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Brainstem structural integrity in the progression of Alzheimer's disease
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Brainstem structural integrity in the progression of Alzheimer's disease
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Running head: BRAINSTEM IN AD PROGRESSION 1
Brainstem Structural Integrity in the Progression of Alzheimer’s Disease
Shubir Dutt
University of Southern California
Department of Psychology
Thesis Submitted to the Faculty of the USC Graduate School
in Partial Fulfillment of the Requirements for the Degree of
Master of Arts in Psychology
August 2018
BRAINSTEM IN AD PROGRESSION 2
Table of Contents
Abstract 3
Introduction 4
Methods 11
Results 20
Discussion 23
References 30
Tables & Figures 46
BRAINSTEM IN AD PROGRESSION 3
Abstract
Prior research has established the brainstem as the earliest site of tau pathology in
Alzheimer’s disease (AD), but few studies have examined the utility of brainstem
structural MRI in predicting dementia risk or related brainstem volumes to AD
biomarkers. The present study compared brainstem, midbrain, and pons volumes
across the spectrum of neurocognitive decline and AD biomarker abnormality,
examined neuropsychological profiles linked to these regional brainstem volumes and
investigated their predictive value for future dementia. Alzheimer’s Disease
Neuroimaging Initiative (ADNI) participants (N = 1677) classified as cognitively normal
(CN), mild cognitive impairment (MCI) or AD underwent baseline MRI scanning with
clinical follow-up (6-120 months). We observed significantly smaller brainstem and
midbrain volumes in AD and MCI patients relative to CN, with no difference in pons
volumes. Among CN individuals, those who never progressed to dementia exhibited
larger baseline brainstem and midbrain volumes, and larger midbrain volume conveyed
decreased risk of progression to AD. CN older adults who were AD biomarker-positive
also showed larger brainstem, midbrain, and pons volumes relative to those who were
biomarker-negative. Among MCI patients, greater brainstem volumes correlated with
better neuropsychological performance. Findings demonstrate reduced brainstem
volume in MCI and AD, and implicate whole brainstem and midbrain volume in risk for
future dementia in preclinical populations. These volumetric differences early in the AD
process are consistent with neuropathological findings of AD-related pathology first
appearing in specific brainstem nuclei. Brainstem volumes may thus be an independent
biomarker for identifying preclinical individuals at risk for dementia.
BRAINSTEM IN AD PROGRESSION 4
Introduction
With the advent of modern neuroimaging techniques, in vivo imaging studies
have implicated the hippocampus and entorhinal cortex as key regions of volumetric
loss in Alzheimer’s disease (AD) dementia and memory decline (Deweer et al., 1995; Di
Paola et al., 2007; Dickerson et al., 2001; Mori et al., 1997). The original Braak staging
of AD emphasized the importance of early pathology in the entorhinal cortex, but recent
postmortem studies and revised pathological staging have highlighted the involvement
of specific brainstem nuclei that precede entorhinal involvement in the progression of
AD (H. Braak & Braak, 1995; Heiko Braak & Del Tredici, 2011b, 2015; Grinberg et al.,
2009). Despite these findings implicating the brainstem in the pathogenesis of AD,
relatively few studies have detailed the structural integrity of the brainstem, and
relationships between brainstem volumes and established AD biomarkers have not
been detailed. Additionally, brainstem volumes have not been quantified in individuals
diagnosed with mild cognitive impairment (MCI), nor have they been evaluated as risk
markers for longitudinal progression to dementia.
AD is a progressive neurodegenerative disorder that involves the formation of
neuritic plaques comprised of beta-amyloid (Aβ) protein as well as the accumulation of
neurofibrillary tangles (NFTs) comprised of hyperphosphorylated tau protein
(Alzheimer's Association, 2016). Aβ is generated when the amyloid precursor protein
(APP) is cleaved by B-secretase and gamma-secretase (Vassar et al., 1999). The
aggregation of Aβ into soluble oligomers, which are toxic to neurons, and insoluble
plaques, which are the neuropathological hallmark of AD, is thought to occur when toxic
Aβ species cannot be cleared from the brain (Haass & Selkoe, 2007; Nedergaard,
BRAINSTEM IN AD PROGRESSION 5
2013). NFTs form when the microtubule-associated protein tau becomes
hyperphosphorylated and aggregates into bundles of insoluble paired helical filament,
which eventually form tangles that disrupt neuronal communication and function (Brion,
1998). Both AD and MCI, an intermediate stage with notable deficits in cognitive
abilities, have traditionally been diagnosed based on clinical judgments, cognitive
testing, and subjective complaints, but recent biomarker studies have developed more
nuanced frameworks for describing varying levels of AD-related pathology among
individuals (Albert et al., 2011; Jack et al., 2011, 2016; G. Mckhann et al., 1984). The
most recent framework is the A/T/N system, which categorizes individuals based on
biomarker positivity or negativity in the domains of Aβ, NFT, and neurodegeneration
(Jack et al., 2016). By classifying individuals across a spectrum of multidomain
biomarkers, subtle differences may be observable earlier in the disease process, such
as in the preclinical phase of AD in cognitively normal (CN) adults (Sperling et al.,
2011). These AD-vulnerable preclinical populations are desirable candidates for early
therapeutic interventions and preventative research, and it is thus of great interest to
study mechanisms underlying the onset of AD-related pathophysiological changes.
Braak & Braak originally conceptualized the pathological progression of AD as
proceeding in stages, with NFTs first appearing in transentorhinal cortex (stages I-II),
then spreading to limbic (stages III-IV) and isocortical (stages V-VI) regions (H. Braak &
Braak, 1995). Following this establishment of cortical involvement in AD pathogenesis,
several studies emerged highlighting the pathological processes occurring in specific
brainstem nuclei, the locus coeruleus (LC) of the pons and dorsal raphe nucleus (DRN)
of the midbrain, that precede any observable cortical changes (Grinberg et al., 2009;
BRAINSTEM IN AD PROGRESSION 6
Overk, Kelley, & Mufson, 2009; Simic et al., 2009). These findings were incorporated
into the updated Braak staging framework, defining stages a-c as the accumulation of
NFTs in the LC, the magnocellular nuclei of the basal forebrain, and the oral nuclei of
the raphe system, followed by cortical stages I-VI (Heiko Braak & Del Tredici, 2011b,
2015).
This initial subcortical deposition of tau may be the first sign of the cascading
effects of AD pathophysiological processes, a hypothesis that is supported by the early
appearance of tau pathology in younger individuals. In one post-mortem study, a large
proportion of subjects as young as six years old had depositions of abnormally
phosphorylated tau in subcortical regions. These intracellular pretangles, despite being
benign early in life, may eventually develop into NFTs (Heiko Braak & Del Tredici,
2011b). A leading hypothesis that follows is that subcortical tau deposition in brainstem
nuclei represents the start of the AD pathological process, and it is only later joined 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). NFT accumulation in specific regions of the brain may be the primary
source of neurodegeneration and consequent dysfunction in AD, with Aβ accumulation
serving a permissive role in facilitating the onset of the disease process.
Histopathological studies have further examined the role of the brainstem in AD
pathogenesis. One recent post-mortem study correlated decreased LC volume with
increased Braak stage and found that NFT accumulation and corresponding volumetric
loss preceded actual neuronal loss, while other studies have demonstrated
accumulation of NFTs in both the LC and DRN, with significant increases in LC NFT
BRAINSTEM IN AD PROGRESSION 7
accumulation between Braak stages 0 and 1 and DRN NFT accumulation proportional
to Braak stage (Ehrenberg et al., 2017; Grinberg et al., 2009; Rüb et al., 2000; Theofilas
et al., 2016). Together, these studies provide postmortem evidence supporting the
theory that subcortical NFTs appear in brainstem nuclei prior to cortical deposition and
degeneration.
The DRN and LC are two of four nuclei that comprise the isodendritic core, a
network of nuclei that share many similar morphological and functional features
including distally projecting axons, neurons with large somata, neurotransmitter
production and transmission, and overlapping dendritic fields (Ramón ‐Moliner &
Nauta, 1966). The DRN, housed in the midbrain, is the main site of serotonin
production, and disease-related degeneration may partially explain certain clinical
phenomena in AD such as the onset of depressive symptoms prior to changes in
cognition (Grinberg et al., 2009; Grinberg, Rüb, & Heinsen, 2011; Panza et al., 2010) .
Furthermore, degeneration of the ascending serotonergic system, the source of which is
located at the oral raphe nuclei of the midbrain and pons, has been linked to sleep
dysregulation and depressive symptoms in post-mortem clinical-pathological studies of
AD (Halliday et al., 1992; Hendricksen, Thomas, Ferrier, Ince, & O’Brien, 2004; Parvizi,
Van Hoesen, & Damasio, 2001; Tabaton, Schenone, Romagnoli, & Mancardi, 1985).
Together, these studies implicate the DRN and the midbrain in early AD-related
processes.
The LC, located in the rostral pons, is the primary site of norepinephrine
production and is linked to autonomic and vascular regulation, as well as behavioral
arousal, memory, and attention (Mather & Harley, 2016). Integrity of the LC, as
BRAINSTEM IN AD PROGRESSION 8
measured by neuronal density postmortem and neuromelanin signal intensity via
magnetic resonance imaging (MRI) in vivo, is linked to optimal cognitive functioning in
healthy controls, and similar MRI methods have confirmed the ability to image LC signal
intensity in individuals with AD and MCI (Clewett et al., 2015; Mather & Harley, 2016;
Takahashi et al., 2014; Wilson et al., 2013). An additional key role of the LC may be in
the development of “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). These findings suggest that
preserved structural integrity of the LC 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. Since there is a lack of neuromelanin-related MRI scans in
existing natural history studies of AD, it remains to be seen whether a volumetric MR
proxy measure of the LC may exist. Overall, these prior studies of the early involvement
of the LC in the pathogenesis of AD emphasize the need to further study the pons and
its nuclei.
Together, the prior literature highlights the central importance of brainstem-
related tau pathological distribution in the progression of clinical symptoms of AD, as
well as the unique role of specific brainstem nuclei in AD pathogenesis. In order to link
early neuropathological events to manifestations of the disease during life, in vivo
BRAINSTEM IN AD PROGRESSION 9
studies of brainstem volumetrics with structural MRI are needed to establish the
potential for a feasible neuroimaging biomarker. Few prior studies of AD have attempted
analysis of brainstem volumetrics, with different groups obtaining conflicting results.
Two volumetric studies using MRI determined that volumes of the whole brainstem,
midbrain, and pons were reduced in AD dementia compared to a CN group, but these
changes were not compared to any clinical measures of disease progression. In
contrast, an earlier 2014 study reported no differences in a combined brainstem and
cerebellar region between AD dementia and CN, though the inclusion of the cerebellum
likely obscured any brainstem-specific changes (Iglesias et al., 2015; Lee, Ryan,
Andreescu, Aizenstein, & Lim, 2015; Yucel et al., 2014). Other similar efforts have
resulted in conflicting outcomes, with one study finding no difference in pons volume
between AD dementia and CN and another observing significant reduction in both
midbrain and pons volumes (Mrzilková, Zach, Bartoš, Tintěra, & Řípová, 2012; Nigro et
al., 2014). Overall, the majority of MRI studies have instead focused on the traditional
biological correlates of memory, the medial temporal lobe (MTL) and hippocampus, and
the few that have focused on the brainstem have encountered numerous limitations
(e.g. sample size, ROI delineation), leading to conflicting results.
Neuroimaging with MRI techniques can capture brain atrophy representative of
synaptic and neuronal loss, but it is not able to directly image pathological proteins in
vivo. Through the use of lumbar puncture, levels of proteins in cerebrospinal fluid (CSF),
particularly CSF Aβ
1-42
, total tau (t-tau), and phosphorylated tau (p-tau), can be
quantified. Decreased levels of Aβ
1-42
, along with increased levels of t-tau and p-tau,
reliably distinguish AD from NC, track progression of MCI to AD, and correlate with
BRAINSTEM IN AD PROGRESSION 10
levels of pathology observed post-mortem (Blennow & Hampel, 2003; Blennow,
Vanmechelen, & Hampel, 2001; Hampel et al., 2010; Hansson et al., 2006; Herukka,
Hallikainen, Soininen, & Pirttila, 2005; Mattsson et al., 2009; Shaw et al., 2009; Tapiola
et al., 2009). Levels of these CSF biomarkers also correlate with AD-related atrophy of
the hippocampus and entorhinal cortex cross-sectionally and longitudinally, as well as
with hippocampal atrophy in preclinical AD (Apostolova et al., 2010; Henneman et al.,
2009; Tarawneh et al., 2015; Wang et al., 2015). This suggests the potential utility of
jointly using CSF and MRI biomarkers to more accurately classify individuals and
assess risk of progression to AD, but there is a substantial gap in the literature, as MRI-
measured volumes of the brainstem have not yet been studied in relation to CSF
biomarker data.
The recent focus on the brainstem in AD research combined with the rich variety
of biological, neuroimaging, and clinical data and recent understanding of biomarker-
delineated diagnoses is encouraging for efforts to catch AD earlier in the disease
course. Histopathological studies have determined the brainstem to be the first site of
tau pathology, but in vivo imaging studies have yet to definitively corroborate this finding
and relate early brainstem degeneration to the onset of clinical symptoms. The present
study aims to address these gaps by quantifying volumes of the brainstem, midbrain,
and pons in a large sample of AD dementia, MCI, and CN, examining the associations
between baseline volumes and cognitive tests, and exploring the longitudinal predictive
value of brainstem volumes. By combining neuroimaging and clinical data, we aim to
determine the potential utility of structural MR imaging of the brainstem as a biomarker
for identifying individuals at highest risk for progression to AD. We hypothesize that
BRAINSTEM IN AD PROGRESSION 11
brainstem, midbrain, and pons volumes will be larger in CN relative to MCI and AD
dementia, and larger in MCI relative to AD dementia. In all group comparisons, we
expect volumetric differences in the midbrain to be most pronounced, consistent with
prior studies demonstrating midbrain shrinkage in normal aging and AD dementia
(Lambert et al., 2013; Lee et al., 2015). We also hypothesize that within all diagnostic
groups, larger whole brainstem and substructure volumes will be associated with better
performance on neuropsychological tests. Relationships will be weaker or absent in the
domain of memory due to the known associations between hippocampal/MTL volumes
and memory. Finally, we hypothesize that larger brainstem ROI volumes at baseline will
grant significant protection against progression to dementia. Exploratory analyses will
be performed in groups delineated by levels of CSF biomarkers to investigate the
potential role of brainstem volume as a protective reserve factor buffering against the
insidious effects of accumulating pathology.
Methods
Study Participants
A total of 1,677 participants were drawn from the Alzheimer’s Disease
Neuroimaging Initiative (ADNI) online database, which has catalogued study
participants since 2004 from a multisite longitudinal natural history study of AD. Data
were used from three phases of the study: ADNI1, which enrolled CN, MCI, and AD
participants; ADNI Grand Opportunity (ADNI-GO), which followed some ADNI1
participants longitudinally and recruited early MCI participants; and ADNI2, which
continued to follow participants longitudinally while also recruiting additional CN, early
MCI, late MCI, and mild AD participants.
BRAINSTEM IN AD PROGRESSION 12
All study participants were diagnosed at baseline as CN, MCI, or AD dementia,
and individuals with co-morbid neurological disease other than AD, history of substance
abuse, or history of significant head trauma were excluded. Participants were diagnosed
with AD dementia if they met NINCDS/ADRDA criteria for probable AD, scored 0.5 or 1
on the Clinical Dementia Rating (CDR), and scored between 20 and 26 on the Mini-
Mental State Examination (MMSE) (Folstein, Folstein, & McHugh, 1975; Hughes, Berg,
Danziger, Coben, & Martin, 1982; Mckhann et al., 2011). Participants were classified as
MCI if they reported subjective memory complaints, scored between 24 and 30 on the
MMSE, scored 0.5 on the CDR, and demonstrated education-adjusted impairment on
the Wechsler Memory Scale (WMS) Logical Memory II subtest. Participants were
classified as CN if they had an MMSE score ≥ 26, CDR score of 0, and had no history of
cognitive impairment.
For the present study, participants with AD dementia retained these diagnoses in
subsequent analyses due to the subjective clinical judgments involved in diagnosing the
disease. MCI participants were entered into a cluster analysis to identify refined
diagnostic groups due to the previously noted susceptibility of ADNI MCI diagnoses to
false positives (Clark, Delano-Wood, Libon, McDonald, et al., 2013; Delano-Wood et al.,
2009; Edmonds et al., 2015). Briefly, all participants diagnosed as MCI by ADNI were
clustered according to performance on 6 neuropsychological tests across 3 cognitive
domains, resulting in 4 new diagnostic groups: cluster-derived CN, amnestic MCI,
dysnomic MCI, and dysexecutive MCI. For all primary analyses, the cluster-derived CN
group was combined with the original CN group, and the three MCI subtypes were
BRAINSTEM IN AD PROGRESSION 13
collapsed into a single combined MCI group. Additional details regarding this cluster
analysis appear in the Statistical Analyses section.
Neuropsychological Testing
All ADNI participants were administered a standardized battery of
neuropsychological tests spanning several different domains of cognition. The present
study analyzed two measures in each of three cognitive domains (memory, language,
and executive functioning), as well as a word-reading test to assess premorbid
intelligence and a cognitive screener to assess global cognitive function.
Memory. The Rey Auditory Verbal Learning Test (RAVLT) is an episodic
memory test that requires the participant to encode a list of 15 words after several
learning trials and recall them after an immediate and thirty-minute delay (Rey, 1964;
Schmidt, 1996; Strauss, Sherman, & Spreen, 2006). Performance on recall and
recognition after the thirty-minute delay were used as measures of episodic memory.
Language. Two measures of language, Animal Fluency and the 30-item Boston
Naming Test, were used. Animal fluency is a subtest of category fluency, which
examines semantic memory and language by having the participant spontaneously
name as many animals as possible in 60 seconds (Morris et al., 1989). The 30-item
Boston Naming Test assesses an individual’s ability to verbally name 30 objects
represented as line drawings (Kaplan, Goodglass, & Weintraub, 1983).
Executive function. Trail Making Test Parts A & B are tests of executive
functioning, specifically processing speed and set shifting, that involve connecting
circles either sequentially with numbers (Part A) or alternating between numbers and
letters (Part B) (Partington & Leiter, 1949; Reitan, 1958; Reitan & Wolfson, 1985).
BRAINSTEM IN AD PROGRESSION 14
Premorbid intelligence or cognitive reserve. The American National Adult
Reading Tests (ANART) comprises 50 visually presented words with irregular spelling
that are read aloud by a participant (Nelson, 1982). The ANART has been shown to be
a good estimate of premorbid intelligence in both healthy older adults and individuals
with dementia (Bright, Jaldow, & Kopelman, 2002; McGurn et al., 2004). Prior studies
have also conceptualized the ANART, along with demographic variables such as
education and occupation, as a surrogate marker for cognitive reserve (Feinstein,
Lapshin, O’Connor, & Lanctot, 2013; Lo & Jagust, 2013).
Global Functioning. Global cognition was assessed with the mini-mental state
exam (MMSE), a thirty-point test of cognition that queries the domains of orientation,
immediate and short-term memory, language, and others (Folstein et al., 1975).
Neuroimaging
ADNI scan parameters and acquisition. ADNI participants underwent MRI
scanning on Siemens, GE, and Phillips scanners at either 1.5 or 3 tesla 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 sequence parameters for each site are available to view online
(http://adni.loni.usc.edu/methods/documents/mri-protocols/). For all study participants,
T1-weighted images were first downloaded from the ADNI database in raw nifti format
prior to any processing.
Study-specific image processing. Using the “Display” function in SPM12
(http://www.fil.ion.ucl.ac.uk/spm/) within MATLAB, each T1-weighted image was
BRAINSTEM IN AD PROGRESSION 15
individually checked for image quality and manually aligned and rotated to ensure
anterior commissure-posterior commissure (AC-PC) alignment. Images were then
processed through the voxel-based morphometry (VBM) pipeline in SPM12, which has
been previously 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, and smoothed with an 8 mm full-width at half-maximum (FWHM)
isotropic Gaussian kernel.
ROI masking and analysis. Region-of-interest (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 (Iglesias et al., 2015). A previously created 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 (Beissner, Schumann,
Brunn, Eisenträger, & Bär, 2014; Mazziotta et al., 2001; Mazziotta, Toga, Evans, Fox, &
Lancaster, 1995; Mazziotta et al., 2001). ROIs for the hippocampus and MTL were
generated using the AAL atlas and the WFU PickAtlas toolbox within SPM12 (Maldjian,
Laurienti, & Burdette, 2004; Maldjian, Laurienti, Kraft, & Burdette, 2003; Tzourio-
Mazoyer et al., 2002). The hippocampus was derived directly from the AAL atlas, and
the MTL was calculated by summing the entorhinal, perirhinal, and parahippocampal
BRAINSTEM IN AD PROGRESSION 16
cortical regions (Brodmann areas 27, 28, 34, 35, 36). All ROIs from the present study
are displayed in Figure 1.
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, Possatti, Alves, Ribas, & Oliveira, 2016). Volumes for hippocampal
and MTL ROIs were only summed across grey matter maps. 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_summarize”) and the “get_totals” script
(http://www0.cs.ucl.ac.uk/staff/gridgway/vbm/get_totals.m). 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, Crum, Watt, &
Fox, 2001). Normalized volumes were subsequently multiplied by a factor of 10
3
to
facilitate ease of comparisons.
CSF Biomarkers
In order to subdivide preclinical (i.e. cognitively normal) participants by biomarker
positivity, we chose to examine levels of Aβ
1-42
and p-tau as measured in the CSF. A
subset of ADNI participants completed a fasting lumbar puncture to collect CSF
samples at baseline. Levels of Aβ
1-42
and p-tau were quantified in aliquots using the
automated Roche Elecsys B-amyloid(1-42) CSF and Elecsys phosphotau (181P) CSF
electrochemiluminescene immunoassays at the UPenn Biomarker Research
Laboratory, as previously described (Bittner et al., 2016). Participants were categorized
BRAINSTEM IN AD PROGRESSION 17
as amyloid positive with values of Aβ
1-42
below 964 pg/ml and as amyloid negative with
values at 964 pg/ml and above. They were also categorized as p-tau positive with
values of p-tau
181p
above 23.2 and as p-tau negative with values at 23.2 and below
(Seibyl et al., 2017). Participants were then stratified into four groups based on the
thresholds of biomarker positivity or negativity on levels of Aβ
1-42
and p-tau.
Statistical Analyses
Cluster-derived participant groups. Due to the known high false positive rates
and misclassification of MCI participants, all participants with baseline ADNI diagnoses
of MCI were entered into a cluster analysis to resolve any potential misclassifications
(Clark, Delano-Wood, Libon, Mcdonald, et al., 2013; Delano-Wood et al., 2009;
Edmonds et al., 2015). Scores entered into the cluster analysis were derived via a
previously validated method with comparable subjects from the ADNI study (Edmonds
et al., 2015). First, regression coefficients for age and education predicted scores on our
six tests of interest (delayed memory, delayed recall, Trails A, Trails B, Animals fluency,
Boston Naming Test) were calculated for participants who (a) had a CN diagnosis at
baseline, (b) had at least 12 month follow-up data and retention of CN diagnosis, and
(c) never progressed to MCI or AD. These regression coefficients were then used to
calculate expected scores for MCI participants, and z-scores were obtained using the
equation: (observed MCI score – expected MCI score) / standard deviation of CN group.
Z-scores for the MCI group were then entered into a hierarchical cluster analysis using
Ward’s method and a forced solution of 4 clusters based on previous studies (Clark,
Delano-Wood, Libon, Mcdonald, et al., 2013; Delano-Wood et al., 2009; Edmonds et al.,
2015; Ward Jr, 1963). The resulting four groups were inspected and defined as (1)
BRAINSTEM IN AD PROGRESSION 18
cluster-derived cognitively normal, (2) amnestic MCI (predominant impairment on
memory tests), (3) dysnomic MCI (predominant impairment on language tasks), and (4)
dysexecutive MCI (predominant impairment on executive function tasks)
(Supplemental Figure 1). The cluster-derived CN group was combined with the ADNI
CN group and the ADNI significant memory concern (SMC) group to form an “overall
CN” group, which will be referred to as CN. The 3 MCI subtypes were combined to form
a “combined MCI” group, which will be referred to as MCI. The participants with ADNI
diagnoses of AD dementia retained these diagnoses for all analyses.
Cross-sectional analyses. All variables were first checked for normality via
skewness and kurtosis. Substantial departure from normality was noted for the Trails A,
Trails B, and BNT variables, and a log
10
transformation was applied. For BNT, a
reflection was applied (score was subtracted from maximum score plus one) prior to
log
10
transformation in order to avoid undefined values.
One-way analysis of variance (ANOVA) with Tukey’s post hoc test was used to
test diagnostic group differences in age and education. Chi-squared test was used to
test group differences in sex. One-way analysis of covariance (ANCOVA) with age, sex,
and education as covariates was used to test group differences in neuropsychological
and neuroimaging variables, followed by post hoc LSD tests to examine pairwise group
differences. The CN group was further subdivided into four groups based on biomarker
positivity (Aβ-ptau-, Aβ+ptau-, Aβ-ptau+, and Aβ+ptau+) and the same aforementioned
statistical tests (ANOVA, chi-squared, ANCOVA) were performed to examine overall
and pairwise group differences.
BRAINSTEM IN AD PROGRESSION 19
To investigate relationships between brainstem volumes and tests of cognition,
zero-order Pearson correlations were first run within each group, followed by partial
correlations controlling for age, sex, and education. Within groups and subgroups where
significant relationships were observed, multiple linear regressions were run with
neuropsychological test as dependent variable, ROI volume as independent variable,
and age, sex, and education as covariates. Scatter plots for significant associations
were plotted with overlaid linear regression lines and 95% confidence intervals.
Longitudinal analyses. Proportional hazards survival analyses were conducted
via Cox regressions to determine the value of brainstem ROI volumes in predicting
progression from CN or MCI to dementia. For the CN survival analysis, converters were
defined as individuals who had a baseline CN diagnosis and a follow-up dementia
diagnosis at any time-point. Non-converter CN participants were defined by the robust
CN group used as reference in the aforementioned cluster analysis. The MCI survival
analyses used similar methods but required an MCI diagnosis at baseline. ROI volumes
were first reflected to ensure appropriate directionality in the model (smaller volumes
corresponding with the onset of the event of interest—progression to dementia). In Cox
regression models, age, sex, and education were entered as covariates in the first
block, followed by a continuous variable for brainstem ROI volume in the second block.
ROI volumes were also compared across CN and MCI converters and non-converters
at baseline. In order to visualize the predictive value of ROI brainstem volumes, receiver
operating characteristic (ROC) curve analysis was performed to obtain an optimal cutoff
for ROI volume predicting progression to dementia, and survival curves were plotted for
diagnostic groups split by high or low ROI brainstem volume.
BRAINSTEM IN AD PROGRESSION 20
Multiple Comparisons. 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, which included diagnostic group and
biomarker-split group comparisons of ROI volumes, multiple linear regressions within
diagnostic groups, and baseline comparisons and Cox regressions of converters/non-
converters to dementia within CN and MCI groups (Glickman, Rao, & Schultz, 2014).
Results were first examined at uncorrected p < 0.05, then at FDR-corrected thresholds
of 0.05 and 0.10. For all other post-hoc tests, descriptive statistics, and exploratory
analyses, uncorrected significance threshold of p < 0.05 was used.
Results
Demographic Data
Baseline demographic, neuropsychological, and neuroimaging data for the CN,
MCI, and AD dementia groups are displayed in Table 1. AD dementia participants were
older than both MCI (p = 0.001) and CN (p < 0.001) participants. The proportion of
males to females differed significantly between the CN and MCI groups (p < 0.05). The
CN group had higher education levels than both the MCI (p = 0.007) and AD dementia
(p < 0.001) groups, and the MCI group had a higher education level than the AD
dementia group (p = 0.001). Baseline demographic, neuropsychological, and
neuroimaging data for the CN group stratified by CSF biomarker positivity are displayed
in Table 2. Biomarker subgroups differed in age (p < 0.001) but not in sex proportion or
education level.
Cross-sectional Analyses
BRAINSTEM IN AD PROGRESSION 21
The CN group performed better than the MCI and AD dementia groups on
neuropsychological testing across all cognitive domains, with the MCI group also
outperforming the AD dementia group (all p’s < 0.001; Table 1). Whole brainstem
volume was larger in CN relative to MCI (p = 0.023) and AD dementia (p = 0.02)
(Figure 2A). Additionally, midbrain volume was larger in CN relative to MCI (p = 0.001)
and AD dementia (p < 0.001) (Figure 2B). Groups did not differ in total intracranial or
pons volumes (Figure 2C). Hippocampal and MTL volumes differed significantly
between groups in the expected direction (CN>MCI>AD, all pairwise p’s < 0.001). All
volumetric differences remained significant at FDR rate of 0.05.
Within the CN group, the biomarker-positive (Aβ+ptau+) group had larger
brainstem (Figure 3A), midbrain (Figure 3B), and pons (Figure 3C) volumes at
baseline relative to the biomarker-negative (Aβ-ptau-) group and the amyloid positive
(Aβ+ptau-) group (all p’s < 0.05). Additionally, the p-tau positive (Aβ-ptau+) group had
larger brainstem, midbrain, and pons volumes than the Aβ+ptau- group (all p’s < 0.05).
A similar pattern of differences was observed in the MTL, while the hippocampus only
differed between the Aβ-ptau+ and Aβ+ptau- groups (Table 2). All findings remained
significant at FDR of 0.05 except for group differences in pons volume. When FDR was
raised to 0.10, all findings remained significant.
Within each of the CN, MCI, and AD dementia groups, larger brainstem,
midbrain, and pons volumes correlated with older age, and females had larger volumes
than males. There were no associations between brainstem ROI volumes and years of
education (Supplemental Table 1).
BRAINSTEM IN AD PROGRESSION 22
Zero-order Pearson correlations between brainstem ROIs and NP tests are
displayed in Supplemental Table 2. After correcting for age, sex, and education, larger
whole brainstem (p = 0.015), midbrain (p = 0.002) and pons (p = 0.027) volumes were
associated with better performance on Trails A in the MCI group. Additionally, larger
midbrain volume was associated with better Trails B performance (p = 0.019) and
higher Animals fluency score (p = 0.025) in the MCI group (Table 3A; Figure 4). All
findings remained significant at FDR rate of 0.05.
Within the amnestic MCI subgroup, larger midbrain volume was associated with
better Trails A (p = 0.012) and Trails B (p = 0.003) performance. Within the dysnomic
MCI subgroup, larger midbrain volume was associated with higher Animals fluency
score (p = 0.038). Within the dysexecutive MCI subgroup, larger whole brainstem
volume (p = 0.022) and larger pons volume (p = 0.014) were associated with better
Trails A performance (p = 0.022) (Table 3 B-D).
Longitudinal Analyses
Group differences in ROI volumes between individuals who progressed and
never progressed to dementia are presented separately for participants with baseline
diagnoses of CN (Table 4A) and MCI (Table 4B). Within the CN group, individuals who
never progressed to dementia had larger baseline brainstem (p = 0.043), midbrain (p =
0.006), hippocampal (p < 0.001), and MTL (p < 0.001) volumes than those who were
diagnosed with dementia at a follow-up visit. Within the MCI group, individuals who
progressed to dementia had significantly smaller hippocampal and MTL volumes (p’s <
0.001) than non-converters, but did not differ in total intracranial, brainstem, midbrain, or
pons volumes (Figure 5). All findings remained significant at FDR of 0.05, with the
BRAINSTEM IN AD PROGRESSION 23
exception of the difference in whole brainstem volumes within the CN group. When FDR
was raised to 0.10, all findings remained significant.
Results of Cox regressions examining the relationships between baseline ROI
volumes and progression to dementia are presented for participants who were CN and
MCI at baseline in Table 5 and displayed visually in Figure 6. In the CN group, larger
midbrain volume was associated with lower risk of progression to dementia [Odds Ratio
(OR) = 2.25, p = 0.016], as was larger baseline hippocampal volume (OR = 3.41, p =
0.004) and MTL volume (OR = 8.20, p < 0.001). In the MCI group, larger baseline
hippocampal (OR = 2.22, p < 0.001) and MTL (OR = 3.66, p < 0.001) volumes were
associated with lower risk of progression to dementia, and there were no associations
with brainstem volumes. All findings remained significant at FDR rate of 0.05.
Discussion
The present study findings demonstrated preserved whole brainstem and
midbrain volumes in CN older adults relative to MCI patients. The intermediate
diagnostic category of MCI is known to be heterogeneous in clinical presentation and
biomarker accumulation, and the current study performed volumetric comparisons in a
refined MCI diagnostic category by using a previously validated hierarchical clustering
approach to delineating groups (Clark, Delano-Wood, Libon, Mcdonald, et al., 2013;
Delano-Wood et al., 2009; Edmonds et al., 2015). Despite the heterogeneity of
individuals in this group, consistently smaller brainstem and midbrain volumes relative
to CN individuals were observed, emphasizing the importance of the structural integrity
of these regions prior to the onset of disease processes. Prior studies examining pons
volume loss in AD dementia have been mixed (Mrzilková et al., 2012; Nigro et al.,
BRAINSTEM IN AD PROGRESSION 24
2014), and we did not observe difference in pons volume in AD dementia or MCI
relative to the CN group. Consistent with our hypotheses and prior work in the field, the
present study confirmed larger whole brainstem and midbrain volumes in CN patients
relative to AD dementia individuals (Iglesias et al., 2015; Lee et al., 2015).
Interestingly, CN older adults who were AD biomarker-positive showed larger
brainstem, midbrain, and pons volumes relative to those who were biomarker-negative,
despite the fact that patients with MCI and AD dementia exhibited smaller brainstem
and midbrain volumes. This pattern of observations is consistent with the hypothesized
role of brainstem volume as a protective factor since it could suggest that older adults
with greater brainstem volumes may remain CN in spite of greater AD pathological
burden. These findings support the role of larger brainstem structures in neural reserve,
as greater neuronal density in the brainstem may compensate for the higher level of
pathology, working to sustain individuals at a level of cognition commensurate with
other CN individuals (Katzman et al., 1988; Schofield, Logroscino, Andrews, Albert, &
Stern, 1997; Stern, 2012). Furthermore, our findings are in line with emerging research
supportive of a buffering role of the LC, due to high neuronal density and increased
noradrenergic turnover, in protecting against the clinical consequences of increasing AD
pathology (Clewett et al., 2015; Mather & Harley, 2016; Robertson, 2013; Wilson et al.,
2013). Other studies have established a similar protective role of education, another
hypothesized marker of cognitive reserve, demonstrating that individuals with higher
education have more CSF-measured amyloid and tau pathology than those with less
education (Jansen et al., 2015; Sunderland et al., 2003). Thus, our findings further
support the notion that protective factors (e.g. higher brainstem volume, higher
BRAINSTEM IN AD PROGRESSION 25
education) may signify greater cognitive reserve in preclinical populations and buffer
against the detrimental effects of accumulating AD-related pathology.
In further support of a protective role of brainstem volume, MCI participants with
larger brainstem, midbrain, and pons volumes exhibited better attention/processing
speed and language abilities on neuropsychological testing relative to those with
smaller volumes. Whole brainstem, midbrain, and pons volumes were associated with
the Trails A test, which is most sensitive to speed of processing in the construct of
executive function. LC neuronal density in older adults has previously been associated
with cognitive decline in multiple domains, including visuospatial ability and perceptual
speed of processing, and the present findings similarly implicate gross brainstem
structural volumes in affecting cognitive abilities (Mather & Harley, 2016). The midbrain
was additionally associated with Trails B time and Animals fluency, suggesting this
structure may play a role in higher-order cognitive functions such as executive control
and switching, a relationship previously demonstrated in animal studies of neural
circuitry, as well as semantic retrieval, as previously shown in human PET studies
(Duan et al., 2015; Heckers, Weiss, Alpert, & Schacter, 2002). It remains to be seen
whether brainstem atrophy is specific to a particular cognitive domain or is more
generally associated with decline, but the present study nonetheless posits a role for
brainstem volumetrics in the ability to track cognitive decline in older adults.
The theorized protective role of structural brainstem integrity is also supported by
our longitudinal findings, as larger midbrain volumes in CN individuals were associated
with decreased risk for developing dementia. This pattern was absent within the MCI
group, suggesting that the protective effects of larger brainstem structures may be
BRAINSTEM IN AD PROGRESSION 26
specific to the preclinical phase of AD, prior to the onset of any symptoms. The
brainstem-neural reserve hypothesis was again supported, as larger midbrain and
brainstem volumes at baseline were observed in CN individuals who never progressed
to dementia relative to those who did, in addition to larger hippocampal and MTL
volumes. Within the MCI group, the hippocampus and MTL were larger in those who
never progressed to dementia, but there were no differences in brainstem regions,
highlighting that the subtle structural difference of the brainstem may only be noticeable
in asymptomatic preclinical populations. Together, these findings suggest that larger
brainstem and midbrain volumes at baseline could serve as potential MRI-based
biomarkers for AD sensitive to change earlier in preclinical populations. Future studies
following CN groups over longer follow-up periods will be needed to further support our
proposed theoretical framework implicating brainstem volumetrics as early markers for
predicting progression to dementia.
The present study also observed a reduction of the midbrain volume in AD
dementia relative to CN and MCI, underscoring the importance of this region when
conceptualizing the pathological progression of AD. Despite a historical focus on cortical
regions, recent post-mortem studies have identified the development of NFTs in the
DRN of the midbrain as preceding any transentorhinal involvement (Braak & Del Tredici,
2015; Grinberg et al., 2009). The volumetric reduction of the midbrain observed in the
present study in CN individuals who eventually progress to dementia aligns with this
theoretical staging of AD. Prior to any detectable differences in subjective or objective
cognitive impairment, reduced volume of the brainstem and the midbrain may confer
some degree of risk for eventual progression to dementia. The key nuclei affected in the
BRAINSTEM IN AD PROGRESSION 27
early pathogenesis of AD (e.g. DRN, LC) are miniscule compared to the larger
structures (e.g. midbrain, pons) they are housed in, highlighting the surprising ability of
structural MRI to detect differences in the present study. Moreover, recent work has
suggested that the revised Braak & Braak subcortical staging may be underestimating
the number of affected brainstem nuclei. A recent postmortem study found that in
addition to the DRN and LC, numerous other midbrain (periaqueductal grey,
dopaminergic substantia nigra and ventral tegmentum, peripeduncular nucleus) and
pons (parabrachial nuclei) regions encounter AD-related tau pathology in Braak stage 0
individuals (Stratmann et al., 2016). This corroborates our approach of viewing the
gross neuronal structures of the brainstem, midbrain, and pons, since tau cytoskeletal
pathology may be more widespread in these structures than previously thought.
The present study had numerous strengths, including the large sample size, the
standardized and validated approach to volumetric quantification of the brainstem, the
examination of brainstem volumes in novel populations (MCI & biomarker-delineated
preclinical groups), and the use of revised empirically-derived groupings of CN and MCI
participants. Our inclusion of longitudinal diagnostic data is another substantial
improvement over prior studies of brainstem structure in AD progression, and future
studies should employ longitudinal imaging methods and linear mixed-effects models to
quantify volumetric loss over time and track concurrent cognitive changes. However,
several limitations to the present study must also be addressed. Due to the numerous
sites of data collection, there was high variability in individual subject history and
instrumentation (e.g. MRI scanner model and field strength, neuropsychological test
administrators) between sites. Additionally, our approach utilized overlaid ROIs to
BRAINSTEM IN AD PROGRESSION 28
extract volumes, as opposed to individually segmented and extracted volumes across
the whole sample. Future studies should aim to employ existing probabilistic
segmentation algorithms, taking into account the time- and labor-intensive aspects of
such methods when analyzing large datasets (Iglesias et al., 2015; Nigro et al., 2014).
The present study also only analyzed a single modality of neuroimaging data, and future
research centered on the brainstem and its sub-regions with multimodal imaging
techniques (e.g. functional MRI, diffusion tensor imaging, arterial spin labeling) is
needed to examine network dysfunction, structural connectivity, and regional blood flow
changes in preclinical populations. In our analyses involving brainstem structures and
neuropsychological tests, we did not correct for hippocampal or medial temporal
volume, due to high multicolinearity. Volumetric loss of brainstem regions in MCI may
have some degree of association with cognitive decline, but additional evidence and
refined methods are needed to disentangle if and how these effects are independent
from the detrimental impact of hippocampal and MTL atrophy.
Our findings implicate baseline brainstem and midbrain volumes as potential
biomarkers for identifying individuals at risk for progression to dementia, supporting the
theory that certain brainstem regions and associated nuclei are vulnerable to early
neuronal injury and consequent network degradation in AD. The underlying
mechanisms accounting for the selective vulnerability of these regions in the
pathological progression of AD remain unknown, but the present study findings
contribute to a developing theoretical framework in which reduced brainstem structural
integrity earlier in life may impact long-term outcomes. The vast number of afferent and
efferent projections from the brainstem, combined with its involvement in crucial central
BRAINSTEM IN AD PROGRESSION 29
nervous system functions and diffuse projections to various cortical regions, paint the
brainstem as a particularly active structure with wide-reaching interconnectivity. A
growing body of literature suggests that tau pathology propagates from neuron to
neuron in a prion-like fashion, and a highly interconnected structure like the brainstem
may thus be at greater risk for accumulating and distributing tau pathology (Heiko Braak
& Del Tredici, 2011a; Goedert, Masuda-Suzukake, & Falcon, 2017; Spillantini &
Goedert, 2013). Future studies should employ multimodal imaging techniques
longitudinally to examine network connectivity and white matter tractography to further
test the theorized role of the brainstem as the origin of AD-related processes.
Findings from the present study may ultimately contribute to the development of
MRI-based outcome measures for future research study and clinical trial designs under
this framework of a selectively vulnerable and early-targeted brainstem. As new
medications and therapies are developed to halt or even reverse the progression of AD,
our findings further implicate the brainstem as a potential therapeutic target.
BRAINSTEM IN AD PROGRESSION 30
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Table 1. Baseline demographic, neuropsychological, and neuroimaging data
Abbreviations: NP Neuropsychological, MMSE Mini-Mental State Exam, AVLT Rey Auditory Verbal
Learning Test, BNT Boston Naming Test, ANART American National Adult Reading Test, TIV Total
Intracranial Volume, MTL Medial Temporal
F or chi-squared are result of one-way ANOVA (age, Education), chi-square test of independence (sex),
or one-way ANCOVA (all other variables; covariates = age, sex, education)
a
CN vs. MCI n.s.; All other pairwise comparisons p < 0.05
b
CN vs. MCI p < 0.001
c
All pairwise comparisons p < 0.001
d
MCI vs. AD n.s.; All other pairwise comparisons p < 0.001
e
CN > MCI p = 0.023; CN > AD p = 0.02
f
CN > MCI p = 0.001; CN > AD p < 0.001
*
Scores were log transformed
†
Scores were reflected
‡
Higher scores indicate worse performance
§
ROI volumes were normalized using the following equation: (ROI volume / TIV) × 10
3
Cognitively
Normal
Mild Cognitive
Impairment
Alzheimer’s
Disease
F or χ
2
p-value
Demographics
n 827 547 303
Age 73.5 (6.8) 73.6 (7.4) 75.3 (7.7) 7.96 <0.001
a
Sex (M/F) 421/406 335/212 166/137 14.22 0.001
b
Education 16.3 (2.7) 15.9 (2.9) 15.2 (3.0) 17.53 <0.001
c
NP Testing
MMSE 28.78 (1.36) 27.18 (1.82) 23.33 (2.01) 1155.79 <0.001
c
AVLT Recall 7.27 (3.88) 2.15 (2.62) 0.71 (1.53) 645.03 <0.001
c
AVLT Recognition 13.06 (2.20) 8.93 (3.20) 7.01 (3.96) 576.02 <0.001
c
Trails A
*,‡
1.53 (0.14) 1.60 (0.17) 1.74 (0.21) 164.30 <0.001
c
Trails B
*,‡
1.91 (0.17) 2.06 (0.23) 2.24 (0.22) 297.43 <0.001
c
Animals Fluency 20.13 (5.26) 15.71 (4.71) 12.60 (4.89) 263.93 <0.001
c
BNT
†,‡
0.37 (0.28) 0.66 (0.34) 0.81 (0.36) 261.73 <0.001
c
ANART errors
‡
9.57 (7.68) 14.37 (9.92) 15.94 (9.57) 60.13 <0.001
d
Neuroimaging
§
TIV (ml) 1498 (147) 1520 (161) 1501 (169) 0.16 0.853
Brainstem 13.33 (1.18) 13.14 (1.20) 13.07 (1.28) 3.97 0.019
e
Midbrain 3.89 (0.31) 3.82 (0.31) 3.78 (0.33) 11.16 <0.001
f
Pons 7.70 (0.75) 7.60 (0.75) 7.59 (0.81) 1.93 0.146
Hippocampus 6.01 (0.64) 5.51 (0.71) 5.04 (0.68) 280.44 <0.001
c
MTL 3.10 (0.36) 2.83 (0.41) 2.56 (0.38) 262.05 <0.001
c
BRAINSTEM IN AD PROGRESSION 47
Figure 1. ROI Overlays
Regions of interest (ROIs) from the present study overlaid on a template brain in
MNI152 space.
Abbreviations: MTL Medial Temporal Lobe (defined by Brodmann areas 27, 28, 34, 35,
36)
BRAINSTEM IN AD PROGRESSION 48
Figure 2. Diagnostic group comparisons of brainstem, midbrain, and pons
volumes
Group differences in A) brainstem, B) midbrain, and C) pons volumes are represented
by post-hoc pairwise LSD tests following one-way ANCOVAs with age, sex, and
education as covariates. Error bars represent ± 1 SEM. * p < 0.05, ** p < 0.01
BRAINSTEM IN AD PROGRESSION 49
Table 2. Baseline descriptive statistics within the Preclinical CN group
Abbreviations: NP Neuropsychological, MMSE Mini-Mental State Exam, AVLT Rey Auditory Verbal
Learning Test, BNT Boston Naming Test, ANART American National Adult Reading Test, TIV Total
Intracranial Volume, MTL Medial Temporal
F or chi-squared are result of one-way ANOVA (Age, Education), chi-square test of independence (Sex),
or one-way ANCOVA (all other variables; covariates = age, sex, education)
a
Aβ-Ptau- < Aβ+Ptau- p = 0.012; Aβ-Ptau- < Aβ-Ptau+ p = 0.002; Aβ-Ptau- < Aβ+Ptau+ p < 0.001
b
Aβ-Ptau- > Aβ+Ptau+ p < 0.001
c
Aβ-Ptau- < Aβ+Ptau- p = 0.003; Aβ-Ptau- < Aβ+Ptau+ p =0.001; Aβ-Ptau+ < Aβ+Ptau+ p = 0.036
d
Aβ-Ptau- vs. Aβ-Ptau+ n.s.; Aβ+Ptau- vs. Aβ+Ptau+ n.s.; all other pairwise comparisons p ≤ 0.001
e
Aβ-Ptau- < Aβ+Ptau+ p = 0.026; Aβ+Ptau- < Aβ-Ptau+ p = 0.02; Aβ+Ptau- < Aβ+Ptau+ p = 0.005
f
Aβ-Ptau- < Aβ+Ptau+ p = 0.015; Aβ+Ptau- < Aβ-Ptau+ p = 0.042; Aβ+Ptau- < Aβ+Ptau+ p = 0.01
g
Aβ-Ptau- < Aβ+Ptau+ p = 0.043; Aβ+Ptau- < Aβ-Ptau+ p = 0.044; Aβ+Ptau- < Aβ+Ptau+ p = 0.01
h
Aβ+Ptau- < Aβ-Ptau+ p = 0.002
i
Aβ-Ptau- < Aβ-Ptau+ p = 0.019; Aβ+Ptau- < Aβ-Ptau+ p = 0.001; Aβ+Ptau- < Aβ+Ptau+ p = 0.048
*
Scores were log transformed
†
Scores were reflected
‡
Higher scores indicate worse performance
§
ROI volumes were normalized using the following equation: (ROI volume / TIV) × 10
3
Aβ-Ptau- Aβ+Ptau- Aβ-Ptau+ Aβ+Ptau+ F or χ
2
p-value
Demographics
n 267 118 104 105
Age 71.32 (6.73) 73.20 (6.43) 73.70 (7.39) 74.95 (6.31) 8.63 <0.001
a
Sex (M/F) 129/138 62/56 49/54 58/47 2.10 0.552
Education 16.31 (2.60) 16.42 (2.67) 16.38 (2.49) 16.01 (2.62) 0.55 0.646
NP Testing
MMSE 28.90 (1.19) 28.92 (1.32) 28.79 (1.37) 28.63 (1.55) 0.50 0.681
AVLT Recall 8.03 (3.90) 7.06 (3.59) 7.18 (3.77) 5.98 (3.44) 4.62 0.003
b
AVLT
Recognition
13.24 (2.15) 12.75 (2.50) 13.12 (1.92) 12.99 (2.10) 0.89 0.445
Trails A
*,‡
1.49 (0.14) 1.54 (0.14) 1.52 (0.14) 1.57 (0.14) 5.44 0.001
c
Trails B
*,‡
1.87 (0.16) 1.94 (0.17) 1.88 (0.15) 1.97 (0.18) 8.87 <0.001
d
Animals Fluency 20.58 (5.17) 20.53 (5.68) 20.48 (4.80) 19.09 (4.67) 0.89 0.445
BNT
†,‡
0.36 (0.28) 0.39 (0.30) 0.33 (0.28) 0.41 (0.27) 1.99 0.115
ANART errors
‡
9.97 (7.94) 9.93 (8.09) 8.93 (6.90) 9.79 (7.66) 0.58 0.628
Neuroimaging
§
TIV (ml) 1499 (152) 1508 (149) 1489 (148) 1491 (147.1) 0.91 0.435
Brainstem 13.35 (1.17) 13.13 (1.19) 13.50 (1.22) 13.50 (1.21) 3.55 0.014
e
Midbrain 3.89 (0.32) 3.85 (0.32) 3.93 (0.30) 3.92 (0.30) 3.36 0.018
f
Pons 7.70 (0.73) 7.58 (0.75) 7.79 (0.80) 7.81 (0.80) 2.87 0.036
g
Hippocampus 6.11 (0.63) 5.92 (0.68) 6.13 (0.62) 5.95 (0.55) 3.22 0.022
h
MTL 3.16 (0.34) 3.06 (0.38) 3.20 (0.35) 3.10 (0.32) 4.11 0.007
i
BRAINSTEM IN AD PROGRESSION 50
Figure 3. CSF biomarker group comparisons of preclinical CN group
CSF biomarker group differences in A) brainstem, B) midbrain, and C) pons volumes
are represented by post-hoc pairwise LSD tests following one-way ANCOVAs with age,
sex, and education as covariates. Error bars represent ± 1 SEM. * p < 0.05, ** p < 0.01
BRAINSTEM IN AD PROGRESSION 51
8 10 12 14 16 18 20
1.0
1.5
2.0
2.5
(Brainstem/TIV) × 10
3
Trails A time
β = -0.11
p = 0.015
A
2.5 3.0 3.5 4.0 4.5 5.0 5.5
1.0
1.5
2.0
2.5
(Midbrain/TIV) × 10
3
Trails A time
β = -0.14
p = 0.002
C
2.5 3.0 3.5 4.0 4.5 5.0 5.5
0
10
20
30
40
(Midbrain/TIV) × 10
3
Animals Fluency
β = 0.10
p = 0.025
E
4 6 8 10 12
1.0
1.5
2.0
2.5
(Pons/TIV) × 10
3
Trails A time
β = -0.10
p = 0.027
B
2.5 3.0 3.5 4.0 4.5 5.0 5.5
1.5
2.0
2.5
3.0
(Midbrain/TIV) × 10
3
Trails B time
β = -0.10
p = 0.019
D
Figure 4. Scatter plots of neuropsychological tests and ROI volumes within the
MCI group
Scatter plots show correlations between neuropsychological tests and brain volumes in
the MCI group. Linear regression line is overlaid in purple, with a shaded 95%
confidence interval. Parameter estimates (β) are from multiple linear regression
controlling for age, sex, and education.
BRAINSTEM IN AD PROGRESSION 52
Table 3. Multiple linear regressions within the MCI group and subgroups
A
Brain ROI NP Test β t p-value
Brainstem Trails A -0.11 -2.44 0.015
Pons Trails A -0.10 -2.21 0.027
Midbrain Trails A -0.14 -3.06 0.002
Trails B -0.10 -2.34 0.019
Animals Fluency 0.10 2.24 0.025
B
Brain ROI NP Test β t p-value
Brainstem Trails A -0.10 -1.71 0.089
Pons Trails A -0.08 -1.37 0.173
Midbrain Trails A -0.16 -2.53 0.012
Trails B -0.18 -3.01 0.003
Animals Fluency 0.10 1.50 0.136
C
Brain ROI NP Test β t p-value
Brainstem Trails A 0.00 0.01 0.992
Pons Trails A 0.01 0.16 0.872
Midbrain Trails A -0.04 -0.46 0.647
Trails B -0.00 -0.05 0.959
Animals Fluency 0.16 2.09 0.038
D
Brain ROI NP Test β t p-value
Brainstem Trails A -0.27 -2.33 0.022
Pons Trails A -0.29 -2.52 0.014
Midbrain Trails A -0.21 -1.82 0.073
Trails B 0.05 0.44 0.659
Animals Fluency 0.09 0.75 0.453
Results from multiple linear regression within the A) Overall MCI group (n = 547), B) Amnestic MCI
subgroup (n = 298), C) Dysnomic MCI subgroup (n = 167), and D) Dysexecutive MCI subgroup (n = 82).
Note: each line denotes a separate model with Brain ROI as IV, NP test as DV, and age, sex, education
as covariates
BRAINSTEM IN AD PROGRESSION 53
Table 4. Baseline neuroimaging data for groups with longitudinal diagnostic data
A
Converters Non-Converters F p-value
n 86 386
TIV 1518.54 (132.42) 1494.89 (144.64) <0.01 0.997
Brainstem 13.07 (1.30) 13.42 (1.14) 4.10 0.043
Midbrain 3.79 (0.34) 3.91 (0.30) 7.60 0.006
Pons 7.59 (0.83) 7.76 (0.74) 2.34 0.127
Hippocampus 5.49 (0.63) 6.11 (0.57) 72.99 <0.001
MTL 2.81 (0.34) 3.15 (0.32) 69.64 <0.001
B
Converters Non-Converters F p-value
n 250 268
TIV 1518.65 (177.08) 1521.64 (144.95) 1.72 0.191
Brainstem 13.19 (1.14) 13.09 (1.23) 0.46 0.497
Midbrain 3.83 (0.30) 3.81 (0.32) 0.09 0.766
Pons 7.65 (0.72) 7.56 (0.77) 1.23 0.269
Hippocampus 5.34 (0.65) 5.69 (0.72) 46.08 <0.001
MTL 2.72 (0.37) 2.93 (0.41) 47.62 <0.001
F is result of one-way ANCOVA controlling for age, sex, and education. Comparisons are between
individuals who progressed to dementia (Converters) and individuals who never progressed to dementia
(Non-Converters) within the A) CN group and B) MCI group
Abbreviations: TIV Total intracranial volume, MTL Medial temporal lobe
BRAINSTEM IN AD PROGRESSION 54
Figure 5. Group comparisons of ROI volumes between converters and non-
converters to dementia within CN and MCI groups
Converter vs. non-converter group differences in A) brainstem, B) midbrain, and C)
pons volumes are represented by post-hoc pairwise LSD tests following one-way
ANCOVAs with age, sex, and education as covariates. Error bars represent ± 1 SEM.
* p < 0.05, ** p < 0.01
BRAINSTEM IN AD PROGRESSION 55
Table 5. Survival Analyses
A
Brain ROI Χ
2
β Wald Odds Ratio 95% CI p-value
Whole Brainstem 3.10 0.16 3.12 1.18 [0.98,1.41] 0.078
Midbrain 5.69 0.81 5.83 2.25 [1.17,4.36] 0.016
Pons 2.23 0.22 2.23 1.24 [0.93,1.65] 0.135
Hippocampus 51.21 1.23 57.28 3.41 [2.48,4.68] <0.001
MTL 49.25 2.10 58.14 8.20 [4.77,14.08] <0.001
B
Brain ROI Χ
2
β Wald Odds Ratio 95% CI p-value
Whole Brainstem 0.01 -0.01 0.01 0.99 [0.89,1.11] 0.912
Midbrain 0.15 0.09 0.15 1.09 [0.70,1.69] 0.703
Pons 0.31 -0.05 0.31 0.95 [0.80,1.13] 0.576
Hippocampus 55.82 0.80 56.47 2.22 [1.80,2.73] <0.001
MTL 55.45 1.30 57.06 3.66 [2.61,5.12] <0.001
Results from Cox regressions are displayed within the A) CN at baseline (event cases =
86; censored cases = 386) and B) MCI at baseline (event cases = 250; censored cases
= 268) groups.
Chi-square indicates change from block 1 (age, sex, education only). β, Wald, Odds
Ratio (OR), 95% CI, and p-value indicate contribution of region in predicting risk for
future dementia after controlling for age, sex, education.
Note: each line denotes a separate model with reflected Brain ROI as IV and binarized
variable for conversion to dementia as event of interest.
BRAINSTEM IN AD PROGRESSION 56
Figure 6. Survival Analyses
Results from Cox regression visually displayed via survival curves. ROC curve analysis
was conducted to determine the optimal cutoff point for midbrain volume, and diagnostic
groups were subsequently split into “high” and “low” midbrain groups for visualization.
BRAINSTEM IN AD PROGRESSION 57
Cluster-derived CN
Amnestic MCI
Dysnomic MCI
Dysexecutive MCI
-5
-4
-3
-2
-1
0
1
Corrected z-score
MCI Cluster Analysis
Delayed Recall
Delayed Recognition
Trails A
Trails B
Animals Fluency
BNT
Cluster-derived CN
Amnestic MCI
Dysnomic MCI
Dysexecutive MCI
-5
-4
-3
-2
-1
0
1
Corrected z-score
MCI Cluster Analysis
Delayed Recall
Delayed Recognition
Trails A
Trails B
Animals Fluency
BNT
Supplemental Figure 1. MCI Cluster Analysis
BRAINSTEM IN AD PROGRESSION 58
Supplemental Table 1
r or t CN
n = 827
MCI
n = 547
AD
n = 303
Age Sex Educ Age Sex Educ Age Sex Educ
Brainstem -0.21** -4.33** -0.01 -0.28** -5.52** -0.05 -0.22** -4.90** -0.04
Midbrain -0.26** -4.72** -0.04 -0.32** -5.37** -0.05 -0.30** -5.47** -0.03
Pons -0.15** -3.61** -0.01 -0.22** -4.82** -0.04 -0.15** -4.14** -0.03
Relationships between brainstem volumes and demographic variables (Pearson
correlations (r) for age and education; two-sample t-test (t) for sex)
* p < 0.05, **p < 0.01
Supplemental Table 2
CN
n = 827
Delayed
Recall
Delayed
Recognition
Trails A Trails B Animals
Fluency
BNT ANART
Brainstem 0.07 0.06 -0.09** -0.08** 0.07** 0.02 -0.01
Midbrain 0.10** 0.07** -0.12** -0.10** 0.09* 0.01 -0.003
Pons 0.04 0.05 -0.07* -0.05 0.05 0.02 -0.01
MCI
n = 547
Delayed
Recall
Delayed
Recognition
Trails
A
Trails
B
Animals
Fluency
BNT ANART
Brainstem 0.03 0.05 -0.14** -0.11** 0.09* -0.004 0.05
Midbrain 0.06 0.04 -0.17** -0.15** 0.12** -0.05 0.04
Pons 0.003 0.04 -0.12** -0.09* 0.07 0.01 0.04
AD
n = 303
Delayed
Recall
Delayed
Recognition
Trails
A
Trails
B
Animals
Fluency
BNT ANART
Brainstem 0.06 -0.10 0.02 0.04 0.09 -0.04 -0.04
Midbrain 0.083 -0.07 0.01 0.06 0.10 -0.08 -0.03
Pons 0.04 -0.10 0.04 0.04 0.08 -0.04 -0.05
Relationships between brainstem volumes and neuropsychological tests as represented
by zero order Pearson correlations in CN, MCI, and AD groups
* p < 0.05, **p < 0.01
Abstract (if available)
Abstract
Prior research has established the brainstem as the earliest site of tau pathology in Alzheimer’s disease (AD), but few studies have examined the utility of brainstem structural MRI in predicting dementia risk or related brainstem volumes to AD biomarkers. The present study compared brainstem, midbrain, and pons volumes across the spectrum of neurocognitive decline and AD biomarker abnormality, examined neuropsychological profiles linked to these regional brainstem volumes and investigated their predictive value for future dementia. Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants (N = 1677) classified as cognitively normal (CN), mild cognitive impairment (MCI) or AD underwent baseline MRI scanning with clinical follow-up (6-120 months). We observed significantly smaller brainstem and midbrain volumes in AD and MCI patients relative to CN, with no difference in pons volumes. Among CN individuals, those who never progressed to dementia exhibited larger baseline brainstem and midbrain volumes, and larger midbrain volume conveyed decreased risk of progression to AD. CN older adults who were AD biomarker-positive also showed larger brainstem, midbrain, and pons volumes relative to those who were biomarker-negative. Among MCI patients, greater brainstem volumes correlated with better neuropsychological performance. Findings demonstrate reduced brainstem volume in MCI and AD, and implicate whole brainstem and midbrain volume in risk for future dementia in preclinical populations. These volumetric differences early in the AD process are consistent with neuropathological findings of AD-related pathology first appearing in specific brainstem nuclei. Brainstem volumes may thus be an independent biomarker for identifying preclinical individuals at risk for dementia.
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Creator
Dutt, Shubir
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Core Title
Brainstem structural integrity in the progression of Alzheimer's disease
School
College of Letters, Arts and Sciences
Degree
Master of Arts
Degree Program
Psychology
Publication Date
08/05/2018
Defense Date
04/19/2018
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Tag
Alzheimer's disease,brainstem,cognitive aging,OAI-PMH Harvest,structural MRI
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Nation, Daniel A. (
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), Mather, Mara (
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Tags
Alzheimer's disease
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