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The role of the locus coeruleus in Alzheimer’s disease and cerebrovascular function: insights from neuroimaging, neuropsychology, and biofluid markers

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The role of the locus coeruleus in Alzheimer’s disease and cerebrovascular function: insights from neuroimaging, neuropsychology, and biofluid markers

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Content The Role of the Locus Coeruleus in Alzheimer’s Disease and Cerebrovascular Function:
Insights from Neuroimaging, Neuropsychology, and Biofluid Markers
by
Shubir Dutt
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
(PSYCHOLOGY)
May 2023
Copyright 2023 Shubir Dutt
ii
Dedication
For my grandparents, Anantha & Nandini Pai, who inspired us all to be kind and to be curious.

iii
Acknowledgements
I owe an enormous amount of gratitude to the people in my life who have supported me
throughout my graduate studies. I am forever grateful to my advisor and committee co-chair, Dr.
Dan Nation, for showing me what it means to be a curious and hardworking scientist, a
thoughtful neuropsychologist, and an astute mentor—next round is on me. To my advisor and
committee co-chair, Dr. Mara Mather, I am grateful for being welcomed into her lab and for her
dedicated mentorship as I learned from her about scientific rigor and integrity, how to say “no,”
and how to begin wrestling with that fabled time management skill that continues to elude me.
Thank you to my mentor and committee member Dr. Gayla Margolin for teaching me clinical
skills I continue to use to this day and for always being there to chat about academic and non-
academic matters alike. And a huge thank you to my other committee members, Drs. Margy Gatz
and Danny JJ Wang, as well as my proposal committee member Dr. Judy Pa, for their guidance
and thoughtful critiques throughout this process.
I am thankful for my lab mates and friends at the VaSC Lab with whom I have
experienced the highs of weddings and the lows of data freezes—Belinda Yew, Jean Ho, Jung
Jang, Anna Blanken, Elissa Hemmingsen, Aimee Gaubert, Isabel Sible, Anisa Marshall, Aru
Kapoor, Momo Li, JP Alitin, Kim Hoang, Amy Nguyen, and all our fantastic RAs. Huge thanks
to everyone from the Emotion and Cognition Lab for welcoming me and teaching me so much
about that small nucleus in the pons—Shelby Bachman, Martin Dahl, Hyun Joo Yoo, Kaoru
Nashiro, Alex Ycaza Herrera, Briana Kennedy, Sara Gallant, Jungwon Min, Shai Porat, Padideh
Nasseri, Christine Cho, Ringo Huang, Paul Choi, and our RAs. Thank you to my friends,
colleagues, and mentors from the USC Clinical Science program for always lifting me up and
helping me believe in myself— Crystal Wang, Annemarie Kelleghan, Mariel Bello, Nina
iv
Jhaveri, Geoff Corner, Kelly Durbin, Alyssa Morris, Yehsong Kim, Hannah Rasmussen, Jeff
Newell, Erika Quinly, Chris Beam, Duke Han, Shannon O’Flinn, and many, many others. Thank
you to Kathy Tingus for your enriching clinical supervision and for always making me laugh. I
am grateful to Joel Kramer, who motivated me to enter the field of neuropsychology and who
has continued to take chances on me—from when I was an aimless 22-year-old college graduate
to now as an intern and incoming postdoc. I also want to thank everyone at the UCSF Memory
and Aging Center who I continue to look up to as scientists and human beings, with special
thanks to Wil Irwin and Josiah Leong for teaching me how to “do good work.”
To all of my friends, I thank you for supporting me throughout my life and believing in
me. I want to especially thank Tim, Bobak, Kaveh, and John for telling me I would make a great
clinical psychologist and encouraging me to apply to clinical PhD programs on that fateful night
in New York City. To my sister, Shefali, you inspire me every day with your strength, courage,
and kindness— thank you for being my (only sometimes annoying) little sister and keeping me
humble. To my father, Annu, you have shown me what it means to be truly passionate about
your work and how to take a step back and put things into perspective— and of course, how to
make bad puns. To my mother, Amma, you are the most selfless and caring person I know and I
am forever grateful for the loving upbringing you provided me and the poems you wrote to me.
To my dog Ruby, thank you for bringing pure joy to me and every person who meets you. And
finally, to my wife Whitney, I can never thank you enough for all of your sacrifice, support, and
love over the years, for moving up and down the California coast with me, and for always
reminding me that I could actually do this.
v
Table of Contents

Dedication ....................................................................................................................................... ii
Acknowledgements ........................................................................................................................ iii
List of Tables .................................................................................................................................. vi
List of Figures .............................................................................................................................. viii
Abstract ........................................................................................................................................... x
Chapter 1: Introduction ................................................................................................................... 1
Chapter 2: Brainstem Volumetric Integrity in Preclinical and Prodromal Alzheimer’s Disease .... 8
Introduction ........................................................................................................... 11
Materials and Methods .......................................................................................... 12
Results ................................................................................................................... 19
Discussion ............................................................................................................. 23
Chapter 3: Brainstem Substructures and Cognition in Prodromal Alzheimer’s Disease .............. 55
Introduction ........................................................................................................... 57
Methods ................................................................................................................. 58
Results ................................................................................................................... 62
Discussion ............................................................................................................. 64
Conclusions ........................................................................................................... 67
Chapter 4: Links Between Locus Coeruleus MRI Contrast and Cerebral Perfusion are
Moderated by Plasma Alzheimer’s Biomarkers in Older Adults ................................ 80
Introduction ........................................................................................................... 82
Methods ................................................................................................................. 84
Results ................................................................................................................... 91
Discussion ............................................................................................................. 93
Conclusions ......................................................................................................... 100
Chapter 5: Discussion .................................................................................................................. 112
References ................................................................................................................................... 121

vi
List of Tables
Chapter 2
Table 1. Baseline demographics and neuroimaging data for cognitively normal, MCI, and
AD ................................................................................................................................................. 37
Table 2. Baseline demographics and neuroimaging data for cognitively normal participants
who progressed to dementia (converters) and did not progress to dementia (non-converters) ..... 38
Table 3. Cox regression models predicting dementia risk from baseline brainstem volumes ...... 39
Supplementary Table 1. p values from LSD pairwise comparisons of diagnostic groups ............ 40
Supplementary Table 2. ROI analyses normalized to whole brainstem volume .......................... 41
Supplementary Table 3. Life table for cognitively normal participants (n = 785) displaying
censored cases and events of interest (progression to dementia) .................................................. 42
Supplementary Table 4. Fixed 48-month follow-up Cox Regression models .............................. 43
Supplementary Table 5. ADNI CN to ADNI MCI progression .................................................... 45
Supplementary Table 6. VBM Coordinates table from 2-sample t-tests comparing CN to
MCI and AD at FWE-corrected height threshold of p < .05 ......................................................... 46
Supplementary Table 7. VBM Coordinates table from 2-sample t-test comparing CN
converters to CN non-converters ................................................................................................... 47
Supplementary Table 8. MNI coordinate overlap between VBM findings and T1-FSE/TSE
derived LC masks from the literature ............................................................................................ 48

Chapter 3
Table 1. Descriptive statistics for demographic, cognitive, and neuroimaging variables ............. 72
Table 2. MNI coordinates from voxel-wise correlation between category fluency and locus
coeruleus volume ........................................................................................................................... 73
Supp. Table 1. MNI coordinates table for pons-corrected VBM analysis .................................... 78

Chapter 4
Table 1. Participant Characteristics and Demographic Data ....................................................... 101
Supplementary Table 1. Regression models predicting regional perfusion from LC-CR in the
overall sample (n=66) and predicting cognition from LC-CR in a subset (n=39) ...................... 105
Supplementary Table 2. Regression models predicting regional perfusion from LC-CR in the
overall sample (n=66) in other regions ....................................................................................... 106
vii
Supplementary Table 3. Regression models for LC and cognition (non-significant tests) ......... 107
Supplementary Table 4. Plasma biomarker moderation models for regional perfusion ............. 108
Supplementary Table 5. Regression models predicting regional perfusion from LC-CR in
women only (n=46) and predicting cognition from LC-CR (n=25) ........................................... 109
Supplementary Table 6. Regression models predicting regional perfusion from LC-CR in
men only (n=20) and predicting cognition from LC-CR in a subset (n=14) .............................. 110
Supplementary Table 7. Sex moderation models ........................................................................ 111

viii
List of Figures
Chapter 2
Figure 1. Brainstem gross regional volumes in cognitively normal, MCI, and Alzheimer’s
disease participants ........................................................................................................................ 32
Figure 2. Brainstem gross regional volumes in progression to AD dementia ............................... 33
Figure 3. Brainstem-masked VBM comparisons and regional LC volumes ................................. 34
Figure 4. LC volume predicts progression to dementia in cognitively normal older adults ......... 35
Supplementary Figure 1. Region-of-interest masks ...................................................................... 49
Supplementary Figure 2. Brainstem-masked VBM analyses comparing CN individuals who
prospectively progress to dementia versus those who did not progress to dementia .................... 50
Supplementary Figure 3. Brainstem-masked VBM analyses comparing MCI and AD to CN
participants with TIV-normalized pons as an alternative covariate .............................................. 51
Supplementary Figure 4. Brainstem-masked VBM analyses comparing MCI and AD to CN
participants with TIV-normalized whole brainstem as an alternative covariate ........................... 52
Supplementary Figure 5. Brainstem-masked VBM analyses comparing CN individuals who
prospectively progress to dementia versus those who did not progress to dementia with TIV-
normalized pons as an alternative covariate .................................................................................. 53
Supplementary Figure 6. Brainstem-masked VBM analyses comparing CN individuals who
prospectively progress to dementia versus those who did not progress to dementia with TIV-
normalized whole brainstem volume as an alternative covariate .................................................. 54

Chapter 3
Fig. 1. Regression analyses predicting cognition from locus coeruleus volume .......................... 69
Fig. 2. Regression analyses predicting cognition from midbrain volume ..................................... 70
Fig. 3. Voxel-wise correlation between category fluency and locus coeruleus volume ............... 71
Supp. Fig. 1. Voxel-wise correlation between category fluency and locus coeruleus volume
corrected for pons volume ............................................................................................................. 77

Chapter 4
Figure 1. Regression analyses demonstrating links between regional perfusion and rostral
LC-CR ......................................................................................................................................... 102

ix
Figure 2. Regression analyses demonstrating links between verbal episodic memory
performance and rostral LC-CR .................................................................................................. 103
Figure 3. Associations between regional perfusion and rostral LC-CR displayed at varying
levels of plasma AD biomarkers ................................................................................................. 104
x
Abstract
Autopsy-based neuropathological studies suggest the tau pathology observed in
Alzheimer’s disease (AD) originates in brainstem nuclei such as the locus coeruleus (LC). The
LC also critically innervates the cerebrovasculature, and vascular dysfunction is known to occur
preclinically in AD. The present three-study dissertation comprehensively examines interplay
among these systems with structural and functional neuroimaging, neuropsychological testing,
and biofluid (cerebrospinal fluid and blood plasma) markers. Together, these three studies
explore the role of the LC and brainstem substructures in dementia risk, cognitive performance
across the AD spectrum, and cerebrovascular function. In Study 1 (N=1,629; Dutt et al., 2020),
we found that LC and brainstem substructure volumes are reduced in clinically-diagnosed and
biomarker-confirmed mild cognitive impairment (MCI) and AD populations compared to
cognitively intact individuals, with low baseline midbrain and LC volumes predicting future AD
dementia in asymptomatic individuals. In Study 2 (N=1,356; Dutt et al., 2021), we found that LC
and brainstem substructure volumes were specifically linked to attentional and executive
function abilities in clinically-diagnosed MCI and those with biomarker-confirmed prodromal
AD. In Study 3 (N=66, Dutt et al. 2023, in preparation), we demonstrated that neuromelanin-
sensitive quantification of LC integrity is associated with frontal and limbic brain perfusion and
verbal episodic memory, and AD-related tau and amyloid pathology measured from blood
plasma biomarkers attenuates the strengths of LC-perfusion relationships. The findings from this
dissertation establish the utility of neuroimaging, cognitive, and biofluid measures in better
characterizing the LC across the AD spectrum and propose concurrent study of subcortical and
cerebrovascular abnormalities to develop novel biomarkers and targeted treatments.
1
Chapter 1: Introduction
Aging remains one of the great mysteries of the natural world – an inevitable process
that, despite our best efforts, cannot be cheated, avoided, or reversed. Alzheimer’s disease (AD)
accounts for 6.5 million diagnosed cases, hundreds of billions of dollars in associated costs in the
United States alone, and no established cures or treatments. Excess deaths, emotional and
economic costs, and caregiver burden will only increase in the coming decades, with almost 13
million people expected to develop AD by the year 2050 (Association, 2022). Despite these
mounting societal, financial, and existential costs, underlying root causes of the disease are still
not fully understood, rendering prevention and treatment efforts largely unsuccessful.
Alzheimer’s Disease Pathophysiology
The “amyloid cascade hypothesis” has dominated the literature, funding mechanisms, and
media coverage surrounding dementia research for several decades. This theory posits that
pathological accumulation of beta-amyloid (Ab-42) leading to plaque formation is the primary
cause of the onset of AD dementia processes and should thus be the chief focus of therapeutic
intervention (Hardy & Selkoe, 2002; Selkoe & Hardy, 2016). However, this leading mechanistic
explanation has been met with increasing resistance due to growing evidence for alternative
theories of dementia pathogenesis. Several anti-amyloid therapeutics have received FDA
authorization to purportedly treat AD but have been met with heavy criticism due to their small
effect sizes, limited generalizability to diverse populations, astronomical financial cost to the
general public, their targeted applicability to a small subset of individuals in the early stages of
disease, their dangerous side effects, and ultimately, their inability to stop or cure the disease
(Knopman et al., 2020; Liu & Howard, 2021; Rosenblum, 2014; Thambisetty & Howard, 2023).
Amyloid plaque deposition alone may thus be better considered a feature or symptom of the
2
disease rather than an underlying cause, as other biological abnormalities occur earlier than
amyloid deposition.
A second well-established pathological sign of AD is the presence of tau neurofibrillary
tangles (NFTs) that form when the microtubule-stabilizing tau protein becomes
hyperphosphorylated and misfolded and ends up disrupting neuronal communication and
function (Brion, 1998). The “tau hypothesis” accordingly suggests that early subcortical
deposition of tau may be the earliest and most prominent sign of the disease and thus may be the
most appropriate target for intervention and treatment. Pathological amyloid and tau aggregation
in the neocortex have been well-established as downstream consequences of AD, but numerous
studies over the past decade have suggested that tau protein-related AD pathophysiological
processes originate in specific brainstem nuclei, including the locus coeruleus (LC) and the
dorsal raphe nucleus (DRN), and precede any observable cortical changes (Grinberg et al., 2009;
Simic et al., 2009). The classic Braak staging of AD pathology originally stated that tau tangles
begin accruing in the transentorhinal cortex (Braak stage I) and stereotypically spread to other
limbic regions and eventually diffusely across the cortex in the late stages (stages II-VI) (Braak
& Braak, 1995). After mounting histopathological data emerged implicating brainstem structures
in the earliest disease stages, this staging framework was subsequently updated to include
precortical stages whereby NFTs first appear in the LC, followed by other brainstem nuclei like
the DRN and later spread to limbic and cortical regions stereotypically (Braak et al., 2011; Braak
& Del Tredici, 2015). Mechanisms of tau & amyloid interactions at various neuronal sites (e.g.
dendritic vs. synaptic) have been hypothesized to ultimately lead to the development of AD, but
a consensus synergistic framework has not yet been established and efforts continue to consider
3
other potential biological causes of the disease (Ittner & Götz, 2011; Spires-Jones & Hyman,
2014).
Cerebrovascular Contributions to AD Risk
Vascular pathways play a key role in AD pathogenesis and early vascular abnormalities
may be better predictors of later disease development than pathological protein aggregation (de
la Torre, 2018; Drachman, 2014; Iturria-Medina et al., 2016). The “vascular hypothesis” of AD
broadly conceptualizes neurodegeneration in dementia as a downstream consequence of
numerous preclinical neurovascular abnormalities, including neurovascular unit (i.e., neurons,
vascular cells, glial cells) insult with resulting brain hypoperfusion (i.e., reduced cerebral blood
flow [CBF]) and blood-brain barrier (BBB) dysfunction (de la Torre, 2018; Nation et al., 2019;
Zlokovic, 2011).
Structural and functional neuroimaging studies of vascular dysregulation in older adults
have demonstrated the potential for identifying individuals in the preclinical and prodromal
stages of disease when they may be at highest risk for progression to dementia (Montagne et al.,
2016). For example, early hypoperfusion is known to predict future cognitive decline and
increase risk of dementia in individuals with initially normal cognition, and brain atrophy and
cerebral perfusion have been shown to have bidirectional deleterious effects on each other
(Wolters et al., 2017; Zonneveld et al., 2015). Some studies suggest that early hypoperfusion
may even trigger the pathological spread of amyloid and tau (Korte et al., 2020). Thus, it is well-
established that reduced brain perfusion and BBB breakdown lead to neurodegeneration, but it
remains unclear whether this brain insult in the context of AD is a direct consequence of vascular
dysfunction or a secondary effect due to interactions with the well-established hallmark Ab
plaque and tau tangle pathologies of AD. Furthermore, it is still unknown whether the emerging
4
cognitive symptoms observed in AD are directly driven by vascular abnormalities or by the
interacting effects of vascular, amyloid, and tau pathologies.
The Locus Coeruleus in Alzheimer’s Disease Progression
One candidate epicenter linking vascular dysregulation to tau-related dementia
progression is the LC, a small nucleus located in the dorsal rostral pons that contains the highest
concentration of noradrenergic cells in the brain and is the chief source of brain norepinephrine.
The LC is linked to a host of cognitive functions, including behavioral arousal, memory, and
attention (Mather & Harley, 2016). Integrity of the LC is essential for optimal cognitive
functioning in healthy controls, and MRI fast-spin echo (FSE) methods examining neuromelanin
signal intensity have successfully imaged attenuated signal intensity in AD and MCI (Clewett et
al., 2015; Mather & Harley, 2016; Takahashi et al., 2015; Wilson et al., 2013). The LC is also
thought to be key in developing sufficient “cognitive reserve,” or the ability to maintain
appropriate function despite the accumulation of pathology (Mather & Harley, 2016). Proposed
markers of cognitive reserve include high education, high occupational attainment, and
continued cognitive stimulation (Stern, 2009). Studies have described relationships between
increased LC neuromelanin intensity and a composite measure of cognitive reserve, as well as
associations between lower LC neuronal density and greater cognitive decline (Clewett et al.,
2015; Robertson, 2013; Wilson et al., 2013). Together, these findings suggest that preserved
structural integrity of the LC should be closely studied in cognitive aging, as it may allow
preclinical individuals with increasing levels of AD-related pathology to maintain appropriate
levels of cognitive function prior to the onset of the disease.
The LC is one of four key nuclei that comprise the isodendritic core, a network of nuclei
that share many similar morphological and functional features including distally projecting
5
axons, neurons with large somata, neurotransmitter production and transmission, and
overlapping dendritic fields (Ramón‐Moliner & Nauta, 1966). This isodendritic core sees the
first appearance of intracellular pretangles, the precursor to NFT pathology (Rüb et al., 2016;
Stratmann et al., 2016). Early tau pathology is observed in individuals as young as six years old,
and despite being benign early in life, may eventually develop into NFTs (Heiko Braak & Del
Tredici, 2011). Subcortical tau deposition in the LC and other brainstem nuclei is thought to
represent the start of AD pathological processes, joined later by Aβ accumulation that further
contributes to the spread of pathology to neocortical regions and the corresponding progression
of clinical symptoms (Jack et al., 2013; Price & Morris, 1999). Once tau pathology deposits into
the medial temporal lobe, co-occurring amyloidosis leads to the acceleration of tau propagation
to neocortical regions, which coincides with disease progression (He et al., 2018; Lewis &
Dickson, 2016; Pooler et al., 2015). Thus, NFT accumulation originating in the LC and
migrating to limbic structures and isocortical regions may be the primary source of
neurodegeneration and consequent dysfunction in AD, with Aβ accumulation serving a
permissive role in the propagation of tau and the onset of the disease process.
Imaging the Locus Coeruleus and the Cerebrovasculature
Though the majority of studies have traditionally interrogated the structure of the LC in
aging and neurodegenerative disease postmortem, the aforementioned neuromelanin-sensitive
MRI scans allow for in vivo estimates of LC integrity in clinical settings. LC integrity, as
represented by contrast ratio, correlates with both Aβ and tau pathology measured in the CSF and
via PET scanning (Betts, Cardenas-Blanco, et al., 2019; Betts, Kirilina, et al., 2019). Recent
studies of LC integrity have outlined its utility in predicting future tau and amyloid aggregation,
tracking subjective and objective cognition, and detecting behavioral and neuropsychiatric
6
changes (Bell et al., 2022; Cassidy et al., 2022; Ciampa et al., 2022; Dahl et al., 2022; Elman et
al., 2021; Jacobs et al., 2022; Parent et al., 2022; Sanchez et al., 2021). Thus, there is a renewed
focus on the LC as a potential biomarker for neurodegenerative disease processes as it plays a
vital role in optimal cognitive function via its neuromodulatory role in noradrenergic supply as
well as in early AD-related pathology accumulation.
The LC is also thought to be closely tied to optimal neurovascular function via several
different mechanisms. The cerebral microvasculature is known to be preferentially innervated by
noradrenergic projections from the LC, while LC-mediated noradrenergic control also regulates
central nervous system blood flow (Cohen et al., 1997; Paspalas & Papadopoulos, 1996; Raichle
et al., 1975; Toussay et al., 2013). When the LC is stimulated, noradrenergic supply to the cortex
increases, with specific targets that include astrocytes, pyramidal neurons, interneurons, and
astrocytic processes of microvessels (Branchereau et al., 1996; Cohen et al., 1997; Paspalas &
Papadopoulos, 1996; Séguéla et al., 1990). LC-projected noradrenaline acts at both alpha- and
beta-adrenergic receptors in these cortical cells. The activation of astrocytes in particular results
in increased Ca
2+
signaling, and differing studies have pointed to Ca
2+
evoked vasodilation or
vasoconstriction of arterioles and capillaries that ultimately lead to increased regional CBF
(Carmignoto & Gómez-Gonzalo, 2010; Mulligan & MacVicar, 2004). One proposed model
suggests that LC-mediated vasodilation and vasoconstriction work in concert to optimize
temporal and spatial allocation of cortical blood supply (i.e., neurovascular coupling) (Bekar et
al., 2012). Another animal study demonstrated in a rat model that LC stimulation recruits a
broader network of neurons, both excitatory and inhibitory in nature, leading to the observed
increases in CBF; of note, lesioning the LC resulted in the disappearance of the increased CBF
response, further suggesting the LC plays a key role in local and global cerebrovascular
7
regulation (Toussay et al., 2013). Other studies have attempted to specify the mechanisms by
which observed vasoconstriction occurs, demonstrating that the constriction of capillaries may be
initiated by pericytes. Stimulation of pericytes was shown to induce local capillary constriction,
suggesting that pericytes play an important role in CBF modulation (Peppiatt et al., 2006).
Furthermore, LC-lesion studies in rats have indicated that the LC’s noradrenergic innervation of
the cerebrovasculature plays a protective role in BBB integrity (Harik & McGunigal, 1984). The
noradrenergic projections of the LC are evidently essential in optimal cerebrovascular regulation,
but this mechanism has not been clearly linked to the onset of dementia processes. One study
lesioned the LC projection system in a transgenic rat model of AD and observed worse memory,
increased amyloid and inflammation, and compromised BBB integrity (Kelly et al., 2019). The
LC’s diffuse noradrenergic projection system and coupled vasculo-regulatory role underscores
the importance of studying LC structure, function, and physiology in aging and dementia
processes, and in particular, its connection to the cerebrovasculature (Giorgi et al., 2020).
It is clear that the LC is selectively vulnerable early in AD progression, experiences
selective degeneration, and is tied to vascular systems that may also be experiencing preclinical
alterations. However, in vivo human studies of these intertwining systems are scarce and few
attempts have been made to systematically characterize structural LC changes across the AD
spectrum and how they relate to the cerebrovasculature. The present three-study dissertation
addresses this by establishing links between MRI-measured LC degeneration in preclinical AD,
progression to future dementia, and cognitive abilities in prodromal AD in a large national
dataset (Studies 1 and 2). With original data collected in the greater Los Angeles Area
demonstrates, for the first time in vivo in humans, associations between integrity of the LC and
regional perfusion and moderating effects of blood plasma AD biomarkers (Study 3).
8
Chapter 2: Brainstem Volumetric Integrity in Preclinical and Prodromal Alzheimer’s Disease

Shubir Dutt
a,b
, Yanrong Li
c
, Mara Mather
a,b
, & Daniel A. Nation
c,d
for the Alzheimer’s Disease
Neuroimaging Initiative*

a
Department of Psychology, University of Southern California, Los Angeles, CA, USA
b
Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
c
Institute for Memory Impairments and Neurological Disorders, University of California, Irvine,
Irvine, CA, USA
d
Department of Psychological Science, University of California, Irvine, Irvine, CA, USA

*Data used in preparation of this article were obtained from the Alzheimer’s Disease
Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within
the ADNI contributed to the design and implementation of ADNI and/or provided data but did
not participate in analysis or writing of this report.

Chapter 2 is a reprint of the manuscript as it appears in Journal of Alzheimer’s Disease:
Dutt, S., Li, Y., Mather, M., & Nation, D.A. for the Alzheimer’s Disease Neuroimaging
Initiative. (2020). Brainstem volumetric integrity in preclinical and prodromal Alzheimer’s
disease. Journal of Alzheimer’s Disease, 77(4), 1579-1594. https://doi.org/10.3233/JAD-200187

9
Abstract
Background: Neuropathological studies have suggested the tau pathology observed in
Alzheimer’s disease (AD) originates in brainstem nuclei, but no studies to date have quantified
brainstem volumes in clinical populations with biomarker-confirmed mild cognitive impairment
(MCI) or dementia due to AD or determined the value of brainstem volumetrics in predicting
dementia.
Objective: The present study examined whether MRI-based brainstem volumes differ among
cognitively normal older adults and those with MCI or dementia due to AD and whether
preclinical brainstem volumes predict future progression to dementia.
Methods: Alzheimer’s Disease Neuroimaging Initiative participants (N = 1,629) underwent
baseline MRI scanning with variable clinical follow-up (6-120 months). Region of interest and
voxel-based morphometric methods assessed brainstem volume differences among cognitively
normal (n = 814), MCI (n = 542), and AD (n = 273) participants, as well as subsets of CSF
biomarker-confirmed MCI (n = 203) and AD (n = 160) participants.
Results: MCI and AD cases showed smaller midbrain volumes relative to cognitively normal
participants when normalizing to whole brainstem volume, and showed smaller midbrain, locus
coeruleus, pons, and whole brainstem volumes when normalizing to total intracranial volume.
Cognitively normal individuals who later progressed to AD dementia diagnosis exhibited smaller
baseline midbrain volumes than individuals who did not develop dementia, and voxel-wise
analyses revealed specific volumetric reduction of the locus coeruleus.
Conclusion: Findings are consistent with neuropathological observations of early AD-related
pathology in brainstem nuclei and further suggest the clinical relevance of brainstem
substructural volumes in preclinical and prodromal AD.
10

Keywords: Alzheimer’s disease; Biomarkers; Brainstem; Cognitive aging; Locus coeruleus;
Mild cognitive impairment; Neuroimaging; Magnetic resonance imaging

11
1. Introduction
Neuropathological studies have suggested tau protein-related Alzheimer’s disease (AD)
pathophysiological processes begin in midbrain and pontine nuclei and precede any observable
cortical changes (Grinberg et al., 2009; Simic et al., 2009). The classic Braak staging of AD
pathology was subsequently updated to include precortical stages whereby neurofibrillary tangles
first appear in brainstem nuclei and later spread to transentorhinal, hippocampal, and neocortical
regions in a stereotypical fashion (Heiko Braak et al., 2011; Heiko Braak & Del Tredici, 2015).
However, there has been recent debate regarding whether brainstem nuclei represent the actual
origin sites of tau seeding activity or simply the earliest regions showing phospho-tau signal
(Heiko Braak & Tredici, 2018; Heinsen & Grinberg, 2018; Kaufman, Del Tredici, Braak, et al.,
2018; Kaufman, Del Tredici, Thomas, et al., 2018). Thus, identification of an origin site for tau
seeding in AD remains controversial. Despite strong evidence from postmortem autopsy studies,
it remains unclear whether corresponding pathological abnormalities may be detected with in
vivo brain MRI and whether observable brainstem pathology is clinically relevant for cognitive
impairment and dementia.
A growing number of studies have identified progressive accumulation of neurofibrillary
tangle pathology in midbrain (e.g., raphe nuclei, substantia nigra) and pontine (e.g., locus
coeruleus, pedunculopontine nucleus) nuclei with increasing Braak stage, implicating the
disruption of ascending neurotransmitter systems in the manifestation of atypical AD symptoms
such as sleep-wake dysregulation, attentional/dysexecutive deficits, and neuropsychiatric
abnormalities (Rüb et al., 2016; Uematsu et al., 2018; Weinshenker, 2008). These
histopathological approaches are supported by in vivo neuroimaging studies observing reduced
midbrain and pontine volumes in clinically-diagnosed AD compared to cognitively normal older
12
adults (Iglesias et al., 2015b; Ji et al., 2020; Lee et al., 2015; Mrzilková et al., 2012; Nigro et al.,
2014). A shape analysis of the brainstem in AD patients and normal controls demonstrated
deformation of a dorsal rostral brainstem region, and a recent voxel-wise study of the brainstem
in AD and controls similarly showed differences in the dorsal rostral brainstem (Ji et al., 2020;
Lee et al., 2015). However, these studies were limited by relatively small sample sizes and a lack
of sub-regional analyses. Furthermore, brainstem volumetric differences remain unexamined in
biomarker-confirmed AD populations, the prodromal mild cognitive impairment (MCI) stage of
disease, or the asymptomatic preclinical stage in cognitively normal individuals who eventually
develop AD dementia. The present study aimed to address the dearth of knowledge regarding in
vivo brainstem imaging in AD by quantifying brainstem subregions in a large, longitudinal study
of MCI and AD dementia patients, conducting a sub-study in biomarker-confirmed AD cases,
and examining the potential utility of brainstem volumetrics in predicting development of AD
dementia in initially asymptomatic individuals.

2. Materials and Methods
2.1 Study Participants
Participant data were drawn from the Alzheimer’s Disease Neuroimaging Initiative
(ADNI) database (adni.loni.usc.edu). The ADNI began in 2003 to test whether serial MRI,
positron emission tomography, biofluid markers, and clinical and neuropsychological assessment
can be combined to measure progression of MCI and early AD. For up-to-date information, see
www.adni-info.org. Inclusion criteria for ADNI consisted of participants ages 55-90 years
(inclusive), available study partner capable of accompanying participant to visits, Geriatric
Depression Scale score < 6, Hachinski Ischemic Score ≤ 4, stability of permitted medications for
13
4 weeks, adequate visual and auditory abilities for neuropsychological testing, adequate general
health with no diseases expected to interfere with study participation, minimum of 6
th
grade
education or equivalent work history, and fluency in English or Spanish. Exclusion criteria
consisted of significant co-morbid neurological disease, history of substance abuse within the
past 2 years, and history of significant head trauma. All participants received baseline clinical
diagnoses of cognitively normal (CN), MCI, or AD dementia according to ADNI diagnostic
criteria, which have been previously described (Petersen et al., 2010). This study was conducted
in compliance with the Declaration of Helsinki and was approved at all sites by local
Institutional Review Boards. All participants or legal representatives of participants gave written
informed consent prior to participation in the study. For the present study, participant data
consisted of 1,629 older adults enrolled in ADNI-1, ADNI-GO, or ADNI-2 with complete
baseline data for all variables of interest (demographics, neuropsychological testing, baseline
structural MRI). Age, sex, years of education, and apolipoprotein (APOE) e4 carrier status were
included as demographic variables. Further information regarding APOE e4 genotyping is online
(http://adni.loni.usc.edu/data-samples/data-types/genetic-data/).
2.2 Cluster Analysis
Due to the previously noted susceptibility of ADNI MCI diagnoses to false positives, all
participants with baseline ADNI diagnoses of MCI were entered into a cluster analysis to resolve
potential misclassifications (L. R. Clark et al., 2013; Delano-Wood et al., 2009; Edmonds et al.,
2015). First, a consistently cognitively normal reference group was formed from participants
who were ADNI-diagnosed CN and remained CN for the length of their participation in the
study (n = 383). Next, linear regression models were run within this group to predict cognitive
performance from age and education for six neuropsychological tests (Rey Auditory Verbal
14
Learning Test delayed memory recall, Rey Auditory Verbal Learning Test delayed memory
recognition, Animal fluency, Boston Naming Test, Trail Making Test Parts A & B) across three
cognitive domains (memory, language, executive function). Resulting regression coefficients
were then used to calculate expected performance of MCI participants on the six
neuropsychological tests based on their age and education. Finally, age- and education-adjusted
z-scores (calculated based on their observed versus expected performance) were used in a
hierarchical cluster analysis (Ward’s method & forced 4-cluster solution) in line with prior
studies to reclassify MCI participants into four previously described diagnostic groups: a cluster-
derived cognitively normal group, amnestic MCI, dysnomic MCI, and dysexecutive MCI. The
cluster-derived cognitively normal group was combined with ADNI-diagnosed cognitively
normal individuals to form the CN group for the present study, while the three MCI subtypes
were combined into a single neuropsychologically-confirmed MCI group. ADNI-diagnosed AD
dementia represented the AD group.
2.3 Neuroimaging Acquisition and Analyses
ADNI participants underwent MRI scanning on Siemens, GE, or Phillips scanners at 1.5T
or 3T magnet strength. T1-weighted structural images were acquired using either a volumetric
magnetization prepared rapid gradient-echo sequence (MPRAGE) or a sagittal 3D inversion-
recovery prepared spoiled gradient echo imaging pulse sequence (IR-SPGR). Specific
parameters for each sequence are available to view online (http://adni.loni.usc.edu/methods/mri-
tool/mri-analysis/). Combining data from 1.5T and 3T magnetic field strengths has been
previously shown to be feasible by the ADNI investigators and independent researchers, and we
accordingly merged MRI scans from both 1.5T and 3T field strengths (Jack et al., 2015;
Marchewka et al., 2014). For all study participants, baseline T1-weighted images were first
15
downloaded from the ADNI database (http://adni.loni.usc.edu) in raw NIfTI format prior to any
processing. Using the “Display” function in SPM12 (http://www.fil.ion.ucl.ac.uk/spm/) within
MATLAB (MATLAB R2018a, MathWorks Inc., Natick, MA, USA) on macOS, each T1-
weighted image was individually checked for image quality and manually aligned and rotated to
ensure AC-PC (anterior commissure-posterior commissure) alignment. Aligned images were
processed through the voxel-based morphometry (VBM) pipeline in SPM12, which has been
described in detail (Ashburner & Friston, 2000). Briefly, each AC-PC aligned T1-weighted
image was segmented into grey matter, white matter, and CSF tissue classes using SPM12’s
unified segmentation procedure, followed by the creation of a study-specific DARTEL template
(Ashburner, 2007; Ashburner & Friston, 2005, 2009). Segmented images were then iteratively
aligned to the DARTEL template, spatially normalized, modulated, and smoothed with an 8 mm
full-width at half-maximum isotropic Gaussian kernel. Resulting smoothed, modulated, and
warped tissue segmentations were used in subsequent analyses.
Region-of-interest (ROI) masks extracted whole brainstem, midbrain, pons, and locus
coeruleus (LC) volumes (Supplementary Fig. 1). A previously established ROI mask defined by
the grey and white matter tissue maps from the ICBM152 template was used to assess whole
brainstem volumes comprising the pons, medulla, and midbrain (Supplementary Fig. 1A)
(Beissner et al., 2014; J. Mazziotta et al., 2001; J. C. Mazziotta et al., 1995; John Mazziotta et al.,
2001). ROI masks for the midbrain and pons were obtained from an atlas created as part of a
study establishing a probabilistic Bayesian segmentation procedure for automated delineation of
the brainstem and its sub-regions, and these masks have been validated in clinical populations
(e.g., progressive supranuclear palsy, corticobasal syndrome) known to experience atrophy of
these regions (Supplementary Fig. 1B-C) (Dutt, Binney, Heuer, Luong, Attygalle, et al., 2016;
16
Iglesias et al., 2015b). To approximate LC volume, we used a previously created ROI mask
derived by averaging coordinates for peak voxels of functional activity and neuromelanin
sensitivity from two prior studies that localized the LC on functional MRI and T1-weighted turbo
spin echo MRI scans (Supplementary Fig. 1D)
(https://rcweb.dartmouth.edu/CANlab/brainstemwiki/doku.php/lc.html) (Astafiev et al., 2010;
Keren et al., 2009). Volumes for brainstem ROIs were calculated by summing grey and white
matter voxel values from the VBM-processed images; both grey and white matter were included
for the brainstem due to the mixed tissue classifications that make up the structure (Parraga et al.,
2016). Total intracranial volume (TIV) was calculated as the sum of all voxels across the grey
matter, white matter, and CSF segmented maps. Volume extractions for TIV and all ROIs were
performed using built-in SPM12 functions (e.g. “spm_summarise”) and the “get_totals” script
(http://www0.cs.ucl.ac.uk/staff/gridgway/vbm/get_totals.m). To correct for head size, ROI
volumes were normalized via simple division by TIV, a widely used method for volumetric
normalization (Mrzilková et al., 2012; Nesteruk et al., 2016; Whitwell et al., 2001). Additional
volume normalizations for midbrain, pons, and LC ROIs were performed via division by whole
brainstem volume to determine specificity of observed volumetric differences. Normalized
volumes were subsequently multiplied by a factor of 10
3
(whole brainstem, midbrain, pons) or
10
4
(LC) to facilitate ease of comparisons.
In addition to ROI analyses, voxel-wise two sample t-tests examined morphometric
differences between groups. White matter maps were selected due to SPM’s predominant
classification of the brainstem as white matter, and prior work has demonstrated the feasibility of
detecting brainstem abnormalities with VBM in clinical populations (Dutt, Binney, Heuer,
Luong, Attygalle, et al., 2016; Ji et al., 2020; Nigro et al., 2014). VBM maps were statistically
17
compared between groups using two-sample t-tests with age, sex, education, APOE e4 status,
and TIV included as covariates. Additional models replaced the TIV covariate with TIV-
normalized pons volume or TIV-normalized brainstem volume to address potential concerns of
regional specificity. An explicit mask combining the midbrain and pons restricted analyses to
rostral brainstem structures. A height threshold of p < .05 with family-wise error (FWE)
correction for multiple comparisons was used and resulting maps were inspected for significant
clusters representing groupwise volumetric differences. Where noted below, less stringent height
thresholds of uncorrected p < .05 and p < .01 were used for exploratory analyses in cases where
no differences were observed at FWE-corrected p < .05. VBM analyses listed below compared
clinically-diagnosed groups, rather than biomarker-confirmed groups, to preserve subject
numbers and ensure sufficient statistical power.
2.4 CSF Biomarkers
A subset of ADNI participants underwent fasting lumbar puncture at baseline, and levels
of amyloid β 1-42 (Aβ1-42) and phosphorylated tau (pTau) were quantified using the automated
Roche Elecsys Aβ1-42 CSF and Elecsys phosphotau (181P) CSF electrochemiluminescene
immunoassays at the UPenn Biomarker Research Laboratory (Bittner et al., 2016). Participants
were categorized as amyloid-positive with values of Aβ1-42 below 980 pg/ml and as amyloid-
negative with values at 980 pg/ml and above. They were also categorized as pTau-positive with
values of pTau181p at or above 21.8 pg/ml and as pTau-negative with values below 21.8 pg/ml
(see http://adni.loni.usc.edu/methods/ for more information) (Hansson et al., 2018a). Participants
who were both amyloid-positive and pTau-positive were considered biomarker-confirmed AD or
MCI due to AD (MCI n= 203; AD n = 160) (Hansson et al., 2018a; Jack et al., 2018).
Statistical Analyses
18
All continuous variables were checked for normality via skewness and kurtosis.
Substantial departure from normality was noted for the Trails A, Trails B, and Boston Naming
Test variables, and a log10 transformation was applied. The Boston Naming Test variable was
reflected prior to log10 transformation to avoid undefined values. ANOVA with Tukey’s post-hoc
test was used to test group differences in age and education. c
2
test was used to test group
differences in sex and APOE e4 status. ANCOVA with age, sex, education, and APOE e4 status
as covariates tested group differences in neuroimaging variables, with post-hoc LSD tests used
for pairwise group comparisons.
Longitudinal analyses were restricted to cognitively normal participants with at least one
follow-up timepoint of clinical diagnostic data. Proportional hazards survival analyses were
conducted via Cox regressions to determine the value of brainstem ROI volumes in predicting
progression from normal cognition to clinically-diagnosed AD dementia. In Cox regression
models, continuous brainstem ROI volume was first entered as a sole predictor variable, with AD
dementia diagnosis as event of interest and months to diagnosis as time variable. Next, for
models that were significant with a single predictor variable, relevant covariates of age, sex,
education, and APOE e4 status were added in a second block. Hazard ratios (HR) and 95%
confidence intervals (CI) were reported from all Cox regression models with brainstem ROI
volumes entered as continuous predictor variables. Median splits were performed solely for
visualization of risk profiles at high versus low volumes, with survival curves plotted for groups
split by high or low brainstem ROI volume. To address potential bias introduced by selective
attrition, all Cox regression models were initially run with variable follow-up (6-120 months)
and repeated with a fixed 48-month follow-up. Additional Cox regression analyses assessed
19
value of brainstem ROI volumes in predicting progression from ADNI-diagnosed normal
cognition to ADNI-diagnosed MCI.
To address the issue of multiple comparisons, false discovery rate (FDR) correction via
the Benjamini-Hochberg procedure was applied to omnibus tests for all planned comparisons,
including diagnostic group and biomarker-split group comparisons of ROI volumes, post-hoc
pairwise tests, and Cox regressions of converters/non-converters to dementia (Glickman et al.,
2014). Results were considered significant at FDR-corrected threshold of .05 and .10. For all
other unplanned comparisons (e.g., demographic variables), results were considered significant
at Bonferroni adjusted p < .05. All statistical analyses were performed in SPSS (IBM SPSS
Statistics 25, IBM, Armonk, NY, USA) and Prism (GraphPad Prism 7, GraphPad Software Inc.,
San Diego, CA, USA).

3. Results
3.1 Demographics
Baseline demographic data are displayed for clinically-diagnosed and biomarker-
confirmed groups in Table 1 and for AD dementia converters and non-converters in Table 2.
3.2 Brainstem Region-of-interest Analyses
Relative to the cognitively normal group, participants with neuropsychologically-
confirmed MCI or clinically-diagnosed AD dementia had smaller TIV-normalized whole
brainstem [F(2,1615) = 7.13, p = .001, hp
2
= .009], midbrain [F(2,1617) = 16.80, p < .001, hp
2
=
.02], and pons [F(2,1616) = 3.26, p = .039, hp
2
= .004] volumes (Table 1; Fig. 1A-C;
Supplementary Table 1). Diagnostic groups did not differ in TIV. When normalizing brainstem
substructures to whole brainstem volume, participants with AD dementia had smaller midbrain
20
relative to whole brainstem volume [F(2,1617) = 5.70, p = .003, hp
2
= .007] and larger pons
relative to whole brainstem volume [F(2,1620) = 7.12, p = .001 hp
2
= .009] as compared to the
cognitively normal group (Supplementary Table 2A,D). When constraining analyses to
biomarker-confirmed groups, AD dementia participants exhibited smaller TIV-normalized
midbrain [F(2,1150) = 4.58, p = .01, hp
2
= .008] volumes than cognitively normal participants,
and there were no differences in whole brainstem or pons volumes (all p’s > .10) (Table 1; Fig.
1D-F; Supplementary Table 1). When normalizing brainstem substructures to whole brainstem
volume, biomarker-confirmed AD dementia participants had smaller midbrain relative to whole
brainstem volume [F(2,1150) = 5.07, p = .006, hp
2
= .009] as compared to the cognitively normal
group and larger pons relative to whole brainstem volume [F(2,1152) = 7.84, p < .001, hp
2
=
.013] as compared to the cognitively normal and MCI groups (Supplementary Table 2B,D).
Cognitively normal participants who progressed to AD dementia (converters) had smaller
baseline TIV-normalized midbrain [F(1,775) = 8.68, p = .003, hp
2
= .011] volumes than those
who did not progress to dementia (non-converters), and no differences were observed in TIV,
TIV-normalized whole brainstem volume, TIV-normalized pons volume, or brainstem-
normalized ROIs (Table 2; Fig. 2A-C; Supplementary Table 2C; see Supplementary Table 3 for
life table). With FDR limited to 0.05, Cox regression analyses of relationships between baseline
TIV-normalized ROI volumes and progression to dementia demonstrated that smaller baseline
midbrain volume was associated with higher risk of progression to dementia (HR 3.24, 95% CI
[1.51, 6.96], p = .003) (Fig. 2E). With FDR limited to 0.10, smaller baseline whole brainstem
volume was associated with higher risk of progression to dementia (HR = 1.24, 95% CI [1.02,
1.52], p = .033) (Fig. 2D). Cox regression with baseline pons volume as predictor was not
significant, nor were repeated Cox regression models with baseline brainstem-normalized ROI
21
volumes as predictor variables (Fig. 2F; Supplementary Table 2E). Repeated Cox regression
models with fixed 48-month follow-up periods confirmed that smaller baseline midbrain volume
was associated with higher risk of progression to dementia (HR 3.14, 95% CI [1.47, 6.69], p =
.003], however relationships for brainstem and pons were not significant (Supplementary Table
4A-B).
ROI analyses centered on the LC indicated that participants with neuropsychologically-
confirmed MCI or clinically-diagnosed AD dementia had smaller TIV-normalized LC
[F(2,1616) = 4.50, p = .011, hp
2
= .006] volumes relative to the cognitively normal group, while
no differences were observed when constraining analyses to biomarker-confirmed groups or
when normalizing to whole brainstem volume (Table 1; Fig. 3C-D; Supplementary Table 1;
Supplementary Table 2A). Baseline LC ROI volumes did not differ between AD dementia
converters and non-converters at baseline when normalizing to TIV or to whole brainstem
volume (Table 2; Supplementary Table 2C). With FDR limited to 0.10, Cox regression analyses
demonstrated that smaller baseline LC volume conferred higher risk of progression to dementia
(HR 9.10, 95% CI [1.20, 69.22], p = .033) (Fig. 4A-B). Baseline LC volume was not predictive
of progression to dementia in repeated Cox regression models with fixed 48-month follow-up
period or in models with brainstem-normalized LC volume (Supplementary Table 2E;
Supplementary Table 4).
ROI analyses were additionally repeated with raw ROI volumes (as opposed to TIV-
normalized volumes) and TIV included as a covariate in statistical models; this approach did not
affect any results. In a risk analysis examining ADNI-diagnosed CN individuals who progress to
an ADNI diagnosis of MCI, baseline midbrain volume was associated with higher risk of MCI
diagnosis (HR 2.26, 95% CI [1.20, 4.27], p = .012 (Supplementary Table 5A-B).
22
3.3 Brainstem-Masked VBM Analyses
Brainstem-masked VBM analyses of the entire MCI sample, regardless of biomarker
positivity, compared to cognitively normal participants indicated smaller regional volume of
clusters overlapping the bilateral LC and bilateral clusters in the anterolateral midbrain (Fig. 3A;
Supplementary Table 6A). Patterns of regional volume difference between AD dementia and
cognitively normal participants similarly indicated smaller clusters overlapping bilateral LC,
anterolateral midbrain, and dorsal rostral pontine regions (Fig. 3B; Supplementary Table 6B). All
VBM findings were significant at an FWE-corrected height threshold of p < .05. Brainstem-
masked VBM analyses within cognitively normal participants showed smaller regional volume
of bilateral clusters corresponding to the anatomical distribution of the LC in AD dementia
converters compared to non-converters, the only clusters that remained significant at an
uncorrected height threshold of p < .01 (Fig. 4C; Supplementary Table 7A). When observed at a
less stringent threshold of uncorrected p < .05, the clusters of interest extended caudally, further
overlapping the anatomical distribution of the LC (Supplementary Table 7B; Supplementary Fig.
2). We compared MNI coordinates from our voxel-wise analyses and found specific overlap with
prior VBM studies of the brainstem and with studies that have localized the structure of the LC
on neuromelanin-sensitive T1-weighted sequences (Supplementary Table 8). In order to
demonstrate that voxel-wise findings were not a reflection of overall pons or overall brainstem
volume difference, VBM analyses were repeated with covariates for TIV-normalized pons
volume and TIV-normalized whole brainstem volume in place of the TIV covariate. Clusters
overlapping the LC remained significant at FWE-corrected p < .05 in AD compared to CN with
pons covariate, and at uncorrected p < .05 in AD compared to CN with whole brainstem
covariate, as well as MCI compared to CN with pons covariate and whole brainstem covariate
23
(Supplementary Figs. 3-4). Similarly, clusters overlapping the LC remained significant at
uncorrected p < .05 in AD dementia converters compared to non-converters when controlling for
TIV-normalized pons and whole brainstem volumes (Supplementary Figs. 5-6).

4. Discussion
The current study found that older adults with biomarker-confirmed dementia due to AD
exhibited smaller midbrain volumes than cognitively normal individuals. Furthermore, smaller
midbrain volumes were observed in cognitively normal older adults who later went on to develop
AD dementia compared to those who did not progress to dementia, and lower baseline brainstem,
midbrain, and LC volumes were predictive of future progression to AD dementia diagnosis.
These findings confirm prior MRI studies implicating brainstem volumetric differences in
clinically-diagnosed AD dementia and further clarify that these differences are observable earlier
in AD pathophysiological processes (Iglesias et al., 2015b; Lee et al., 2015; Nigro et al., 2014).
A brainstem-masked analysis using voxel-level methods revealed further brainstem differences
between neuropsychologically-confirmed MCI and clinically-diagnosed dementia due to AD
compared to cognitively normal individuals in a small cluster corresponding with the anatomical
location of the LC along the midbrain-pontine axis. Our findings provide preliminary in vivo
evidence of structural brainstem abnormalities detectible on traditional MRI sequences,
mirroring neuropathological studies that have localized early AD pathology to brainstem nuclei
(Heiko Braak & Del Tredici, 2011; Stratmann et al., 2016). Taken together, the patterns of
brainstem volumetric differences across clinically-diagnosed and biomarker-confirmed AD
groups suggest early brainstem pathology in the midbrain and LC is observable on MRI, and this
pathology is predictive of clinical progression from the earliest preclinical phase of the disease.
24
A strength of the present study was the use of VBM in addition to ROI-based volumetrics
to compare diagnostic groups and constrain analyses to brainstem substructures. Our finding of
reduced LC volume in MCI and AD dementia compared to cognitively normal individuals
provides a volumetric analogue to previous studies showing reduced LC neuromelanin contrast
ratios in AD and MCI relative to cognitively normal individuals (Betts, Cardenas-Blanco, et al.,
2019; Takahashi et al., 2015). To our knowledge, no prior studies have outlined proxy estimates
of LC volume on traditional T1-weighted images and instead have focused on neuromelanin-
sensitive T1-weighted scans. Our study represents the first known effort to evaluate LC integrity
with volumetrics as opposed to neuromelanin contrast ratio. Despite the difficulty in quantifying
the volume of a nucleus as small as the LC on structural MRI, groupwise differences in LC
volume were in the expected direction, with smaller LC volume seen in cognitive impairment
and predicting future cognitive impairment. Clusters corresponding to the bilateral LC resulting
from our voxel-wise analyses overlap with coordinates reported from prior studies using
neuromelanin-sensitive T1-weighted FSE scans, demonstrating the potential utility of traditional
T1-weighted scans in detecting LC volumetric differences (Betts et al., 2017; Dahl et al., 2019;
Keren et al., 2009). Our analyses indicated gross detectible differences in the midbrain while
voxel-level analyses revealed sub-regional differences in midbrain and pontine regions adjacent
to nuclei known to degenerate with advancing Braak stage (Stratmann et al., 2016). Our
midbrain-specific findings in biomarker-confirmed AD cases emphasize that volumetric
differences are detectible in a structure known to undergo selective neuronal loss in the earliest
precortical stages of AD and may be indicative of compromised optimal regulation of various
arousal-related systems (e.g., serotonergic, glutamatergic, cholinergic, noradrenergic) in AD
progression. (Grinberg et al., 2009; Rüb et al., 2017; Stratmann et al., 2016). Future ex vivo
25
neuropathological studies will be helpful in clarifying whether our findings represent a proxy
measure of neurodegeneration, synaptic loss, axonal deterioration, or a different
neuropathological marker, but in any case our approach has established detectible differences in
brainstem MRI in very early stage AD.
Given that smaller midbrain volume in cognitively normal older adults predicts future
dementia, it is possible that greater premorbid midbrain volume confers a degree of protection
against insidious tau deposition and consequent disease progression. This is consistent with the
neural reserve literature suggesting the LC as a site of reserve due to its known involvement in
higher-order executive cognitive processes, neuroplasticity, memory, and arousal, and our
findings support a critical role of the LC in protecting against the deleterious effects and
corresponding clinical consequences of increasing AD pathology (Clewett et al., 2015; Mather &
Harley, 2016; Robertson, 2013). Alternatively, our findings could be interpreted as suggesting
brainstem regions are selectively vulnerable to neurodegenerative disease. As we demonstrated,
reduced premorbid structural integrity of the midbrain and LC bestows greater risk for cognitive
decline and disease progression, in line with studies observing reduced LC neuromelanin
contrast ratios in AD dementia and MCI populations compared to cognitively normal
individuals, inverse correlations between LC neuromelanin contrast ratio and Aβ pathological
burden, and general declines in LC neuromelanin contrast with age (Betts, Cardenas-Blanco, et
al., 2019; Liu et al., 2019; Olivieri et al., 2019; Takahashi et al., 2015). Prior studies have
examined LC degeneration in the early stages of AD pathophysiology in postmortem tissue, and
the present study provides preliminary evidence that these differences may be apparent in vivo
with widely used structural neuroimaging techniques (Ehrenberg et al., 2017; Grinberg et al.,
2009; Simic et al., 2009; Theofilas et al., 2016). The LC’s role as the primary site of
26
noradrenergic production and regulation has increased its potential as a biomarker for
neurodegenerative disease, and our findings suggest that gross anatomical differences quantified
on T1-weighted scans may be useful when assessing preclinical and prodromal populations in
contexts where more advanced imaging sequences are unavailable (Betts, Kirilina, et al., 2019;
Dordevic et al., 2017).
Alternative pathways of dysfunction must also be considered when examining a nucleus
as functionally diverse as the LC. Recent animal studies have suggested that LC integrity is
linked to optimal regulation of cerebral blood flow, with degeneration of the LC-norepinephrine
projection system predicting downstream vascular consequences in AD-related regions (Bekar et
al., 2012; Kelly et al., 2019). Multimodal neuroimaging and biomarker studies of well-
characterized clinical populations are needed to disentangle whether subcortical brainstem nuclei
represent sites of selective vulnerability or resilience to AD pathological burden. In the context
of ongoing debate over whether subcortical regional pathology in the LC and other brainstem
nuclei represent the initial site of tau seeding and hyperphosphorylated tau deposition in AD, our
study cannot argue one way or the other due to the lack of histopathological analysis and Braak
staging. However, the observation of early brainstem volume differences in MCI due to AD and
cognitively normal older adults at risk for future AD dementia diagnosis clearly emphasizes the
importance of integrating in vivo neuroimaging studies with histopathological studies to continue
characterizing the disease-related progression of tau pathology.
The present study is not without several caveats. One limitation is the estimation of LC
volume from structural T1 images via approximated ROI masks. The LC is a notoriously
difficult nucleus to localize on images and neuromelanin-sensitive T1-weighted fast spin echo or
turbo spin echo sequences are thought to best visualize and capture the integrity of the LC (Betts
27
et al., 2017; Clewett et al., 2015; Keren et al., 2009). These specialized scans leverage the natural
accumulation of neuromelanin in noradrenergic cells of the LC over the lifespan, which has
paramagnetic T1-shortening effects, and apply targeted scan parameters to capture this
neuromelanin concentration as hyperintensities visible in the pons (Dahl et al., 2019; Sasaki et
al., 2008). The present study used T1-weighted volumetric sequences and an LC mask centered
on average peak voxels from multiple studies to approximate LC volume. Future studies will
need to compare this methodology to neuromelanin-sensitive sequences (Astafiev et al., 2010;
Keren et al., 2009). Although we demonstrated groupwise differences among diagnostic groups
in ROI volume of LC when normalizing to TIV, this finding did not remain significant when
normalizing to whole brainstem volume. This may represent artifact specific to our methodology,
as various processing steps including warping to template space and spatial smoothing make it
difficult to ensure regional specificity in a mixed tissues structure such as the brainstem. VBM
methodologies contain inherent limitations for evaluating grey/white matter contrast in a mixed
tissue class structure such as the brainstem, and it has been previously noted that VBM may have
a limited capacity to detect subtle changes in white matter regions that are largely homogenous
in nature (Kurth et al., 2015; Mechelli et al., 2005). However, prior studies have used VBM
analysis of white matter maps to successfully detect volume loss within brainstem substructures
in disease populations known to experiences specific volume loss within the brainstem
(progressive supranuclear palsy and corticobasal degeneration), as well as in Alzheimer’s disease
compared to healthy controls (Dutt, Binney, Heuer, Luong, Attygalle, et al., 2016; Nigro et al.,
2014). Our study further adds to this literature by providing additional support for the feasibility
of this approach within the greater context of the limitations VBM analyses pose. Additionally,
regional findings from our voxel-wise analyses remained significant when correcting for total
28
pons volume or total brainstem volume. Nevertheless, future studies should conduct similar
analyses in cohorts with specialized neuromelanin-sensitive MRI sequences designed to
specifically assess LC structural integrity (Betts, Kirilina, et al., 2019).
Survival analyses indicated that baseline LC volume confers significant risk for dementia,
but the large 95% CI suggests the stability of this prediction is highly variable, and predictive
value was no longer significant when examining over a fixed 48-month follow-up. Additionally,
the present study analyzed a sample with high rates of MCI and AD cases likely not present in
real-world settings, suggesting that a similar analysis in a smaller sample would result in lower
predictive value. Thus, the predictive value of the LC should be interpreted with caution and
future studies in more representative samples will help to determine the stability of predictive
values and may aim to make comparisons with regions well-established to experience atrophic
changes in AD dementia (e.g., hippocampus, medial temporal lobe). Nevertheless, the close
anatomical localization provided from the LC ROI mask, combined with the marked volumetric
differences observed in VBM analyses, implicate midbrain and pontine regions adjacent to and
overlapping the LC in preclinical AD. Given the clinical subjectivity of differential diagnoses in
ADNI and the lack of pathological diagnostic confirmation in our cohort, it is possible that our
findings in the clinically-defined groups were influenced by individuals with co-morbid
subcortical dementias, primary age-related tauopathies, and other non-AD processes. We
examined cross-sectional volumetric differences at baseline rather than progression of brain
atrophy over time purposefully in an effort to comprehensively quantify baseline volumetrics.
Future studies will aim to detail the longitudinal progression of brainstem substructure atrophy in
each of our diagnostic groups. It should be noted that although our analysis predicting AD
dementia used neuropsychologically-confirmed cluster analysis to refine baseline MCI
29
diagnoses, our analysis predicting MCI used original ADNI MCI diagnoses. Future efforts
should use neuropsychological data to inform serial MCI diagnoses to avoid potential
misclassifications, as demonstrated by recent studies (Edmonds et al., 2020). Additionally, our
longitudinal risk analyses included clinically-diagnosed groups and were not performed in
biomarker-confirmed groups due to limited sample sizes. Thus, longitudinal studies are needed
to detail brainstem atrophy progression in biomarker-confirmed MCI and AD. Finally, the ADNI
database comprises an ethnically homogenous, highly educated sample that is not necessarily
representative of the general population, and future studies will need to replicate our findings in
diverse cohorts.
Despite these limitations, our study demonstrates brainstem MRI abnormalities that are
detectible in preclinical populations, highlighting the importance of considering the brainstem
when developing novel biomarkers and innovative therapeutics. Future studies should leverage
additional neuroimaging modalities, including functional MRI, diffusion tensor imaging, and
arterial spin labeling, to address issues of neurovascular coupling, structural white matter tract
degeneration, and cerebral perfusion as they relate to the brainstem and disease progression.
Future endeavors should also separate individuals by Braak stage and quantify these brainstem
substructure volumes to examine whether brainstem atrophy progresses in temporal conjunction
with the widely accepted pathophysiological staging advancement. Future MRI-based studies of
dementia populations may aim to include brainstem volumetrics as outcome variables and
address their potential utility as clinical trial endpoints (Iglesias et al., 2015b; Nigro et al., 2014;
Sander et al., 2019). In summary, we provide here preliminary evidence that in vivo visualization
of brainstem substructure and LC-specific differences are detectible with widely used MRI
sequences in preclinical and prodromal AD populations.
30

Acknowledgements
We would like to thank the participants and their families, investigators, and researchers
from the ADNI study. Data used in preparation of this article were obtained from the ADNI
database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the
design and implementation of ADNI and/or provided data but did not participate in analysis or
writing of this report. A complete listing of ADNI investigators can be found at:
http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
Author funding for this study was obtained through grants from the National Institutes of
Health (R01AG060049, R21AG055034, P01AG052350, P50AG00514, P50AG016573,
R01AG025340, R01AG64228), the Alzheimer’s Association (AA008369), and the National
Science Foundation (DGE1418060). Data collection and sharing for this project was funded by
the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant
U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-
0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical
Imaging and Bioengineering, and through generous contributions from the following: AbbVie,
Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech;
BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.;
Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and
its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen
Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical
Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale
Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals
31
Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and
Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to
support ADNI clinical sites in Canada. Private sector contributions are facilitated by the
Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the
Northern California Institute for Research and Education, and the study is coordinated by the
Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data
are disseminated by the Laboratory for NeuroImaging at the University of Southern California.

Disclosure Statement
The authors have no conflicts of interest to report.

32
Figure 1. Brainstem gross regional volumes in cognitively normal, MCI, and Alzheimer’s
disease participants

Relative to older adults who are cognitively normal (CN; blue dots), those with mild cognitive
impairment (MCI; orange dots) and clinically-diagnosed Alzheimer’s disease dementia (AD;
dark red dots) exhibit smaller volumes of overall brainstem (A), midbrain (B), and pons (C). In a
subgroup with biomarker-confirmed AD pathology based on CSF Aβ1-42 and pTau abnormalities,
brainstem volume differences are specific to the midbrain in AD dementia (bright red dots) (D-
F). All p-values reported are the results of post-hoc Fisher’s LSD pairwise comparisons
following one-way ANCOVA controlling for age, sex, education, and APOE e4 status. *Remains
significant with FDR limited to .10; **Remains significant with FDR limited to .05 or .10. Error
bars represent ± 1 standard deviation. Abbreviations: AD = Alzheimer’s disease; CN =
cognitively normal; MCI = mild cognitive impairment; NS = non-significant.

33
Figure 2. Brainstem gross regional volumes in progression to AD dementia

CN older adults who prospectively progress to dementia (AD dementia converters; blue dots)
demonstrated smaller volume of the midbrain (B) at baseline relative to those who did not
progress to dementia (non-converters; purple dots), while no differences were observed in whole
brainstem (A) and pons (C). CN older adults with smaller baseline midbrain volumes (pink lines)
are more likely to later develop cognitive impairment and receive a clinical diagnosis of AD
dementia over 6 to 120 months of follow-up than those with larger baseline midbrain volumes
(blue lines) (D-F). Hazard ratios (HR) and p values report results of proportional hazards Cox
regressions with continuous brainstem ROI volume as predictor variable, AD dementia diagnosis
as event of interest, and months to diagnosis as time variable (event cases = 83; censored cases =
702). ROI volume was first entered as a sole continuous predictor variable, and significant
models added in covariates of age, sex, education, and APOE e4 status. For display purposes,
median splits were performed on brainstem ROI volumes to show risk at high and low volumes.
*Remains significant with FDR limited to .10; **Remains significant with FDR limited to .05 or
.10. Abbreviations: AD = Alzheimer’s disease; CN = cognitively normal; NS = non-significant.

34
Figure 3. Brainstem-masked VBM comparisons and regional LC volumes

Brainstem-masked VBM analyses in the overall sample revealed a specific pattern of reduced
regional dorsal rostral brainstem volume at baseline in MCI (A) and clinically-diagnosed AD (B)
relative to those who are CN that closely corresponds to the anatomical distribution of the LC.
VBM analyses consisted of two-sample t-tests with age, sex, education, APOE e4 status, and
TIV as covariates. Results are displayed at FWE-corrected height threshold of p < .05,
represented by color bar. Images are displayed in neurological orientation. LC ROI volumes,
extracted using an LC atlas mask, (C) were smaller in MCI (orange dots) and AD (dark red dots)
relative to cognitively normal older adults (blue dots). No significant differences were observed
between biomarker-confirmed subgroups (D). p-values reported are the results of post-hoc
Fisher’s LSD pairwise comparisons following one-way ANCOVA controlling for age, sex,
education, and APOE e4 status. *Remains significant with FDR limited to .10; **Remains
significant with FDR limited to .05 or .10. Error bars represent ± 1 standard deviation.
Abbreviations: AD = Alzheimer’s disease; CN = cognitively normal; MCI = mild cognitive
impairment; NS = non-significant.
35
Figure 4. LC volume predicts progression to dementia in cognitively normal older adults

AD dementia converters (blue dots) did not differ from non-converters (purple dots) in LC
volume at baseline (A). CN older adults with smaller baseline LC volumes (pink lines) are more
likely to later develop cognitive impairment and receive a clinical diagnosis of AD dementia
over 6 to 120 months of follow-up than those with larger baseline midbrain volumes (blue lines)
(B). Hazard ratios (HR) and p-values report results of proportional hazards Cox regressions with
36
continuous brainstem ROI volume as predictor variable, AD dementia diagnosis as event of
interest, and months to diagnosis as time variable (event cases = 83; censored cases = 702). ROI
volume was first entered as a sole continuous predictor variable, and significant models added in
covariates of age, sex, education, and APOE e4 status. For display purposes, median split was
performed on LC ROI volume to show risk at high and low volumes. Brainstem-masked VBM
analyses revealed a specific pattern of reduced regional dorsal rostral brainstem volume at
baseline in AD dementia converters compared to non-converters (C) that closely corresponds to
the anatomical distribution of the LC. VBM analyses consisted of two-sample t-tests with age,
sex, education, APOE e4 status, and TIV as covariates. VBM results are displayed at uncorrected
height threshold of p < .01, represented by color bar. Images are displayed in neurological
orientation. *Remains significant with FDR limited to .10. Error bars represent ± 1 standard
deviation. Abbreviations: AD = Alzheimer’s disease; CN = cognitively normal; L = left; NS =
non-significant; R = right.

37
Table 1. Baseline demographics and neuroimaging data for cognitively normal, MCI, and AD

Mean (standard deviation) are shown for all variables except for sex and APOE e4. F or χ
2
are result of one-way
ANOVA (age, education), chi-square test of independence (sex, APOE e4), or one-way ANCOVA (all other
variables; covariates = age, sex, education, APOE e4). Effect sizes are displayed as hp
2
(age, education,
neuroimaging variables) or Cramer’s V (sex, APOE e4). Results from pairwise comparisons are displayed in
Supplementary Table 1. Omnibus p-values for planned comparisons (neuroimaging variables) remained significant
when FDR rate was limited to 0.05 (exception: pons CN vs. MCI vs. AD) and 0.10 (all comparisons).

a
TIV is presented in milliliters. ROI volumes were normalized using the following equation: (ROI volume / TIV) ×
10
3
for brainstem, midbrain, and pons or (ROI volume / TIV) x 10
4
for LC
b
ROI neuroimaging variables were screened for outliers (± 3 standard deviations from group mean) prior to
statistical analyses. Revised n’s (CN/MCI/AD) by brainstem ROI are as follows: TIV(813/541/273); brainstem
(809/541/272); midbrain (810/541/273); pons (810/541/272); LC (808/542/273)
c
Revised n’s (CN/MCI [Aβ+pTau+]/AD [Aβ+pTau+]) by brainstem ROI are as follows: TIV(813/202/145);
brainstem (809/202/145); midbrain (810/202/145); pons (810/202/145); LC (808/202/145)

Abbreviations: Aβ, amyloid β; AD, Alzheimer’s disease; APOE e4, apolipoprotein e4; CN, cognitively normal;
FDR, false discovery rate; LC, locus coeruleus; MCI, mild cognitive impairment; pTau, phosphorylated tau; ROI,
region-of-interest; TIV, total intracranial volume
CN MCI AD F or χ
2
p-value hp
2
or
Cramer’s V
Demographics
n 814 542 273
Age 73.49 (6.76) 73.54 (7.35) 75.12 (7.74) 5.78 .003 .007
Sex (M/F) 417/397 332/210 153/120 13.30 .001 .09
Education 16.29 (2.65) 15.85 (2.92) 15.23 (2.92) 14.20 <.001 .017
APOE e4
(0/1/2 e4)
536/246/32 249/221/72 87/132/54 138.36 <.001 .206
Neuroimaging
a,b

TIV 1499.35 (146.12) 1517.81 (157.29) 1504.57 (166.64) 0.47 .628 .001
Brainstem 13.33 (1.14) 13.13 (1.18) 13.05 (1.24) 7.13 .001 .009
Midbrain 3.89 (0.30) 3.82 (0.31) 3.78 (0.33) 16.80 <.001 .02
Pons 7.69 (0.73) 7.60 (0.74) 7.58 (0.78) 3.26 .039 .004
LC 1.22 (0.11) 1.20 (0.12) 1.19 (0.12) 4.50 .011 .006

Demographics CN MCI [Aβ+pTau+] AD [Aβ+pTau+]
n 814 202 145
Age 73.49 (6.76) 73.61 (7.13) 73.91 (8.01) 0.23 .797 <.001
Sex (M/F) 417/397 112/90 78/67 1.31 .521 .034
Education 16.29 (2.65) 15.98 (2.86) 15.58 (2.72) 4.68 .009 .008
APOE e4
(0/1/2 e4)
536/246/32 57/104/41 31/77/37 197.6 <.001 .292
Neuroimaging
a,c

TIV 1499.35 (146.12) 1505.68 (166.34) 1508.55 (172.89) 1.05 .35 .002
Brainstem 13.33 (1.14) 13.26 (1.17) 13.24 (1.15) 0.98 .376 .002
Midbrain 3.89 (0.30) 3.85 (0.30) 3.82 (0.30) 4.58 .01 .008
Pons 7.69 (0.73) 7.67 (0.73) 7.70 (0.73) 0.15 .859 <.001
LC 1.22 (0.11) 1.21 (0.11) 1.21 (0.11) 0.76 .469 .001
38
Table 2. Baseline demographics and neuroimaging data for cognitively normal participants who
progressed to dementia (converters) and did not progress to dementia (non-converters)

Mean (standard deviation) are shown for all variables. F or χ
2
are result of one-way ANOVA (age, education), chi-
square test of independence (sex, APOE e4), or one-way ANCOVA (all other variables; covariates = age, sex,
education, APOE e4). Effect sizes are displayed as hp
2
(age, education, neuroimaging variables) or Cramer’s V (sex,
APOE e4). All omnibus p-values for planned comparisons (neuroimaging variables) remained significant when FDR
rate was limited to 0.05 and 0.10

a
TIV volume is presented in milliliters. ROI volumes were normalized using the following equation: (ROI volume /
TIV) × 10
3
for brainstem, midbrain, and pons or (ROI volume / TIV) x 10
4
for LC
b
ROI neuroimaging variables were screened for outliers (± 3 standard deviations from group mean) prior to statistical
analyses. Revised n’s (non-converters/converters) by brainstem ROI are as follows: TIV (701/83), brainstem (697/83),
midbrain (698/83), pons (697/83), LC (696/83)

Abbreviations: APOE e4, apolipoprotein e4; FDR, false discovery rate; LC, locus coeruleus; ROI, region-of-interest;
TIV, total intracranial volume

Non-converters Converters F or χ
2
p-value hp
2
or Cramer’s V
Demographics
n 702 83
Age 73.38 (6.79) 75.17 (6.29) 5.18 .023 .007
Sex (M/F) 350/352 54/29 6.87 .009 .094
Education 16.36 (2.68) 15.71 (2.40) 4.50 .034 .006
APOE e4 (0/1/2
e4)
482/198/22 35/38/10 30.01 <.001 .196
Neuroimaging
a,b

TIV 1498.76 (147.91) 1520.87 (132.93) 0.16 .688 <.001
Brainstem 13.36 (1.12) 13.07 (1.28) 3.64 .057 .005
Midbrain 3.90 (0.29) 3.79 (0.33) 8.68 .003 .011
Pons 7.71 (0.72) 7.59 (0.82) 1.63 .202 .002
LC 1.22 (0.11) 1.19 (0.12) 2.96 .086 .004

39
Table 3. Cox regression models predicting dementia risk from baseline brainstem volumes

Brainstem ROI volumes were reflected prior to Cox regression analyses to ensure consistent directionality, with
smaller volume predicting greater risk. p-values for brainstem ROI variables were significant with FDR limited to
0.05 (midbrain) and 0.10 (brainstem, midbrain, LC).

HR 95% CI p-value
Model 1a
Brainstem 1.22 [1.01, 1.48] .043

Model 1b
Brainstem 1.24 [1.02, 1.52] .033
Age 1.03 [1.00, 1.07] .085
Sex 0.68 [0.42, 1.09] .105
Education 0.93 [0.86, 1.00] .059
APOE e4 2.76 [1.98, 3.84] <.001

Model 2a
Midbrain 3.05 [1.47, 6.32] .003

Model 2b
Midbrain 3.24 [1.51, 6.96] .003
Age 1.03 [0.99, 1.06] .167
Sex 0.70 [0.44, 1.12] .139
Education 0.92 [0.85, 1.00] .047
APOE e4 2.81 [2.01, 3.92] <.001

Model 3
Pons 1.24 [0.92, 1.68] .154

Model 4a
LC 9.16 [1.32, 63.55] .025

Model 4b
LC 9.10 [1.20, 69.22] .033
Age 1.03 [0.99, 1.07] .107
Sex 0.67 [0.42, 1.07] .091
Education 0.93 [0.86, 1.01] .065
APOE e4 2.74 [1.97, 3.82] <.001
40
Supplementary Table 1. p values from LSD pairwise comparisons of diagnostic groups

* Remains significant with FDR limited to 0.10
** Remains significant with FDR limited to 0.05 and 0.10

CN vs. MCI CN vs. AD MCI vs. AD CN vs. MCI
[Aβ+pTau+]
CN vs. AD
[Aβ+pTau+]
MCI vs. AD
[Aβ+pTau+]
TIV .464 .395 .781 .485 .159 .476
Brainstem .003
**
.001
**
.359 .29 .245 .826
Midbrain <.001
**
<.001
**
.081 .049 .006
**
.363
Pons .033
*
.038
*
.677 .581 .889 .766
LC .013
**
.014
**
.597 .339 .315 .874
41
Supplementary Table 2. ROI analyses normalized to whole brainstem volume

NOTE. Mean (standard deviation) are shown for all variables. F is result of one-way ANCOVA (covariates = age,
sex, education, APOE e4). Effect sizes are displayed as hp
2
. ROI volumes were normalized using the following
equation: (ROI volume / whole brainstem volume) × 10
2
for midbrain and pons or (ROI volume / whole brainstem
volume) x 10
3
for LC. ROI neuroimaging variables were screened for outliers (± 3 standard deviations from group
mean) prior to statistical analyses. Revised n’s (CN/MCI/AD) by brainstem ROI are as follows: midbrain
(810/542/272); pons (812/542/273); LC (810/537/271). Revised n’s (CN/MCI [Aβ+pTau+]/AD [Aβ+pTau+]) by
brainstem ROI are as follows: midbrain (810/202/145); pons (812/202/145); LC (810/200/143). Revised n’s (non-
converters/converters) by brainstem ROI are as follows: midbrain (698/83), pons (701/83), LC (698/83).
* Remains significant with FDR limited to 0.10
** Remains significant with FDR limited to 0.05 and 0.10
A CN MCI AD F p-value hp
2

n 814 542 273
Midbrain 29.19 (0.97) 29.10 (0.97) 28.95 (0.98) 5.70 .003** .007
Pons 57.73 (1.13) 57.85 (1.05) 58.05 (1.12) 7.12 .001** .009
LC 9.14 (0.34) 9.14 (0.33) 9.13 (0.35) 0.04 .965 <.001

B CN MCI
[Aβ+pTau+]
AD
[Aβ+pTau+]
F p-value hp
2

n 814 202 145
Midbrain 29.19 (0.97) 29.07 (0.87) 28.91 (0.91) 5.07 .006** .009
Pons 57.73 (1.13) 57.83 (1.02) 58.12 (1.16) 7.84 <.001** .013
LC 9.14 (0.34) 9.12 (0.32) 9.11 (0.32) 0.27 .765 <.001

C Non-converters Converters F p-value hp
2

n 702 83
Midbrain 29.20 (0.97) 29.02 (0.98) 2.18 .14 .003
Pons 57.71 (1.14) 58.00 (1.17) 2.95 .086 .004
LC 9.14 (0.34) 9.13 (0.35) 0.001 .981 <.001

D CN vs. MCI CN vs. AD MCI vs. AD CN vs. MCI
[Aβ+pTau+]
CN vs. AD
[Aβ+pTau+]
MCI vs. AD
[Aβ+pTau+]

Midbrain .072 <.001** .06 .119 .002** .124
Pons .042 <.001** .038 .16 <.001** .016*
LC .939 .834 .793 .559 .561 .946
E HR 95% CI p-value

Midbrain 1.18 [0.95, 1.47] .142
Pons 0.89 [0.73, 1.07] .206
LC 1.32 [0.69, 2.53] .397
42
Supplementary Table 3. Life table for cognitively normal participants (n = 785) displaying
censored cases and events of interest (progression to dementia)
Months of
Follow-up
Cases Entering
Interval
Censored
Cases
Progression to
Dementia
6 785 29 4
12 752 43 8
24 701 160 22
36 519 118 11
48 390 178 10
60 202 56 3
72 143 22 5
84 116 9 2
96 105 17 9
108 79 32 5
120 42 38 4

43
Supplementary Table 4. Fixed 48-month follow-up Cox Regression models
A. Hazard ratios

HR 95% CI p-value
Model 1a
Whole Brainstem 1.40 [1.09, 1.79] .008

Model 1b
Whole Brainstem 1.29 [0.99, 1.67] .056
Age 1.04 [1.00, 1.08] .037
Sex 0.54 [0.29, 0.99] .045
Education 0.93 [0.83, 1.03] .160
APOE e4 2.38 [1.64, 3.46] <.001

Model 2a
Midbrain 5.31 [2.05, 13.76] .001

Model 2b
Midbrain 4.00 [1.46, 10.95] .007
Age 1.04 [1.00, 1.08] .080
Sex 0.56 [0.30, 1.02] .058
Education 0.92 [0.83, 1.02] .124
APOE e4 2.40 [1.64, 3.50] <.001

Model 2a
Pons 1.52 [1.03, 2.23] .034

Model 3
Pons 1.34 [0.90, 2.00] .151
Age 1.05 [1.01, 1.09] .024
Sex 0.52 [0.28, 0.95] .034
Education 0.93 [0.83, 1.03] .156
APOE e4 2.36 [1.63, 3.43] <.001

Model 4
LC 23.34 [2.15, 252.94] .010

Model 3
LC 8.92 [0.73, 109.25] .087
Age 1.04 [1.00, 1.08] .059
Sex 0.51 [0.28, 0.93] .029
Education 0.93 [0.83, 1.03] .164
APOE e4 2.35 [1.62, 3.42] <.001
44
B. Life table
Months of
Follow-up
Cases Entering
Interval
Censored
Cases
Progression to
Dementia
6 583 29 4
12 550 43 8
24 499 160 22
36 317 118 11
48 188 178 10

45
Supplementary Table 5. ADNI CN to ADNI MCI progression

A

B

Non-Converters CN->MCI
Converters
F or χ
2
p-value

n 397 90
Brainstem 13.40 (1.14) 13.06 (1.20) 4.82 .029
Midbrain 3.90 (0.30) 3.79 (0.30) 7.95 .005
Pons 7.74 (0.74) 7.57 (0.78) 3.07 .08
LC 1.22 (0.12) 1.21 (0.14) 0.34 .56

HR 95% CI p-value
Model 1a
Whole Brainstem 1.19 [1.00, 1.41] .049

Model 1b
Whole Brainstem 1.19 [1.00, 1.41] .053
Age 1.04 [1.00, 1.08] .043
Sex 0.67 [0.43, 1.03] .069
Education 0.93 [0.86, 1.00] .059
APOE e4 1.70 [1.16, 2.49] .007

Model 2a
Midbrain 2.31 [1.24, 4.33] .009

Model 2b
Midbrain 2.26 [1.20, 4.27] .012
Age 1.04 [1.00, 1.08] .053
Sex 0.67 [0.43, 1.04] .073
Education 0.93 [0.87, 1.00] .062
APOE e4 1.70 [1.16, 2.50] .007

Model 3
Pons 1.26 [0.96, 1.66] .093

Model 4
LC 1.89 [0.36, 10.04] .457

46
Supplementary Table 6. VBM Coordinates table from 2-sample t-tests comparing CN to
MCI and AD at FWE-corrected height threshold of p < .05
A. MCI < CN

Cluster-level Peak-level
PFWE kE PFWE T x y z
0.045 51 0.005 3.67 -16 -18 -16
0.047 19 0.013 3.39 -6 -40 -24
0.05 1 0.032 3.07 16 -16 -15
0.049 2 0.04 2.99 6 -40 -24

B. AD < CN

Cluster-level Peak-level
PFWE kE PFWE T x y z
0.033 292 <0.001 4.83 -16 -18 -16
0.016 3.33 -16 -24 -28
0.036 206 0.001 4.24 16 -16 -15
0.016 3.35 2 -27 -15
0.021 3.26 15 -21 -24
0.046 27 0.013 3.41 8 -39 -22
0.049 2 0.04 3.03 -6 -40 -24

47

Supplementary Table 7. VBM Coordinates table from 2-sample t-test comparing CN
converters to CN non-converters
A. Uncorrected height threshold of p < .01

Cluster-level Peak-level
Puncorr kE Puncorr T x y z
0.985 5 0.005 2.55 6 -40 -24
0.981 7 0.007 2.47 -6 -40 -24

B. Uncorrected height threshold of p < .05

Cluster-level Peak-level
Puncorr kE Puncorr T x y z
0.903 282 0.005 2.56 6 -40 -24
0.007 2.47 -6 -40 -24
0.020 2.06 2 -40 -34
0.957 78 0.010 2.31 -15 -24 -20
0.949 103 0.022 2.01 16 -20 -16
0.026 1.94 16 -24 -27
0.993 5 0.043 1.71 0 -26 -14

48
Supplementary Table 8. MNI coordinate overlap between VBM findings and T1-FSE/TSE
derived LC masks from the literature

MNI x-range MNI y-range MNI z-range
Dutt et al., 2020 MCI < CN 8 to -8 -39 to -42 -21 to -28
Dutt et al., 2020 AD < CN 9 to -6 -33 to -41 -17 to -26
Dutt et al., 2020
Converters < Non-Converters
8 to -8 -39 to -41 -21 to -26
Keren et al., 2009 9 to -9 -36 to -39 -18 to -33
Betts et al., 2017 9 to -9 -36 to -43 -15.5 to -37.5
Dahl et al., 2019 8 to -10 -29 to -42 -18 to -38
Ji et al., 2020
(only peak coordinates available)
-6, -9 -36, -36 -24, -29

49
Supplementary Figure 1. Region-of-interest masks

Region-of-interest (ROI) masks used to extract volumes are displayed for whole brainstem (A),
midbrain (B), pons (C), and locus coeruleus (D). Details regarding mask creation are available in
the main text.

50
Supplementary Figure 2. Brainstem-masked VBM analyses comparing CN individuals who
prospectively progress to dementia versus those who did not progress to dementia.

Results of two-sample t-test with age, sex, education, APOE e4 status, and TIV as covariates.
VBM results are displayed at uncorrected height threshold of p < .05, represented by color bar.
Images are displayed in neurological orientation. Blue lines indicate corresponding slices
displayed in each row.

51
Supplementary Figure 3. Brainstem-masked VBM analyses comparing MCI and AD to CN
participants with TIV-normalized pons as an alternative covariate

Results of two-sample t-test showing (A) MCI < CN and (B) AD < CN with age, sex, education,
APOE e4 status, and TIV-normalized pons volume as covariates. VBM results are displayed at
uncorrected height threshold of p < .05 (MCI < CN) and FWE-corrected p < .05 (AD < CN),
represented by color bars. Images are displayed in neurological orientation.

52
Supplementary Figure 4. Brainstem-masked VBM analyses comparing MCI and AD to CN
participants with TIV-normalized whole brainstem as an alternative covariate

Results of two-sample t-test showing (A) MCI < CN and (B) AD < CN with age, sex, education,
APOE e4 status, and TIV-normalized whole brainstem volume volume as covariates. VBM
results are displayed at uncorrected height threshold of p < .05, represented by color bars. Images
are displayed in neurological orientation.

53
Supplementary Figure 5. Brainstem-masked VBM analyses comparing CN individuals who
prospectively progress to dementia versus those who did not progress to dementia with
TIV-normalized pons as an alternative covariate

Results of two-sample t-test with age, sex, education, APOE e4 status, and TIV-normalized pons
volume as covariates. VBM results are displayed at uncorrected height threshold of p < .05,
represented by color bar. Images are displayed in neurological orientation.

54
Supplementary Figure 6. Brainstem-masked VBM analyses comparing CN individuals who
prospectively progress to dementia versus those who did not progress to dementia with
TIV-normalized whole brainstem volume as an alternative covariate

Results of two-sample t-test with age, sex, education, APOE e4 status, and TIV-normalized
whole brainstem volume as covariates. VBM results are displayed at uncorrected height
threshold of p < .05, represented by color bar. Images are displayed in neurological orientation.

55
Chapter 3: Brainstem substructures and cognition in prodromal Alzheimer’s disease

Shubir Dutt
a,b
, Yanrong Li
c
, Mara Mather
a,b
, & Daniel A. Nation
c,d
for the Alzheimer’s Disease
Neuroimaging Initiative*

a
Department of Psychology, University of Southern California, Los Angeles, CA, USA
b
Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
c
Institute for Memory Impairments and Neurological Disorders, University of California, Irvine,
Irvine, CA, USA
d
Department of Psychological Science, University of California, Irvine, Irvine, CA, USA

*Data used in preparation of this article were obtained from the Alzheimer’s Disease
Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within
the ADNI contributed to the design and implementation of ADNI and/or provided data but did
not participate in analysis or writing of this report.

Chapter 3 is a reprint of the manuscript as it appears in Brain Imaging and Behavior:
Dutt, S., Li. Y., Mather M., & Nation, D.A. for the Alzheimer’s Disease Neuroimaging Initiative.
(2021). Brainstem substructures and cognition in prodromal Alzheimer’s disease. Brain Imaging
and Behavior, 15, 1-11. https://doi.org/10.1007/s11682-021-00459-y

56
Abstract
Neuropathological research suggests the tau pathology of Alzheimer’s disease may originate in
brainstem nuclei, yet it remains unknown whether tau-mediated degeneration of brainstem nuclei
influences cognitive impairment in prodromal Alzheimer’s disease. The present study examined
cognitive domains impacted in prodromal Alzheimer’s disease and brainstem substructure
volume in cognitively normal older adults (n = 814) and those with mild cognitive impairment (n
= 542). Subsamples of cognitively normal (n = 112) and mild cognitive impairment (n = 202)
also had cerebrospinal fluid Alzheimer’s disease biomarker characterization. Region-of-interest
and voxel-level analyses related whole brainstem, midbrain, pons, and locus coeruleus volumes
to cognition with multiple linear regression models corrected for age, sex, education,
apolipoprotein-e4 carrier status, and MRI magnet strength. Within mild cognitive impairment
participants, smaller midbrain and locus coeruleus volumes were significantly related to poorer
performance on tests of attention and executive function, and the relationship between locus
coeruleus volume and executive abilities remained significant in the mild cognitive impairment
subsample with biomarker-confirmed Alzheimer’s disease. A brainstem-masked voxel-wise
regression further demonstrated an association between locus coeruleus volume and executive
abilities. Brainstem volumes were not significantly related to memory processes. Study findings
implicate midbrain and locus coeruleus volume in attention and executive deficits in mild
cognitive impairment. Together with prior neuropathological studies, our data suggest a link
between Alzheimer’s disease-related degeneration of brainstem nuclei and cognitive deficits in
prodromal Alzheimer’s disease.
Keywords: Alzheimer’s disease, brainstem, cognition, locus coeruleus, magnetic resonance
imaging
57
1. Introduction
Recent updated Braak staging of Alzheimer’s disease (AD) implicates the brainstem as
the first site of tau-related pathology, with the locus coeruleus (LC) the first nucleus to
demonstrate signs of pretangles (i.e., precursors to neurofibrillary tangle pathology) (Heiko
Braak & Del Tredici, 2015). Although the origin of tau seeding activity remains controversial,
recent histopathological studies demonstrated the presence of tau cytoskeletal pathology in the
LC prior to allocortical cytoskeletal changes (Heinsen & Grinberg, 2018; Kaufman, Del Tredici,
Braak, et al., 2018; Rüb et al., 2016; Stratmann et al., 2016). The LC is the noradrenergic
epicenter of the brain and helps regulate autonomic and neurovascular function and modulate
aspects of cognition. Human and animal studies reveal the LC-noradrenergic system modulates
attentional shifts, executive function, cognitive control and memory processes (Aston-Jones &
Cohen, 2005; Mather, 2020; Mather et al., 2016; Sara, 2009). Recent efforts have highlighted the
importance of characterizing LC integrity in aging and neurodegenerative disease (Mather, 2020;
Mather & Harley, 2016), and neuroimaging studies have employed T1-weighted neuromelanin-
sensitive scans to approximate LC structural integrity in vivo (Betts, Kirilina, et al., 2019; Liu et
al., 2017). Neuroimaging studies using these specialized scans have demonstrated associations
between LC integrity and episodic memory encoding for stimuli of varying salience (Dahl et al.,
2019; Hämmerer et al., 2018; Liu et al., 2020; Olivieri et al., 2019). However, to our knowledge
no studies have evaluated associations between cognition and LC volume derived from standard
structural T1-weighted scans.
Prior studies examining brainstem volumetrics with standard structural T1-weighted
scans in AD populations found volume differences in rostral midbrain and pons regions in AD
relative to cognitively normal (CN) individuals (Ji et al., 2020; Lee et al., 2015). Furthermore,
58
we recently demonstrated volumetric differences specific to the midbrain and LC in the
prodromal phase of AD, mild cognitive impairment (MCI), compared to CN individuals, and at
an earlier preclinical stage in asymptomatic CN individuals who later received a diagnosis of AD
dementia (Dutt et al., 2020). The methodology from this study adjusted for total brainstem
volume and found overlap with prior LC masks, demonstrating that functionally-relevant LC
volume estimates can be quantified from standard T1-weighted MRI scans. Thus, brainstem
substructures, and the LC in particular, experience volumetric loss detectible on traditional MRI
sequences during the early preclinical phase of AD pathophysiology. However, no studies have
evaluated whether AD-related brainstem volume changes are associated with cognitive deficits.
The present study investigated how neuropsychological deficits associated with brainstem
substructure volume in prodromal AD, building upon our prior efforts to detail brainstem
substructure volumes across the AD spectrum (Dutt et al., 2020). Based on the growing literature
linking LC integrity with cognition, we hypothesized that smaller brainstem substructure
volumes, and smaller LC volumes in particular, would be linked to worse performance on tests
of attention, executive function and episodic memory encoding.

2. Methods
2.1 Study Design
Data were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI)
online database. The ADNI is a multisite natural history study that has collected clinical,
biomarker, and neuropsychological data since 2003 to measure progression of normal aging,
MCI, and AD. Detailed study information is available online (http://adni.loni.usc.edu/). 1,356
participants with a baseline clinical diagnosis of CN or MCI and available neuropsychological
59
and structural neuroimaging data were included from the ADNI1, ADNI GO, and ADNI2
cohorts. Participant data represented a subset of a larger study of brainstem volumetrics in
preclinical and prodromal AD (Dutt et al., 2020). This study was conducted in accordance with
the Helsinki Declaration and approved by all local Institutional Review Boards.
2.2 Neuropsychological Testing
Participants completed a standardized battery of neuropsychological tests at baseline.
Trail Making Test parts A & B assessed attentional/executive abilities (visual attention & set-
shifting, respectively). Rey Auditory Verbal Learning Test (RAVLT) delayed free recall and
recognition assessed memory consolidation/retrieval abilities. RAVLT trial 1 performance
assessed auditory attention and working memory, while RAVLT trials 1-5 total score indexed
episodic memory encoding. Category fluency (Animals) tested both language (semantic
retrieval) and executive abilities, while the Boston Naming Test (BNT) assessed language
(confrontation naming) specific to lexical-semantic retrieval abilities.
2.3 Cluster-Derived Diagnoses
We entered all participants clinically diagnosed as MCI at baseline into a cluster analysis
to address previously described high rates of MCI misclassification (Bondi et al., 2014; L. R.
Clark et al., 2013; Delano-Wood et al., 2009; Edmonds et al., 2015). First, participants diagnosed
as CN by ADNI and who remained CN throughout enrollment were designated the normal
reference group. Linear regression models predicted cognitive performance on six tests (Trails A,
Trails B, RAVLT free recall, RAVLT recognition, Animals fluency, Boston Naming Test) from
age and education within this normal reference group. Expected cognitive performance of MCI
participants based on their age and education was calculated using the resulting regression
coefficients from these models, and the expected scores were used along with the MCI
60
participants’ observed performance to calculate age- and education-adjusted z-scores. Finally, z-
scores were entered into a hierarchical cluster analysis using Ward’s method and a forced 4-
cluster solution. An emergent cluster-derived CN group was combined with the ADNI-diagnosed
CN group to form the CN group (n = 814), while the remaining three MCI sub-groups (amnestic,
dysnomic, and dysexecutive) formed the MCI group (n = 542).
2.4 Neuroimaging Acquisition & Analyses
T1-weighted structural images were collected from all ADNI participants using either a
3D-MPRAGE or 3D IR-SPGR sequence. Sequence parameters are available online
(http://adni.loni.usc.edu/methods/documents/mri-protocols/). MRI scans from 1.5T and 3T
magnetic field strengths were combined for analyses, an approach previously shown to be
feasible in voxel-based analyses of the ADNI dataset (Dutt et al., 2020; Jack et al., 2015;
Marchewka et al., 2014). Images were downloaded from the ADNI-LONI database, checked for
image quality, and manually reoriented in SPM12 within MATLAB
(http://www.fil.ion.ucl.ac.uk/spm/). Images were processed using the voxel-based morphometry
(VBM) pipeline via segmentation into tissue classes, creation of and alignment to a study-
specific DARTEL template, spatial normalization, modulation, and 8 mm smoothing (Ashburner
& Friston, 2000). Region-of-interest (ROI) masks for midbrain, pons, and whole brainstem were
derived from previously published atlases (Iglesias et al., 2015a; J. Mazziotta et al., 2001). We
used a pre-existing LC ROI mask that averaged peak voxel coordinates from studies that
localized the LC on functional MRI and neuromelanin-sensitive T1-weighted scans
(https://rcweb.dartmouth.edu/CANlab/brainstemwiki/doku.php/lc.html) (Astafiev et al., 2010;
Keren et al., 2009). To adjust for whole brain volume and facilitate comparisons, we divided
61
ROI volumes by total intracranial volume and multiplied them by 10
3
(midbrain, pons, whole
brainstem) or 10
4
(LC) (Whitwell et al., 2001).
2.5 CSF Biomarkers
MCI participants who were both amyloid-β (Aβ) and phosphorylated tau (pTau) positive
based on pre-established cutoffs (Hansson et al., 2018b) comprised the MCI due to AD group
(MCIAβ+pTau+, n = 202). Aβ-positive and pTau-positive CN participants comprised the preclinical
AD group (CNAβ+pTau+, n = 112). For detailed information on CSF biomarker quantification, see
Supplemental Methods.
2.6 Statistical Analyses
For all ROI volumes and cognitive measures, Pearson correlations were first examined to
confirm the presence or absence of zero-order relationships (Keith, 2014; Kraha et al., 2012),
followed by multiple linear regression models with TIV-adjusted brainstem ROI volume as
independent variable, neuropsychological test as dependent variable, and age, sex, education,
apolipoprotein-e4 (APOE-e4) carrier status, and MRI magnet strength as covariates. In order to
demonstrate that our substructural findings were independent of total brainstem volume changes,
we repeated analyses with an additional covariate for total brainstem volume. False discovery
rate (FDR) correction via the Benjamini-Hochberg procedure (Glickman et al., 2014) was
controlled at 0.10 to address multiple comparisons, similar to prior AD studies (Readhead et al.,
2018; Yew & Nation, 2017). Further information regarding statistical analyses is available in
Supplemental Methods.
For all significant multiple regressions, we conducted exploratory voxel-wise regression
analyses in SPM12 with neuropsychological test of interest as independent variable and
segmented white matter map as dependent variable, consistent with prior studies (Dutt et al.,
62
2020; Dutt, Binney, Heuer, Luong, Marx, et al., 2016; Nigro et al., 2014). An explicit mask of
the midbrain and pons constrained analyses to rostral brainstem regions, and age, sex, education,
APOE-e4 carrier status, MRI magnet strength, and total intracranial volume were included as
covariates. Voxel-wise analyses were repeated with an additional covariate for pons volume to
determine regional specificity. Results were examined at family-wise error (FWE)-corrected p <
0.05 and uncorrected p < 0.05.

3. Results
3.1 Demographic, Clinical, and Cognitive Variables
Descriptive statistics for demographic, cognitive, and neuroimaging variables are
displayed in Table 1.
3.2 Memory
Multiple linear regression models predicting memory performance (RAVLT trials 1-5,
delayed recall, and recognition) from ROI volumes were not significant within CN, MCI,
CNAβ+pTau+, or MCIAβ+pTau+.
3.3 Attention and Executive Function Measures
Within the overall MCI group, multiple linear regression models indicated smaller LC
volume predicted worse performance on Trails A (β = 0.13, p = 0.003; Fig. 1A), RAVLT trial 1
(β = 0.11, p = 0.015; Fig. 1B), and Animals fluency (β = 0.12, p = 0.009; Fig. 1C). When
including an additional covariate for whole brainstem volume, the relationship between LC
volume and Animals fluency (β = 0.29, p = 0.008) remained significant. When constraining
analyses to AD biomarker-positive MCIAβ+pTau+ participants, smaller LC volume predicted worse
63
performance on Animals fluency (β = 0.20, p = 0.007; Fig. 1D), and this finding remained
significant with an additional covariate for whole brainstem volume (β = 0.48, p = 0.007).
Within the overall MCI group, smaller midbrain volume predicted worse performance on
Trails A (β = 0.13, p = 0.004; Fig. 2A), Trails B (β = 0.10, p = 0.022; Fig. 2B), RAVLT trial 1 (β
= 0.11, p = 0.011; Fig. 2C), and Animals fluency (β = 0.11, p = 0.02; Fig. 2D), while smaller
whole brainstem volume (β = 0.10, p = 0.02) and smaller pons volume (β = 0.09, p = 0.031)
predicted worse performance on Trails A. When correcting for whole brainstem volume, smaller
midbrain volume predicted worse performance on Trails B (β = 0.28, p = 0.016) and RAVLT
trial 1 (β = 0.26, p = 0.026). Within AD biomarker-positive MCIAβ+pTau+ participants, midbrain,
pons, or whole brainstem volumes were not associated with neuropsychological testing.
Regression models predicting attention and executive function performance from ROI volumes
were not significant within the CN or CNAβ+pTau+ groups.
Brainstem-masked voxel-wise regressions relating brain volume to neuropsychological
tests within the overall MCI group were not significant at FWE-corrected p < 0.05. At a less
stringent threshold of uncorrected p < 0.05, worse Animals fluency correlated with smaller
volume of clusters overlapping the bilateral LC and right anterolateral midbrain (Fig 3; Table 2),
and a similar cluster emerged when including an additional covariate for total pons volume
(Supp Fig. 1, Supp Table 1).
3.4 Language
Multiple regression models predicting BNT performance from ROI volumes were not
significant within participant subgroups (CN, MCI, CNAβ+pTau+, MCIAβ+pTau+).

64
4. Discussion
The present study found that MCI patients with smaller midbrain and LC volumes
performed worse on tests of visual attention (Trails A), verbal attention (RAVLT trial 1),
executive function (Trails B), and category fluency (Animals), suggesting brainstem
substructural volumes may be related to underlying attention, processing speed, and executive
abilities. In MCI patients with biomarker-confirmed AD, the relationship between LC volume
and Animals fluency remained significant in the presence of prodromal AD pathology. Whole
brainstem, midbrain, pons and LC volumes were not associated with episodic memory (RAVLT
encoding, delayed recall, and recognition) or a confrontation naming test of language ability
(BNT), highlighting the specific association between brainstem substructure volumes and
measures of attention, processing speed, and executive function. This is the first study to report
associations between cognition and brainstem substructure volumes in MCI populations. We
provided preliminary evidence that well-documented relationships between the LC noradrenergic
system and attention (Aston-Jones et al., 1999; Mather et al., 2020; Sara, 2009) are detectible
when examining LC volume in the prodromal phase of AD.
The critical MCI phase preceding AD dementia may be a window when neural and
cognitive reserve in brainstem regions are integral to maintaining optimal cognitive function.
Within this prodromal period, we found that individuals with smaller midbrain and LC volumes
performed worse on tasks of executive function and visual and verbal attention. This echoes the
neuropathology literature demonstrating that individuals with greater pathological burden (i.e.,
greater subcortical tau deposition) exhibit diminished volumes of nuclei known to contain the
first signs of AD-related pathology and perform worse on corresponding cognitive tests (Heiko
Braak & Del Tredici, 2015; Grudzien et al., 2007). Alternatively, our findings could reflect that
65
greater premorbid LC volume supports better performance on attentional tasks. This supports a
previously theorized buffering role of the LC, due to its high lifetime noradrenergic turnover and
neuronal density, in protecting against the detrimental effects of accumulating AD-related
pathology (Clewett et al., 2015; Mather & Harley, 2016; Robertson, 2013). Although the exact
role of brainstem degeneration in cognitive dysfunction is not well-understood, degeneration of
the LC appears to be related to cognitive function in normal aging (Dahl et al., 2019; Langley et
al., 2020) and correlates with cognitive abilities and pathological protein accumulation in animal
models of AD (Chalermpalanupap et al., 2017; James et al., 2020; Kelberman et al., 2020). Of
note, we found attenuated brain-behavior relationships in the biomarker-confirmed MCI due to
AD group compared to the overall MCI group, likely due to the smaller sample size.
Interestingly, we did not observe relationships between brainstem structure and cognition in the
CN group, despite observable first signs of tau pathology in postmortem adult cognitively normal
samples (Heiko Braak & Del Tredici, 2015). We previously demonstrated that LC structural
abnormalities are observable using MRI with cognitively normal participants (Dutt et al., 2020);
however, the current findings suggest these individuals do not yet exhibit cognitive decline that
correlates with brainstem structure. Future studies will be necessary to clarify whether LC
function, as opposed to structure, in the early preclinical AD phase better correlates with
cognition.
The category fluency task was the cognitive test most strongly associated with midbrain
and LC volumes in MCI and biomarker-confirmed MCI due to AD. The category fluency task,
though often broadly categorized under the domain of language processing, also requires
executive abilities subserved by frontal-subcortical systems, including monitoring, shifting, and
inhibition (Shao et al., 2014). Furthermore, the category fluency task is similar to other tests
66
from the present study (e.g., Trails A & B) because it represents a timed test requiring adequate
attention and processing speed to complete successfully (Auriacombe et al., 2001; Baddeley &
Della Sala, 1996). Subcortical dementias experience specific impairments in attention, executive
function, and processing speed (Cummings, 1986; Salmon & Filoteo, 2007), and our findings
may similarly reflect subcortical contributions to cognitive impairment in prodromal AD.
The present study did not find relationships between brainstem volumes and episodic
verbal memory encoding, which contrasts with associations observed in studies of LC signal
intensity and memory encoding performance during verbal learning and immediate recall tasks in
older adults and AD populations (Dahl et al., 2019; Olivieri et al., 2019). Memory performance
on the immediate recall trial and across the encoding trials is linked to an individual’s ability to
engage attention during the presentation of stimuli and store items in working memory (Buckner
et al., 2000), and our study findings suggest a role of brainstem volume in attention and working
memory. Interestingly, relationships between brainstem volumes and measures of episodic verbal
memory abilities linked to integrity of medial temporal and hippocampal structures (Squire &
Zola-Morgan, 1991), were not observed. Our approach did not include hippocampal and medial
temporal structures, as these areas are well-studied and known to experience profound atrophy in
AD neurodegenerative processes (Jack et al., 1998; Mori et al., 1997). Our study was not
designed to determine if brainstem substructures are better predictors of cognition than medial
temporal and hippocampal regions, but rather to independently assess relationships between
brainstem substructure volumes and cognition. Our findings complement a growing body of
evidence supporting the role of LC structural integrity (as measured by neuromelanin-sensitive
T1-weighted imaging) and functional activity (as measured by fMRI) in diverse memory
processes when the stimuli involved are particularly salient or emotionally charged (Clewett et
67
al., 2018; Hämmerer et al., 2018; Jacobs et al., 2020; Liu et al., 2020). The relative neutrality of
word stimuli in the present study may partially explain why no relationships between brainstem
volumes and recall or recognition were observed, yet a recent diffusion-weighted imaging study
found an association between LC microstructure and RAVLT delayed recall performance in
healthy older adults (Langley et al., 2020). More multimodal neuroimaging work is needed in
MCI populations to disentangle the specific associations between brainstem substructures and
memory for stimuli of varying emotional arousal.
A study limitation is the use of segmented structural T1 images to estimate volumes of
deep brainstem nuclei, which inherently lack information regarding the boundaries of structures
such as the LC. Although prior studies have demonstrated an ability to detect structural
brainstem differences between disease groups with a similar method (Dutt et al., 2020), our
approach should be further validated in cohorts with MRI sequences specialized for assessment
of LC structure (Betts, Kirilina, et al., 2019). Another limitation is the racially homogeneous and
highly educated nature of the ADNI cohort, which limits the generalizability of our findings.
Future studies should examine diverse populations. Given the cross-sectional study design,
directionality of brainstem-cognition relationships cannot be determined. Other limitations
include the overlaid ROI approach to volume extraction as opposed to individual structural
segmentation and the high variability in individual subject history and instrumentation between
sites, all of which should be addressed in follow-up studies.

5. Conclusions
The present study examined relationships between brainstem volumes and cognition by
quantifying VBM-estimated brainstem substructure and LC volumes from structural MRI images
68
in individuals with normal cognition, biomarker-confirmed preclinical AD,
neuropsychologically-confirmed MCI, and biomarker-confirmed MCI due to AD. Midbrain and
LC volumes were associated with measures of attention, processing speed, and executive
function, but not with episodic memory performance or confrontation naming. A growing
number of studies have implicated subcortical brainstem structures as the earliest sites of AD-
related tau pathology, and MRI-measured volumes of these regions appear to correlate strongest
with tasks that require greater executive control and attention in the MCI phase preceding the
later onset of dementia.

Acknowledgements
We would like to thank the participants and their families, investigators, and researchers
from the ADNI study. Data used in preparation of this article were obtained from the ADNI
database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the
design and implementation of ADNI and/or provided data but did not participate in analysis or
writing of this report. A complete listing of ADNI investigators can be found at:
http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

69
Fig. 1. Regression analyses predicting cognition from locus coeruleus volume

Scatter plots and regression lines showing associations between TIV-normalized LC volume and
(A) Trails A performance, (B) RAVLT trial 1 performance, and (C) category fluency
performance in the MCI group (n = 542), and (D) between LC volume and category fluency
performance in the MCIAβ+pTau+ group (n = 202). Plotted data are unadjusted values, and red text
indicates b and p-value corresponding to multiple linear regression models with ROI volume as
independent variable, cognitive test as dependent variable, and age, sex, education, APOE-e4
carrier status, and MRI magnet strength as covariates. Abbreviations: Ab = amyloid-b, APOE-e4
= apolipoprotein e4, LC = locus coeruleus, MCI = mild cognitive impairment, pTau =
phosphorylated tau, RAVLT = Rey Auditory Verbal Learning Test, ROI = region of interest,
TIV = total intracranial volume

70
Fig. 2. Regression analyses predicting cognition from midbrain volume

Scatter plots and regression lines showing associations between TIV-normalized midbrain
volume and (A) Trails A performance, (B) Trails B performance, (C) RAVLT trial 1
performance, and (D) category fluency performance in the MCI (n = 542) group. Plotted data are
unadjusted values, and red text indicates b and p-value corresponding to multiple linear
regression models with ROI volume as independent variable, cognitive test as dependent
variable, and age, sex, education, APOE-e4 carrier status, and MRI magnet strength as
covariates. Abbreviations: Ab = amyloid-b, APOE-e4 = apolipoprotein e4, LC = locus coeruleus,
MCI = mild cognitive impairment, pTau = phosphorylated tau, RAVLT = Rey Auditory Verbal
Learning Test, ROI = region of interest, TIV = total intracranial volume

71
Fig. 3. Voxel-wise correlation between category fluency and locus coeruleus volume

Results of voxel-wise multiple regression correlating brain volume with category fluency
performance in the MCI (n = 542) group with covariates for total intracranial volume, age, sex,
education, APOE-e4 carrier status, and MRI magnet strength. (A) Significant clusters emerged
overlapping the bilateral locus coeruleus and right lateral midbrain at an uncorrected height
threshold of p < 0.05. (B) Significant clusters at p < 0.05 (orange) overlaid on an unthresholded
statistical map (red). Explicit mask comprising the midbrain and pons was applied to limit search
volume to rostral brainstem structures. Images are shown in neurological orientation. Text
indicates MNI coordinates of corresponding axial slices. Abbreviations: APOE-e4 =
apolipoprotein e4, MCI = mild cognitive impairment, MNI = Montreal Neurological Institute

72
Table 1. Descriptive statistics for demographic, cognitive, and neuroimaging variables

Means (SD) are reported for continuous variables unless otherwise noted. Biomarker-positive groups are subsets of
respective diagnostic groups. ROI volumes (LC, midbrain, pons, brainstem) were normalized via division by TIV.
Scores for Trails A, Trails B, and BNT were log-transformed and reflected. Abbreviations: Ab = amyloid-b, APOE-
e4 = apolipoprotein e4, BNT = Boston Naming Test, CN = cognitively normal, LC = locus coeruleus, MCI = mild
cognitive impairment, pTau = phosphorylated tau, RAVLT = Rey Auditory Verbal Learning Test, ROI = region of
interest, TIV = total intracranial volume

Total Sample Prodromal AD Subsets

CN MCI CNAβ+pTau+ MCIAβ+pTau+
Demographics
n 814 542 112 202
Age 73.49 (6.76) 73.54 (7.35) 74.75 (6.18) 73.61 (7.13)
Sex (M/F) 417/397 332/210 59/53 112/90
Education 16.29 (2.65) 15.85 (2.92) 15.90 (2.66) 15.98 (2.86)
APOE-e4 (0/1/2 e4) 536/246/32 249/221/72 39/61/12 57/104/41
MRI Scanner (1.5T/3T) 304/510 305/237 34/78 91/111
Cognitive Testing
Trails A -1.53 (0.14) -1.60 (0.17) -1.56 (0.14) -1.63 (0.17)
Trails B -1.91 (0.17) -2.06 (0.23) -1.97 (0.19) -2.08 (0.21)
RAVLT Trial 1 5.23 (1.78) 4.14 (1.41) 4.81 (1.62) 4.01 (1.38)
RAVLT Encoding 43.47 (10.42) 30.10 (8.14) 39.88 (9.56) 28.71 (7.39)
RAVLT Recall 7.26 (3.88) 2.16 (2.62) 6.11 (3.39) 1.55 (2.23)
RAVLT Recognition 13.05 (2.20) 8.93 (3.21) 12.97 (2.09) 8.58 (3.07)
Animals Fluency 20.13 (5.26) 15.73 (4.72) 19.22 (4.60) 15.46 (4.59)
BNT -0.37 (0.28) -0.65 (0.34) -0.42 (0.27) -0.67 (0.32)
Neuroimaging
TIV 1499.92 (146.93) 1518.99 (159.50) 1488.19 (146.29) 1505.68 (166.34)
LC 1.22 (0.12) 1.20 (0.12) 1.24 (0.13) 1.21 (0.11)
Midbrain 3.88 (0.31) 3.82 (0.31) 3.92 (0.30) 3.85 (0.30)
Pons 7.70 (0.75) 7.60 (0.75) 7.81 (0.81) 7.67 (0.73)
Brainstem 13.32 (1.18) 13.14 (1.20) 13.50 (1.23) 13.26 (1.17)
73
Table 2. MNI coordinates from voxel-wise correlation between category fluency and locus
coeruleus volume

A

Set-level Cluster-level Peak-level
p puncorr kE puncorr T x y z
0.032 0.983 19 0.023 1.99 8 -40 -24
0.994 4 0.036 1.79 -8 -40 -22
0.987 13 0.043 1.70 14 -21 -21

B
MNI x-range MNI y-range MNI z-range
Voxel-wise correlation with
Animals fluency
8 to -9 -38 to -41 -21 to -27
MCI < CN (Dutt et al., 2020) 8 to -8 -39 to -42 -21 to -28
AD < CN (Dutt et al., 2020) 9 to -6 -33 to -41 -17 to -26
Converters < Non-Converters
(Dutt et al., 2020)
8 to -8 -39 to -41 -21 to -26
LC mask (Keren et al., 2009) 9 to -9 -36 to -39 -18 to -33
LC mask (Betts et al., 2017) 9 to -9 -36 to -43 -15.5 to -37.5
LC mask (Dahl et al., 2019) 8 to -10 -29 to -42 -18 to -38
NC vs. AD peak coordinates (Ji
et al., 2020)
-6, -9 -36, -36 -24, -29

(A) Coordinates from voxel-wise multiple regression in MCI (n = 542) group regressing
category fluency onto brain volume with an explicit mask comprising the midbrain + pons and
covariates for total intracranial volume, age, sex, education, APOE-e4 carrier status, and MRI
magnet strength. (B) MNI coordinate range for significant clusters from present study and from
prior brainstem VBM studies and established locus coeruleus masks. Abbreviations: AD =
Alzheimer’s disease, CN = cognitively normal, kE = cluster size, LC = locus coeruleus, MCI =
mild cognitive impairment, NC = normal controls, MNI = Montreal Neurological Institute,
uncorr = uncorrected

74
Declarations
Funding
Author funding for this study was obtained through grants from the National Institutes of
Health (D.N., grant numbers R01AG060049, R01AG64228, P01AG052350, P50AG016573),
(M.M., grant number R01AG025340); the Alzheimer’s Association (D.N., grant number
AA008369); and the National Science Foundation (S.D., grant number DGE1418060). Data
collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging
Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI
(Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National
Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and
through generous contributions from the following: AbbVie, Alzheimer’s Association;
Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-
Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli
Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company
Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy
Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development
LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx
Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal
Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian
Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private
sector contributions are facilitated by the Foundation for the National Institutes of Health
(www.fnih.org). The grantee organization is the Northern California Institute for Research and
Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the
75
University of Southern California. ADNI data are disseminated by the Laboratory for
Neuroimaging at the University of Southern California.

Conflicts of interest/Competing interests
The authors have no disclosures or conflicts of interest to report.

Ethics approval
The present study was conducted in accordance with the Helsinki Declaration and approved at all
ADNI sites by local Institutional Review Boards.

Consent to participate
Written informed consent was obtained from all participants.

Consent for publication
Not applicable

Availability of data and material
All data used in the present study are publicly available via the ADNI website
(http://adni.loni.usc.edu/) and the ADNI-LONI Image & Data Archive
(https://ida.loni.usc.edu/login.jsp).

Code availability
Not applicable
76

Authors’ Contributions
The listed authors contributed to the present manuscript in the following ways: study
concept and design (SD, YL, MM, DAN), analysis and interpretation of data (SD, YL), statistical
analyses (SD), drafting the manuscript (SD), critical revision of the manuscript (YL, MM,
DAN), and final approval of and accountability for the manuscript (SD, YL, MM, DAN).

77
Supp. Fig. 1. Voxel-wise correlation between category fluency and locus coeruleus volume
corrected for pons volume

Results of voxel-wise multiple regression correlating brain volume with category fluency
performance in the MCI (n = 542) group with covariates for age, sex, education, APOE-e4
carrier status, MRI magnet strength, and total intracranial volume. Significant cluster emerged
overlapping the right locus coeruleus at an uncorrected height threshold of p < 0.05. Explicit
mask comprising the midbrain and pons was applied to limit search volume to rostral brainstem
structures. Image is shown in neurological orientation.

78
Supp. Table 1. MNI coordinates table for pons-corrected VBM analysis

Cluster-level Peak-level
puncorr kE puncorr T x y z
0.984 2 0.048 1.66 8 -40 -24

Coordinates represent finding from voxel-wise multiple regression in MCI (n = 542) group
regressing category fluency onto brain volume with an explicit mask comprising the midbrain +
pons and covariates for total intracranial volume, age, sex, education, APOE-e4 carrier status,
and MRI magnet strength. Abbreviations: kE = cluster size, MCI = mild cognitive impairment,
MNI = Montreal Neurological Institute, uncorr = uncorrected

79
Supplemental Methods
CSF Biomarkers
Levels of CSF amyloid-β (Aβ) 1-42 and phosphorylated tau (pTau) were quantified in
aliquots using the automated Roche Elecsys β-amyloid (1-42) CSF and Elecsys phosphotau
(181P) CSF electrochemiluminescene immunoassays at the UPenn Biomarker Research
Laboratory; detailed information is available online (http://adni.loni.usc.edu/methods/).
Participants were categorized as Aβ-positive and pTau-positive based on pre-established cutoffs
of 980 pg/mL and 21.8 pg/mL, respectively (Hansson et al., 2018b).
Statistical Analyses
Prior to analyses, distributions of continuous variables were checked for normality via
skewness and kurtosis. The Trails A, Trails B, and BNT variables had highly skewed
distributions and were corrected with log-transformation. Scores for Trails A and Trails B were
reflected to ensure consistent directionality across all neuropsychological tests, with higher
scores indicating better performance. BNT scores were reflected prior to log-transformation to
avoid undefined values. Outliers ± 3 standard deviations from the mean were identified and
analyses were run with and without these datapoints; none of the reported results were affected
by the removal of outliers, thus all datapoints were included.

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Chapter 4: Links between locus coeruleus MRI contrast and cerebral perfusion are moderated by
plasma Alzheimer’s biomarkers in older adults

Shubir Dutt, Shelby L. Bachman, Martin J. Dahl, Yanrong Li, Belinda Yew, Jung Y. Jang, Jean
K. Ho, Kaoru Nashiro, Jungwon Min, Hyun Joo Yoo, Aimee Gaubert, Amy Nguyen, Anna E.
Blanken, Isabel J. Sible, Anisa J. Marshall, Arunima Kapoor, John P. Alitin, Kim Hoang,
Alessandra Martini, Lorena Sordo, Elizabeth Head, Xingfeng Shao, Danny J. J. Wang, Mara
Mather, & Daniel A. Nation

81
Abstract
The locus coeruleus (LC) and its ascending noradrenergic projections innervate the
cerebrovasculature and moderate brain perfusion. Links between the locus coeruleus (LC) and
cerebral perfusion these systems have been demonstrated in animal models, but relationships
have not been well-studied in humans with widely available neuroimaging techniques. Our study
quantified associations between LC integrity and 1) cerebral perfusion and 2) cognition, as well
as the moderating effects of blood plasma Alzheimer’s disease (AD) biomarkers. Community-
dwelling older adults (N=66) received structural (T1-MPRAGE; T1-FSE) and functional (resting
pCASL) MRI scans to quantify rostral LC contrast ratios (LC-CR) and regional gray matter
perfusion. Subsets of participants underwent comprehensive neuropsychological testing (n=39)
and fasting blood draw to quantify levels of plasma AD biomarkers for amyloid via Aβ42/40
ratio (n=55) and tau via pTau181 (n=39). Greater integrity of the rostral LC was associated with
higher lateral and medial orbitofrontal perfusion but lower entorhinal and limbic perfusion.
Greater rostral LC was also associated with better verbal episodic memory performance, but not
with visual memory or executive functions. Plasma amyloid and tau levels moderated
relationships between rostral LC and perfusion such that links were attenuated in the presence of
greater pathology. Previously unstudied links between LC integrity and cerebral perfusion are
quantifiable in older adults and may represent a novel system to study in aging and AD research.

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1. Introduction
The locus coeruleus (LC), a small nucleus in the pons responsible for chiefly sourcing the
brain and spinal cord with norepinephrine, has emerged as a key region when studying
contributing factors to cognitive aging and Alzheimer’s disease (Betts, Kirilina, et al., 2019;
Ehrenberg et al., 2023). In normal aging trajectories, LC integrity is crucial for maintaining
optimal cognition (e.g., memory, attention), supporting sufficient cognitive reserve, and
regulating core physiological responses (e.g., arousal, sleep-wake cycles) and functions (e.g.,
blood pressure, heart rate) (Yuqing Chen et al., 2022; Mather & Harley, 2016). The LC is
additionally selectively vulnerable to Alzheimer’s disease (AD) related processes and
experiences early cell loss with corresponding adverse clinical outcomes (Eser et al., 2018;
Matchett et al., 2021; Parvizi et al., 2001). Greater focus has been placed on the LC in preclinical
studies of AD since its identification as the first site of pretangle tau pathology aggregation,
increasing its profile as a candidate site for the epicenter of tau-related AD pathogenesis (Heiko
Braak et al., 2011; Heiko Braak & Del Tredici, 2015).
Recent advances in MR imaging allow for structural and functional quantification of the LC
in vivo in concordance with cognitive function, brain metrics, and disease progression (Mather &
Harley, 2016). Specialized MRI sequences (e.g., T1-fast spin echo, magnetization transfer)
leverage the inherent paramagnetic properties of neuromelanin, a byproduct of catecholamine
synthesis in noradrenergic cells, and image them as hyperintense relative to surrounding tissue
(Liu et al., 2017; Sasaki et al., 2008). This resulting LC contrast has been validated as a proxy
measure of LC integrity and has been shown to spatially localize the LC (Keren et al., 2015) and
correlate with AD pathology and clinical diagnoses (Betts, Cardenas-Blanco, et al., 2019; Jacobs
et al., 2021). In only the last few years, these novel LC-MRI methods have revealed associations
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between the LC and multiple facets of cognition, behavior, and pathology in aging (Bachman et
al., 2020; Bachman, Nashiro, et al., 2022; Bell et al., 2022; Dahl et al., 2019, 2022; Elman et al.,
2021; Liu et al., 2019; Takahashi et al., 2015).
One line of research that remains understudied is the relationship between the LC and the
cerebrovasculature. Physiologically, the LC critically innervates the cerebrovasculature (Cohen
et al., 1997; Toussay et al., 2013), and animal models have demonstrated how selectively
lesioning the LC results in vascular pathological outcomes in the basal forebrain and remodeling
of the walls of the microvasculature (Kelly et al., 2019). Furthermore, vascular dysfunction has
been shown in large human studies to precede cognitive decline, brain atrophy, biomarker
abnormality of AD pathological proteins (Iturria-Medina et al., 2016), highlighting the possible
co-occurrence of cerebrovascular dysregulation and tau-related changes in the LC in the
preclinical phase of AD. Yet, human studies have not yet attempted to link the LC to the
cerebrovasculature. The present study aimed to demonstrate for the first time in humans whether
LC integrity (as measured by neuromelanin-sensitive MRI) is associated with regional cerebral
blood flow (as measured by perfusion MRI) in dementia-free older adults, as well as whether
associations exist between LC integrity and multiple cognitive domains. Additionally, we
examined if relationships change at varying levels of AD-associated proteins in blood plasma. To
our knowledge, this is the first study to date to 1) explore relationships between the LC and brain
perfusion in humans, 2) specifically examine potential relationships between LC MRI contrast
ratio and regional cerebral blood flow, and 3) explore how the LC may be related to blood
plasma biomarkers of Alzheimer’s disease.

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2. Methods

2.1 Participants & Study Design
Participants were recruited from two research studies conducted at the University of
Southern California (USC): the Vascular Senescence and Cognition study (VASC) and the
Emotion and Cognition (HRV) (EMO) study. The VASC study, an ongoing natural history
research program investigating how age-related changes in the cerebrovascular system contribute
to cognitive aging and dementia risk, recruited older adults ages 55-90 from the greater Los
Angeles community with no known history of dementia, stroke, traumatic brain injury with loss
of consciousness greater than 15 minutes, insulin-dependent diabetes, myocardial infarction,
major psychiatric or neurological disease, substance abuse leading to hospitalization, vitamin
B12 deficiency, or hypothyroidism. Participants in the EMO study were recruited as part of a
clinical trial investigating the effects of a heart rate variability biofeedback training intervention
on emotion regulation (Nashiro, Min, et al., 2022); data from EMO study participants were only
included from their initial pre-intervention visit, prior to any knowledge of the study intervention
or actual study intervention taking place. In this study, older adults ages 55-80 were recruited
from the greater Los Angeles community and were included if they had no known major
medical, neurological, or psychiatric conditions, no history of major heart disease, arrhythmia,
angina, or stroke, were not actively practicing regular biofeedback techniques, had no current use
of psychoactive drugs other than prescribed antidepressants or anti-anxiety medications, and no
MRI contraindications. Both studies were approved by the USC Institutional Review Board and
all study participants provided written informed consent.
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In the present study, participants who had available and usable MRI data from the T1-
MPRAGE, T1-FSE, and pCASL-ASL sequences were included (N = 66). Initially, there were 72
total participants with available data from all three MRI sequences; 2 participants were excluded
due to unusable pCASL data (e.g., excessive motion, significant artifacts, pipeline processing
errors) and 4 participants were excluded due to unusable T1-FSE data (e.g., excessive motion,
significant artifacts).

2.2 Neuroimaging Measures
All participants from both studies were scanned on the same 3-Tesla Siemens Prisma
scanner at the USC Dana and David Dornsife Cognitive Neuroimaging Center.

2.2.1 T1-MPRAGE acquisition and processing
For the EMO cohort, a T1-weighted MPRAGE sequence was acquired with the following
parameters: TR = 2300 ms, TE = 2.26 ms, TI = 1060 ms, slice thickness = 1.00 mm, flip angle =
9°, field of view = 256 mm. For the VASC cohort, a T1-weighted MPRAGE sequence was
acquired with the following parameters: TR = 2300 ms, TE = 2.98 ms, TI = 900 ms, slice
thickness = 1.20 mm, flip angle = 9°, field of view = 256 mm. For generation of grey matter
mask images used in later pCASL processing, T1-MPRAGE images were processed with the
voxel-based morphometry (VBM) pipeline in SPM12 (https://www.fil.ion.ucl.ac.uk/spm/) within
MATLAB; processing steps have been previously described in detail and included segmentation
into distinct tissues classes using SPM12’s unified segmentation procedure, spatial
normalization, modulation, and smoothing (Ashburner & Friston, 2000, 2005, 2009).

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2.2.2 ASL acquisition and processing
For the EMO cohort, a 3D pseudo-continuous arterial spin labeling (pCASL) sequence
was acquired with the following parameters: TR = 3880 ms, TE = 36.48 ms, slice thickness =
3.00 mm, flip angle = 120°, field of view = 220 mm, bolus duration = 700 ms, labeling time =
1.517 s, post-labeling delay = 1.8 s, RF block number = 82, RF block duration = 18500 µs, 1 M0
acquisition, 10 total tag and control acquisitions (5 tag-control pairs). For the VASC cohort, a 3D
pCASL sequence was acquired with the following parameters: TR = 5000 ms, TE = 36.3 ms,
slice thickness = 3.42 mm, flip angle = 120°, field of view = 240 mm, bolus duration = 700 ms,
labeling time = 1.517 s, post-labeling delay = 2 s, RF block number = 82, RF block duration =
18500 µs, 1 M0 acquisition, 30 total tag and control acquisitions (15 tag-control pairs).
Raw pCASL scans were pre-processed using the ASL Perfusion MRI Data Processing Toolbox
(ASLtbx) based in SPM12 and MATLAB (Wang, 2012; Wang et al., 2008). ASL pre-processing
involved motion correction, subject-specific co-registration to T1-MPRAGE images, spatial
smoothing with a 6mm full-width at half-maximum Gaussian kernel, and tag-control subtraction,
resulting in 5 tag-control pairs for subjects in the EMO cohort and 15 tag-control pairs for
subjects in the VASC cohort. Within each subject, tag-control pairs were averaged to generate a
single map of CBF in mL/100 tissue/min. Mean CBF maps were warped to MNI space,
thresholded between 10-150 mL/100g/min to exclude values outside of the expected
physiological range for grey matter, and visually inspected for quality control (Bangen et al.,
2014; A. L. Clark et al., 2020; Nation et al., 2013; Yew et al., 2022). Images were partial volume
corrected with subject-specific grey matter masks from the aforementioned SPM12 pipeline with
additional exclusion of the cerebellum to limit analyses to CBF in the grey matter (Petr et al.,
2018; Yew et al., 2022).
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We selected a priori regions-of-interest (ROIs) that were implicated in dementia risk,
known to experience age-related changes in perfusion, and established as functionally connected
to the LC; ROIs included the amygdala, entorhinal cortex, hippocampus, lateral orbitofrontal
cortex (latOFC), medial orbitofrontal cortex (medOFC), posterior cingulate cortex (PCC), and
precuneus, as well as a control region where CBF is relatively unaffected by aging (precentral
gyrus) (Jacobs et al., 2018; Mattsson et al., 2014; Staffaroni et al., 2019; Yew & Nation, 2017).
ROI masks were drawn from the AAL3 atlas and WFUPickAtlas and used to extract regional
CBF values, while subject-specific whole brain grey matter masks were used to extract whole
brain grey matter (Maldjian et al., 2003; Rolls et al., 2020). CBF values for individual ROIs were
normalized to whole brain grey matter CBF via division to account for global perfusion and
increase sensitivity (Aslan & Lu, 2010; Y. Chen et al., 2011).

2.2.3 T1-FSE Acquisition and Processing
For both the EMO and VASC cohorts, a 2D T1-fast spin echo (T1-FSE) scan was
acquired with a field-of-view perpendicular to the brainstem capturing the entire rostrocaudal
extent of the pons with the following parameters: TR = 750 ms, TE = 12 ms, slice gap = 1 mm,
flip angle = 120°, bandwidth = 287 Hz/pixel, voxel size = 0.43 x 0.43 x 2.5 mm, 11 total axial
slices. We used a semi-automated approach to delineate the LC on FSE scans for the purpose of
computing LC contrast ratios, which has been previously fully described (Bachman, Nashiro, et
al., 2022; Dahl et al., 2022). All participants’ FSE scans were warped to MNI152 0.5mm linear
standard space processes within Advanced Normalization Tools (ANTs) Version 2.3.4 (Avants
et al., 2009). First, all FSE and MPRAGE scans were resampled to twice their native resolution.
Next, MPRAGE scans from the VASC study were pooled to create a whole-brain template. We
88
note that this step was performed using MPRAGE scans from the VASC study only, due to
MPRAGE sequences from the different studies having different resolutions. In order to move
scans from both studies to a common space, at this step, resampled MPRAGE scans from the
EMO study were co-registered to the space of the MPRAGE template created from the VASC
study. Then, all resampled FSE scans from both studies were co-registered to whole-brain
template-co-registered MPRAGE scans, and resulting scans were pooled to generate an FSE
template. Following co-registration of the FSE template to the MPRAGE template and the
MPRAGE template to MNI 0.5mm linear space, transformations from previous steps were used
to warp resampled FSE scans and the FSE template to MNI 0.5mm linear space.
ANTs routines and parameters used for LC delineation are described in detail in Dahl et
al. (2022), with the following exceptions. For construction of the initial (as opposed to the full)
whole-brain template, we used a subset of 29 MPRAGE scans from the VASC study, all of
which had `qoffset_x`, `qoffset_y`, and `qoffset_z` values within 1 standard deviation of the
mean across all MPRAGE scans. This resulted in an initial template with high spatial alignment
which was then used for alignment of all scans during construction of the whole-brain template.
To extract intensity values from the LC, a previously-created, binarized consensus mask
was applied onto each participant’s FSE scan in standard space (Dahl et al., 2022). The peak LC
intensity value across all slices in the z-dimension (rostrocaudal axis) in the masked region was
extracted for each hemisphere separately. We also applied the central pontine reference region
map from Dahl et al. (2022) as a mask on each participant’s FSE scan in standard space and,
within the masked region, extracted the peak intensity value across all slices in the z-dimension.
LC contrast ratio (LC-CR) for each hemisphere was then calculated from peak intensities as a
ratio (Liu et al., 2017):
89

LC contrast ratio = peak(LC) - peak(reference)
peak(reference)

Due to spatial differences in age-related LC neuronal loss (Manaye et al., 1995), we also
computed values of rostral and caudal LC-CR: Peak LC and reference intensities in each slice in
the z-dimension were first used to calculate values of left and right LC contrast for each z-slice,
and left and right LC contrast values were then averaged for each z-slice. We then identified
ranges of z-slices corresponding to rostral and caudal clusters where we previously identified age
differences in associations between LC-CR and cortical thickness and episodic memory
(Bachman et al., 2020; Dahl et al., 2019). For each participant, contrast ratios were averaged
over these rostral and caudal clusters of z-slices to obtain rostral and caudal LC-CR values.
Primary analyses used the rostral LC-CR values due to the well-established role of the rostral LC
in aging and AD-related processes (Bell et al., 2022; Betts et al., 2017; Dahl et al., 2019; Liu et
al., 2019; Manaye et al., 1995; Zarow et al., 2003).

2.3 Plasma Biomarkers
A subset of participants (n = 60) underwent fasting blood draw, and samples were collected in
EDTA tubes to quantify levels of the plasma AD biomarkers Aβ1-42, Aβ1-40, and pTau181. The
following Quanterix assays were used for AB and pTau samples respectively: Simoa Neuroloy
3-Plex A Advantage Kit and Simoa pTau-181 Advantage V2 Kit. We used values for Aβ42/40
ratio (n = 56) and pTau181 in pg/ml (n = 35) for subsequent analyses.

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2.4 Neuropsychological Testing
Participants from the VASC study (n = 39) received a standardized comprehensive
neuropsychological testing battery to assess several cognitive domains (e.g., episodic memory,
executive function, language, visuospatial function). The following cognitive domains and
respective test scores were included in primary analyses: verbal episodic memory (Rey Auditory
Verbal Learning Test (RAVLT) trials 1-5 learning, immediate free recall, delayed free recall,
delayed recognition), visual episodic memory (Visual Reproduction immediate free recall,
delayed free recall, delayed recognition), and executive function (Trail Making Test part B, F-A-
S phonemic fluency, animals category fluency).

2.5 Statistical Analyses
All variables of interest were screened for normality of distribution prior to analyses
using the Shapiro-Wilk test (n < 50) or Kolmogorov-Smirnov test (n >= 50) (Mishra et al.,
2019). For primary analyses, multiple linear regression models included rostral LC-CR as
independent variable and CBF ROI or neuropsychological test performance as dependent
variable. Covariates were included for age, due to the previously established effect of age on LC
contrast (Bachman et al., 2020; Liu et al., 2019, 2020), and study, due to minor variations in T1-
MPRAGE and pCASL sequences across the two studies. Age and study were not included in
models comparing LC contrast and cognition due to a) cognitive data were age-adjusted Z-scores
based on normative data and b) only one study having cognitive data available.
In order to investigate the moderating effects of plasma AD biomarkers, subsequent
regression models included additional terms for plasma AD biomarker concentration (Aβ42/40
ratio or pTau181) and a rostral LC contrast * plasma AD biomarker interaction term. Interactions
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were probed using the Johnson-Neyman technique to identify regions of significance. All
statistical analyses were conducted in IBM SPSS Statistics version 27, R project 4.2.1, and
RStudio. Post hoc exploratory analyses examined whether relationships were present separately
in men and women for all significant findings, as well as whether sex moderated any significant
relationships (Shapiro et al., 2021).

3. Results
Participant demographic and clinical data are presented in Table 1.
3.1 LC-CR and Regional Perfusion
In the overall sample (N = 66), higher rostral LC-CR was associated with greater CBF in
latOFC (β = .23, 95% CI [.05, .41], p = .015) and medOFC (β = .24, 95% CI [.004, .47], p =
.047) (Figure 1A-B; Supplementary Table 1). In limbic regions, higher rostral LC-CR was
associated with lower CBF in entorhinal cortex (β = -.21, 95% CI [-.39, -.02], p = 0.032) and the
amygdala (β = -.26, 95% CI [-.51, -.01], p = 0.046) (Figure 1C-D; Supplementary Table 1).
Rostral LC-CR was not linked to CBF in the whole brain, hippocampus, posterior cingulate,
precuneus, or precentral gyrus (Supplementary Table 2).
3.2 LC-CR and Cognition
In the subset of participants with neuropsychological testing (n = 39), higher rostral LC-
CR was associated with better performance on the 5 RAVLT learning trials (β = .43, 95% CI
[.13, .73], p = .014), immediate free recall (β = .43, 95% CI [.12, .73], p = .007), delayed free
recall (β = .39, 95% CI [.09, .70], p = .013), and RAVLT recognition (β = .39, 95% CI [.08, .69],
p = .016) (Figure 2A-D; Supplementary Table 1). Rostral LC-CR was not associated with tests of
visual episodic memory or executive function (Supplementary Table 3).
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3.3 Moderating Effects of pTau181 and Aβ42/40 ratio
In cases where rostral LC significantly predicted regional perfusion or cognition, we
performed moderation analyses to test the moderating effects of plasma biomarker levels of
amyloid or tau. In participants with available plasma pTau181 data (n = 35), pTau181 moderated
the relationship between rostral LC-CR and latOFC CBF such that increasing pTau181 (i.e.,
increasing pathology) attenuated the significant positive association between rostral LC-CR and
latOFC CBF (pTau181*rostral LC-CR interaction β = -.52, 95% CI [-.87, -.18], p = .0043)
(Figure 3A; Supplementary Table 4). Johnson-Neyman analyses indicated that the positive
association between rostral LC and latOFC CBF was no longer significant at plasma pTau181
levels higher than 2.27 pg/mL. Similarly, increasing pTau181 attenuated the significant positive
association between rostral LC-CR and medOFC CBF (pTau181*rostral LC-CR interaction β = -
.52, 95% CI [-.87, -.18], p = .0044) (Figure 3B; Supplementary Table 4), with the Johnson-
Neyman technique indicating that the association was no longer significant at pTau181 levels
higher than 2.29 pg/mL. For limbic regions, pTau181 moderated the relationship between rostral
LC-CR and entorhinal CBF such that increasing pTau181 (i.e., increasing pathology) attenuated
the significant negative association between LC-CR and entorhinal CBF (pTau181*rostral LC-
CR interaction β = .64, 95% CI [.28, .99], p = .001) (Figure 3C; Supplementary Table 4).
Johnson-Neyman analysis indicated that the association was no longer significant at pTau181
levels higher than 2.04 pg/mL.
In participants with available plasma amyloid data (n = 55), Aβ42/40 ratio moderated the
relationship between rostral LC-CR and amygdala CBF such that decreasing Aβ42/40 ratio (i.e.,
increasing pathology) attenuated the significant negative association between rostral LC-CR and
amygdala CBF (Aβ42/40 ratio*rostral LC-CR interaction β = -.33, 95% CI = [-.61, -.06], p =
93
.0196) (Figure 3D; Supplementary Table 4). Johnson-Neyman analysis revealed a nonsignificant
association at Aβ42/40 ratio lower than 0.06. Plasma biomarker concentrations did not moderate
observed relationships between rostral LC and verbal episodic memory.
3.4 Post hoc exploratory analyses
LC-perfusion relationships were not observed when limiting analyses to women-only and
men-only subgroups, while rostral LC-CR was associated with all verbal episodic memory
measures in women but not in men (Supplementary Tables 5-6). Sex did not significantly
moderate observed relationships between rostral LC and regional perfusion or rostral LC and
cognition (Supplementary Table 7).

4. Discussion
The present study found that rostral LC integrity was differentially linked to limbic and
cortical perfusion, and the strengths of these associations were altered in the presence of
accumulating AD pathology as measured by blood plasma. Greater rostral LC integrity was
linked to higher frontal perfusion in the medial and lateral orbitofrontal cortices but not in the
presence of elevated plasma tau. Greater rostral LC integrity was linked to lower limbic
perfusion in the amygdala and entorhinal cortex but not in the presence of elevated levels of
plasma amyloid and tau, respectively. This represents the first study to investigate and
demonstrate a direct association between cerebral perfusion and an in vivo measure of LC
integrity in humans. We provide preliminary evidence that previously documented associations
between the structure and function of the LC and the cerebrovasculature in animal studies (Bekar
et al., 2012; Kelly et al., 2019; Toussay et al., 2013) is quantifiable in humans with widely used
neuroimaging techniques. Additionally, we add to a growing body of literature emphasizing the
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utility of blood-based biomarkers in tracking preclinical dementia processes in aging populations
(Fandos et al., 2017; Janelidze et al., 2020; Vergallo et al., 2019), with the added insight that
plasma markers of AD pathology moderate the relationship between LC integrity and
cerebrovascular function.
Analyses of cognitive data revealed that greater rostral LC integrity was related to better
verbal episodic memory performance but not visual episodic memory or executive functions.
This complements prior findings of rostral LC integrity correlating with verbal list learning
(Dahl et al., 2019) with added insight that the LC may also support better immediate and delayed
recall and recognition performance. This also converges with diffusion MRI studies linking the
LC and verbal memory recall (Langley et al., 2020). Interestingly, we did not observe
associations between rostral LC integrity and measures of executive function or visual memory,
suggesting a specific link with verbally mediated learning processes.
The preclinical phase of Alzheimer’s disease, prior to cognitive symptoms or biomarker
abnormality, represents a critical window when therapeutic interventions can target proteins to
slow or stop the eventual progression of AD. As hyperphosphorylated tau accumulates in the LC
during this early asymptomatic period (Heiko Braak et al., 2011; Heiko Braak & Del Tredici,
2015), the cerebrovasculature experiences subtle alterations that confer dementia risk (Iturria-
Medina et al., 2016). Thus, the LC-NE system and the cerebrovasculature represent two potential
targets for intervention and treatment due to their well-established early role in dementia risk.
Amongst dementia-free older adults with varying cognitive abilities, we found that greater LC
integrity is linked to both frontal hyperperfusion and limbic hypoperfusion. The LC’s
noradrenergic supply acts at several cortical cellular targets (e.g., astrocytes, pyramidal neurons,
interneurons), ultimately facilitating vasodilation and vasoconstriction of arterioles and
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capillaries and modulation of regional perfusion (Carmignoto & Gómez-Gonzalo, 2010; Cohen
et al., 1997; Mulligan & MacVicar, 2004; Paspalas & Papadopoulos, 1996; Séguéla et al., 1990).
More recently, the LC has been implicated in regulation of blood-brain barrier via action at
pericytes, offering another potential mechanism explaining noradrenergic control over cerebral
blood flow (Kelly et al., 2019; Korte et al., 2023). Although our study did not probe specific
cellular or molecular mechanisms, we provide preliminary in vivo evidence that the
noradrenergic and cerebrovascular systems are interconnected in aging humans by demonstrating
that the integrity of an individual’s LC as measured by neuromelanin-sensitive MRI tracks
regional perfusion alterations in AD risk regions.
We unexpectedly found that LC integrity was positively associated with frontal perfusion
but negatively associated with limbic perfusion. LC-mediated noradrenergic innervation of the
microvasculature optimizes functional hyperemia based on demand by simultaneously inducing
vasodilation and vasoconstriction across brain regions (Bekar et al., 2012), and our regional
findings may reflect this propensity of the LC to balance blood flow distribution to frontal versus
limbic regions in a pre-disease state based on demand. Amongst dementia-free older adults, a
structurally intact LC (as measured by higher LC-CR) and corresponding adequate noradrenergic
supply to the vessels is tied to a resting brain perfusion pattern whereby frontal regions require
higher compensatory perfusion. This mirrors well-established alterations in perfusion, glucose
metabolism, and cerebrovascular burden in the frontal lobes in normal aging (Aghakhanyan et
al., 2022; Gunning-Dixon et al., 2009; Jagust, 2013; Zhang et al., 2018) and posits that the LC
may be supplying these regions with compensatory blood supply preclinically. In contrast, limbic
regions, which are not yet affected by AD-related pathological processes, may not require similar
levels of supportive perfusion and perhaps experience diverted blood flow in favor of frontal
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regions. In animal models, selective lesioning of the LC alters microvasculature wall structures
and compromises blood-brain barrier integrity (Kelly et al., 2019). The present study did not
directly alter the LC in humans but nevertheless demonstrates that a naturally less robust LC, as
measured by lower contrast ratio, is linked to lower frontal perfusion and higher entorhinal
perfusion. In individuals with lower LC structural integrity and possible noradrenergic
dysfunction, known to occur preclinically and throughout AD pathogenesis (Weinshenker, 2008,
2018), greater perfusion in AD risk regions like the entorhinal cortex may represent a
compensatory phenomenon previously observed in cognitively unimpaired individuals with
elevated AD risk (Alsop et al., 2008; Dai et al., 2009; Fleisher et al., 2009; Thomas et al., 2021;
Wierenga et al., 2012). At higher levels of LC integrity and sufficient noradrenergic supply,
brain perfusion patterns resemble those at lower risk, with an absence of limbic hyperperfusion.
Further exploration of mechanisms underpinning discrepant perfusion patterns will be needed to
fully characterize how the LC is supporting regional perfusion in aging.
Associations between LC integrity and regional perfusion were attenuated in the presence
of heightened plasma AD biomarkers, potentially representing a shift in the LC’s optimal
distribution of regional perfusion in response to aggregating tau and amyloid pathology. In the
presence of greater tau pathology (i.e., higher plasma pTau181), the LC may shift to supporting
higher perfusion in limbic regions, as lower LC integrity in cognitively unimpaired subjects is
associated with greater entorhinal tau deposition (Jacobs et al., 2021). The LC may thus
preferentially allocate available noradrenergic innervation of vessels to the entorhinal cortex, due
to the spread of tau pathology to this region and divert resources away from the frontal cortex
leading to the observed negative association with frontal regions. Accrual of pathological tau
precursors in this early preclinical phase may ultimately be co-occurring with early vascular
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abnormalities (Giorgi et al., 2020). One possible interpretation of our findings is that LC
integrity and brain perfusion maintain a strong link in non-pathological aging, with differentially
higher and lower blood flow distribution to frontal versus limbic regions based on need (i.e.,
lower blood flow needed in limbic regions when at relatively lower risk for dementia). As AD-
related amyloid and tau pathology accumulate in the brain, the LC’s ability to optimally regulate
and distribute blood flow based on regional demand may be disrupted, reflected by our plasma
biomarker moderation analyses.
In our study, LC integrity was linked to verbal episodic memory but not visual memory
or executive functions, consistent with prior structural and functional studies tying LC integrity
to verbal memory processes (Ciampa et al., 2022; Dahl et al., 2019, 2022; James et al., 2020;
Langley et al., 2020, 2022). Much of the prior work in this field has used verbal list-learning
tasks as a singular measure of memory performance, and we further clarified that rostral LC
integrity may be uniquely associated with verbal but not visual memory performance. It is not
well-understood what aspects of the LC are more relevant for verbal versus visual learning and
may relate to the relatively more arousing nature of verbally presented stimuli (i.e., words that
may hold emotional significance to participants) versus visual stimuli (i.e., simple line
drawings), as well as the relatively more stressful nature of a list-learning task that may
preferentially engage the LC (Mather et al., 2016; Mather & Harley, 2016; Sara & Bouret, 2012).
Verbal memory may also be more affected by age than visual memory (Shaw et al., 2006),
offering another explanation for why age-related LC alterations track verbal learning alone,
though verbal versus visual memory trajectories in aging are mixed (Park et al., 2002; Salthouse,
2003). A more plausible explanation may relate to the nature of the memory tasks themselves, as
the verbal list-learning task involves repeated exposure to the same stimuli and may engage LC-
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involved attentional resources more readily and better represent encoding and consolidation
processes compared to the visual memory task that involves a single exposure to each stimulus.
Additional studies are needed to distinguish the specificity of LC integrity in supporting verbal
versus visual episodic memory.
The absence of relationships between LC integrity and frontally-mediated executive
functions was unanticipated, as prior studies using both LC contrast quantification (Eckert et al.,
2023) and volumetric LC measures (Dutt et al., 2021) have demonstrated associations with
executive function and attention in aging. Methodological differences in rostral LC delineation
and neuropsychological test instrumentation may partially explain our divergent results, and
future studies should clarify links between LC integrity and domain-specific cognition in large,
well-characterized samples. Additionally, the present sample consisted of older adults free of
dementia, and it may be that only when looking specifically in individuals with prodromal AD
pathology and objective memory dysfunction does LC volume begin to relate more broadly to
cognitive functions apart from memory (Dutt et al., 2021). LC metrics as captured by
neuromelanin-sensitive MRI in the present study versus LC quantification on traditional
structural MRI (Dutt et al., 2020, 2021; Ji et al., 2020; Lee et al., 2015; Plini et al., 2021) or
diffusion MRI (Langley et al., 2020, 2022; Porat et al., 2022; Solders et al., 2022) are also likely
characterizing different aspects of LC integrity, and future studies incorporating multimodal LC
metrics should focus on specific neuropsychological correlates of age-related LC decline. Taken
together, additional studies of LC multimodal imaging may provide more insight into the specific
mechanisms underlying LC-cognition links.
The present study was limited by its cross-sectional and correlational approach and
cannot fully speak to mechanisms underlying the links between LC integrity and cerebral
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perfusion. Our group’s prior work has developed in vivo methods of inducing hypocapnia and
hypercapnia with concurrent perfusion imaging (Yew et al., 2022) and future studies may
examine whether individuals with varying levels of LC integrity experience altered
cerebrovascular reactivity. The present study imaged the LC with 3T MR, but high-resolution 7T
MR imaging of the LC results in better spatial resolution and more fine-grained characterization
of LC structure (Jacobs et al., 2018; Liebe et al., 2022; Priovoulos et al., 2018; Ye et al., 2021,
2022), which should be used in conjunction with functional imaging to better characterize LC-
vascular relationships. Noradrenergic PET methods combined with neuromelanin-sensitive MRI
could also increase sensitivity and clarify the specificity of LC-cerebrovascular associations, as
noradrenergic PET has been shown to detect LC-related memory performance and
neuropsychiatric symptoms (Ciampa et al., 2022; Parent et al., 2022). Studies involving direct
modulation of the LC-noradrenergic system and quantification of vascular outcomes are needed,
as noradrenergic drugs have been increasingly highlighted as a potential alternative treatment for
AD-related dementia (M. C. B. David et al., 2022; Levey et al., 2022). As the LC has established
links to the BBB via action at pericytes (Korte et al., 2023), the potential link between the LC
and BBB should also be explored further in humans using novel DCE-MRI to determine whether
alterations in BBB permeability may relate to LC integrity and explain the LC-vascular
connection (Montagne et al., 2016, 2020; Nation et al., 2019). Finally, in addition to studies
focused on underlying mechanisms, the approach in the present study should be applied
longitudinally and in well-characterized clinical populations of varying disease severity to
further elucidate the degree to which AD biomarkers affect the LC’s regulation of cerebral
perfusion.

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5. Conclusions
We quantified associations between LC integrity via neuromelanin-sensitive MRI and
cerebral perfusion in older adults and observed attenuated relationships in the presence of
growing plasma-derived AD pathology. Strength of LC-perfusion associations may represent an
important preclinical marker of vascular health and the brain’s resilience against accruing AD
pathology. Early tau accumulation in the LC may have specific vascular consequences leading to
altered brain blood flow and cognitive outcomes. Study findings highlight the importance of
studying the LC and the cerebrovasculature in aging and AD and inform future interventions
targeting subcortical and vascular systems.

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Table 1. Participant Characteristics and Demographic Data

N 66
Age 67.98 (8.18)
Females, N (%) 46 (70%)
Race/Ethnicity, N (%)
White 42 (64%)
Black or African American 8 (12%)
Hispanic or Latino/a/x 11 (17%)
Asian 5 (7%)
Study (VASC/EMO) 39/27
Rostral LC-CR 0.002 (0.068)
GM CBF (ml/100g/min)
Whole brain 44.58 (9.09)
Entorhinal 44.17 (10.87)
Hippocampus 38.89 (8.89)
Amygdala 38.81 (8.91)
PCC 56.95 (13.74)
Precuneus 48.20 (10.30)
LatOFC 52.36 (11.51)
MedOFC 43.29 (10.86)
Precentral 46.24 (10.80)
N 55
Plasma Aβ42/40 (ratio) 0.06 (0.01)
N 35
Plasma pTau181 (pg/mL) 2.08 (1.07)
N 39
RAVLT T1-5 Learning (Z) 1.23 (1.67)
RAVLT Immediate Recall (Z) 0.41 (1.51)
RAVLT Delayed Recall (Z) 0.75 (1.77)
RAVLT Recognition (Z) -0.53 (2.20)
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Figure 1. Regression analyses demonstrating links between regional perfusion and rostral LC-
CR. Scatter plots, regression lines, and 95% confidence intervals (shaded blue) show
associations between rostral LC-CR and (A) lateral OFC perfusion, (B) medial OFC perfusion,
(C) amygdala perfusion, and (D) entorhinal perfusion in the overall cohort (n = 66). Perfusion
values represent regional CBF in ml/100g tissue/min divided by whole brain CBF. Reported
statistics correspond to regression coefficient and p-value for rostral LC-CR predictor in multiple
regression models controlling for age and study. Abbreviations: CBF = cerebral blood flow, CR
= contrast ratio, LatOFC = lateral orbitofrontal cortex, LC = locus coeruleus, MedOFC = medial
orbitofrontal cortex, OFC = orbitofrontal cortex

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Figure 2. Regression analyses demonstrating links between verbal episodic memory performance
and rostral LC-CR. Scatter plots, regression lines, and 95% confidence intervals (shaded blue)
show associations between rostral LC-CR and (A) RAVLT trials 1-5 total performance (B)
RAVLT immediate free recall, (C) RAVLT delayed free recall, and (D) RAVLT recognition in
the subset of participants with neuropsychological test data (n = 39). RAVLT memory scores
represent age-adjusted Z-scores. Reported statistics correspond to regression coefficient and p-
value for rostral LC-CR predictor in regression models. Abbreviations: CR = contrast ratio, LC =
locus coeruleus, RAVLT = Rey Auditory Verbal Learning Test

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Figure 3. Associations between regional perfusion and rostral LC-CR displayed at varying levels
of plasma AD biomarkers. Scatter plots demonstrate how the association between rostral LC and
(A) latOFC CBF is moderated by plasma pTau181, (B) medOFC CBF is moderated by plasma
pTau181, (C) entorhinal CBF is moderated by plasma pTau181, and (D) amygdala CBF is
moderated by Aβ42/40 ratio. Red, yellow, and blue lines represent associations at 1 SD below
the mean plasma biomarker level, at the mean plasma biomarker level, or 1 SD above the mean
plasma biomarker level as defined by figure legends. Reported statistics correspond to regression
coefficient and p-value for the plasma biomarker*LC-CR interaction term in multiple regression
models with rostral LC-CR as predictor, regional CBF as outcome, covariates for continuous
covariates for plasma biomarker levels and age, and a plasma biomarker*LC-CR interaction
term.

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Supplementary Table 1. Regression models predicting regional perfusion from LC-CR in the
overall sample (n=66) and predicting cognition from LC-CR in a subset (n=39). Each row
represents the standardized coefficient, 95% CI, t-value, and p-value for the listed predictor, with
covariates included for age and study.

Predictor Outcome β 95% CI t p
Regional Perfusion (n = 66)
Rostral LC Amygdala CBF -.255 [-.505, -.005] -2.039 .046
Rostral LC Entorhinal CBF -.205 [-.392, -.018] -2.193 .032
Rostral LC Lateral OFC CBF .227 [.045, .410] 2.494 .015
Rostral LC Medial OFC CBF .236 [.004, .469] 2.03 .047

Cognition (n = 39)
Rostral LC RAVLT Trials 1-5 .430 [.130, .731] 2.899 .006
Rostral LC RAVLT Immediate Recall .426 [.124, .727] 2.863 .007
Rostral LC RAVLT Delayed Recall .394 [.087, .700] 2.605 .013
Rostral LC RAVLT Recognition .385 [.077, .692] 2.535 .016

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Supplementary Table 2. Regression models predicting regional perfusion from LC-CR in the
overall sample (n=66) in other regions. Each row represents the standardized coefficient, 95%
CI, t-value, and p-value for the listed predictor, with covariates included for age and study.

Predictor Outcome β 95% CI t p
Regional Perfusion (n = 66)
Rostral LC Hippocampus CBF -.189 [-.436, .058] -1.53 .131
Rostral LC Posterior cingulate CBF .040 [-.183, .263] .361 .720
Rostral LC Precuneus CBF .170 [-.083, .422] 1.343 .184
Rostral LC Precentral gyrus CBF -.148 [-.396, .099] -1.201 .234

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Supplementary Table 3. Regression models for LC and cognition (non-significant tests). Each
row represents the standardized coefficient, 95% CI, t-value, and p-value for the listed predictor,
with covariates included for age and study.

Predictor Outcome β 95% CI t p
Cognition (n = 39)
Rostral LC VR Immediate Recall .175 [-.153, .503] 1.082 .286
Rostral LC VR Delayed Recall .124 [-.207, .454] .758 .453
Rostral LC VR Recognition -.001 [-.335, .332] -.009 .993
Rostral LC Trails B .173 [-.155, .501] 1.068 .292
Rostral LC FAS Fluency -.136 [-.466, .194] -.834 .409
Rostral LC Animals Fluency -.018 [-.351, .315] -.111 .912

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Supplementary Table 4. Plasma biomarker moderation models for regional perfusion. Each row
represents the standardized coefficient, 95% CI, t-value, and p-value for the listed interaction
term, with covariates included for age and study.

Predictor Outcome Interaction Term β 95% CI t p
AB42/40 moderation
(n=55)

Rostral LC Amygdala
CBF
AB42/40 ratio x Rostral
LC
-.334 [-.613, -.056] -2.412 .0196

Ptau181 moderation
(n=35)

Rostral LC Entorhinal
CBF
Ptau181 x Rostral LC .637 [.279, .996] 3.636 .0011
Rostral LC Lateral OFC
CBF
Ptau181 x Rostral LC -.521 [-.866, -.176] -3.086 .0043
Rostral LC Medial OFC
CBF
Ptau181 x Rostral LC -.522 [-.868, -.176] -3.085 .0044

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Supplementary Table 5. Regression models predicting regional perfusion from LC-CR in women
only (n=46) and predicting cognition from LC-CR (n=25). Each row represents the standardized
coefficient, 95% CI, t-value, and p-value for the listed predictor, with covariates included for age
and study.

Predictor Outcome β 95% CI t p

Rostral LC Amygdala CBF -.153 [-.466, .160] -.985 .330
Rostral LC Entorhinal CBF -.148 [-.381, .085] -1.281 .207
Rostral LC Lateral OFC CBF .135 [-.102, .373] 1.148 .257
Rostral LC Medial OFC CBF -.006 [-.278, .266] -.046 .964

Rostral LC RAVLT Trials 1-5 .432 [.043, .821] 2.297 .031
Rostral LC RAVLT Immediate Recall .511 [.140, .882] 2.851 .009
Rostral LC RAVLT Delayed Recall .497 [.123, .872] 2.75 .011
Rostral LC RAVLT Recognition .519 [.150, .888] 2.913 .008

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Supplementary Table 6. Regression models predicting regional perfusion from LC-CR in men
only (n=20) and predicting cognition from LC-CR in a subset (n=14). Each row represents the
standardized coefficient, 95% CI, t-value, and p-value for the listed predictor, with covariates
included for age and study.

Predictor Outcome β 95% CI t p

Rostral LC Amygdala CBF -.201 [-.736, .344] -.774 .451
Rostral LC Entorhinal CBF -.059 [-.445, .329] -.319 .754
Rostral LC Lateral OFC CBF .079 [-.289, .447] .455 .655
Rostral LC Medial OFC CBF .375 [-.154, .903] 1.503 .152

Rostral LC RAVLT Trials 1-5 .288 [-.315, .890] 1.04 .319
Rostral LC RAVLT Immediate Recall .186 [-.432, .804] .656 .524
Rostral LC RAVLT Delayed Recall .135 [-.488, .758] .471 .646
Rostral LC RAVLT Recognition .210 [-.405, .825] .743 .472

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Supplementary Table 7. Sex moderation models. Each row represents the standardized
coefficient, 95% CI, t-value, and p-value for the listed interaction term, with covariates included
for age, sex, and study.

Predictor Outcome Interaction Term β 95% CI t p
Sex as moderator
Rostral LC Amygdala CBF sex * Rostral LC .006 [-.249, .250] .004 .997
Rostral LC Entorhinal CBF sex * Rostral LC .062 [-.118, .242] .688 .494
Rostral LC latOFC CBF sex * Rostral LC -.044 [-.216, .128] -.511 .611
Rostral LC medOFC CBF sex * Rostral LC .103 [-.123, .330] .914 .365
Rostral LC RAVLT Trials 1-
5
sex * Rostral LC -.111 [-.435, .214] -.693 .493
Rostral LC RAVLT
Immediate
Recall
sex * Rostral LC -.129 [-.457, .199] -.799 .430
Rostral LC RAVLT Delayed
Recall
sex * Rostral LC -.231 [-.554, .092] -1.454 .155
Rostral LC RAVLT
Recognition
sex * Rostral LC -.109 [-.441, .224] -.663 .512

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Chapter 5: Discussion
Treatments for numerous health-related diseases have been developed, yet the root causes
of and potential cures for AD remain elusive. Amyloid-targeted therapeutics centered on one of
the primary biological proteins of the disease have proven largely fruitless, requiring the field to
pursue alternative routes to study early detection, treatment, and prevention. Atypical pathways
of AD pathogenesis represent crucial targets for research and funding, and there is growing
optimism that shifting our focus to traditionally understudied pathways may yield unique insights
into how to treat and stop this devastating disease. In this dissertation, a case is outlined for
greater focus to be placed on a subcortical nucleus, the LC, due to its early accrual of AD-related
pathology, diffuse involvement in cognitive functions, and interplay with cerebrovascular
systems. The three studies of this dissertation jointly represent the first intensive effort to
quantify LC, brainstem substructure, and cerebrovascular metrics in normal aging and
neurodegenerative processes through analyses of neuroimaging, neuropsychological, and
biofluid marker data from secondary datasets and original data collection.
In Study 1, we performed a secondary data analysis of the Alzheimer’s Disease
Neuroimaging Initiative (ADNI), a well-characterized dataset of older adults with normal
cognition, MCI, and dementia due to AD (Dutt et al., 2020). In this work, we analyzed data from
1,629 participants from ADNI who had baseline MRI scans and variable clinical follow-up and
used ROI and voxel-wise imaging analyses to quantify clinical group differences in brainstem
substructure (midbrain, pons, and LC) volumes. We found that individuals with MCI and AD
had smaller volumes of the whole brainstem, midbrain, pons, and LC compared to cognitively
normal participants, and midbrain findings sustained in CSF-biomarker-confirmed groups of
individuals MCI or dementia due to AD. Voxel-wise analyses centered on the brainstem revealed
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that groupwise volumetric differences were localized to the LC. Interestingly, risk analyses in
preclinical cognitively normal individuals who later progressed to a diagnosis of AD dementia
evidenced smaller baseline midbrain volumes than individuals who did not develop dementia.
Longitudinal Cox regression analyses found that having a smaller volume of the midbrain or LC
at baseline conferred greater risk of future progression to an AD dementia diagnosis independent
of other common AD risk factors. Our study complements other volumetric MRI studies
centered on the brainstem in MCI and AD (Ji et al., 2020; Lee et al., 2015) and expands upon
them to demonstrate that 1) brainstem and LC differences among clinically-diagnosed groups are
quantifiable and differentiable with widely used structural MRI sequences, and 2) brainstem and
LC volumetrics have unique predictive utility in determining preclinical asymptomatic
individuals who are at greater risk for progression to dementia.
Study 2 expanded upon analyses from Study 1 by characterizing the neuropsychological
correlates of brainstem and LC volumetrics in clinically normal, MCI, and AD dementia
individuals from the same ADNI cohort (Dutt et al., 2021). We examined associations between
brainstem substructure volumes and neuropsychological test performance across the domains of
episodic memory, attention/executive function, and language. Regression analyses revealed that,
among patients with MCI, smaller volumes of the LC and midbrain were associated with worse
performance on tests of attention and executive function, with LC-executive function
relationships sustaining in CSF-biomarker-confirmed MCI due to AD. Additionally, brainstem-
masked voxel-wise analyses found that executive function correlated with LC volume, lending
further confidence to the specificity of the relationship between LC volume and
attentional/executive function abilities. This work provides new insight into the phase at which
brainstem substructure volume reductions have cognitive implications (i.e., when they correlate
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with cognition), as associations were present in the MCI group but not the cognitively normal
group. Our work suggests that LC volume differences shown in Study 1 may only correlate with
cognitive performance once individuals are further into disease progression and are accumulating
pathological levels of amyloid and tau.
In Study 3, we used cutting-edge structural and functional neuroimaging techniques to
study LC integrity and cerebrovascular function in dementia-free older adults from the greater
Los Angeles area. This represents the first human study to directly investigate links between LC
integrity, as measured by neuromelanin-sensitive MRI, and cortical perfusion, as measured by
ASL-MRI. Greater integrity of the rostral LC was associated with frontal hyperperfusion and
limbic hypoperfusion, and these observed links were attenuated by increasing levels of plasma
AD biomarkers for amyloid and tau. Furthermore, rostral LC integrity was uniquely correlated
with verbal episodic memory learning, recall, and recognition but not with visual episodic
memory or attention/executive function. This study with original data critically implicates the
LC in cerebrovascular function and complements animal studies that have long demonstrated
direct relationships between the LC-NE system and the cerebrovasculature. These two systems
known to be tied to preclinical risk for dementia are interconnected and represent a novel
candidate for diagnostic assessment, intervention, and therapeutic development.
Together, these three studies demonstrate how widely used (T1-MPRAGE) and
specialized (T1-FSE) structural scans, in combination with functional brain perfusion imaging
(pCASL) and biofluid markers (CSF and blood plasma), have utility in tracking the LC across
AD-related disease processes and in quantifying novel relationships amongst physiologically
relevant systems in cognitive aging and dementia. We have established robust markers of LC
degeneration and dysfunction, but if translational efforts to therapeutic drug and intervention
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development are to succeed, future efforts must elucidate specific mechanisms for our various
findings. In studies 1 & 2, the volumetric reductions we observed may correspond with neuronal
loss caused by pathological tau accumulation and underlying LC degeneration in AD
progression, though additional studies are needed with postmortem confirmation (Beardmore et
al., 2021; Theofilas et al., 2016, 2018). The mechanisms underlying connections between the LC
and cerebrovasculature in humans also remain unclear. As previously discussed, animal models
have extensively demonstrated how LC-originating noradrenergic projections innervate brain
microvessels and modulate vasodilation and vasoconstriction (Bekar et al., 2012; Toussay et al.,
2013). One possible mechanism by which the LC directly exerts control over the
cerebrovasculature is via action at pericytes, which are essential for optimal functioning of the
blood-brain barrier (BBB) (Korte et al., 2023). The BBB is compromised early in the AD
dementia course (Nation et al., 2019; Sweeney et al., 2018; Zlokovic, 2011) and is directly
altered by lesioning the LC in rats (Kelly et al., 2019). As the LC experiences preclinical tau-
related pathology, this may adversely affect the availability of norepinephrine at alpha-2
adrenergic receptors on capillary pericytes, which could in turn compromise integrity of the BBB
and lead to leakage of pathological proteins and the pathophysiological cascade and dysregulated
CBF seen throughout AD progression.
The work in this dissertation emphasizes the need to further study these vascular
phenomena in humans with multimodal neuroimaging and biofluid markers to determine
whether they may represent potential therapeutic targets. This would pave the way for a growing
subfield of aging and dementia research centered on novel human studies exploring how these
systems are interconnected—from the behavioral to neuronal to molecular levels. In addition to
the amyloid, tau, and vascular hypotheses, this dissertation presents a working “LC-vascular”
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hypothesis linking preclinical brainstem tau aggregation, early vascular dysregulation, and LC
structure and function. As early tau accumulation throughout the lifespan occurs first and
primarily in the LC due to the high lifetime activity in virtually all waking/attention/arousal-
related processes, individuals with heightened cerebrovascular (e.g. hypertension,
atherosclerosis) and microvascular (e.g. neurovascular unit insult, BBB breakdown) risk factors
may be especially vulnerable to the deleterious effects of accumulating brainstem tau. In those
individuals with vascular risk factors, the LC-noradrenergic system may be overburdened,
leading those noradrenergic cells to become especially vulnerable to pretangles that have already
accumulated earlier in life and consequently insufficient regulation of cortical perfusion. Due to
the well-established role of the LC in innervating and regulating the cerebrovasculature and the
noted vulnerability of LC cells to toxins and environmental stress, this may lead to the initiation
of the pathophysiological cascade of tau and stereotypical spread to limbic and cortical regions.
Amongst our many findings, some inconsistencies arose regarding how the LC relates to
cognitive abilities. In study 2, we found that LC-VBM metrics were correlated with attentional
and executive abilities but unrelated to episodic memory measures. In contrast, study 3
demonstrated that LC-CR was closely tied to verbal episodic memory but not to attentional and
executive abilities. Although initially counterintuitive, this contradiction has several plausible
explanations. The MRI scans used to measure LC volume versus LC contrast ratio are likely
capturing different aspects of the LC, as the different scan parameters lead to measurements of
volume versus concentration of neuromelanin-pigmented neurons, albeit both rely on underlying
voxel intensity (Ashburner & Friston, 2000; Sasaki et al., 2008). While our LC-VBM metrics are
better thought of as a direct measure of the tissue volume in that region, LC-CR represents the
brightness of neuromelanin-containing neurons in that region and may represent noradrenergic
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activity and turnover (Betts, Kirilina, et al., 2019). Additionally, our LC-VBM metric combined
gray and white matter tissue classes, most of which does not contain neuromelanin due to the
very small size of the LC, whereas the LC-CR metric is specific to neuromelanin and is
constrained to only a few slices in the pons (Sasaki et al., 2008). Finally, the LC-CR findings
were specific to the rostral subdivision of the LC while our LC-VBM metrics captured volume
across the entire rostrocaudal axis. Ultimately, we are likely quantifying different aspects of LC
structure with these methods that may offer unique insights into domain-specific cognition, and
future studies should examine how these metrics relate to each other and to post-mortem LC
pathology.
As explored in detail in our studies, the LC, along with other subcortical brainstem
nuclei, has generated considerable interest in recent years as a unique brain region to target with
therapeutics in dementia, as it experiences profound cell loss and degeneration in the late stages
of disease but is relatively preserved early in life despite the accumulation of AD-associated
pathological proteins (Betts, Kirilina, et al., 2019; Ehrenberg et al., 2023). This uniquely
positions the LC and other subcortical nuclei as candidates for intervention early in the AD
disease course (M. David & Malhotra, 2021). Deficiency of noradrenaline elevates levels of
pathological amyloid in mouse models (Kalinin et al., 2007) and may be overall reduced in
humans with AD dementia, mirroring corresponding LC cell loss (Portela Moreira et al., 2023).
Attempts are already underway to repurpose noradrenergic drugs for use in MCI and AD, with
promising preliminary benefits for AD biomarkers, as well as cognitive and affective symptoms
(M. C. B. David et al., 2022; Husain, 2022; Levey et al., 2022). In the phase II trial by Levey et
al., atomoxetine, a norepinephrine reuptake inhibitor, reduced CSF levels of tau and modulated
brain function in prodromal AD, showing promise for future therapies targeting the
118
noradrenergic system. This clinical trial investigated resting state functional MRI to demonstrate
brain connectivity changes, and our study findings suggest that further investigation is warranted
of how modifying the LC and its noradrenergic system affects cerebrovascular function through
direct measurements of brain perfusion. Additional studies in mouse and rat models have also
demonstrated the beneficial effects of noradrenergic therapies, though caution should be
exercised as evidence also exists that disproportionate elevation of norepinephrine can have
negative effects, emphasizing the need to carefully study how these drugs may be used in MCI
and AD (Gutiérrez et al., 2022; Mather, 2021).
This dissertation focused on the LC-NA system due to its established role as the first site
of tau pathology, but as studies 1 & 2 demonstrated, widespread brainstem substructure atrophy
is observable on MRI and other neuromodulatory subcortical systems and circuitry may be
involved in AD risk. The most widely prescribed AD drugs modulate subcortical
neuromodulatory systems to slow symptom progression; despite no benefit in treating the
underlying disease itself, drugs like memantine and donepezil act at the level of acetylcholine
and glutamate neurotransmitters, among others (Howard et al., 2012). The cholinergic system,
primarily based in the basal forebrain has long been implicated in AD progression (Francis et al.,
1999). However, the lack of specificity of cholinergic interventions sees these anticholinergic
medications prescribed for a wide range of neurodegenerative diseases (e.g., Parkinson’s disease,
Lewy body disease). The “cholinergic hypothesis” continues to evolve and should be considered
in the context of dysfunction in other (i.e., noradrenergic) neurotransmitter systems (Hampel et
al., 2019).
As newly prescribed anti-amyloid agents receive FDA approval for individuals in the
mild stages of AD, there continues to be a dearth of preventative treatments for individuals in
119
preclinical, asymptomatic disease stages or via alternative non-amyloid pathways (Perneczky et
al., 2023). One of the more promising trials targeting the cerebrovasculature, the SPRINT MIND
trial, attempted to control AD risk through use of antihypertensive medications targeting blood
pressure (Williamson et al., 2019). Although the trial ultimately failed to reduce risk of future
dementia, it showed promise in mitigating dementia risk factors, including benefits for
cerebrovascular disease burden (i.e., white matter lesions), cognition, and cerebral blood flow
(Dolui et al., 2022; Goldstein et al., 2022; Rashid et al., 2023; Williamson et al., 2019). Blood
pressure variability has also emerged as a useful tool in tracking AD-related processes across the
disease spectrum (Sible et al., 2021, 2022, 2023). In this context, the benefits of intensive blood
pressure control may have indirect benefits on overall cerebrovascular health but may not be
sufficient to reduce dementia risk, and perhaps an additional concurrent physiological
modification (i.e., at the level of the LC-NE system) is necessary.
Non-pharmaceutical interventions should also be considered for modulation of both the
LC-NE and cerebrovascular systems. Research has long demonstrated the beneficial impact of
physical activity on cognitive decline, dementia risk, and vascular health (Casaletto et al., 2020;
Laurin et al., 2001; Podewils et al., 2005; Rabin et al., 2019). Some studies have suggested that
physical activity can induce noradrenergic activity and result in cognitive benefits, paving the
way for future studies to consider physical activity interventions that concurrently track vascular
outcome, LC-NE structure and function, and dementia progression in aging populations (da Silva
de Vargas et al., 2017; Segal et al., 2012). Biofeedback interventions involving modulation of
heart rate variability have also shown promise in potential benefits for cognition, brain network
connectivity, and direct modulation of LC contrast in both older and younger adults (Bachman,
Cole, et al., 2022; Nashiro, Min, et al., 2022; Nashiro, Yoo, et al., 2022). Future studies should
120
continue to explore whether these direct interventions targeting heart rate oscillations may
benefit older adults in the preclinical and prodromal stages of AD. The LC’s critical regulation of
sleep-wake cycles (Oh et al., 2019; Van Egroo et al., 2022) along with the established link
between sleep disruption and AD pathology (Mander et al., 2016) implicate sleep as another
potential target for intervention. Future sleep studies in aging populations may benefit from
tracking LC structure and function along with cerebrovascular outcomes to determine whether
sleep-based interventions may also modify these systems.
Together, the three studies of this dissertation describe novel brainstem- and
cerebrovascular-related aspects of cognitive aging and AD progression previously understudied
and largely ignored in the field until the last decade. Through both secondary analyses of large
publicly available datasets and original data collection from older adults in the local Los Angeles
community, we observed previously undocumented relationships between structural integrity of
the brainstem, its substructures, and its nuclei with disease risk, cognition, and cerebrovascular
function. Ultimately, we posit critical roles of the brainstem, the locus coeruleus, and the
cerebrovasculature in AD risk and progression and propose their viability as targets in future
interventions.

121
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Abstract (if available)

Abstract Autopsy-based neuropathological studies suggest the tau pathology observed in Alzheimer’s disease (AD) originates in brainstem nuclei such as the locus coeruleus (LC). The LC critically innervates the cerebrovasculature, and vascular dysfunction occurs preclinically in AD. The present three-study dissertation comprehensively examines interplay among these systems with structural and functional neuroimaging, neuropsychological testing, and biofluid (cerebrospinal fluid and blood plasma) markers. Together, these studies explore the role of the LC and brainstem substructures in dementia risk, cognitive performance across the AD spectrum, and cerebrovascular function. In Study 1 (N=1,629; Dutt et al., 2020), we found that LC and brainstem substructure volumes are reduced in clinically-diagnosed and biomarker-confirmed mild cognitive impairment (MCI) and AD populations compared to cognitively intact individuals, with low baseline midbrain and LC volumes predicting future AD dementia in asymptomatic individuals. In Study 2 (N=1,356; Dutt et al., 2021), we found that LC and brainstem substructure volumes were specifically linked to attentional and executive function abilities in clinically-diagnosed MCI and those with biomarker-confirmed prodromal AD. In Study 3 (N=66, Dutt et al. 2023, in preparation), we demonstrated that neuromelanin-sensitive quantification of LC integrity is associated with frontal and limbic brain perfusion and verbal episodic memory, and AD-related tau and amyloid pathology from plasma biomarkers attenuates the strengths of LC-perfusion relationships. The findings from this dissertation establish the utility of neuroimaging, cognitive, and biofluid measures in better characterizing the LC across the AD spectrum and propose concurrent study of subcortical and cerebrovascular abnormalities to develop novel biomarkers and targeted treatments.

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Asset Metadata

Creator Dutt, Shubir (author)

Core Title The role of the locus coeruleus in Alzheimer’s disease and cerebrovascular function: insights from neuroimaging, neuropsychology, and biofluid markers

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Psychology

Degree Conferral Date 2023-05

Publication Date 11/16/2023

Defense Date 03/27/2023

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

Tag Alzheimer's disease,biomarkers,brain perfusion,brainstem,locus coeruleus,magnetic resonance imaging,OAI-PMH Harvest

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Mather, Mara (committee chair), Nation, Daniel (committee chair), Gatz, Margaret (committee member), Margolin, Gayla (committee member), Wang, Danny (committee member)

Creator Email shubir.dutt@gmail.com,shubirdu@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC113127154

Unique identifier UC113127154

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Legacy Identifier etd-DuttShubir-11859

Document Type Dissertation

Format theses (aat)

Rights Dutt, Shubir

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Source 20230516-usctheses-batch-1045 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

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