele frequencies, βˆ is the estimated effect of the variant
within the meta-analysis and σˆ is the estimated variance of the trait (for which we used the pooled variance
of the trait across UKB and ABCD. In order to determine the number of independent traits, matrix spectral
decomposition was computed using matSpD in R on the phenotypic correlations between CC traits using
the method proposed by Li and Ji (J. Li & Ji, 2005; Nyholt, 2004). This resulted in 8.16 effective independent
61

variables, and a significance threshold of p = 5 x 10−8
/8.16 = 6.13 x 10−9
. Meta-analyses were also completed
for non-European individuals. To determine if the global measure of brain intracranial volume (ICV) would
have an impact on the analysis, all GWAS were completed again using ICV as an additional covariate.
3.3.3.1 Heritability and genetic correlations within and between cohorts
To determine SNP heritability (h
2
SNP) tagged from SNPs used in the analysis, we used the GREML approach
implemented in GCTA (Yang et al., 2010, 2011), while adjusting for the same covariates as in the GWAS. The
SNP heritability (h
2
SNP) from LDSC (B. K. Bulik-Sullivan et al., 2015), was also computed, which estimates
heritability casually explained by common reference SNPs. Genetic correlations between the UKB and
ABCD cohorts for area and thickness of each parcellation of the CC defined by the Witelson scheme, and
total CC were completed using LDSC (B. K. Bulik-Sullivan et al., 2015). Between cohort heterogeneity of
h2SNP should not be considered unusual, as the genetic influence observed on the corpus callosum has the
potential to be different between the UKB and ABCD cohorts due to age - white matter volume is known to
increase through childhood and start decreasing in middle adulthood (Bethlehem et al., 2022), as well as the
smaller sample size in ABCD making it harder for LDSC to detect polygenic effects.
3.3.4 Gene-mapping and gene enrichment analyses
Genetic variants (SNPs) were mapped to genes using information about genomic position, expression
quantitative trait loci (eQTL) information, and 3D chromatin interaction mapping as implemented in
FUMA v1.5.2 with the experiment-wide significance threshold (p = 6.13 x 10−9
) (Watanabe et al., 2017).
Pathway enrichment analyses using the results from the full meta-analyses with no pre-selection of genes
via MAGMA v1.08 (C. A. de Leeuw et al., 2015) gene-set analysis in FUMA. Genes located in the MHC
region were excluded (hg19: chromosome 6: 26Mb - 34Mb). There were 19,021 gene sets from MSigDB
v7.0 (Liberzon et al., 2015) (Curated gene sets: 5500, GO terms: 9996), and 9 other data resources including
62

KEGG, Reactome and Biocarta (https://www.gsea-msigdb.org/gsea/msigdb/collection_details.jsp#C2).
MAGMA uses gene-based P-values to identify genes that are more strongly associated with a phenotype
than would be expected by chance. MAGMA then applies a competitive test to compare the association of
genes in a gene set to the association of genes outside of the gene set. This allows MAGMA to identify
gene sets that are enriched for association signals. MAGMA corrects for a number of confounding factors,
such as gene length and size of the gene set, to ensure that the results are not due to chance. A gene-based
association analysis (GWGAS) in MAGMA was completed using the full summary statistics for each trait
from METAL. Corrections for multiple comparisons were completed using the Bonferroni approach.
To determine whether genes associated with CC morphometry cluster into biological functions, tissue
types, or specific cell types, we used the full results of the meta-analyzed genome-wide association studies
(GWAS) rather than prioritizing genes. Pathway analysis as described above was completed.
We performed gene-property and gene-set analysis using the MAGMA software on 54 tissue types from
the GTEx v8 database and BrainSpan (analysts: Aguet François 1 Brown Andrew A. 2 3 4 Castel Stephane
E. 5 6 Davis Joe R. 7 8 He Yuan 9 Jo Brian 10 Mohammadi Pejman 5 6 Park YoSon 11 Parsana Princy 12 Segrè
Ayellet V. 1 Strober Benjamin J. 9 Zappala Zachary 7 8 et al., 2017; J. A. Miller et al., 2014), which includes
29 samples from individuals representing 29 different ages of brains, as well as 11 general developmental
stages.
Single cell RNA-sequencing data sets used in the cell-type specific analyses included the human
developmental and adult brain samples from the PsychENCODE consortium (D. Wang et al., 2018), human
brain samples of the middle temporal gyrus and lateral geniculate nucleus from the Allen Brain Atlas (Hodge
et al., 2019), human brain samples using DroNc-seq (Habib et al., 2017), two datasets of human prefrontal
cortex brain samples across developmental stages which show per cell type average across different ages,
and per cell type per age average expression (Zhong et al., 2018), two datasets of human brain samples with
and without fetal tissue (Darmanis et al., 2015), human brain samples from the temporal cortex (Hochgerner
63

et al., 2017), and human samples from the ventral midbrain from 6–11 week old embryos (La Manno et al.,
2016). A 3-step workflow is implemented in FUMA to determine association between cell-type specific
expression and CC morphometry-gene association supported by multiple independent datasets, which has
been extensively described (Watanabe et al., 2019). All tests were corrected using the Bonferroni approach.
3.3.5 Partitioned heritability of meta-analysis results by cell and tissue type with LDSC
Partitioned heritability analysis was completed to estimate the amount of heritability explained by annotated
regions of the genome (Finucane et al., 2015, 2018). We tested for enrichment of CC h
2 of variants located
in multiple tissues and cell types using the LDSC-SEG approach, with all analyses being corrected for
the FDR (Finucane et al., 2018). Annotations indicating specific gene expression in multiple tissues/cell
types from the Genotype-Tissue Expression (GTEx) project and Franke lab were downloaded from https:
//alkesgroup.broadinstitute.org/LDSCORE/LDSC_SEG_ldscores/. We also downloaded 489 tissue-specific
chromatin-based annotations from narrow peaks for six epigenetic marks from the Roadmap Epigenomics
and ENCODE projects (E. P. Consortium et al., 2012; Kundaje et al., 2015). These annotations were
downloaded from the URL mentioned above. This would allow us to either verify or identify new findings
from the gene expression analysis from an independent source using a different type of data. Finding new
patterns of chromatin enrichment can help us to understand how genes are regulated. For example, if
we find that a particular epigenetic mark is enriched in a region of the genome that is associated with a
specific gene in a specific tissue type, this could suggest that the gene is regulated by that epigenetic mark
in that specific tissue type. Gene expression data from the Immunological Genome (ImmGen) project (Heng
et al., 2008), which contains microarray data on 292 immune cell types from mice, was used to test immune
cell-type-specific enrichments. Data was downloaded from the aforementioned link.
64

3.3.6 LAVA Transcriptome-Wide Association Study
We used the LAVA-TWAS framework to investigate the relationship between CC traits and gene expression
in brain tissues, fibroblasts, lymphocytes, and whole blood from the GTEx consortium (v8) (G. Consortium,
2020) in all protein coding genes, as it has ability to model the uncertainty of eQTL effects compared
to other commonly used TWAS approaches, which have been shown to be prone to high type-I errors
(false positives), and provides a directly interpretable effect size in the rG estimate (C. de Leeuw et al.,
2023). Analyses were performed on all protein coding genes (N = 18,380) between all CC phenotypes and
eQTLs/sQTLs for each tissue. Genotype data from the European sample of the 1000 Genomes (phase 3)
project (1. G. P. Consortium et al., 2015) was used to estimate SNP LD for LAVA. For each eQTL/sQTL that
had a significant genetic signal for both the CC phenotype and cortical phenotype (univariate p-values
less than 1 x 10−4
), the local bivariate genetic correlation between the two was estimated and tested.
All LAVA-TWAS results were corrected using the Bonferroni approach. Following TWAS, trait specific
enrichment analysis via a Fisher’s exact test of the top 1% of genes, to evaluate overrepresentation in 7,246
MSigDB v6.2 (Liberzon et al., 2015) gene sets and gain insight into biological pathways, was conducted.
Gene sets were subset such that they must have consisted of at least one of the top 1% of genes, to avoid
testing gene-sets with no significantly associated genes. All enrichment testing for eQTLs and sQTLs was
performed with Bonferroni correction.
3.3.7 Global and local genetic correlations with cortical morphometry and Mendelian
randomization
The CC develops in such a manner that callosal projections are over-produced then refined during development. The majority of cortical projections are refined during postnatal stages and are under the influence
of guidance cues (De León Reyes et al., 2020). As many genes are responsible for callosal axon guidance, we
sought to investigate the genetic relationship between our derived CC traits and the genetic architecture
65

of the human cerebral cortex (De León Reyes et al., 2020). We used LDSC to determine the global genetic
correlation between area and thickness of the total and parcellated regions of the corpus callosum, and
the GWAS summary statistics of each globally corrected region-of-interest of the cerebral cortex from the
ENIGMA-3 GWAS (Grasby et al., 2020). We performed bi-directional Mendelian Randomization analyses
to investigate if significant genetic correlations observed could be driven by genetic causal relationships
between an exposure (e.g., area and thickness of different regions of the CC) and outcome (e.g., regional
surface area & cortical thickness). Analyses were performed with summary statistics using GSMR (Zhu
et al., 2018). All analyses were corrected using the Bonferroni approach. To capture potential local shared
genetic effects across the genome, we ran LAVA (Werme et al., 2022) for all protein coding genes (N = 18,380)
between all CC phenotypes and surface area and cortical thickness of regions in the ENIGMA-3 GWAS.
Genotype data from the European sample of the 1000 Genomes (phase 3) project (1. G. P. Consortium et al.,
2015) was used to estimate SNP LD for LAVA. Sample overlap was estimated using the intercepts from
bivariate LDSC and integrated into the analysis (B. Bulik-Sullivan et al., 2015; Werme et al., 2022). For each
gene that had a significant genetic signal for both the CC phenotype and cortical phenotype (univariate
p-values less than 1 x 10−4
), the local bivariate genetic correlation between the two was estimated and
tested. All results were corrected using the Bonferroni approach.
3.3.8 Global and local genetic correlations with neuropsychiatric conditions and Mendelian
randomization
Abnormalities of the corpus callosum have also been heavily implicated in several neurological and neuropsychiatric conditions such as autism spectrum disorders (ASDs), ADHD, bipolar disorder, schizophrenia,
visual impairments, epilepsy, as well is chronic pain (Aboitiz & Montiel, 2003; Di Paola et al., 2013; Horton
et al., 2004; Khoury et al., 2022; Kitayama et al., 2007; Lau et al., 2013; Luders et al., 2016; Saar-Ashkenazy
et al., 2016, 2023; Shatokhina et al., 2021; Tao et al., 2021; Unterberger et al., 2016; Westerhausen et al.,
66

2018). We used LDSC to determine the global genetic correlation between area and thickness of the total
and parcellated regions of the corpus callosum, 19 neuropsychiatric traits, and chronic overlapping pain
conditions. Mendelian randomization analysis, and local genetic correlations were run as done for the brain
cortical phenotypes.
3.4 Results
3.4.1 Characterization of corpus callosum shape associated loci
We conducted a GWAS of area and mean thickness of the whole corpus callosum, and five regions of the
Witelson parcellation scheme (Fig 3.2) (Hofer & Frahm, 2006; Witelson, 1989), using data from participants
of European ancestry from the UKB (N = 41,979) and ABCD cohorts (N = 4,706). A meta-analysis of
GWAS summary statistics of all CC derived metrics in UKB and ABCD was performed using METAL
and the random-metal extension (Willer et al., 2010), based on the DerSimonian-Laird random-effects
model (Methods). To examine the generalizability of single nucleotide polymorphism (SNP) effects across
ancestries, these same analyses were run using data from non-European participants (total N = 7,040).
The GWAS meta-analysis identified 48 independent significant SNPs for total area and 18 independent
SNPs for total mean thickness. Independent significant SNPs were determined in FUMA using the default
threshold of r
2 = 0.6, and genomic loci were determined at r
2 = 0.1. This identified 28 genomic loci for
total cross-sectional area, and 11 genomic loci for total mean thickness. All significant loci for total area
and mean thickness showed concordance in the direction of effect between the two cohorts. There were
5 loci, all in intronic regions, each positionally mapped to genes (K. Wang et al., 2010) that overlapped
between area and mean thickness. These included IQCJ-SHIP1 (multimolecular complexes of initial axon
segments and nodes of Ranvier, and calcium mediated responses) (Martin et al., 2008), FIP1L1 (RNA binding
and protein kinase activity) (Kaufmann et al., 2004), HBEGF (growth factor activity and epidermal growth
67

Figure 3.2: Regions of the midsagittal corpus callosum and associated genomic loci. An ideogram
representing loci that influence total corpus callosum area, its mean thickness, and area and thickness of
individual parcellations determined by the Witelson parcellation scheme in a rostral-caudal gradient (1–5).
All loci are significant at the Bonferroni corrected, experiment-wide threshold of p < 6.13 x 10−9
.
68

factor receptor binding) (Oyagi & Hara, 2012), CDKN2B-AS1 (involved in the NF-κB signaling pathway
with diverse roles in the nervous system) (Kaltschmidt & Kaltschmidt, 2009; Song et al., 2020), and FAM107B
(cytoskeletal reorganization in neural cells and cell migration/expansion) (Nakajima & Koizumi, 2014). The
genomic locus mapped to IQCJ-SHIP1 had a positive effect for total area (rs11717303, effect allele: C, effect
allele frequency (EAF): 0.689, β = 4.28, s.e. = 0.51, p = 4.54 x 10−17). The same locus showed a negative
effect for a different SNP on total thickness (rs12632564, effect allele: T, EAF: 0.305, β = -0.042, s.e. = 0.006,
p = 2.59 x 10−12). The strongest locus for total area (rs7561572, effect allele: A, EAF: 0.532, β = -4.13, s.e. =
0.46, p = 1.98 x 10−18) was positionally mapped to the STRN gene. The strongest locus for mean thickness
(rs4150211, effect allele: A, EAF: 0.265, β = -0.05, s.e. = 0.006, p = 8.20 x 10−18) was mapped to the HBEGF
gene.
Loci for area overlapped between parcellations in a rostral-caudal gradient (1–5), such that: rs1122688
on the SHTN1 (or KIAA1598) gene (involved in positive regulation of neuron migration) overlapped between
the genu (1) and anterior body (2); rs1268163 near the FOXO3 gene (involved in IL-9 signaling and FOXOmediated transcription) overlapped between the posterior body (3) and isthmus (4); and rs11717303 on
the IQCJ-SCHIP1 gene overlapped between the isthmus (4) and splenium (5). This gradient pattern was
not observed for mean thickness. The strongest regional association was observed with splenium area
(rs10901814, effect allele: C, EAF: 0.584, β = -1.69, s.e. = 0.16 p = 2.02 x 10−24) and thickness (rs11245344,
effect allele: T, EAF: 0.570, β = -0.11, s.e. = 0.11, p = 6.28 x 10−22), both on the FAM53B gene. FAM53B
is involved in positive regulation of the canonical Wnt signaling pathway. We observed a concordance
in direction and similar magnitude effect sizes in the analyses within the data from the non-European
participants. Detailed annotations and regional association plots of all genomic loci, independent significant
SNPs and genes are in Supplementary Tables S1-S4 and Extended Data 1 in the published article.
69

3.4.2 SNP heritability and genetic correlation between cohorts
Moderate to high genetic correlations were seen across CC phenotypes between cohorts, with rg ranging
from 0.54 (s.e. = 0.27) and 0.92 (s.e. = 0.63) for area metrics, and 0.30 (s.e. = 0.16) and 0.99 (s.e. = 0.69)
for thickness metrics. We used the GREML approach implemented in GCTA (Yang et al., 2010, 2011) to
estimate SNP heritability (h
2
SNP) for each cohort. Within the UKB, heritability values ranged for different
CC phenotypes from 0.42 – 0.71, with similar results seen in the ABCD cohort (Supplementary Tables
S5-S8). Total area (UKB h
2
SNP = 0.71, s.e. = 0.01; ABCD h
2
SNP = 0.74, s.e. = 0.03) and mean thickness (UKB
h
2
SNP = 0.60, s.e. = 0.02; ABCD h
2
SNP = 0.77, s.e. = 0.03) showed the highest h
2
SNP across both cohorts. LDSC
(B. K. Bulik-Sullivan et al., 2015) h
2
SNP estimates from the meta-analysis ranged between 0.10 (s.e. = 0.01)
and 0.18 (s.e. = 0.05) for area, and 0.12 (s.e. = 0.01) and 0.16 (s.e. = 0.02) for thickness, with the area of
the genu showing the highest, and area of the splenium showing the lowest h
2
SNP estimates. As shown in
Supplementary Tables S5-S8 in the published article, all LDSC rg estimates between meta-analyzed CC
phenotypes were significant.
3.4.3 Gene-mapping and gene-set enrichment analyses
Gene-based association analysis in MAGMA (C. A. de Leeuw et al., 2015) identified 30 genes for the total
area, and 34 genes for total mean thickness of the CC, with 5 genes overlapping between area and thickness
(IQCJ-SCHIP1, IQCJ, BPTF, PADI2, CHIC2). The strongest association seen with area was AC007382.1 and
the strongest association with mean thickness was HBEGF (Figure 3A). There were between 15 and 31
genes for area, and between 7 and 25 genes for thickness identified within regions of the CC. Notably, IQCJ,
IQCJ-SCHIP1, and STRN overlapped for all parcellations of CC area. AC007382.1 overlapped for four out
of five parcellations, and STRN and PARP10 overlapped for three out of five parcellations of CC thickness
(Figure 3.3B, Supplementary Tables S1-S4). Enrichment of SNP heritability in 53 functional categories for
each trait was determined via LDSC (Finucane et al., 2015). The majority of enrichment and the strongest
70

effects across parcellations of the CC were observed in categories related to gene regulation/transcription
in chromatin (Figure 3.4A-4B).
Gene-set enrichment analyses were also completed in MAGMA (Figure 3.4C). Strongest effects of
significant gene sets included those involved in postsynaptic specialization for total CC area, including GO:009901 (postsynaptic specialization, intracellular component) and GO:009902 (postsynaptic density,
intracellular component). A theme of signal transduction related pathways was observed for splenium
area including R-HSA-6785631 (ERBB2 regulates cell motility) and R-HSA-8857538 (PTK6 promotes HIF1A
stabilization). Enrichment of the “CARM1 and regulation of the estrogen receptor” was found for the posterior body thickness and is implicated transcriptional regulation via histone modifications. Enrichment
of GO:1904714 (regulation of chaperone-mediated autophagy) was found for the isthmus area, which is
implicated in lysosomal-mediated protein degradation. All significant results across all CC phenotypes are
in Supplementary Table 18 in the published article.
3.4.4 Tissue-specific and cell-type specific expression of corpus callosum associated
genes
Gene-property enrichment analyses were completed in MAGMA with 54 tissue types from GTEx v8 and
BrainSpan (analysts: Aguet François 1 Brown Andrew A. 2 3 4 Castel Stephane E. 5 6 Davis Joe R. 7 8 He
Yuan 9 Jo Brian 10 Mohammadi Pejman 5 6 Park YoSon 11 Parsana Princy 12 Segrè Ayellet V. 1 Strober
Benjamin J. 9 Zappala Zachary 7 8 et al., 2017; J. A. Miller et al., 2014), which includes 29 samples from
individuals representing 29 different ages, as well as 11 general developmental stages. An enrichment
of genes associated with isthmus thickness were expressed in the cerebellum (p(Bon) = 0.017). Area and
thickness across parcellations of the CC showed an enrichment of expression of genes in the brain from
early prenatal to late mid-prenatal developmental stages. An enrichment of expression of genes associated
with area and thickness of the anterior body of the CC was observed in brain tissue prenatally 9 to 24
71

Figure 3.3: GWAS meta-analysis of midsagittal corpus callosum area and thickness (A) Miami plot
for SNPs (top) and genes (bottom) based on MAGMA gene analysis for total area and total mean thickness.
(B) Miami plot for SNPs (top) and genes (bottom) based on MAGMA gene analysis for area of thickness of
the CC split by the Witelson parcellation scheme (Witelson, 1989). Significant SNPs and genes are color
coded by corpus callosum traits.
72

Figure 3.4: Partitioned heritability, functional annotation and enrichment of gene-sets of CC
morpholog associated genetic variants (A) Significant enrichment of SNP heritability across 53 functional
categories compute by LD Score regression for area (left) and mean thickness (right). Error bars indicate
95% confidence intervals. (B) Proportion of GWAS SNPs in each functional category from ANNOVAR
across each CC phenotype. (C) Significant gene-sets across CC phenotypes computed via MAGMA gene-set
analysis at the Bonferroni corrected threshold of 3.23 x 10−6
. GOBP: Gene-ontology biological processes,
GOCC: Gene-Ontology Cellular Components. 73

weeks post conception. Enrichment of expression of genes associated with area of the genu was observed
in brain tissue 19 weeks post conception. Enrichment of expression of genes associated with total mean
thickness of the CC was observed in brain tissue 19 weeks post conception. All results are shown in
Supplementary Tables S19-S21 in the published article. These results, along with the gene-sets involved in
histone modifications, were supported by LDSC-SEG analyses using chromatin-based annotations from
narrow peaks (Finucane et al., 2018), which showed a significant enrichment in the heritability by variants
located in genes specifically expressed in DNase in the female fetal brain for total CC thickness (p(Bon)
= 0.0105). Chromatin annotations showed a consistent and significant enrichment of splenium area and
thickness associated variants in histone marks of the fetal brain and neurospheres (Supplementary Table
S25 in the published article).
Using microarray data from 292 immune cell types, area of the posterior body showed a significant
enrichment in the heritability by variants located in genes specifically expressed in multiple types of
myeloid cells (p(Bon) < 0.05), and area of the isthmus showed enrichment in innate lymphocytes (p(Bon)
= 0.047). This further validates the aforementioned significant locus on gene FOXO3, which overlapped
between the posterior body and isthmus (Supplementary Table S26 in thep published article).
Cell-type specific analyses were performed in FUMA using data from 13 single-cell RNA sequencing
datasets from the human brain. This tests the relationship between cell-specific gene expression profiles
and phenotype-gene associations (Watanabe et al., 2019). Of the 12 phenotypes tested, only total CC
thickness showed significant results after going through the 3-step process using conditional analyses to
avoid bias from batch effects from multiple scRNA-seq datasets. The most significant association was seen
with oligodendrocytes located in the middle temporal gyrus (MTG, p(Bon) = 0.001) from the Allen Human
Brain Atlas (AHBA). Oligodendrocytes (p(Bon) = 0.03) and non-neuronal cells (p(Bon) = 0.03) located in the
lateral geniculate nucleus (LGN) from the AHBA also showed significant associations but were collinear
(Supplementary Table S22 in the published article).
74

LAVA-TWAS analyses (C. de Leeuw et al., 2023; Werme et al., 2022) (Figure 3.5) of expression quantitative
trait loci (eQTLs) and splicing quantitative trait loci (sQTLs) of protein-coding genes in 16 different brain, cell
type, and whole blood tissues revealed the strongest eQTL associations of area and thickness with CROCC
expression in whole blood for the isthmus (ρ = -0.53, p = 1.29 x 10−10). Other notable eQTL (Supplementary
Table S29) findings included total CC area and isthmus area and thickness being positively associated
with ATP13A2 expression in fibroblasts (ρ = 0.48, p = 1.58 x 10−7
). The strongest sQTL association was a
positive association observed with KANSL1 cluster 11710 in fibroblasts for genu area (ρ = 0.83, p = 1.46
x 10−14), which was the tissue type where most observed associations occurred across CC phenotypes
(Supplementary Table S30). Moreover, a negative association was observed in a KANSL1 (cluster 11707) in
fibroblasts for the genu area (ρ = 0.82, p = 3.11 x 10−7
). An sQTL in MFSD13A (cluster 7894) in the anterior
cingulate showed very strong yet opposite associations for total CC thickness (ρ = 0.42, p = 1.12 x 10−13)
and total CC area (ρ = -0.44, p = 2.98 x 10−11). Other notable findings across tissue types included CRHR1 in
the cortex, nucleus accumbens, and putamen, as well as UGP2 in fibroblasts, whole blood, and the putamen.
No significant results from LAVA-TWAS gene-set enrichment analyses were observed after Bonferroni
correction (Supplementary Tables S31-S32 in the published article).
3.4.5 Genetic overlap of corpus callosum and cerebral cortex architecture
Broadly, we observed a pattern of negative genetic correlations with area and thickness of the CC with
cortical thickness across regions of the cingulate cortex, but positive genetic correlations with regions’
cortical thickness across the neocortex (Figure 3.6A). Specifically, we observed a significant negative genetic
correlation between total area with cortical thickness of the rostral anterior cingulate (rg = -0.35, SE = 0.06)
and posterior cingulate (rg = -0.28, SE = 0.06). Mean thickness was negatively genetically correlated with
cortical thickness of the rostral anterior cingulate (rg = -0.29, SE = 0.06) and posterior cingulate (rg = -0.23,
SE = 0.05). Positive genetic correlations were observed with cortical thickness of the lingual gyrus (rg =
75

Figure 3.5: LAVA-TWAS analyses of corpus callosum traits with gene-expression (eQTLs) and
splicing (sQTLs). Results of local genetic correlations between CC traits and eQTLs and sQTLs from GTEx
v8 using the LAVA-TWAS framework. Associations between (A) CC area and eQTLs, (B) CC thickness and
eQTLs, (C) CC are and sQTLs, and (D) CC thickness and sQTLs are shown via − log10 p values scaled by
the direction of association (y-axis) and chromosomal location (x-axis). All significant points are colored by
tissue type and labeled by CC trait. Significance thresholds for eQTLs (p < 2.01 × 10−6
) and sQTLs (p < 5.45
× 10−7
) were determined by Bonferroni correction.
76

0.26, SE = 0.05) and cuneus (rg = 0.27, SE = 0.06). When parcellating by the Witelson scheme, negative
genetic correlations were observed for area and mean thickness with cortical thickness of regions across
the cortex and the cingulate, but positive genetic correlations with regions in the occipital lobe. We also
observed a significant negative genetic correlation between total area of the CC with surface area of the
precuneus (rg = -0.20, SE = 0.04). (Supplementary Table S9-S10).
Genetic correlations can reflect direct causation, pleiotropy, or genetic mediation. To explore potential
causal relationships between CC phenotypes and morphometry of the cerebral cortex, we ran Generalized
Summary-data-based Mendelian Randomization (GSMR) analyses (Zhu et al., 2018) directional effect of
CC phenotypes on morphometry of the cerebral cortex, but not vice-versa. (Figure 3.6B, Supplementary
Table S14 in the published article). There was a strong negative unidirectional effect of total CC area on the
precuneus surface area (bxy = -0.50, SE = 0.13, p = 0.0002), implying a greater total area and thickness of the
CC results in a lower surface area of the precuneus. There was also a negative unidirectional effect of total
CC mean thickness and cortical thickness of the posterior cingulate (bxy = -0.02, SE = 0.008, p = 0.02), but
not vice versa. When using the Witelson parcellation scheme, there was a strong negative unidirectional
effect on the area of the genu on the cortical thickness of the rostral anterior cingulate (bxy = -0.001, SE =
0.0003, p = 0.003).
Local genetic correlations of area phenotypes of the CC and surface area of the cerebral cortex with
LAVA (Werme et al., 2022) showed many significant negative correlations in genes between the total area
and posterior body and the precuneus SA along the 2p22.2 cytogenetic band (QPCT, PRKD3, SULT6B1,
NDUFAF7, EIF2AK2, HEATR5B, GPATCH11, CEBPZ, CEBPZOS, CDC42EP3, STRN, VIT) (Figure 3.6C-D).
Negative genetic correlations between total CC area and caudal middle frontal gyrus SA in 5 genes along
the 17q24.2 cytogenetic band (HELZ, PSMD12, PITPNC1, ARSG, BPTF) were also observed. Positive local
genetic correlations along the 2p22.2 cytogenetic band were observed with anterior body area and the
77

Figure 3.6: The genetic overlap of the corpus callosum and cerebral cortex. (A) Global genetic correlations (LDSC - rg) between CC phenotypes and cerebral cortex phenotypes. The Bonferroni significance
threshold was set at p = 6.1 × 10−5
. Surface area and cortical thickness of significant cortical regions with
each CC phenotype are displayed on brain plots. (B) Of the significant global genetic correlations, significant
Mendelian randomization (GSMR) results are displayed, representing the effect of CC phenotypes on cortical
phenotypes free of non-genetic confounders. (C) Chord plot displaying the number of significant bivariate
local genetic correlations (LAVA) between CC and cortical phenotypes. Underlined numbers represent the
total number of genes shared with that phenotype. (D) Volcano plots showing degree (− log10 p p-values)
and direction (rg) of local genetic correlations (LAVA) between cortical and CC phenotypes. Colors represent
cortical regions labeled on the chord plot in section C. Significant genes (Bonferroni significance threshold
was set at p = 2.18 × 10−6
) across all phenotypes are labeled. 78

surface area of the posterior cingulate (CDC42EP3, PRKD3), as well as total area of the CC and precentral
gyrus surface area (HEATR5B).
Many negative local genetic correlations were observed with mean thickness of the splenium and cortical
thickness of the superior parietal gyrus (TEX36, EDRF1, UROS, BCCIP, DHX32) and the parahippocampal
gyrus (ZNF879) along the 10q26.13–10q26.2 cytogenetic bands, while positive genetic correlations were
observed with isthmus cingulate cortical thickness along the 10q26.13–10q26.2 cytogenetic bands (EDRF1,
TEX36, UROS, BCCIP, DHX32, CTBP2, CPXM2, GPR26, ZRANB1, FAM53B).
Area of the posterior body showed a negative local genetic correlation with pericalcarine gyrus cortical
thickness (GPATCH11). Area of the isthmus showed positive local genetic correlations with the cortical
thickness of the superior parietal gyrus (LRRC73), caudal middle frontal gyrus (GPATCH2L), and isthmus
cingulate (PLPPR3, CFD, R3HDM4, PTBP1, ELANE, MED16, PALM) along the 19p13.3 cytogenetic band.
Mean thickness of the posterior body showed negative local genetic correlations with the surface area
of the lingual gyrus (STC2, NKX2–5, 5q35.2) and pericalcarine gyrus (NKX2–5). Mean thickness of the
isthmus showed negative local genetic correlations with the precuneus (EIF2AK2, GPATCH11, 2p22.2) and
superior frontal gyrus (TBX19) surface area. Total mean thickness of the CC showed a positive genetic
correlation with surface area of the insula (PDZRN3). The anterior body mean thickness showed positive
local genetic correlations with surface area of the superior parietal gyrus (RETN, FCER2). Splenium mean
thickness showed positive genetic correlations with inferior temporal gyrus surface area (ZNF318, CRIP3,
SLC22A7) along the 6p21.1 cytogenetic band.
3.4.6 Genetic overlap of corpus callosum and associated neuropsychiatric phenotypes
We observed a significant genetic correlation (Figure 3.7A, Supplementary Table S11 in the published article)
between total CC area and ADHD (rg = -0.11, SE = 0.03), bipolar disorder (BD, rg = -0.10, SE = 0.03), and
bipolar I disorder (BD-I, rg = -0.10, SE = 0.03). Total mean thickness was genetically correlated with BD (rg
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= -0.10, SE = 0.03) and BD-I (rg = -0.10, SE = 0.03). When analyzing the regional Witelson parcellations, the
area of the genu was genetically correlated with ADHD risk (rg = -0.13, SE = 0.03), and the mean thickness
of the splenium was genetically correlated with risk for BD (rg = -0.13, SE = 0.03) and BD-I (rg = -0.12, SE =
0.03). A significant genetic correlation between chronic overlapping pain conditions and thickness of the
genu of the corpus callosum was also observed (rg = 0.12, SE = 0.04).
GSMR analyses showed causal bidirectionality of genetic liability of BD (bxy = -0.06, SE = 0.02, p =
0.006) and BD-I (bxy = -0.05, SE = 0.02, p = 0.003) on total mean thickness of the CC, and mean thickness of
the CC on BD (bxy = -0.19, SE = 0.08, p = 0.01) and BD-I (bxy = -0.23, SE = 0.09, p = 0.02). When using the
Witelson parcellation, GSMR analyses showed causal directionality of genetic liability of BD-I on mean
thickness of the splenium (bxy = -0.09, SE = 0.04, p = 0.01), but not vice versa (Figure 3.7A, Supplementary
Table S15). No significant relationships were observed with chronic overlapping pain.
Local genetic correlations with LAVA (Werme et al., 2022) (Figure 3.7B, Supplementary Table S17 in the
published article) showed 3 negative local genetic correlations between area of the genu and neuroticism
(CRHR1, KANSL1, and STH), and one positive local genetic correlation between anterior body area and
tourette’s syndrome (DFFB).
3.5 Discussion
We conducted the first GWAS meta-analysis of CC morphometry, leveraging our artificial intelligencebased tool, SMACC, to extract detailed CC phenotypes from 46,685 individuals across the UKB and ABCD
studies. While prior research into the genetic basis of CC structure and development has primarily relied
on candidate gene approaches in animal models and post-mortem human studies, our work addresses
the notable differences between the human CC and its counterparts in animal models (De León Reyes
et al., 2020). This study offers genome-wide insights into the genetic architecture of the human CC in vivo,
significantly advancing our understanding of its development and variation. Previous GWAS efforts focused
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Figure 3.7: The genetic overlap of the corpus callosum and neuropsychiatric phenotypes. (A) Global
genetic correlations between CC traits and neuropsychiatric phenotypes. The Bonferroni significance
threshold was set at p = 0.0019. Of the significant global genetic correlations, significant Mendelian
randomization (GSMR) results ar displayed, representing the effect of CC phenotypes on neuropsychiatric
phenotypes free of non-genetic confounders. (B) Volcano plots showing degree (− log10 p p-values) and
direction (rg) of local genetic correlations (LAVA) between neuropsychiatric and CC phenotypes. Phenotypes
with significant associations are colored (IQ an bipolar II disorder). Significant genes (Bonferroni significance
threshold was set at p = 2.23 x 10−6
) across all neuropsychiatric phenotypes. AD: alzheimer’s disease,
ADHD: attention deficit hyperactivity disorder, ASD: autism spectrum disorder, BD: bipolar disorder, BD-I:
bipolar I disorder, BD-II: bipolar II disorder, COPC: chronic overlapping pain conditions, IQ: intelligence
quotient, OCD: obsessive-compulsive disorder, PTSD: post-traumatic stress disorder, SCZ: schizophrenia.
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on CC volume using FreeSurfer-derived measures in the UKB cohort (Campbell et al., 2023; S.-J. Chen et al.,
2023). However, it is well-established that area and thickness measures of neuroimaging phenotypes have
distinct genetic influences (Panizzon et al., 2009b). Our findings strongly support this distinction, as our
meta-analysis revealed zero overlapping significant loci between the area and thickness phenotypes of the
CC. This underscores the value of separating these metrics to identify unique genetic contributions to CC
morphometry. Furthermore, while previous studies have reported genetic correlations between CC volume
and neuropsychiatric traits such as bipolar disorder and ADHD (Campbell et al., 2023; S.-J. Chen et al.,
2023), our investigation extends these findings by exploring the specific genetic influences on CC area and
thickness. This approach enables a deeper understanding of the mechanistic underpinnings behind these
associations. Notably, we identified localized, distinct genetic relationships between CC morphometry and
traits like neuroticism and Tourette’s syndrome—associations that had not been previously reported.
We show the genetic architecture of the CC is highly polygenic, and specific genetic variants influence
CC subregions along a rostral-caudal gradient. Five loci that were positionally mapped to genes were
identified to influence both total area and mean thickness of the CC (IQCJ-SHIP1, FIP1L1, HBEGF, CDKN2BAS1, and FAM107B). IQCJ-SHIP1 had the strongest effect across total area and mean thickness, implicating
mechanisms such as conduction of action potentials in myelinated cells via organizing molecular complexes
at the nodes of Ranvier and axon initial segments, calcium mediated responses, as well as axon outgrowth
and guidance (Papandréou et al., 2015). The strongest locus for total area was mapped to the STRN gene.
STRN has been heavily implicated in the Wnt signaling pathway, which controls the expression of genes
that are essential for cell proliferation, survival, differentiation, and migration via transcription factors
(Chenn & Walsh, 2002; J. Liu et al., 2022; Munji et al., 2011). The HBEGF gene was the strongest locus for
total mean thickness, implicating mechanisms in early development. HBEGF expression is localized in
the ventricular zone and cortical layers during development (Caric et al., 2001), and has been implicated
in regulating cell migration via chemoattractive mechanisms (Caric et al., 2001). Significant enrichment
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of heritability of total mean thickness in various histone marks from chromatin data (ATAC-seq) of the
fetal brain and cortex derived primary cultured neurospheres, significant tissue expression in the brain
19-weeks post conception, as well as enrichment of gene sets involving regulation of histone modification,
suggests genetic variants in regions of open chromatin and transcriptional activity regulation in early
development are key mechanisms underlying CC morphometry. When histones are acetylated, they become
more negatively charged. This negative charge repels the negatively charged DNA, causing the DNA
to be “pushed away” from the histones. This loosening of the DNA-histone complex makes it easier for
transcription factors to access the DNA and initiate transcription (Zentner & Henikoff, 2013).
Parcellation of the CC into the five regions defined by the Witelson scheme allowed for further refinement and genetic understanding of its morphometry in a rostral-caudal gradient. Our results provide
insight as to which molecular mechanisms influence this functionally defined gradient (i.e. prefrontal,
premotor/supplementary motor, primary motor, primary sensory, and parietal/temporal/occipital) (Hofer
& Frahm, 2006). An overlap of genetic loci along the most anterior (genu and anterior body, SHTN1) and
most posterior (isthmus and splenium, IQCJ-SCHIP1) regions of the CC, along with splenium heritability
enrichment of in histone chromatin marks of the fetal brain and dorsolateral prefrontal cortex, implicates
regulation of neuron migration and action potential conduction. But the overlap of the FOXO3 along the area
of the posterior body and isthmus implicates IL-9 signaling and FOXO-mediated transcription responsible
for triggering apoptosis (Huang & Tindall, 2007). Only the posterior body and isthmus showed heritability
enrichment in immune cells including myeloid cells and innate lymphocytes. The thinning of the CC (along
the posterior body and isthmus) occurs in a functional gradient connecting the somatosensory and parietal
association areas of the brain (Aboitiz & Montiel, 2003; Aboitiz et al., 1992; De León Reyes et al., 2020). This
follows activity dependent pruning by functional area (De León Reyes et al., 2020), where somatosensory
circuits are pruned in early development in an experience dependent context (Faust et al., 2021). As immune
cells are increasingly being recognized as key players in brain maturation and neurodevelopment (Zengeler
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& Lukens, 2021), our results suggest IL-9 mediating a neuroprotective effect in the CC during the cell
dieback phase (Renault et al., 2009; Zengeler & Lukens, 2021), and may play a significant role in posterior CC
morphometry. LAVA-TWAS results showed another potential mechanism of isthmus pruning via expression
of ATP13A2 in fibroblasts, and splicing of genes involved in NF-κB signaling (T. Liu et al., 2017). ATP13A2
is involved in lysosomal-mediated apoptosis (van Veen et al., 2020), suggesting such regulation of fibroblast
mediated growth of callosal projections (K. M. Smith et al., 2006). This is also supported by the current
discovery of enrichment of genes related to isthmus area in the “regulation of chaperone mediated autophagy
pathway”, which may influence isthmus morphometry.
The topographic organization of the CC correlates with the homotopic bilateral regions of the cortex it is
known to connect (Caminiti et al., 2013). A variety of genetically regulated principal mechanisms influence
CC neuronal and glial proliferation, neuronal migration and specification, midline patterning, axonal growth
and guidance, and post-guidance refinement to homotopic analogs in the cortex (Pânzaru et al., 2022; Paul
et al., 2007). Our results suggest potential genetic mechanisms contributing to callosal-cortical organization.
We show an overall negative global genetic correlation of CC phenotypes with the cortical thickness of the
cingulate and surface area of the posterior parietal cortices, including a unidirectional negative effect of genu
area on rostral anterior cingulate thickness, and total area on precuneus surface area free of any non-genetic
confounders. Positive global genetic correlations of total CC area and splenium thickness with cortical
thickness in the occipital cortex were also observed. Local genetic correlations of the CC were observed
throughout the cerebral cortex, most pronounced with total CC area and splenium thickness. Notable
findings included numerous genes in the chr2p22 cytogenetic band showing negative correlations between
total CC and posterior body area with precuneus surface area, including the significant STRN gene observed
across all CC phenotypes, further implicating the Wnt signaling pathway and dendritic calcium signaling
in the context of neurodevelopment (Bartoli et al., 1998; Castets et al., 1996). Within this cytogenetic
band, HEATR5B was also positively genetically associated with precentral gyrus surface area. Opposing
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genetic effects were observed between splenium thickness with isthmus cingulate thickness (i.e. positive)
vs. superior parietal cortex thickness (i.e. negative) in genes in the chr10q26.13 cytogenetic band. Clinical
phenotypes associated with the central nervous system due to copy number variations of chr10q26.13 include
abnormal cranium development, global developmental delay and learning difficulties, and neuropsychiatric
manifestations including ADHD, impulsivity or autistic behaviors (Lin et al., 2016; Vera-Carbonell et al.,
2015; Yatsenko et al., 2009). This provides a novel testable hypothesis for functional follow up studies,
as alterations in the isthmus cingulate and superior parietal cortex have been observed in large-scale
studies of various neurodevelopmental disorders (Thompson et al., 2020). Positive genetic associations in
the chr19p13.3 cytogenetic band were observed between the isthmus area and isthmus cingulate cortical
thickness, which has been implicated with microcephaly, ventriculomegaly and developmental delay
(Palumbo et al., 2016; Swan & Coman, 2018).
Our results demonstrate opposing genetic relationships between CC phenotypes (area and thickness of
the entire CC and it’s subregions) and thickness of the cingulate cortex (negative) vs the neocortex (positive),
which suggests a strong genetic component underlying the development of the CC via pioneer axons and
chemotaxis. Coupled with the observed negative phenotypic correlations (Supplementary Table 13 in the
published article), this suggests that the relationship between the corpus callosum (CC) and the thickness
of the cingulate cortex (but not surface area) is influenced by distinct genetic mechanisms that govern their
development. Developmentally, pioneer axons emerge in the cingulate and project their axons across the
midline using guidance cues. A large portion of these callosal projections are pruned and myelinated in an
activity dependent manner, such that axonal remodeling is highly dependent on correlated neural activity
in the cortex (De León Reyes et al., 2020; Edwards et al., 2014; Gavrish et al., 2024; Innocenti & Price, 2005).
The strongest local genetic correlation supporting this finding was observed between total mean thickness
of the CC and rostral anterior cingulate thickness on TGIF1. As TGIF1 is implicated in holoprosencephaly
(i.e. where the brain fails to develop two hemispheres), forebrain development via alterations in the Sonic
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Hedgehog (SHH) pathway, and disruption of axonal guidance via chemoattractive mechanisms (Okada
et al., 2006; Taniguchi et al., 2017), these results provide a potential genetic localization for functional
follow-up. The isthmus cingulate, in relation to the isthmus and splenium, was the only cingulate region
showing positive local genetic correlations, providing further evidence of distinct molecular mechanisms
(e.g. immune-mediated apoptosis and regulation of callosal projections) compared to the rest of the CC
underlying its structure and development.
Abnormalities of the CC have also been associated with various neurological/neuropsychiatric disorders
(De León Reyes et al., 2020). This was the first study to demonstrate a significant negative genetic relationship
between the CC and ADHD (utilizing the latest ADHD GWAS findings) (Demontis, Walters, Athanasiadis,
Walters, Therrien, Nielsen, Farajzadeh, Voloudakis, Bendl, Zeng, et al., 2023), and also replicates previously
observed negative genetic associations with bipolar disorder (Campbell et al., 2023; S.-J. Chen et al., 2023). It
is important to note, however, that prior studies focused on brain volume phenotypes, whereas the current
study examines area and thickness, which are known to be influenced by different genetic factors (Panizzon
et al., 2009b). The negative global genetic correlations observed in CC area with ADHD and CC thickness
with bipolar disorder, indicate that the allelic differences resulting in smaller CC area and thickness are
partly shared with those resulting in a greater risk for ADHD and bipolar disorder, respectively. Further
evidence of the negative genetic relationship between ADHD and CC area is provided by studies that show
the CC is smaller in individuals with ADHD across various ages (Hutchinson et al., 2008; Luders et al.,
2016), suggesting that impaired inter-hemispheric communication between sensorimotor and attentional
systems may contribute to symptoms of hyperactivity, impulsivity, and inattention. Our results also provide
a credence to future studies investigating the genetic relationship between the CC and bipolar disorder, as
differences in the CC in bipolar disorder have been well established (Sarrazin et al., 2015; Videtta et al., 2023;
F. Wang et al., 2008). Negative local genetic correlations on the 17q21.31 cytogenetic band between genu area
and neuroticism implicated the closely located CRHR1 and KANSL1 genes, which were also highly significant
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genes observed with genu area and splicing QTLs (sQTLs) in various cortical and subcortical tissue types
in the TWAS analysis. Neuroticism, a construct historically describing a cluster of negative emotions,
thoughts, and behaviors under the umbrella of “negative affect,” is increasingly recognized as a significant
predictor of susceptibility to stress-related psychiatric disorders, including anxiety and depression (Binder
& Nemeroff, 2010). CRHR1 encodes the corticotropin-releasing hormone receptor 1, a receptor widely
expressed in the cortex and central nervous system that mediates the effects of corticotropin-releasing
factor (CRF). The CRF system plays a central role in orchestrating the body’s stress response through the
hypothalamic-pituitary-adrenal (HPA) axis and autonomic nervous system. Dysregulation of this system
has been extensively linked to the pathophysiology of stress-related anxiety and mood disorders, with
neuroticism often serving as a measurable intermediate phenotype (Binder & Nemeroff, 2010). CRHR1
polymorphisms have been associated with differential responses to stress and heightened susceptibility to
psychiatric disorders (Binder & Nemeroff, 2010), potentially mediated via altered connectivity between
the prefrontal cortices via the genu and altered RNA splicing of the CRHR1 gene inside cortical tissue.
A positive local genetic correlation was observed between Tourette’s syndrome (TS) and the DFFB gene
for the cortical thickness of the anterior body of the corpus callosum, which connects callosal fibers to
premotor and supplementary motor cortical areas (Hofer & Frahm, 2006). DFFB is involved in apoptosis
during development, a process critical for proper neural pruning and brain maturation. Disruptions in
this gene may influence the structural and functional integrity of the corpus callosum, which could have
downstream effects on motor planning and execution. Individuals with TS have been reported to exhibit
larger corpus callosum morphometry compared to neurotypical controls, and greater CC size has also been
positively correlated with tic severity, suggesting that alterations in callosal morphology may play a role in
the pathophysiology of TS (Baumgardner et al., 1996; Plessen et al., 2004). The anterior body, in particular,
is critical for coordinating motor functions and integrating cortical activity across hemispheres, regions
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closely tied to tic generation and suppression (Hampson et al., 2009; Hofer & Frahm, 2006; Tübing et al.,
2018).
In summary, this work identifies genome-wide significant loci of morphometry of the overall CC and
its sectors, convergence on biological functions with a particular importance of apoptosis and pruning
during development, tissues and cell types, as well as the genetic overlap with the cerebral cortex and
neuropsychiatric conditions.
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Chapter 4
Integrated Neurogenetic Biomarkers Predict Chronic and Widespread
Pain Development in Children
4.1 Abstract
Chronic pain in children has reached epidemic proportions, underscoring the urgent need for biomarkers to
improve prediction and guide targeted treatments. Pediatric chronic pain is often accompanied by a range
of comorbid symptoms, including mood and anxiety disorders, sleep disturbances, stress-related conditions,
and cognitive fatigue. Emerging evidence highlights significant alterations in brain structure and function,
immune activation, and neurochemical processes that contribute to the pathophysiology of chronic pain.
Genome-wide association studies (GWAS) have further advanced our understanding of the mechanistic
underpinnings of centralized chronic pain. The Adolescent Brain Cognitive Development (ABCD) study,
with its richly phenotyped and genotyped longitudinal cohort, provides an unprecedented opportunity to
investigate these mechanisms in a representative population. This chapter explores two primary hypotheses:
first, that the genetic architecture of chronic widespread pain (CWP) overlaps with that of commonly
observed comorbidities, including anxiety, depression, attention-deficit/hyperactivity disorder (ADHD),
neuroticism, intelligence, insomnia, suicide attempts, and the chronic visceral pain condition irritable bowel
syndrome (IBS). Second, it is hypothesized that integrating clinical data, multimodal neuroimaging features,
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and genetic markers can predict pain widespreadness in a sex-specific manner. The findings reveal significant
genetic overlap between CWP and all examined comorbidities, with evidence of causal genetic liability,
enrichment in specific biological pathways, tissue types, and protein-protein interactions. Additionally, this
chapter demonstrates the potential of multi-omics integration—combining brain structural and functional
data, polygenic risk scores for CWP and its comorbid traits, and clinical/behavioral measures—to enhance
the understanding and prediction of chronic pain. These results provide critical insights into the complex
interplay of genetic, neurobiological, and clinical factors driving chronic pain and its comorbidities in
children.
4.2 Introduction
Chronic pain is a biopsychosocial phenomenon that has reached epidemic proportions in children. Hospitalization rates of children with chronic pain have increased 831% in the last decade and doubled for opioid
poisonings (Coffelt et al., 2013a; Gaither et al., 2016b). Between 5-38% of children suffer from chronic pain,
73% of which will continue to have chronic pain in adulthood often due to undertreatment or neglect (Dunn
et al., 2011; Eccleston et al., 2021; Hassett et al., 2013; Hotopf et al., 1998a; Huguet & Miró, 2008a; Jones
et al., 2007; King et al., 2011; Perquin et al., 2000).
Pain development across multiple body sites with functional impairment, without any sign of peripheral
injury, is indicative of involvement of the central nervous system (CNS) and central sensitization (Fitzcharles
et al., 2021b; Harte et al., 2018). Individual differences in pain sensitivity, via biological predisposition,
and acquired mechanisms from the environment that set off priming mechanisms, are crucial for the
development of chronic pain (Denk et al., 2014; Mogil, 2021), and associated developmental and health
outcomes (Boyce, 2016b). Strong evidence of differential susceptibility shows sex (Denk et al., 2014),
environmental influences (Boyce, 2016b; Denk et al., 2014), gene-by-environment interactions (Denk
et al., 2014), the locus coeruleus-norepinephrine (LC-NE) system (Suárez-Pereira et al., 2022), cortical
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sensory processing sensitivity (Acevedo et al., 2014), and a continuing state of neural sensitization via
kindling and long-term potentiation (LTP) (Boyce, 2016b), to be underlying mechanisms of chronic pain
development (Boyce, 2016b; Suárez-Pereira et al., 2022). Studies in adults and children investigating
supraspinal mechanisms have shown hyperresponsiveness of the brain to painful stimuli, hyperconnectivity
between brain regions involved in pain, decreased activity in descending inhibitory pathways, elevated
inflammatory markers and glutamate in the cerebrospinal fluid, changes in gray matter and white matter
structure, and glial cell activation in the brain (Bhatt et al., 2020; Fitzcharles et al., 2021b; Martucci &
Mackey, 2018; Nijs, George, Clauw, Fernández-de-las-Peñas, et al., 2021). The plasticity and the critical
period of the developing brain in relation to nociception (Verriotis et al., 2016a) makes it a crucial target for
prevention and early treatment.
Children with common chronic pain conditions such as primary headache, irritable bowel syndrome,
and musculoskeletal and joint pain conditions (Friedrichsdorf et al., 2016; Howard, 2011) commonly present
with co-morbid symptoms such as mood/anxiety and stress-related disorders, sleep disturbances and
cognitive fatigue (Bair et al., 2003; Fisher et al., 2022; Ginsburg et al., 2006; Hofflich et al., 2006; Lerman
et al., 2015; Soltani et al., 2019; Vinall et al., 2016; Zernikow et al., 2012). A biological framework of shared
neurobiological mechanisms involving the differential susceptibility of the stress-response (Boyce, 2016b;
L. Miller, 2000; L. Zeltzer et al., 1997), sensory over-responsivity (Carpenter et al., 2019; Kaplan et al., 2021;
Schwarzlose et al., 2022), brain network reorganization (Bhatt et al., 2020; Martucci & Mackey, 2018; Mayer,
Labus, et al., 2015b; Mercer Lindsay et al., 2021b; Zhuo, 2016), and central sensitization (Clark et al., 2019b;
Harte et al., 2018; Nijs, George, Clauw, Fernández-de-las-Peñas, et al., 2021; Walker et al., 2012; L. Zeltzer
et al., 1997) warrant further investigation into individualized neurobiological biomarkers able to predict
these overlapping phenotypes with high specificity and sensitivity.
A culmination of studies using neuroimaging have provided seminal understanding of brain regions and
networks. Brain structure, function, immune activation, and neurochemical processes have been observed
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in children and adults that develop chronic pain (Bhatt, Gadewar, et al., 2024; Bhatt et al., 2020; Fitzcharles
et al., 2021b; Kaplan et al., 2021, 2024; Martucci & Mackey, 2018; Mercer Lindsay et al., 2021b). It’s now
accepted that multiple intrinsic brain networks and modulatory control systems work together to produce
the experience and maintenance of chronic pain (Canavero & Bonicalzi, 2015a; Fitzcharles et al., 2021b;
Kaplan et al., 2024; Martucci & Mackey, 2018; Mercer Lindsay et al., 2021b) and studying the relationship
with associated non-pain comorbidities such as mood/anxiety disorders and sleep disturbances would
provide comprehensive mechanistic insight into the development of chronic pain in children and adolescents.
Genome-wide association studies (GWAS) have provided considerable insight into the pathophysiology of
various pain conditions (Diatchenko, Parisien, Jahangiri Esfahani, & Mogil, 2022), including biochemical
pathways specific to neurogenesis and sympathetic plasticity specific to chronic pain vs. acute pain (Bortsov
et al., 2022), and specificity to chronic widespread pain involving over 60 pathways involved in neural
function and development (Khoury et al., 2021). Using multimodal brain imaging, expression of the top
identified gene was shown to be expressed in corticolimbic microstructure which was altered in those
with chronic widespread pain (Khoury et al., 2021). Given the observed implication of the CNS in chronic
widespread pain due to brain differences, neurogenesis, and synaptic plasticity via genetics, integration of
neuroimaging and genetics can provide insight into joint mechanisms and develop more accurate predictive
and prognostic biomarkers (Tracey et al., 2019).
The Adolescent Behavioral Cognitive Development (ABCD) study provides an unparalleled opportunity
to study chronic pain in a population sample of over 11,000 children and adolescents from a wide range of
racial and socio-economical backgrounds, largely representative of the general US population (Karcher
& Barch, 2021). The longitudinal cohort is genotyped and deeply phenotyped, including measures of
pain location (i.e. body maps), intensity, duration and impairment. Using deeply phenotyped clinical
data, multimodal neuroimaging and genotypes, we aimed to establish the neurobiological mechanisms
underlying pain vulnerability are shared across commonly observed phenotypes. We first hypothesized
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biopsychosocial signatures using clinically relevant features would characterize pain widespreadness and
chronicity over 2 years in a sex-specfic manner. Our second hypothesis was that the genetic architecture
of chronic widespread pain would have substantial overlap, and show causal genetic relationships with
commonly observed traits/comorbidities including anxiety, attention-deficit/hyperactivity disorder (ADHD),
depression, neuroticism, intelligence, insomnia, suicide attempt, and a chronic visceral pain condition,
irritable bowel syndrome (IBS). Following the identification of overlapping genetic markers of chronic
widespread pain and commonly observed comorbidities, we hypothesized integration of clinical data,
multi-modal neuroimaging features and genetic markers would predict pain widespreadness and chronicity
longitudinally in a sex-specific manner.
4.3 Methods
4.3.1 Pain Phenotyping
The ABCD Pain Questionnaire, based on the Seattle’s Children’s "Child and Adolescent Pain Questionnaire"
(“Child and Adolescent Pain Questionnaire”, n.d.), was used to characterize measures of pain intensity,
pain duration, pain impairment, as well as endorsement of pain in 74 body locations based on the CHOIR
body map for boys and girls (Scherrer et al., 2021). Body maps are powerful tools for characterizing pain
type, patient stratification, and determining individualized treatments based on the widespreadness of
pain (Clauw, 2024). To accurately characterize pain widespreadness, as simply counting the number of
regions endorsed can lead to adjacent localized pain being characterized as multisite pain (Clauw, 2024),
the body map was divided into 9 body regions including the head, left arm, right arm, chest, abdomen,
back, pelvis, left leg and right leg (Hah et al., 2022). Endorsement of any of the 74 regions within the nine
broader regions resulted in the broader region being endorsed. Pain widespreadness was defined as the
number of the aforementioned nine regions being endorsed (Figure 4.1). Worsening and improvement of
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Figure 4.1: Body maps from participants in the ABCD study, where varying numbers of body areas can be
endorsed, are harmonized into a standardized map representing 9 contiguous body regions. This harmonized
body map is designed to capture nociplastic pain with high validity (Clauw, 2024; Hah et al., 2022).
pain widespreadness was determined by taking the difference of the number of 9 broader body regions
endorsed between baseline and 1-year follow-up, and baseline and 2-year follow-up.
After harmonizing pain body maps to represent divisions of the body that effectively capture central
sensitization and nociplastic pain (Clauw, 2024), all participants were categorized based on pain localization
in 0, 1, 2, 3, or 4+ body regions. This classification follows a previously validated approach in the UK
Biobank study (Tanguay-Sabourin et al., 2023). To create a risk score for pain spreading and recovery,
changes in the number of chronic pain sites over time, with scores ranging from a decrease of 4 or more
sites to an increase of 4 or more sites (-4 to 4) were computed. This Pain Spreading Score (PSS) was refined
by adjusting for the number of pain sites (including their squared values) at baseline, allowing variations in
the adjusted score to indicate either recovery or potential for pain spreading during the follow-up visit.
Pain intensity was reported on a 1-10 numerical rating scale (NRS) to the question Over the past month,
how much did it hurt on AVERAGE (0 = not at all, 10 = the worst)? Pain duration was reported on a 1-4
ordinal scale to the question How long do your aches or pains usually last? Pain impairment was reported on
a 1-10 NRS to the question Over the past month, how much did the pain stop you from doing your normal
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activities (0 = not at all, 10 = stop me from doing anything)? If children endorsed pain in the same, one of
nine body regions 1 year (1 Year Chronic Pain) and 2 years (2 Years Chronic Pain) following the initial pain
questionnaire, they were considered as having chronic pain.
4.3.2 Biopsychosocial clinical assessment
4.3.2.1 Demographics
Using the demographics questionnaire (abcd_p_demo), ages and sex were extracted for each timepoint.
As there were only 3 children classified as intersex, they were excluded from the analysis. Anthropometric information including height (anthroheightcalc), weight (anthroweightcalc), and waist circumference
(anthro_waist_cm) was also extracted.
4.3.2.2 Pubertal status
Pubertal status was assessed using the youth reported Puberty Development Scale (PDS) (A. C. Petersen
et al., 1988) (ph_y_pds). Previous work has shown the relationship between pubertal development and
multiple pain characteristics in ABCD (R. Li et al., 2023). In concordance with previous works using
the ABCD study (Herting et al., 2020; R. Li et al., 2023), and empirical research associating hormonal
and pubertal development (Shirtcliff et al., 2009), adrenal-specific and gonadal-specific PDS scores were
computed for each sex. Adrenal specific PDS scores were computed by averaging body hair growth and
skin changes (e.g. blemishes) variables for males and females. Gonadal specific PDS scores were computed
by growth spurt, voice deepening and facial hair growth PDS items for males, and growth spurt, breast
development and menarche PDS items for females. Puberty category scores ranging between “prepubertal”,
“early pubertal”, “midpubertal”, “late pubertal”, and “postpubertal” for males and females were categorized
as previously published (Herting et al., 2020). In ABCD, in order to be able to further parse abdominal pain
which may be due to menstrual pain in girls, measures of menstrual pain intensity, length and impairment
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were extracted. As pubertal development is characterized by adrenarche (release of androgens such as
DHEA) and gonadarche (a rise in testosterone and estradiol levels), the hormones dehydroepiandrosterone
(DHEA), testosterone and estradiol were also assessed via saliva specimens (Herting et al., 2020).
4.3.2.3 Developmental history
Information on children’s birth and development was characterized using the developmental history
questionnaire completed by the parent (ph_p_dhx). Early life pain, along with sensitizing medical events
are known to be strong risk factors for altered sensitivity to pain and stress in later life via altered sensory
transduction/modulation, endocrine function, psychobehavioral outcomes, and brain development (Thapar
et al., 2020; Victoria & Murphy, 2016; L. K. Zeltzer et al., 1997). Items relating to birth complications
included premature birth (in weeks), cesarean delivery, blue at birth, slow heartbeat, no breathing at birth,
convulsions, jaundice, requirement of oxygen, Rh incompatibility, and fever of 104 degrees Fahrenheit or
more in the first 12 months. These items were summed together to form a total birth complication score.
Motor (devhx_20_p) and speech (devhx_21_p) development milestones were also assessed by comparing to
“most other children” as “much earlier”, “somewhat earlier”, “about average”, “somewhat later”, and “much
later”. The number of months breastfed was extracted from the Breastfeeding Questionnaire (ph_p_bfq) as
1 = Several days; 2 = 1-3 months; 3 = 4-6 months; 4 = 7-9 months; 5 = 10-12 months; 6 = 13-18 months; 7 =
19-24 months; 8 = more than 24 months.
4.3.2.4 Sleep quality
The effects of sleep deficiency on neurobiological systems/mediators which have hyperalgesic or analgesic
properties in chronic pain is well documented (Haack et al., 2019), with the majority of children with
chronic pain exhibiting difficulties with sleep onset and maintenance (Palermo et al., 2011). Using the
parent-reported Sleep Disturbance Scale (SDS, ph_p_sds) (Bruni et al., 1996), continuous scales of Disorders
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of Initiating and Maintaining Sleep (DIMS, sds_p_ss_dims), Sleep Breathing Disorders (SBD, sds_p_ss_sbd),
Disorder of Arousal (DA, sds_p_ss_da), Sleep-Wake transition disorder (SWTD, sds_p_ss_swtd), Disorders
of Excessive Solomance (DOES, sds_p_ss_does), Sleep Hyperhidrosis (SHY, sds_p_ss_shy), and a total sleep
disturbance score (sds_p_ss_total) were extracted.
4.3.2.5 Psychological factors
The parent-reported child behavior checklist (CBCL), which has shown to have high internal consistency
and validity (Garavan et al., 2018; Senger-Carpenter et al., 2022), was used to assess many psychological
factors. Anxiety symptoms were assessed using raw scores of the anxiety disorder DSM5 subscale (cbcl_-
scr_dsm5_anxdisord_r). Depression symptoms were assessed using raw scores of the depression DSM5
subscale (cbcl_scr_dsm5_depress_r). ADHD symptoms were assessed using raw scores of the ADHD DSM5
subscale (cbcl_scr_dsm5_adhd_r). Oppositional defiance symptoms were assessed using raw scores of
the oppositional defiance DSM5 subscale (cbcl_scr_dsm5_opposit_r). Conduct disorder symptoms were
assessed using raw scores of the it’s DSM5 subscale (cbcl_scr_dsm5_conduct_r). Inattention symptoms
were assessed using raw scores of the CBCL attention subscale (cbcl_scr_syn_attention_r). The Short Social
Responsiveness Scale (SDS) was used to assess behaviors related to the autism spectrum (Reiersen et al.,
2008). Measures known to be high risk factors such as narrow interests (ssrs_39_p) and sensory sensitivity
(ssrs_42_p) (Allely, 2013; Bogdanova et al., 2022), as well as the total sum (ssrs_p_ss_sum) were extracted.
Higher scores represented greater symptoms for each respective subscale.
4.3.2.6 Medical history - physical comorbidities
Following the approach used in previous works for reporting comorbidity scores based on physiological
health (Senger-Carpenter et al., 2022; Sun et al., 2021; Torres-Espíndola et al., 2019), we created a score
for physiological comorbidities. The score was computed by summing 39 parent-reported items from the
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medical history questionnaire (ph_p_mhx). These items represented unplanned medical visits for various
medical conditions (e.g. asthma, bad headaches) or injuries (e.g. broken bones, serious wounds). The
scores ranged from 1-22. General somatic symptoms were also assessed using the raw scores of the somatic
problems DSM5 subscale of the CBCL (cbcl_scr_dsm5_somaticpr_r). These were separate from general aches
and pains (cbcl_q56a_p), headaches (cbcl_q56b_p) and stomachaches (cbcl_q56f_p) which were assessed
separately.
4.3.2.7 Sociocultural factors
Psychosocial factors such as early life trauma, family conflict, parental psychopathology, and school
functioning impact early life programming of neurobiological systems are strong risk factors for pediatric
chronic pain (McKillop & Banez, 2016; Thapar et al., 2020). Adverse childhood experiences (ACEs) and
benevolent childhood experiences (BCEs) were assessed using the youth-reported life events questionnaire
(mh_y_le). The total number of good (ple_y_ss_total_good) and bad events (ple_y_ss_total_bad), as well as
the sum (ple_y_ss_affected_good_sum, ple_y_ss_affected_bad_sum) and mean (ple_y_ss_affected_good_mean,
ple_y_ss_affected_bad_mean) of how much good and bad events affected each individual were extracted. The
degree of family conflict was assessed using the parent reported Family Environment Scale’s conflict subscale
(fes_p_ss_fc_pr). Parental symptoms of anxiety (asr_scr_anxdisord_r) and depression (asr_scr_depress_r) as
well as somatic symptoms (asr_scr_somaticpr_r) were assessed via adult self-report. The number of excused
(sag_miss_school_excuse_p) and unexcused school days missed (sag_miss_school_unexcuse_p), and grade
levels (ce_p_sag) were also extracted (Dick & Pillai Riddell, 2010).
4.3.2.8 Neurocognition
Variability in cognitive functioning in children with chronic pain is well documented, with differences in
working memory and sustained attention being the most disrupted cognitive processes. However, they
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are known to have normal or above-average scores on tests of general intelligence, as well as verbal,
performance and processing speed subscales (Dick & Pillai Riddell, 2010; Weiss et al., 2018). Tests assessing
language/verbal intellect (nihtbx_picvocab_agecorrected), cognitive control/attention (nihtbx_flanker_-
agecorrected), working memory (nihtbx_list_agecorrected), flexible thinking (nihtbx_cardsort_agecorrected),
processing speed (nihtbx_pattern_agecorrected), visuospatial sequencing/episodic memory (nihtbx_picture_-
agecorrected), reading ability (nihtbx_reading_agecorrected), and fluid reasoning (nihtbx_fluidcomp_agecorrected), along with two composite scores - crystallized intelligence (nihtbx_cryst_agecorrected) and a total
score (nihtbx_totalcomp_agecorrected) - were extracted (Luciana et al., 2018).
4.3.2.9 Medications
Trends in the prevalence of prescription medication usage in children in the United States over the last
20 years has the potential to inform the presence of conditions which are risk factors, or sequelae due to
widespread/chronic pain (Hales et al., 2018; McKillop & Banez, 2016). Using the parent-reported medications
inventory (ph_p_meds), all reported prescription and over-the-counter (OTC) medications were queried
against the National Library of Medicine RxNorm RESTful API using RxCUI with the R package rxnorm
(Nelson et al., 2011; Williams, n.d.). All medications were classified using the Anatomical Therapeutic
Chemical (ATC) Classification System at the first four levels indicating (1) anatomical or pharmacological
group, (2) pharmacological or therapeutic subgroup, and (3 & 4) Chemical, Pharmacological or Therapeutic
subgroup (“Anatomical therapeutic chemical (ATC) classification”, n.d.). The number of individuals taking
each classification of medication was computed.
4.3.3 The genetic overlap of chronic widespread pain and comorbidities
In order to establish shared genetic liability for chronic widespread pain and commonly associated symptoms/traits, genome-wide summary statistics were first obtained for chronic overlapping pain conditions
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(COPC) (Khoury et al., 2021), autism spectrum disorder (ASD) (Grove et al., 2019), attention deficit hyperactivity disorder (ADHD) (Demontis, Walters, Athanasiadis, Walters, Therrien, Nielsen, Farajzadeh,
Voloudakis, Bendl, Zeng, Zhang, Grove, Als, Duan, Satterstrom, Bybjerg-Grauholm, Bækved-Hansen,
Gudmundsson, Magnusson, Baldursson, Davidsdottir, Haraldsdottir, Agerbo, Hoffman, Dalsgaard, Martin,
Ribasés, Boomsma, Soler Artigas, et al., 2023), anxiety (Friligkou et al., 2024), depression (Als et al., 2023),
intelligence (Savage et al., 2018), insomnia (Watanabe et al., 2022), suicide attempt (Mullins et al., 2022),
and a visceral chronic pain condition with nociplastic features, irritable bowel syndrome (Eijsbouts et al.,
2021). In order to determine the genetic correlation of COPC and associated symptoms/traits, global genetic
correlations were run using the LD score regression framework (LDSC) (B. K. Bulik-Sullivan et al., 2015).
We performed bi-directional Mendelian Randomization analyses using CAUSE (Morrison et al., 2020) to
investigate if significant genetic correlations observed could be driven by genetic causal relationships
between an exposure (e.g. anxiety) and outcome (e.g. COPC) and vise-versa. CAUSE considers correlated and uncorrelated pleiotropy, which helps to reduce bias due to horizontal pleiotropy. All p-values
were Bonferroni corrected based on the number of tests run. All SNPs for the exposure GWAS with a
genome-wide significant p-value less than 5 x 10−8 were considered as instrumental variables, and were
LD-clumped using the default recommended settings by CAUSE (r
2 < 0.01 within 10,000 kb window) using
PLINK and the 1000 Genomes European reference panel. Following the confirmation of a significant CAUSE
model, SNP level importance scores were computed. These scores measure how much each genetic variant
contributes to the causal effect. They are based on the difference in a value called ELPD (Expected Log
Pointwise Posterior Density). The most negative ∆ELP D score indicates the variant with the strongest
evidence for influencing the outcome.
As global genetic correlations may not be able to capture local genetic correlations in opposing directions,
and local genetic correlations in the absence of global genetic correlations can go undetected, local genetic
correlations across all protein coding genes (N = 18,380 genes) were conducted between COPC and associated
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symptoms/traits using LAVA (Werme et al., 2022). Genotype data from the European sample of the 1000
Genomes (phase 3) project (1. G. P. Consortium et al., 2015) was used to estimate SNP LD for LAVA. For each
gene that had a significant genetic signal for both the COPC phenotype and associated symptoms/traits
(univariate p-values less than 1 x 10−4
), the local bivariate genetic correlation between the two was
estimated and tested. All results were corrected using the Bonferroni approach.
In order to characterize genetic loci jointly associated with COPC and associated symptoms/traits, we
used multi-trait analysis of GWAS (MTAG) (Turley et al., 2018). MTAG jointly analyzes GWAS summary
statistics from multiple traits, while considering sample overlap using bivariate LD Score regression,
taking advantage of the genetic correlation between them and boosting statistical power. Unlike other
multi-trait methods that do not provide estimated effect sizes and often combine traits into a single, often
difficult-to-interpret meta-trait (Smeland et al., 2020), MTAG preserves the distinct characteristics of each
individual trait. This allows to accurately differentiate between genetic loci that are genuinely associated
with multiple traits and those that are specific to just one COPC, while taking into account its genetic
correlation with all other comorbidities. It also enhances the discovery of genetic loci, PRS prediction
accuracy, and informativeness of downstream bioinformatic analysis, which has been successfully applied
in other clinical conditions (Han et al., 2023; Khunsriraksakul et al., 2023). SNPs around the extended
major histocompatibility complex (MHC) region (genome build positions 19 chromosome 6:26000000-
34000000) before starting MTAG analyses to avoid bias due to complex regional linkage disequilibrium
(LD) (Schwartzman & Lin, 2011). Identification of independent genomic loci were determined using the
FUMA platform (Watanabe et al., 2017). We identified independent, statistically significant SNPs with low
linkage disequilibrium (r
2 < 0.60 and MTAG p < 5 x 10−8
). From these, lead SNPs in approximate linkage
equilibrium (r
2 < 0.1) were chosen. Candidate SNPs were defined by significant association (MTAG p <
5 x 10−8
) and strong linkage (r
2 > 0.60) with a lead SNP. Finally, loci were formed by merging nearby
SNPs (<250 kb) and selecting the SNP with the most significant MTAG as the lead SNP for the locus. Locus
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borders were defined by all candidate SNPs in strong LD (r
2 ≥ 0.60) with any independent significant
SNP within the region. All linkage disequilibrium values were obtained from the 1000 Genomes Project
European-ancestry reference panel (1000 Genomes Project Consortium et al., 2015). Polygenic risk scores
(PRS) were computed for 5,899 individuals of European ancestry in the ABCD cohort using sBayesRC,
a Bayesian method that combines effect sizes from MTAG-derived summary statistics with functional
genomic annotations. This approach was applied to assess PRS for chronic overlapping pain conditions
as well as for each co-morbid trait, leveraging the integration of genomic and functional data to enhance
accuracy. (Zheng et al., 2024). The ancestry of the ABCD population was determined using the KING
software and the 1000 Genomes reference panel (Figure 4.2). King uses a multidimensional scaling analysis
(MDS) to identify the top 10 ancestry components, then projected onto the MDS space of the 1000 Genomes
reference panel, and ancestry is determined by using the e107153 support vector machines package in R
(1000 Genomes Project Consortium et al., 2015; Manichaikul et al., 2010).
In order to determine which biological functions are over-represented from the MTAG results, the
genes mapped to the significant loci via FUMA/MAGMA from each trait (Watanabe et al., 2017), were
entered into Metascape platform to perform a multi-gene-list meta-analysis to identify commonly-enriched
and selectively-enriched pathways (Y. Zhou et al., 2019). For each given gene list, pathway and process
enrichment analysis have been carried out with the following ontology sources: GO Biological Processes,
KEGG Pathway, GO Molecular Functions, GO Cellular Components, Reactome Gene Sets, Hallmark Gene
Sets, Canonical Pathways, BioCarta Gene Sets, CORUM, WikiPathways, and PANTHER Pathway. All genes
in the genome have been used as the enrichment background. Terms with a p-value < 0.01, a minimum
count of 3, and an enrichment factor > 1.5 (the enrichment factor is the ratio between the observed counts
and the counts expected by chance) were collected and grouped into clusters based on their membership
similarities. More specifically, p-values are calculated based on the cumulative hypergeometric distribution
(Zar, 1999), and q-values are calculated using the Benjamini-Hochberg procedure to account for multiple
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Figure 4.2: The ancestry of each individual in the ABCD study determined via KING using the 1000 Genomes
reference panel (1000 Genomes Project Consortium et al., 2015; Manichaikul et al., 2010)
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testing (Benjamini & Hochberg, 1995). Kappa scores (J. Cohen, 1960) are used as the similarity metric
when performing hierarchical clustering on the enriched terms, and sub-trees with a similarity of > 0.3
are considered a cluster. The most statistically significant term within a cluster is chosen to represent the
cluster. For each given gene list, protein-protein interaction enrichment analysis has been carried out with
the following databases: STRING (Szklarczyk et al., 2019), BioGrid (Stark et al., 2006), OmniPath (Türei et al.,
2021), and InWeb IM (T. Li et al., 2017). Only physical interactions in STRING (physical score > 0.132) and
BioGrid were used. The resultant network contained the subset of proteins that form physical interactions
with at least one other member in the list. If the network contained between 3 and 500 proteins, the
Molecular Complex Detection (MCODE) algorithm (Bader & Hogue, 2003) was applied to identify densely
connected network components. To identify significantly enriched cell type signatures (Subramanian et al.,
2005), all genes in the genome were used as the enrichment background. Terms with a p-value < 0.01,
a minimum count of 3, and an enrichment factor > 1.5 (the enrichment factor is the ratio between the
observed counts and the counts expected by chance) were collected and grouped into clusters based on
their membership similarities.
4.3.4 Neuroimaging Processing
4.3.4.1 Brain structure
All MRI acquisition parameters for all neuroimaging modalities for the ABCD study have been previously
published (Hagler Jr et al., 2019). All individuals who scored a 3 (consider clinical referral) or 4 (consider
immediate clinical referral) in the MRI clinical report/findings (table name mri_y_qc_clfind) were excluded.
In order to characterize regional metrics of cortical surface area, thickness and subcortical volume, all T1
weighted brain MRIs were processed through FreeSurfer 7.1 (Fischl, 2012b) using the Human Connectome
Project - Multimodal Parcellation cortical atlas (HCP-MMP1) (Glasser et al., 2016) and the Harvard-Oxford
subcortical atlas included in FreeSurfer, resulting in a total of 377 regions-of-interest (ROIs).
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4.3.4.2 Structural connectivity
Diffusion weighted images from the minimally processed release from ABCD included b0 correction,
gradient unwarping, eddy current correction, motion correction, tensor fit outlier-based censoring and
replacement of bad frames, and reorienting to standard orientation (Hagler Jr et al., 2019). Brain extraction
from the T1w images were completed using HD-BET (Isensee et al., 2019). All diffusion images were then
reampled to 1 mm3
to improve co-registration to T1-weighted (T1w) structural images and tractography
accuracy (Dyrby et al., 2014; Schwarz et al., 2014). Bias correction was performed using dwibiascorrect
from the MRtrix3 package (J. D. Tournier et al., 2019). The fiber orientation distributions (FODs) for each
tissue type were determined using the dhollander algorithm for multishell data with the dwi2response
function to determine the basis functions, and dwi2fod function using the msmt_csd option for multi-shell
multi-tissue constrained spherical deconvolution (Dhollander et al., 2016, 2019; J.-D. Tournier et al., 2007).
All FODs were normalized using the mtnormalise function (Dhollander et al., 2021). Diffusion images were
then co-registered to T1w images using boundary based registration (BBR) using flirt in FSL (Greve &
Fischl, 2009). Tissue segmentation was performed on the T1w images using 5ttgen into gray matter, white
matter and CSF (R. E. Smith et al., 2012), and registered to the diffusion images using BBR. Whole brain
tractography was then computed using a parallel transport tractography approach using trekker (Aydogan
& Shi, 2021), which has an advantage over previously tractography algorithms with the ability to generate
completely smooth curves using differential geometry and being able to track along the parameterized
smooth curves. Pathway rules were defined such that 1 million streamlines could only be present in
white matter, and end in gray matter using the segmented images from 5ttgen. To address the issue of
false-positive streamlines, information from microstructure via the diffusion images was incorporated into
the tractograms, and biologically implausible streamlines were filtered out using the COMMIT2 framework
(Battocchio et al., 2022; Schiavi et al., 2020). The resulting 377 x 377 connectome matrices the “weight”
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of each connection, which is the effective cross-sectional area of the number of streamlines from each
ROI-to-ROI (F. Zhang et al., 2022).
4.3.4.3 Resting-state functional connectivity
Preprocessing was performed with fmriprep version 23.2.2 (RRID:SCR_016216) (Esteban et al., 2019). T1w
images were corrected for corrected for intensity non-uniformity (INU) with N4BiasFieldCorrection
(Tustison et al., 2010), distributed with ANTs 2.5.1 (RRID:SCR_004757]) (Avants et al., 2008), and used
as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a Nipype
implementation of the antsBrainExtraction.sh workflow (from ANTs), using OASIS30ANTs as target
template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter
(GM) was performed on the brain-extracted T1w using fast (FSL 6.0.5.2, RRID:SCR_002823) (Y. Zhang
et al., 2001). Brain surfaces were reconstructed using recon-all (FreeSurfer 7.1.1, RRID:SCR_001847)
(Dale et al., 1999), and the brain mask estimated previously was refined with a custom variation of the
method to reconcile ANTs-derived and FreeSurfer-derived segmentations of the cortical gray-matter of
Mindboggle (RRID:SCR_002438) (Klein et al., 2017). T2-weighted images were used when available to
improve pial surface refinement. Brain surfaces were reconstructed using recon-all (FreeSurfer 7.1.1,
RRID:SCR_001847) (Dale et al., 1999), and the brain mask estimated previously was refined with a custom
variation of the method to reconcile ANTs-derived and FreeSurfer-derived segmentations of the cortical
gray-matter of Mindboggle (RRID:SCR_002438) (Klein et al., 2017). Surface-based spatial normalization was
performed by accessing the fsLR via TemplateFlow 24.2.0 (Ciric et al., 2022). Grayordinate "dscalar" files
containing 91k samples were resampled onto fsLR using the Connectome Workbench (Glasser et al., 2013).
The eXtensible Connectivity Pipeline-DCAN (XCP-D) (Mehta et al., 2024) was used to post-process the
outputs of fMRIPrep version 23.2.2 (RRID:SCR_016216) (Esteban et al., 2019). XCP-D was built with Nipype
version 1.8.6 (RRID:SCR_002502) (K. Gorgolewski et al., 2011). Native-space T1w images were transformed
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to MNI152NLin2009cAsym space at 1 mm3
resolution. fsLR-space morphometry surfaces were copied from
the preprocessing derivatives to the XCP-D derivatives. HCP-style mid-thickness, inflated, and very-inflated
surfaces were generated from the white-matter and pial surface meshes. fsnative-space surfaces were then
warped to fsLR space. For each of the resting-state BOLD runs found per subject (across all sessions), the
following post-processing was performed.
Non-steady-state volumes were extracted from the processed confounds and were discarded from both
the BOLD data and nuisance regressors. The six translation and rotation head motion traces were band-stop
filtered to remove signals between 15.0 and 25.0 breaths-per-minute using a fourth-order notch filter, based
on (Fair et al., 2020). The Volterra expansion of these filtered motion parameters was then calculated.
Framewise displacement was calculated from the filtered motion parameters using the formula (Power et al.,
2014), with a head radius of 66.91 mm. Volumes with filtered framewise displacement greater than 0.3 mm
were flagged as high-motion outliers for the sake of later censoring (Power et al., 2014). Nuisance regressors
were selected according to the acompcor_gsr strategy. The top 5 aCompCor principal components from
the white matter and cerebrospinal fluid compartments were selected as nuisance regressors (Behzadi
et al., 2007), along with the six filtered motion parameters and their temporal derivatives, mean white
matter signal, mean cerebrospinal fluid signal, and mean global signal (Ciric et al., 2017; Satterthwaite et al.,
2013). As the aCompCor regressors were generated on high-pass filtered data, the associated cosine basis
regressors were included. This has the effect of high-pass filtering the data as well. The BOLD data were
converted to NIfTI format, despiked with AFNI’s 3dDespike, and converted back to CIFTI format.
Nuisance regressors were regressed from the BOLD data using a denoising method based on Nilearn’s
approach. Any volumes censored earlier in the workflow were first cubic spline interpolated in the BOLD
data. Outlier volumes at the beginning or end of the time series were replaced with the closest low-motion
volume’s values, as cubic spline interpolation can produce extreme extrapolations. The time series were
band-pass filtered using a second-order Butterworth filter, in order to retain signals between 0.009-0.08
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Hz. The same filter was applied to the confounds. The resulting time series were then denoised via linear
regression, in which the low-motion volumes from the BOLD time series and confounds were used to
calculate parameter estimates, and then the interpolated time series were denoised using the low-motion
parameter estimates. The interpolated time series were then censored using the temporal mask. The
denoised BOLD was then smoothed using Connectome Workbench with a Gaussian kernel (FWHM=6.0
mm).
Processed functional time series were extracted from residual BOLD using Connectome Workbench
(Marcus et al., 2011) for the Glasser (Glasser et al., 2016) cortical and HCP CIFTI (Glasser et al., 2013) subcortical atlases. Postprocessing derivatives were then concatenated across resting-state runs. Corresponding
pairwise functional connectivity between all regions (ROIs) was computed for all regions on both atlases,
which was operationalized as the Fisher transformed Pearson’s correlation (Z) of each parcel’s unsmoothed
time series with the Connectome Workbench and the DescTools package in R (Andri et al., 2021; Van Dijk
et al., 2010). Unsmoothed data was used to calculate ROI-to-ROI connectivity matrices as spatial averaging
of voxels within parcels is computed to determine the time-series with each parcel, and smoothing before
parcellation can blur signal across ROIs (Alahmadi, 2021). In cases of partial coverage, uncovered vertices
(values of all zeros or NaNs) were either ignored (when the parcel had >50.0% coverage) or were set to zero
(when the parcel had <50.0% coverage). Thresholding of the functional connectome was completed by taking
the absolute values of the matrix and using the extensively validated and optimized orthogonal minimum
spanning tree (OMST) approach (Dimitriadis et al., 2017; Luppi et al., 2024). Values of the negative weights
were restored after thresholding (Fornito et al., 2016). As resting-state functional connectivity can offer
insight into telescoping levels of information (e.g. local vs. global vs. edge connectivity), global features
of global efficiency and modularity, local features of strength and eigenvector centrality, as well edge
connectivities between each ROI were computed (Hallquist & Hillary, 2018). Within-network correlations
were determined by averaging the Fisher-transformed correlations of every unique ROI pair within that
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network. Between-network correlations were determined by averaging the correlations of every unique
ROI pair between two different networks (DeRosa et al., 2024).
4.3.5 Multi-omics data integration
As shown to be effective previous multiomics studies using neuroimaging (Bhatt et al., 2022, 2023; Sarnoff
et al., 2023), the Data Integration Analysis for Biomarker discover using Latent cOmponents (DIABLO)
(Singh et al., 2019) framework was used. This was done by using clinical/behavioral data, structural
brain data, functional brain data, and PRS scores for each trait used in the MTAG analysis computed via
sBayesRC (Zheng et al., 2024) in european ancestry children. DIABLO aims to identify a limited number
of correlated variables from multiple, matching high-dimensional datasets (Q) to predict an outcome. It
extends sparse generalized canonical correlation analysis to a supervised machine learning framework,
which is a generalization of partial least squares (PLS). Prior to the DIABLO analysis, sparse PLS (sPLS)
models were run between each dataset (e.g. brain structure versus clinical; brain function versus PRS)
to understand the overall correlational structure between data types. A data-driven design matrix was
constructed using correlations derived from the first component of each individual sPLS model to guide data
integration in DIABLO. This weighted Q x Q design matrix, an essential input for DIABLO, was computed
by calculating the correlations of the first principal components between pairwise data blocks. The design
matrix, with values ranging from 0 to 1, quantifies the strength of the correlation between each pair of data
blocks, indicating both the presence and degree of association. The final DIABLO model selects a limited
set of features across data types that are highly correlated, providing insights into which omics data types
contribute most to the discriminatory process and how these data types interact with each other. A DIABLO
model with 2 components was fit for boys and girls to predict "No Pain", "Local Pain" or "Widespread Pain"
at baseline. This included 3778 pain free, 1460 local pain, and 486 widespread pain boys, and 3350 pain free,
1166 local pain, and 683 widespread pain girls. Models were fit by choosing 10 features on each component
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for the clinical, structural, and functional data, and all 9 PRS features. The final performance of the model
was assessed using 5-fold cross validation repeated 10 times and looking at the area under the ROC curve
(AUC) to determine how well the model was performing for each group. The primary output measures of
DIABLO include a set of components (latent variables) selected by the model, loading vectors (coefficients
assigned to variables to define each component), and a list of variables from each dataset associated with
each component. Loadings, the coefficients for each variable, indicate the importance of the variables in the
model, with their absolute values reflecting their contribution to DIABLO. Each loading vector corresponds
to a specific component and is optimized to maximize the covariance between a linear combination of X
variables and Y. Individual sample plots project each sample onto a space defined by the components, with
their coordinates determined by the component scores. Loading plots visualize the coefficients (importance)
assigned to variables for each component within each dataset. Circos diagrams, built on a similarity matrix,
illustrate correlations between variables across datasets. A correlation cutoff of r = 0.7, widely recognized
as a “strong” correlation, was applied to highlight significant relationships.
4.4 Results
4.4.1 Characterizing pain in ABCD
Prior to harmonizing body maps, the endorsement of pain on the CHOIR body maps was computed for
each individual. Individuals with local and widespread pain according to the characterization by (Clauw,
2024) is depicted in Figure 4.3.
In order to determine how the effect of pain intensity, pain duration, and pain impairment on endorsement on all 74 locations on the body, general linear models were run in males and females separately for
pain intensity, pain duration, and pain impairment. All tests were corrected using the Bonferroni approach.
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Figure 4.3: The endorsement of pain is shown for for individuals with local and widespread pain at baseline
in males and females. Darker shades of red represent a higher percentage of children endorsing pain in
those body regions. There were 3778 boys and 3550 girls who did not report any pain.
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The direction of effect for each pain variable on each body site is shown in Figure 4.3. Significant body
regions are outlined in black.
4.4.2 Evidence for substantial genetic overlap between chronic pain and commonly
observed comorbidities
Positive global genetic correlations were observed between COPC and anxiety, ADHD, depression, IBS,
insomnia, neuroticism, and suicide attempt (rg = 0.43 - 0.60). A negative global genetic correlation was
observed with intelligence (rg = -0.33). Local negative genetic correlations were observed between COPC
and all other traits across the genome. Cytogenetic bands showing overlap between COPC and multiple
other traits included anxiety, depression, intelligence and neuroticism on the 3p21.31 cytogenetic band;
depression and neuroticism on the 6p21.1 cytogenetic band; anxiety, ADHD, depression, insomnia, and
neuroticism on the 7p22.3 cytogenetic band; anxiety and depression on the 18q21.2 (DCC) cytogenetic band,
and intelligence and depression on the 20q13.33 cytogenetic band.
Genetic correlations can be caused by direct causation, pleiotropy, or genetic mediation (van Rheenen
et al., 2019). Using the Bayesian CAUSE algorithm, we assessed putative causal relationships between
COPC and commonly observed comorbidities (Morrison et al., 2020). CAUSE also estimates SNP level
importance scores using the difference in Expected Log Pointwise Posterior Density (∆ELP D) (Vehtari
et al., 2017), which were ranked by importance. Bidirectional causal effects were observed with COPC
and anxiety (COPC → anxiety: γ = 0.20, pFDR = 0.0003, anxiety → COPC: γ = 0.17, pFDR = 2.38 x 10-5),
depression (COPC → depression: γ = 0.34, pFDR = 2.30 x 10-8, depression → COPC: γ = 0.22, pFDR = 8.40 x
10-9) neuroticism (COPC → Neuroticism: γ = -1.03, pFDR = 2.20 x 10-5, Neuroticism → COPC: γ = -0.04,
pFDR = 1.09 x 10-7), intelligence (COPC → Intelligence: γ = -0.36, pFDR = 2.38 x 10-5, Intelligence → COPC:
γ = -0.07, pFDR = 6.65 x 10-8), and IBS (COPC → IBS: γ = 0.78, pFDR = 3.73 x 10-6, IBS → COPC: γ = 0.08,
pFDR = 2.38 x 10-5). Unidirectional causal effects were observed with COPC on ADHD (γ = 0.48, pFDR = 6.49
112

a
rG
b
c
Figure 4.4: Evidence for substantial genetic overlap of chronic widespread pain and commonly observed comorbidities (a) Significant global genetic correlations are observed between CWP and comorbid
conditions. (b) Significant local genetic correlations are observed with CWP and all comorbidities across
the genome. Volcano plot shows degree (-log10 p-values) and direction (rG) of local genetic correlations
between COPC and all comorbidities (c) Ideogram shows location of all local genetic correlations and
annotated genes.
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Figure 4.5: Effect size estimates and variant level contribution to CAUSE test statistics with chronic
pain as the exposure, and co-morbid symptoms as the outcome SNP-level effect estimates for chronic
pain (horizontal axis) are plotted against estimates for co-morbid conditions (vertical axis). SNPs with a
negative (∆ELP D) which contribute to the causal relationship are labeled. The size the points represent
the significance level in the chronic overlapping pain GWAS.
x 10-5), insomnia (γ = 0.38, pFDR = 3.22 x 10-11), and suicide attempt (COPC → Suicide Attempt: γ = 0.51,
pFDR = 0.008), but not vice-versa. The genes SPHKAP and RNF123 highly influential to the causal genetic
effect with COPC as the exposure for 5 of the comorbid symptoms, and the genes DCC, FAM212B, FOXP2,
NCAM1, SLC39A8 were highly influential for 4 comorbid symptoms (Figure 4.5).
The highly influential genes ELAVL2, RP11-363J20.2, RP11-436D23.1, and SGCZ were shared with
intelligence and neuroticism as the exposure, and NCAM1 was shared with intelligence and IBS as the
exposure (Figure 4.6).
Using MTAG (Turley et al., 2018), we showed a significant overlap with chronic overlapping pain
conditions and commonly associated traits including anxiety (Friligkou et al., 2024), ADHD (Battison et al.,
2023; Demontis, Walters, Athanasiadis, Walters, Therrien, Nielsen, Farajzadeh, Voloudakis, Bendl, Zeng,
114

Figure 4.6: Effect size estimates and variant level contribution to CAUSE test statistics with
chronic pain as the outcome, and co-morbid symptoms as the exposure SNP-level effect estimates
for comorbid conditions (horizontal axis) are plotted against estimates for chronic pain (vertical axis). SNPs
with a negative (∆ELP D) which contribute to the causal relationship are labeled. The size the points
represent the significance level in the respective comorbid condition GWAS.
115

Zhang, Grove, Als, Duan, Satterstrom, Bybjerg-Grauholm, Bækved-Hansen, Gudmundsson, Magnusson,
Baldursson, Davidsdottir, Haraldsdottir, Agerbo, Hoffman, Dalsgaard, Martin, Ribasés, Boomsma, Artigas,
et al., 2023), depression (Als et al., 2023), neuroticism (Kadimpati et al., 2015; Nagel et al., 2018), intelligence
(Higgins et al., 2018), insomnia (Haack et al., 2019; Watanabe et al., 2022), and suicide attempt (C. Chen
et al., 2023; Mullins et al., 2022), as well as IBS, a known chronic pain condition with nociplastic pain
features and common etiological pathways with anxiety/mood disorders (Eijsbouts et al., 2021; Mayer et al.,
2023). The genes that had the most overlap between chronic overlapping pain conditions and commonly
associated traits included the DCC, TCF4, LAMB2, DAG1, MED19, LSAMP, CTNND1, BPTF, RAD51 and
MPHOSPH9. To determine which biological functions are over-represented from the MTAG results, the
genes mapped to each trait via FUMA (Watanabe et al., 2017), were entered into Metascape (Y. Zhou et al.,
2019) to perform a multi-list multi-gene meta analysis of all traits. Significant enrichment of all traits were
observed in multiple biological processes and pathways, with the highest enrichment being observed in
ontology terms “postsynapse”, “cell morphogenesis”, and “regulation of synapse organization”. The highest
significant cell type enrichment was observed in the medial midbrain neuroblasts which are GABAergic
precursors (hNbML5, hNbGaba) (La Manno et al., 2016). When the MCODE algorithm was applied to the
protein-protein interaction network, five clusters which were significantly enriched for axon guidance
during development, cell adhesion molecule binding, chromatin remodeling, chromosome organization and
G protein-coupled glutamate receptor activity were identified.
4.4.3 DIABLO shows potential neuroimaging genetic signatures for widespread pain in
children
The DIABLO models integrating brain structural features from FreeSurfer, resting-state functional connectivity, polygenic risk scores for chronic overlapping pain conditions and commonly observed comorbidities,
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Figure 4.7: Multi-gene-list meta-analysis of chronic overlapping pain conditions and commonly
observed comorbidities: (a) Circos plot displays the overlap of genes between COPC and all comorbidities.
Genes significant for multiple traits are colored in dark orange, and genes unique to a trait are shown in
light orange. (b) Heatmap of enriched terms across traits in biological pathways and (c) tissue types. (d)
Enrichment network of significant genes of all traits, where the size of a slice represents the percentage
of genes under the trait that originated from the corresponding gene list. The network shows processes
relating to the postsynapse and cell morphogenesis are highly enriched and are shared amongst all traits. (e)
Selected MCODE components identified from the combined list of 854 genes, where each node represents
a protein with a pie chart encoding its which trait it showed significance for. Complexes related to axon
guidance, cell adhesion molecule binding, chromatin remodeling, chromosome organization and G proteincoupled glutamate receptor activity are shared between chronic overlapping pain conditions and commonly
observed comorbidities. Many genes across complexes are observed in all traits.
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and clinical/behavioral data provide insight into potential neurological mechanisms underlying observed
symptoms specific to widespread pain.
The largest discriminatory clinical features contributing to both boys’ and girls’ models included
children with widespread pain having the highest scores for total early adverse life events, how much the
children were affected by early adverse events (ACE), and scores on the behavioral inhibition scale on
component 1, and scores on intelligence testing via the NIH Toolbox on component 2. In both boys and
girls the self reported impact of ACE’s was positively correlated the PRS of COPC, anxiety, ADHD, IBS,
neuroticism, and suicide attempt, but negatively correlated with the intelligence PRS in only girls (Figures
4.11 and 4.12).
Regarding brain structure, boys with widespread pain exhibited the highest surface area values in the
sensorimotor network, including the primary motor cortex (area 4) and primary sensory cortices (areas
1 and 3b). These structural features were positively correlated with functional connectivity between the
right nucleus accumbens and right posterior orbitofrontal cortex, as well as between the right anterior
cingulate (area 25) and posterior orbitofrontal cortex. Furthermore, these brain structure and connectivity
features were positively associated with higher intelligence test scores and elevated polygenic risk scores
for intelligence.
In contrast, girls with widespread pain displayed smaller amygdala volumes and reduced cortical
thickness in sensorimotor regions (areas 4 and 3a). These structural abnormalities were negatively correlated
with polygenic risks for chronic overlapping pain conditions (COPC), ADHD, irritable bowel syndrome (IBS),
depression, neuroticism, and suicide attempt. Additionally, girls showed reduced functional connectivity
within the visual network (specifically between areas V1 and V2) and between the lateral temporal cortex
(area TE1a) and rectus gyrus (area 10v), further highlighting the differential neural profiles between boys
and girls (Figures 4.11 and 4.12).
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After conducting 5-fold cross-validation repeated 10 times for each model, the stability of all features
was assessed. Stability reflects the percentage of times each feature was selected across all folds and
repetitions during the cross-validation process. The results are summarized in Table 4.1 for boys and Table
4.2 for girls.
For clinical features on component 1, the degree to which individuals were affected by adverse childhood
experiences (ACEs), scores on the Behavioral Inhibition Scale (BIS), Negative Urgency scale, and the Somatic
Problems scale of the Child Behavior Checklist were the most stable features for both boys and girls.
Additionally, the adrenal-specific Puberty Development Score was a highly stable feature in girls (80%).
On component 2, all intelligence subscales, as well as the total intelligence score from the NIH Toolbox,
demonstrated high stability in both boys and girls. The BIS score was also a highly stable feature in girls
(96%).
For brain structural features on component 1, the cortical thickness of sensorimotor regions showed the
highest stability in boys (84–90%). In girls, the most stable structural features were the volume of the left
amygdala and the cortical thickness of sensorimotor regions (areas 3a and 4), with stability values ranging
from 76–94%. No brain structural features exceeded 70% stability on component 2 for either boys or girls.
Regarding brain functional connectivity, no features surpassed 70% stability on component 1 in boys.
However, in girls, connectivity within the visual network (specifically, between areas V1 and V2) demonstrated 76% stability. On component 2, no functional connectivity features exceeded 70% stability for either
boys or girls.
Although the area under the curve (AUC) values for predicting widespread pain versus localized or no
pain were statistically significant in boys (AUC = 0.57, p = 0.044) and girls (AUC = 0.57, p = 0.037), the low
AUC values indicate that the models did not exhibit strong predictive capacity.
Feature DataType Component AverageValue
affected_sum Clinical comp1 1.00
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bad_affected_sum Clinical comp1 1.00
bis_sum Clinical comp1 1.00
negative_urgency Clinical comp1 1.00
somatic_r_score Clinical comp1 1.00
total_bad_le Clinical comp1 0.94
sensation_seeking Clinical comp1 0.62
bas_fs Clinical comp1 0.48
crystallized_int Clinical comp1 0.42
internalizing_r_score Clinical comp1 0.40
sleep_disturbance Clinical comp1 0.40
adhd_r_score Clinical comp1 0.30
comorbidy_score Clinical comp1 0.28
externalizng_r_score Clinical comp1 0.20
language_verb_int Clinical comp1 0.20
oppositional_def_r_score Clinical comp1 0.20
positive_urgency Clinical comp1 0.20
school_enviornment Clinical comp1 0.36
sleep_maintainence Clinical comp1 0.52
total_comp_agecorr_nihtb Clinical comp1 0.68
crystallized_int Clinical comp2 0.98
fluid_reasoning Clinical comp2 0.98
language_verb_int Clinical comp2 0.98
reading_ability Clinical comp2 0.98
total_comp_agecorr_nihtb Clinical comp2 0.96
120

working_memory Clinical comp2 0.94
bis_sum Clinical comp2 0.82
school_enviornment Clinical comp2 0.72
breastfed Clinical comp2 0.58
cogcontrol_attention Clinical comp2 0.48
days_60_min_active Clinical comp2 0.42
grades Clinical comp2 0.38
sensation_seeking Clinical comp2 0.30
adhd_r_score Clinical comp2 0.44
adrenal_pds Clinical comp2 0.60
externalizng_r_score Clinical comp2 0.76
family_conflict_yr Clinical comp2 0.84
flexible_thinking Clinical comp2 0.84
inv_rulebreak_peers Clinical comp2 0.92
male_puberty_category_sum Clinical comp2 0.90
school_involvment Clinical comp2 0.82
sleep_disturbance Clinical comp2 0.70
somatic_r_score Clinical comp2 0.62
L_6mp_ROI_thickness Structural comp1 0.90
R_4_ROI_thickness Structural comp1 0.88
R_6d_ROI_thickness Structural comp1 0.88
R_3b_ROI_thickness Structural comp1 0.86
R_55b_ROI_thickness Structural comp1 0.86
R_6mp_ROI_thickness Structural comp1 0.84
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R_6v_ROI_thickness Structural comp1 0.76
R_FEF_ROI_thickness Structural comp1 0.66
L_4_ROI_thickness Structural comp1 0.52
L_6d_ROI_thickness Structural comp1 0.44
R_6a_ROI_thickness Structural comp1 0.34
R_PEF_ROI_thickness Structural comp1 0.28
L_1_ROI_thickness Structural comp1 0.20
L_13l_ROI_thickness Structural comp1 0.20
L_d32_ROI_thickness Structural comp1 0.28
L_IFJp_ROI_thickness Structural comp1 0.36
L_IP2_ROI_area Structural comp1 0.52
L_LO1_ROI_area Structural comp1 0.52
L_LO3_ROI_area Structural comp1 0.52
L_MT_ROI_area Structural comp1 0.52
L_p32pr_ROI_thickness Structural comp1 0.52
L_PF_ROI_area Structural comp1 0.46
L_PFm_ROI_area Structural comp1 0.50
L_PGp_ROI_area Structural comp1 0.46
L_TPOJ3_ROI_area Structural comp1 0.50
L_V3CD_ROI_area Structural comp1 0.52
L_V4t_ROI_area Structural comp1 0.54
L_A4_ROI_area Structural comp2 0.62
L_A5_ROI_area Structural comp2 0.52
L_TF_ROI_area Structural comp2 0.46
122

R_4_ROI_area Structural comp2 0.46
L_1_ROI_area Structural comp2 0.44
L_1_ROI_thickness Structural comp2 0.44
L_4_ROI_thickness Structural comp2 0.42
L_46_ROI_thickness Structural comp2 0.38
L_6d_ROI_thickness Structural comp2 0.38
L_6ma_ROI_area Structural comp2 0.34
L_9-46d_ROI_thickness Structural comp2 0.32
L_A1_ROI_area Structural comp2 0.28
L_d32_ROI_thickness Structural comp2 0.28
L_EC_ROI_area Structural comp2 0.26
L_LBelt_ROI_area Structural comp2 0.24
L_OP1_ROI_area Structural comp2 0.24
L_p32pr_ROI_thickness Structural comp2 0.22
L_PBelt_ROI_area Structural comp2 0.20
L_PF_ROI_area Structural comp2 0.20
L_PFcm_ROI_area Structural comp2 0.20
L_PSL_ROI_area Structural comp2 0.20
L_RI_ROI_area Structural comp2 0.20
L_STSva_ROI_area Structural comp2 0.20
L_TE1a_ROI_area Structural comp2 0.20
R_1_ROI_area Structural comp2 0.20
R_1_ROI_thickness Structural comp2 0.20
R_3b_ROI_area Structural comp2 0.20
123

R_4_ROI_thickness Structural comp2 0.20
R_6a_ROI_area Structural comp2 0.20
R_6ma_ROI_area Structural comp2 0.26
R_6mp_ROI_area Structural comp2 0.32
R_FOP4_ROI_thickness Structural comp2 0.30
R_FST_ROI_area Structural comp2 0.38
R_LO2_ROI_area Structural comp2 0.34
R_MST_ROI_area Structural comp2 0.42
R_MT_ROI_area Structural comp2 0.36
R_PH_ROI_area Structural comp2 0.36
R_PHT_ROI_area Structural comp2 0.36
R_s6-8_ROI_area Structural comp2 0.40
R_SCEF_ROI_area Structural comp2 0.34
R_SFL_ROI_area Structural comp2 0.34
R_TE1m_ROI_area Structural comp2 0.38
R_TPOJ2_ROI_area Structural comp2 0.34
R_TPOJ3_ROI_area Structural comp2 0.32
R_V4t_ROI_area Structural comp2 0.32
Right-Cerebellum-Cortex Structural comp2 0.32
HIPPOCAMPUS_LEFT_L_EC Functional comp1 0.42
L_2_L_6v Functional comp1 0.36
L_2_L_PFop Functional comp1 0.34
L_24dd_L_4 Functional comp1 0.30
L_24dd_R_6mp Functional comp1 0.28
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L_3a_L_6mp Functional comp1 0.26
L_3a_R_1 Functional comp1 0.24
L_47s_R_47s Functional comp1 0.24
L_5m_R_4 Functional comp1 0.22
L_6a_R_7Am Functional comp1 0.20
L_6d_R_2 Functional comp1 0.20
L_7Am_R_7Pm Functional comp1 0.20
L_8BL_L_10v Functional comp1 0.20
L_9p_L_PGs Functional comp1 0.20
L_a47r_L_PFm Functional comp1 0.20
L_FOP2_L_OP4 Functional comp1 0.20
L_FOP3_L_p32pr Functional comp1 0.20
L_Ig_L_6v Functional comp1 0.20
L_LBelt_R_MBelt Functional comp1 0.20
L_LIPd_L_IP2 Functional comp1 0.20
L_LO3_L_PGp Functional comp1 0.20
L_MIP_R_LIPd Functional comp1 0.20
L_PEF_L_55b Functional comp1 0.20
L_PFt_R_2 Functional comp1 0.20
L_PreS_L_POS1 Functional comp1 0.20
L_V3B_L_IP0 Functional comp1 0.20
L_V6A_L_IP0 Functional comp1 0.20
R_1_R_4 Functional comp1 0.20
R_11l_R_13l Functional comp1 0.20
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R_11l_R_p9.46v Functional comp1 0.20
R_2_R_6mp Functional comp1 0.20
R_24dd_R_1 Functional comp1 0.20
R_31pv_R_TE1a Functional comp1 0.20
R_3a_R_6v Functional comp1 0.20
R_47m_R_s32 Functional comp1 0.20
R_7AL_R_2 Functional comp1 0.20
R_7PC_R_2 Functional comp1 0.20
R_7PC_R_6d Functional comp1 0.20
R_7PC_R_7Am Functional comp1 0.20
R_9a_R_d32 Functional comp1 0.20
R_AIP_L_AIP Functional comp1 0.24
R_d23ab_L_PGs Functional comp1 0.26
R_LBelt_R_MBelt Functional comp1 0.24
R_PBelt_R_TA2 Functional comp1 0.24
R_PEF_R_6v Functional comp1 0.26
R_PFt_R_2 Functional comp1 0.28
R_PFt_R_6d Functional comp1 0.30
R_PFt_R_7PC Functional comp1 0.34
R_TPOJ2_R_STV Functional comp1 0.36
R_V3CD_L_IP0 Functional comp1 0.34
ACCUMBENS_LEFT_ACCUMBENS_RIGHT Functional comp2 0.36
AMYGDALA_LEFT_R_TGd Functional comp2 0.30
L_31a_L_PFm Functional comp2 0.24
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L_44_L_45 Functional comp2 0.22
L_44_L_p47r Functional comp2 0.22
L_47l_L_STGa Functional comp2 0.22
L_5m_R_5L Functional comp2 0.20
L_8BL_R_9m Functional comp2 0.20
L_a24_R_d32 Functional comp2 0.20
L_AAIC_R_p24 Functional comp2 0.20
L_FOP2_L_PFop Functional comp2 0.20
L_FOP3_L_43 Functional comp2 0.20
L_MT_R_TPOJ3 Functional comp2 0.20
L_PHA3_R_PHA1 Functional comp2 0.20
L_PoI2_L_Pir Functional comp2 0.20
L_PoI2_R_FOP2 Functional comp2 0.20
L_PreS_L_PHA1 Functional comp2 0.20
L_TE1a_L_STSdp Functional comp2 0.20
L_TE1a_L_STSvp Functional comp2 0.20
L_TE1m_L_TE1p Functional comp2 0.20
L_V2_R_V3 Functional comp2 0.20
L_v23ab_L_10v Functional comp2 0.20
L_V3_L_V2 Functional comp2 0.20
L_V3_R_V3 Functional comp2 0.20
L_VIP_L_IPS1 Functional comp2 0.20
PALLIDUM_RIGHT_PUTAMEN_RIGHT Functional comp2 0.20
R_10v_L_10pp Functional comp2 0.20
127

R_23d_L_RSC Functional comp2 0.20
R_31pd_R_PCV Functional comp2 0.20
R_45_R_9m Functional comp2 0.20
R_47m_R_13l Functional comp2 0.20
R_47s_R_pOFC Functional comp2 0.20
R_6v_R_55b Functional comp2 0.20
R_7m_L_7m Functional comp2 0.20
R_7m_L_POS2 Functional comp2 0.20
R_8C_R_IP1 Functional comp2 0.20
R_a24pr_R_p32pr Functional comp2 0.20
R_A4_R_A1 Functional comp2 0.20
R_a47r_R_10pp Functional comp2 0.20
R_a47r_R_8C Functional comp2 0.20
R_LO3_R_MST Functional comp2 0.20
R_MBelt_R_TA2 Functional comp2 0.20
R_PGi_L_7m Functional comp2 0.20
R_PGp_R_IP0 Functional comp2 0.20
R_PH_L_PH Functional comp2 0.22
R_PHA1_L_PreS Functional comp2 0.22
R_PHA3_R_PHA2 Functional comp2 0.22
R_PHT_R_TPOJ2 Functional comp2 0.24
R_TE1a_R_TE2a Functional comp2 0.30
R_v23ab_R_9m Functional comp2 0.36
adhd_prs PRS comp1 1.00
128

anx_prs PRS comp1 1.00
copc_prs PRS comp1 1.00
dep_prs PRS comp1 1.00
ibs_prs PRS comp1 1.00
ins_prs PRS comp1 1.00
int_prs PRS comp1 1.00
neur_prs PRS comp1 1.00
sui_prs PRS comp1 1.00
adhd_prs PRS comp2 1.00
anx_prs PRS comp2 1.00
copc_prs PRS comp2 1.00
dep_prs PRS comp2 1.00
ibs_prs PRS comp2 1.00
ins_prs PRS comp2 1.00
int_prs PRS comp2 1.00
neur_prs PRS comp2 1.00
sui_prs PRS comp2 1.00
Table 4.1: Feature stability from 5-fold cross validation of the DIABLO model for boys
Feature DataType Component AverageValue
affected_sum Clinical comp1 1.00
bis_sum Clinical comp1 1.00
bad_affected_sum Clinical comp1 0.88
negative_urgency Clinical comp1 0.82
129

somatic_r_score Clinical comp1 0.82
total_bad_le Clinical comp1 0.82
adrenal_pds Clinical comp1 0.80
discrimination Clinical comp1 0.80
family_conflict_yr Clinical comp1 0.70
positive_urgency Clinical comp1 0.54
crystallized_int Clinical comp1 0.26
depression_r_score Clinical comp1 0.32
fluid_reasoning Clinical comp1 0.40
good_affected_sum Clinical comp1 0.48
grades Clinical comp1 0.48
internalizing_r_score Clinical comp1 0.48
language_verb_int Clinical comp1 0.46
reading_ability Clinical comp1 0.46
school_involvment Clinical comp1 0.52
sleep_disturbance Clinical comp1 0.76
sleep_wake_transition Clinical comp1 0.78
total_comp_agecorr_nihtb Clinical comp1 0.68
working_memory Clinical comp1 0.68
bis_sum Clinical comp2 0.96
crystallized_int Clinical comp2 0.90
flexible_thinking Clinical comp2 0.82
fluid_reasoning Clinical comp2 0.82
language_verb_int Clinical comp2 0.82
130

reading_ability Clinical comp2 0.82
total_comp_agecorr_nihtb Clinical comp2 0.82
working_memory Clinical comp2 0.82
grades Clinical comp2 0.72
breastfed Clinical comp2 0.50
cogcontrol_attention Clinical comp2 0.34
somatic_r_score Clinical comp2 0.26
adrenal_pds Clinical comp2 0.40
affected_sum Clinical comp2 0.40
bad_affected_sum Clinical comp2 0.48
discrimination Clinical comp2 0.48
family_conflict_yr Clinical comp2 0.48
negative_urgency Clinical comp2 0.48
positive_urgency Clinical comp2 0.44
school_enviornment Clinical comp2 0.60
total_bad_le Clinical comp2 0.82
Left-Amygdala Structural comp1 0.94
L_3a_ROI_thickness Structural comp1 0.76
L_4_ROI_thickness Structural comp1 0.76
R_TE1p_ROI_thickness Structural comp1 0.66
R_V1_ROI_thickness Structural comp1 0.62
R_V3_ROI_thickness Structural comp1 0.54
L_STSva_ROI_thickness Structural comp1 0.48
L_V1_ROI_thickness Structural comp1 0.46
131

Right-Amygdala Structural comp1 0.42
L_31pd_ROI_area Structural comp1 0.38
L_7Am_ROI_area Structural comp1 0.36
L_7m_ROI_area Structural comp1 0.32
L_7Pm_ROI_area Structural comp1 0.32
L_8C_ROI_thickness Structural comp1 0.28
L_TE1a_ROI_thickness Structural comp1 0.22
Left-Cerebellum-Cortex Structural comp1 0.22
Left-Hippocampus Structural comp1 0.20
Left-Thalamus Structural comp1 0.20
Left-VentralDC Structural comp1 0.20
R_31pd_ROI_area Structural comp1 0.20
R_7Am_ROI_area Structural comp1 0.28
R_7m_ROI_area Structural comp1 0.36
R_7PL_ROI_area Structural comp1 0.36
R_7Pm_ROI_area Structural comp1 0.34
R_PCV_ROI_area Structural comp1 0.40
R_PF_ROI_thickness Structural comp1 0.34
R_RI_ROI_thickness Structural comp1 0.42
R_STSda_ROI_thickness Structural comp1 0.36
R_STSvp_ROI_thickness Structural comp1 0.50
R_V4_ROI_thickness Structural comp1 0.58
Right-Cerebellum-Cortex Structural comp1 0.56
Right-Hippocampus Structural comp1 0.52
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Right-Thalamus Structural comp1 0.50
Right-VentralDC Structural comp1 0.44
L_ProS_ROI_area Structural comp2 0.58
L_V1_ROI_area Structural comp2 0.56
L_V2_ROI_area Structural comp2 0.52
L_V3_ROI_area Structural comp2 0.50
L_V4_ROI_area Structural comp2 0.50
R_46_ROI_thickness Structural comp2 0.48
R_V2_ROI_area Structural comp2 0.48
R_V3_ROI_area Structural comp2 0.46
R_V4_ROI_area Structural comp2 0.44
L_1_ROI_thickness Structural comp2 0.30
L_46_ROI_thickness Structural comp2 0.30
L_8BM_ROI_thickness Structural comp2 0.26
L_8C_ROI_thickness Structural comp2 0.24
L_9m_ROI_thickness Structural comp2 0.22
L_p9-46v_ROI_thickness Structural comp2 0.22
L_RSC_ROI_area Structural comp2 0.20
L_STV_ROI_thickness Structural comp2 0.20
L_V3_ROI_thickness Structural comp2 0.20
Left-Caudate Structural comp2 0.20
Left-Cerebellum-Cortex Structural comp2 0.20
Left-Hippocampus Structural comp2 0.20
Left-Thalamus Structural comp2 0.20
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Left-VentralDC Structural comp2 0.20
R_1_ROI_thickness Structural comp2 0.26
R_9-46d_ROI_thickness Structural comp2 0.26
R_LO2_ROI_area Structural comp2 0.26
R_p9-46v_ROI_thickness Structural comp2 0.26
R_PH_ROI_area Structural comp2 0.26
R_PIT_ROI_area Structural comp2 0.26
R_RI_ROI_thickness Structural comp2 0.26
R_STSvp_ROI_thickness Structural comp2 0.26
R_TE1m_ROI_thickness Structural comp2 0.30
R_TE1p_ROI_thickness Structural comp2 0.24
R_V1_ROI_area Structural comp2 0.32
R_V3_ROI_thickness Structural comp2 0.32
R_V4t_ROI_area Structural comp2 0.34
Right-Caudate Structural comp2 0.42
Right-Cerebellum-Cortex Structural comp2 0.40
Right-Hippocampus Structural comp2 0.38
Right-Thalamus Structural comp2 0.38
Right-VentralDC Structural comp2 0.42
L_V1_L_V2 Functional comp1 0.76
R_V1_R_V2 Functional comp1 0.56
L_V1_R_V2 Functional comp1 0.48
AMYGDALA_RIGHT_R_TGd Functional comp1 0.38
DC_VENTRAL_LEFT_DC_VENTRAL_RIGHT Functional comp1 0.34
134

L_10d_L_10v Functional comp1 0.28
L_10d_L_d23ab Functional comp1 0.28
L_5m_L_6mp Functional comp1 0.26
L_8BL_L_9p Functional comp1 0.24
L_9p_L_10v Functional comp1 0.20
L_A5_R_STGa Functional comp1 0.20
L_DVT_R_V6 Functional comp1 0.20
L_IFSa_L_13l Functional comp1 0.20
L_IFSa_R_13l Functional comp1 0.20
L_LBelt_R_PBelt Functional comp1 0.20
L_MI_L_SCEF Functional comp1 0.20
L_MT_R_TPOJ2 Functional comp1 0.20
L_p47r_L_13l Functional comp1 0.20
L_POS2_L_POS1 Functional comp1 0.20
L_ProS_R_DVT Functional comp1 0.20
L_TF_R_TF Functional comp1 0.20
L_V1_L_V3 Functional comp1 0.20
L_V3A_R_V2 Functional comp1 0.20
R_1_R_4 Functional comp1 0.20
R_10r_HIPPOCAMPUS_RIGHT Functional comp1 0.20
R_10r_R_10v Functional comp1 0.20
R_24dv_R_5L Functional comp1 0.20
R_31pv_R_10v Functional comp1 0.20
R_43_R_MI Functional comp1 0.20
135

R_44_R_AVI Functional comp1 0.20
R_47s_R_TGd Functional comp1 0.20
R_9.46d_R_a32pr Functional comp1 0.20
R_a24_R_25 Functional comp1 0.20
R_a24pr_R_MI Functional comp1 0.20
R_a32pr_R_AVI Functional comp1 0.20
R_A4_R_TA2 Functional comp1 0.20
R_d23ab_R_23d Functional comp1 0.20
R_DVT_R_VMV1 Functional comp1 0.28
R_DVT_R_VMV2 Functional comp1 0.36
R_IFSa_R_13l Functional comp1 0.32
R_PFop_R_43 Functional comp1 0.28
R_POS2_R_PCV Functional comp1 0.38
R_s6.8_R_d32 Functional comp1 0.32
R_STSda_R_TGd Functional comp1 0.48
R_TF_L_PeEc Functional comp1 0.46
R_TPOJ3_R_STV Functional comp1 0.36
R_V1_L_VMV1 Functional comp1 0.34
R_V1_R_ProS Functional comp1 0.26
R_V1_R_VMV1 Functional comp1 0.22
L_ProS_L_DVT Functional comp2 0.36
L_ProS_L_V6 Functional comp2 0.30
L_V1_L_V2 Functional comp2 0.26
ACCUMBENS_RIGHT_R_25 Functional comp2 0.24
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L_1_L_6d Functional comp2 0.20
L_52_R_MBelt Functional comp2 0.20
L_5L_L_4 Functional comp2 0.20
L_6d_R_2 Functional comp2 0.20
L_7Am_R_23c Functional comp2 0.20
L_9.46d_R_a32pr Functional comp2 0.20
L_9p_L_p32 Functional comp2 0.20
L_DVT_L_V6A Functional comp2 0.20
L_DVT_L_VMV1 Functional comp2 0.20
L_DVT_R_ProS Functional comp2 0.20
L_Ig_R_FOP2 Functional comp2 0.20
L_IP0_R_IPS1 Functional comp2 0.20
L_p32pr_L_FOP5 Functional comp2 0.20
L_PHA3_R_POS1 Functional comp2 0.20
L_PreS_HIPPOCAMPUS_LEFT Functional comp2 0.20
L_ProS_R_DVT Functional comp2 0.20
L_SCEF_R_p32pr Functional comp2 0.20
L_TE1a_R_TGd Functional comp2 0.20
L_V1_L_VMV1 Functional comp2 0.20
L_V1_R_ProS Functional comp2 0.20
L_V2_L_VMV1 Functional comp2 0.20
L_V2_R_ProS Functional comp2 0.20
L_V4_R_LO1 Functional comp2 0.20
L_V6_R_ProS Functional comp2 0.20
137

R_10d_R_8BL Functional comp2 0.20
R_3a_R_6v Functional comp2 0.20
R_47l_R_TGd Functional comp2 0.20
R_6mp_R_SCEF Functional comp2 0.20
R_7Am_R_6ma Functional comp2 0.20
R_DVT_R_ProS Functional comp2 0.20
R_DVT_R_VMV1 Functional comp2 0.20
R_FFC_R_LO1 Functional comp2 0.20
R_LO3_L_V3CD Functional comp2 0.20
R_p47r_R_IFSa Functional comp2 0.20
R_PreS_L_PreS Functional comp2 0.20
R_s6.8_R_8Av Functional comp2 0.20
R_STSda_R_TGv Functional comp2 0.20
R_STSva_R_TGd Functional comp2 0.20
R_TGv_R_TGd Functional comp2 0.20
R_V1_R_ProS Functional comp2 0.20
R_v23ab_R_TE1a Functional comp2 0.20
R_V3B_R_IP0 Functional comp2 0.20
R_V3CD_R_LO3 Functional comp2 0.22
R_V6_R_ProS Functional comp2 0.24
R_V6A_R_VMV1 Functional comp2 0.26
R_VMV1_L_DVT Functional comp2 0.30
THALAMUS_LEFT_DIENCEPHALON_VENTRAL_LEFT Functional comp2 0.26
adhd_prs PRS comp1 1.00
138

anx_prs PRS comp1 1.00
copc_prs PRS comp1 1.00
dep_prs PRS comp1 1.00
ibs_prs PRS comp1 1.00
ins_prs PRS comp1 1.00
int_prs PRS comp1 1.00
neur_prs PRS comp1 1.00
sui_prs PRS comp1 1.00
adhd_prs PRS comp2 1.00
anx_prs PRS comp2 1.00
copc_prs PRS comp2 1.00
dep_prs PRS comp2 1.00
ibs_prs PRS comp2 1.00
ins_prs PRS comp2 1.00
int_prs PRS comp2 1.00
neur_prs PRS comp2 1.00
sui_prs PRS comp2 1.00
Table 4.2: Feature stability from 5-fold cross validation of the DIABLO model for girls
4.5 Discussion
This chapter highlights ongoing research integrating neuroimaging and genetics to develop a multiomics
biomarker to predict widespread and chronic pain in children. By leveraging both population-level and
individual-level data, this work aims to uncover the underlying mechanisms driving these conditions.
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Figure 4.8: Circos plots from DIABLO models integrating clinical, brain stucture, brain function,
and polygenic risk score data Circos plots represent the feature selected via DIABLO, the mean value of
each feature for each pain group, and the correlations between features across omics types. The threshold
is set at r = 0.7, to show strong correlations. Abbreviations - COPC: chronic overlapping pain conditions,
ANX: anxiety, DEP: depression, INT: intelligence, SUI: suicide attempt, INS: insomnia, ADHD: Attentiondeficit/hyperactivity disorder, IBS: irritable bowel syndrome, affected sum: how much affected by early life
events, total bad le: total bad life events, bad affected sum: how much affected by bad life events, bis sum:
sum on behavioral inhibition scale (BIS/BAS), somatic r score: somatic problems CBCL raw score, adrenal
pds: adrenal puberty development score, crystallized int: crystinallized intelligence, total comp agecorr
nihtb: cognition total score age corrected, language verb int: Picture vocabulary test score, internalizing
r score: internalizing raw score CBCL, 1: Brodmann area 1, 2: Brodmann area 2, 3a: Brodmann area 3a,
3b: Primary sensory cortex, 4: Primary motor cortex, 6d: Dorsal area 6, 6v: Ventral Area 6, 6mp: Area
6mp, 6r: Rostral area 6, 7AL: Lateral area 7A, PEF: premotor eye field, 8bL: Area 8b lateral, TE2p: Area TE2
posterior, TE1m: Area TE1 middle, TE1p: Area TE1 posterior, 55b: Area 55b, A4: Auditory 4 complex, TF:
Area TF, V1: Primary visual cortex, V2: Second visual area, V3: Third visual area, 10v: Brodmann area 10v,
25: Brodmann area 25, pOFC: Posterior orbitofrontal cortex complex, 43: Brodmann Area 43, PIT: Posterior
inferotemporal complex, 13l: Brodmann area 13l, STSva: Area STSv anterior, STSda: Area STSd anterior,
STSvp: Area STSv posterior, LO1: Area lateral occipital 1, PoI2: Posterior insular area 2, OP4: Area OP4/PV,
AIP: Anterior intraparietal area, AAIC: Anterior agranular insula complex, p24: Area posterior 24, IP2: Area
intraparietal 2, p9-46v: Area posterior 9-46v, Pft: Area PFt
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Figure 4.9: Relevance network from the DIABLO analysis depicting the correlation between
different ’omics types for boys Red lines represent positive correlations and blue lines represent negative
correlations. Cutoff for the correlations was r = 0.5. The first of two clusters shows a positive relationship
between all PRS scores and how affected individuals are by ACEs. All PRSs are negatively associated with
brain region’s thickness in the sensorimotor network. Abbreviations are shown in Figure 4.8
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Figure 4.10: Relevance network from the DIABLO analysis depicting the correlation between
different ’omics types for girls Features which show high correlations with each other are extracted
from the entire DIABLO model for easy visualization. Red lines represent positive correlations and blue
lines represent negative correlations. Cutoff for the correlations was r = 0.5. Abbreviations are shown in
Figure 4.8
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Figure 4.11: Loading plots for the DIABLO model in boys Features which show high correlations with
each other are extracted from the entire DIABLO model for easy visualization. Loadings depict the relative
importance of each feature in discriminating the groups. Colors represent which group has the highest
or lowest mean value for that feature. Plots under maximum contribution represent which group had the
highest mean value for that feature. Plots under minimum contribution represent which group had the
lowest mean value for that feature. Abbreviations are shown in Figure 4.8
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Figure 4.12: Loading plots for the DIABLO model in girls Loadings depict the relative importance of
each feature in discriminating the groups. Colors represent which group has the highest or lowest mean
value for that feature. Plots under maximum contribution represent which group had the highest mean
value for that feature. Plots under minimum contribution represent which group had the lowest mean value
for that feature. Abbreviations are shown in Figure 4.8
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4.5.1 A shared genetic architecture of chronic widespread pain
The first objective was to investigate the shared molecular mechanisms between chronic widespread pain
(CWP) and common comorbid conditions using population-level genetic data. GWAS analyses involving
chronic overlapping pain conditions (COPC), anxiety, depression, insomnia, ADHD, irritable bowel syndrome, neuroticism, and intelligence identified significant global genetic correlations, including a negative
correlation with intelligence. This supports the notion that these traits are not inherited independently.
Local genetic correlations using LAVA found the strongest relationships between COPC and depression
on many genes along the 7p22.3 cytogenetic band, which has been associated with neurodevelopmental
abnormalities, but the research is still in its infancy (Mastromoro et al., 2020; Touhami et al., 2023).
Mendelian randomization using CAUSE revealed several genes, such as DCC, NCAM1, FOXP2, and
SLC39A8, as having causal effects on the development of these comorbid conditions when CWP was the
exposure trait. Conversely, key genes like PLCL2 (anxiety, depression) and TCF4 (neuroticism, depression)
emerged when comorbid conditions were the exposure traits. MTAG analysis identified novel genetic
loci across traits, with enrichment analyses pointing to neurological pathways involved in development,
particularly the postsynapse and cell morphogenesis.
The protein-protein interaction networks highlighted G-protein-coupled glutamate receptor activity,
particularly through GRM8 and GRM7, as a shared mechanism among COPC, anxiety and depression.
Enrichment in neuroblasts, which are GABAergic precursors in the midbrain, further underscores the
significance of these pathways.
Mechanistically, the postsynaptic density (PSD) plays a critical role in nociceptive synapses during
chronic pain and central sensitization. Reorganization of the PSD, including altered receptor trafficking and
activity, amplifies nociceptive signaling through calcium influx and downstream kinases such as PKC and
CaMKII. Reduced GABA and glycine release further exacerbates sensitization (Latremoliere & Woolf, 2009).
145

These findings help explain the efficacy of drugs like pregabalin, which mitigate central sensitization by
modulating calcium channel activity and reducing glutamate release.
4.5.2 Towards a neuroimaging genetics signature of pain in children
Two DIABLO models were constructed to explore whether clinical and behavioral data, brain structure,
brain function, and polygenic risk scores could predict pain phenotypes (no pain, localized pain, widespread
pain) in the ABCD cohort. While the models identified distinct patterns across data types in boys and girls,
predictive accuracy was limited, with AUC values of 0.57.
Girls with widespread pain exhibited the highest COPC polygenic risk scores (PRS) compared to localized
or no pain, a trend not observed in boys. This was associated with lower amygdala volume, greater cortical
thickness in the primary motor cortex, and a greater impact of adverse childhood experiences (ACEs),
which negatively correlated with default mode network connectivity. Similarly, PRS for anxiety, depression,
neuroticism, ADHD, and suicide attempts followed the same pattern. In contrast, the intelligence PRS
positively correlated with dorsal attentional network connectivity, surface area, and intelligence testing,
while showing a negative correlation with ACE impact.
Despite modest predictive accuracy, the stability of clinical (e.g., ACEs, somatic problems, adrenal
puberty development scores) and brain structural features (e.g., amygdala volume, thickness of the sensorimotor network) identified during repeated 5-fold cross-validation aligns with previous findings in children
(Bhatt et al., 2020).
4.5.3 Future work
Future efforts will use longitudinal data from the ABCD study to track the progression of widespread pain
over time, identifying early predictors of its development. To enhance predictive modeling, structural
connectivity data will be incorporated using diffusion tensor imaging (DTI) and whole brain tractography.
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Nonlinear approaches such as random forests, XGBoost, and graph neural networks will be applied to
integrate these multimodal datasets, enabling a more robust identification of complex relationships between
clinical, neuroimaging, and genetic factors. This integrative approach has the potential to uncover novel
biomarkers and pathways to inform early intervention and treatment strategies.
This dissertation’s work holds significant potential for clinical translation. To develop precise neuroimaginggenetics-based biomarkers—acknowledging that both neuroimaging and genetic traits typically exhibit
aggregate small effect sizes—it is essential to utilize large, population-level datasets. These extensive sample
sizes are crucial for ensuring the accuracy, reproducibility, and robustness of the resulting biomarkers.
Based on clinical data alone, it is feasible to develop a screening questionnaire that, with appropriate
psychometric evaluation, could reliably predict which children are at risk for developing chronic widespread
pain. This would be instrumental in identifying high-risk individuals early, enabling timely and targeted
prevention strategies. These strategies could include patient education on preventive care, lifestyle modifications, and ongoing monitoring — areas that are not commonly emphasized in routine medical care.
For example, educational outreach could focus on teaching families about the importance of maintaining
physical activity, improving sleep hygiene, managing stress, and fostering healthy eating habits to reduce
the likelihood of developing chronic pain.
Preventive primary care could involve identifying at-risk individuals based on family history or early
life adversity and subsequently conducting genetic and neuroimaging screening to uncover potential
predispositions. Once identified, regular follow-ups and monitoring of symptomatology would allow for
early interventions before pain becomes chronic. Secondary prevention strategies, such as early physical
therapy, mindfulness-based stress reduction, or cognitive behavioral therapy, could also be introduced to
mitigate symptoms and prevent progression.
This screening questionnaire would incorporate key predictors identified through robust data analysis
and machine learning approaches, ensuring its relevance and accuracy. From our current analyses, several
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consistent themes emerged as critical predictors of chronic widespread pain risk. These include the impact
of early adverse events, higher intelligence testing scores, and pubertal development. To thoroughly capture
the developmental process of puberty which impact pain, it is crucial to utilize both physical development
assessments, such as Tanner staging, and hormonal measurements. Additionally, a comprehensive panel of
pubertal hormones—including gonadotropin-releasing hormone, follicle-stimulating hormone, luteinizing
hormone, dehydroepiandrosterone sulfate (DHEAS), testosterone, and estradiol—could provide valuable
insights into the associations between pain and the early stages of puberty (R. Li et al., 2023). These insights
could inform targeted prevention programs, lifestyle interventions, and ongoing support tailored to the
unique needs of at-risk children. With regular monitoring and an emphasis on education about preventive
care and lifestyle changes, we can fill a crucial gap in current medical practice and improve long-term
developmental and health outcomes for vulnerable populations.
Moreover, this work underscores a shared neurobiological foundation underlying chronic pain and
its comorbidities, including chronic widespread pain, anxiety, ADHD, depression, insomnia, intelligence,
irritable bowel syndrome (IBS), neuroticism, and suicide attempts. Evidence suggests that many of these
conditions converge on overlapping biological pathways, with the postsynaptic density (PSD) emerging as
a central hub. The consistent enrichment of genes related to the PSD across chronic widespread pain and
these comorbid traits highlights its pivotal role in these shared processes.
Key pathways, including cell adhesion molecule binding and axon guidance, are repeatedly implicated,
with genes within these pathways observed across all traits. Specifically, the PSD plays a critical role in
central sensitization — a hallmark mechanism in chronic pain — through its involvement in the activation of
receptors such as NMDAR, mGluR, TrkB, NK1, and CGRP. These receptors activate numerous downstream
proteins, culminating in the activation of extracellular signal-regulated kinase (ERK). ERK, in turn, regulates
over 160 proteins and multiple transcription factors, driving translational and post-translational modifications that sustain central sensitization. Post-translational effects mediated by ERK include enhanced
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NMDAR function via phosphorylation of its NR1 subunit, recruitment of AMPAR to the synaptic membrane
(leading to increased AMPAR and NMDAR currents), and phosphorylation of Kv4.2 channels, which reduces
K+ currents and increases membrane excitability (Latremoliere & Woolf, 2009). Together, these changes
amplify synaptic efficacy and excitability, perpetuating chronic pain states.
These insights point to potential avenues for novel therapeutic development. Pharmacological interventions targeting PSD mechanisms, such as NMDA receptor antagonists (e.g., ketamine), have shown
promise not only in alleviating pain but also in addressing comorbid conditions such as anxiety (J. H. Taylor
et al., 2018), depression (Zanos & Gould, 2018), and insomnia (Kwaśny et al., 2023). While microdosing
with psilocybin is still in research infancy, the analgesic properties of 5-HT2A agonists are promising,
offering not only relief from persistent, debilitating pain but also improved functional mobility (Lyes et al.,
2023). Future research could focus on testing these therapeutics, first in rodent models and eventually in
human clinical trials, to evaluate their efficacy and broader applications in chronic pain and its associated
comorbidities.
Building on this foundation, the integration of neuroimaging and genetic data offers a novel opportunity
to identify individuals at risk for chronic widespread pain more precisely. By leveraging polygenic risk
scores (PRS) for chronic widespread pain, individuals with a higher genetic predisposition can be identified
and further examined for deviations in brain structure and function. Using normative modeling approaches,
which compare individual-level neuroimaging data to population-based norms, we can determine whether
patterns of brain structure and function in genetically at-risk individuals deviate significantly from those
of the general population. For example, deviations in cortical thickness, subcortical volume, or connectivity patterns in regions implicated in pain processing—such as the anterior cingulate cortex, insula, and
thalamus—could serve as early neuroimaging biomarkers of vulnerability to CWP, and if these deviations
can be attenuated or corrected with behavioral or pharmaceutical interventions. This combination of
149

genetic predisposition and neuroimaging analyses provides a powerful framework for identifying high-risk
individuals before the onset of symptoms, enabling proactive interventions.
150

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Abstract (if available)

Abstract Chronic pain is the leading cause of disabilty and disease burden globally. It represents a complex set of conditions where a biopsychosocial approach is needed for a comprehensive mechanistic understanding and treatment. This dissertation explores the multifaceted underpinnings of chronic pain through a combination of clinical, neuroimaging, and genetic analyses, leveraging big data and cutting-edge multi-omics approaches to uncover meaningful biomarkers and mechanisms. The first chapter provides a comprehensive overview of chronic pain, detailing its clinical presentation, neurological pathways—centered on brain mechanisms—and genetic foundations. This foundational work emphasizes the necessity of integrating large datasets and multi-omics approaches to identify reliable biomarkers for chronic pain diagnosis, prognosis, and treatment. The second chapter utilizes data from the UK Biobank to investigate structural brain differences in individuals with chronic single-site and multisite pain, as well as investigating the differences in brain stucture in each body site individually. This analysis identifies pain site-specific brain changes and explores their potential role in mediating the relationship between chronic pain and suicide attempts, providing critical insights into the neurobiological consequences of chronic pain. Chapter three employs state-of-the-art genetic methodologies to perform a genome-wide association study (GWAS) meta-analysis on the morphometry of the corpus callosum, a brain structure crucial for communication and pain processing. Extensive follow-up analyses assess genetic correlations across multiple genomic resolutions and evaluate causal relationships between corpus callosum traits and multiple neurological traits, including chronic overlapping pain conditions through Mendelian randomization. Transcriptome-wide association studies further elucidate the patterns of gene expression and splicing, revealing key tissue-specific regulatory mechanisms. Chapter four focuses on the Adolescent Brain Cognitive Development (ABCD) Study to investigate predictors of chronic and widespread pain development over time in children. First, genetic architectures were examined using Multi-Trait Analysis of GWAS (MTAG) to assess the shared genetic underpinnings of chronic pain and related traits, including ADHD, depression, anxiety, insomnia, intelligence, neuroticism, and irritable bowel syndrome (IBS). These MTAG-enhanced architectures were analyzed using multi-list, multi-gene pathway analysis and protein-protein interaction networks to identify common biological mechanisms across traits. Polygenic risk scores (PRS) were then derived using a Bayesian approach (sBayesRC), which incorporates functional annotations to enhance predictive power. Finally, PRS scores for chronic pain and related traits were integrated with neuroimaging (brain structure and function) and clinical data from the ABCD cohort. This multi-omics approach was used to longitudinally predict which children are most at risk of developing chronic and widespread pain, providing a framework for early identification and intervention. All together, this dissertation advances our understanding of the neurological and genetic underpinnings of chronic pain, offering novel insights into its etiology and paving the way for precision medicine approaches to improve prevention, diagnosis, and treatment.

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University of Southern California Dissertations and Theses

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Asset Metadata

Creator Bhatt, Ravi R. (author)

Core Title Decoding the neurological and genetic underpinnings of chronic pain

School Keck School of Medicine

Degree Doctor of Philosophy

Degree Program Neuroscience

Degree Conferral Date 2024-12

Publication Date 01/10/2025

Defense Date 12/16/2024

Publisher Los Angeles, California (original), University of Southern California (original), University of Southern California. Libraries (digital)

Tag big-data,brain imaging,chronic pain,genetics,machine-learning,Medicine,multi-omics

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Thompson, Paul M. (committee chair), Jahanshad, Neda (committee member), Kutch, Jason (committee member), Mayer, Emeran A. (committee member), Millstein, Joshua (committee member)

Creator Email ravibot93@gmail.com,rbhatt@usc.edu

Unique identifier UC11399F8KI

Identifier etd-BhattRaviR-13731.pdf (filename)

Legacy Identifier etd-BhattRaviR-13731

Document Type Dissertation

Format theses (aat)

Rights Bhatt, Ravi R.

Internet Media Type application/pdf

Type texts

Source 20250110-usctheses-batch-1232 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

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