John C. Morris

YNIMG Journal 2024 Journal Article

Accuracy of TrUE-Net in comparison to established white matter hyperintensity segmentation methods: An independent validation study

• Jeremy F. Strain
• Maryam Rahmani
• Donna Dierker
• Christopher Owen
• Hussain Jafri
• Andrei G. Vlassenko
• Kyle Womack
• Jurgen Fripp

White matter hyperintensities (WMH) are nearly ubiquitous in the aging brain, and their topography and overall burden are associated with cognitive decline. Given their numerosity, accurate methods to automatically segment WMH are needed. Recent developments, including the availability of challenge data sets and improved deep learning algorithms, have led to a new promising deep-learning based automated segmentation model called TrUE-Net, which has yet to undergo rigorous independent validation. Here, we compare TrUE-Net to six established automated WMH segmentation tools, including a semi-manual method. We evaluated the techniques at both global and regional level to compare their ability to detect the established relationship between WMH burden and age. We found that TrUE-Net was highly reliable at identifying WMH regions with low false positive rates, when compared to semi-manual segmentation as the reference standard. TrUE-Net performed similarly or favorably when compared to the other automated techniques. Moreover, TrUE-Net was able to detect relationships between WMH and age to a similar degree as the reference standard semi-manual segmentation at both the global and regional level. These results support the use of TrUE-Net for identifying WMH at the global or regional level, including in large, combined datasets.

YNICL Journal 2024 Journal Article

Predicting cognitive decline: Which is more useful, baseline amyloid levels or longitudinal change?

• Gengsheng Chen
• Nicole S. McKay
• Brian A. Gordon
• Jingxia Liu
• Nelly Joseph-Mathurin
• Suzanne E. Schindler
• Jason Hassenstab
• Andrew J. Aschenbrenner

C-Pittsburgh compound B (PiB) Aβ-PET to predict cognitive decline. A cohort of 153 participants who previously underwent PiB-PET scans and comprehensive clinical assessments were used in this study. Our analyses revealed that baseline Aβ is significantly associated with the rate of change in cognitive composite scores, with cognition declining more rapidly when baseline PiB Aβ levels were higher. In contrast, no signification association was identified between the rate of change in PiB-PET Aβ and cognitive decline. Additionally, the ability of the rate of change in the PiB-PET measures to predict cognitive decline was significantly influenced by APOE ε4 carrier status. These results suggest that a single PiB-PET scan is sufficient to predict cognitive decline and that longitudinal measures of Aβ accumulation do not improve the prediction of cognitive decline once someone is amyloid positive.

YNICL Journal 2023 Journal Article

Neuroinflammation and amyloid deposition in the progression of mixed Alzheimer and vascular dementia

• Chunwei Ying
• Peter Kang
• Michael M. Binkley
• Andria L. Ford
• Yasheng Chen
• Jason Hassenstab
• Qing Wang
• Jeremy Strain

BACKGROUND: Alzheimer's disease (AD) and vascular contributions to cognitive impairment and dementia (VCID) pathologies coexist in patients with cognitive impairment. Abnormal amyloid beta (Aβ) deposition is the hallmark pathologic biomarker for AD. Neuroinflammation may be a pathophysiological mechanism in both AD and VCID. In this study, we aimed to understand the role of neuroinflammation and Aβ deposition in white matter hyperintensities (WMH) progression and cognitive decline over a decade in patients with mixed AD and VCID pathologies. METHODS: C-PiB MCBP) and baseline WMH volume and cognitive function. Moreover, linear mixed-effects models evaluated whether PET biomarkers predicted greater WMH progression or cognitive decline over a decade. RESULTS: C-PiB MCBP. CONCLUSIONS: Neuroinflammation and Aβ deposition may represent two distinct pathophysiological pathways, and both independently contributed to the progression of cognitive impairment in mixed AD and VCID pathologies. Neuroinflammation, but not Aβ deposition, contributed to WMH volume and progression.

YNIMG Journal 2022 Journal Article

Covariance-based vs. correlation-based functional connectivity dissociates healthy aging from Alzheimer disease

• Jeremy F. Strain
• Matthew R. Brier
• Aaron Tanenbaum
• Brian A. Gordon
• John E. McCarthy
• Aylin Dincer
• Daniel S. Marcus
• Jasmeer P. Chhatwal

Prior studies of aging and Alzheimer disease have evaluated resting state functional connectivity (FC) using either seed-based correlation (SBC) or independent component analysis (ICA), with a focus on particular functional systems. SBC and ICA both are insensitive to differences in signal amplitude. At the same time, accumulating evidence indicates that the amplitude of spontaneous BOLD signal fluctuations is physiologically meaningful. We systematically compared covariance-based FC, which is sensitive to amplitude, vs. correlation-based FC, which is not, in affected individuals and controls drawn from two cohorts of participants including autosomal dominant Alzheimer disease (ADAD), late onset Alzheimer disease (LOAD), and age-matched controls. Functional connectivity was computed over 222 regions of interest and group differences were evaluated in terms of components projected onto a space of lower dimension. Our principal observations are: (1) Aging is associated with global loss of resting state fMRI signal amplitude that is approximately uniform across resting state networks. (2) Thus, covariance FC measures decrease with age whereas correlation FC is relatively preserved in healthy aging. (3) In contrast, symptomatic ADAD and LOAD both lead to loss of spontaneous activity amplitude as well as severely degraded correlation structure. These results demonstrate a double dissociation between age vs. Alzheimer disease and the amplitude vs. correlation structure of resting state BOLD signals. Modeling results suggest that the AD-associated loss of correlation structure is attributable to a relative increase in the fraction of locally restricted as opposed to widely shared variance.

YNIMG Journal 2022 Journal Article

Predicting brain age from functional connectivity in symptomatic and preclinical Alzheimer disease

• Peter R. Millar
• Patrick H. Luckett
• Brian A. Gordon
• Tammie L.S. Benzinger
• Suzanne E. Schindler
• Anne M. Fagan
• Randall J. Bateman
• Jae-Hong Lee

"Brain-predicted age" quantifies apparent brain age compared to normative neuroimaging trajectories. Advanced brain-predicted age has been well established in symptomatic Alzheimer disease (AD), but is underexplored in preclinical AD. Prior brain-predicted age studies have typically used structural MRI, but resting-state functional connectivity (FC) remains underexplored. Our model predicted age from FC in 391 cognitively normal, amyloid-negative controls (ages 18-89). We applied the trained model to 145 amyloid-negative, 151 preclinical AD, and 156 symptomatic AD participants to test group differences. The model accurately predicted age in the training set. FC-predicted brain age gaps (FC-BAG) were significantly older in symptomatic AD and significantly younger in preclinical AD compared to controls. There was minimal correspondence between networks predictive of age and AD. Elevated FC-BAG may reflect network disruption during symptomatic AD. Reduced FC-BAG in preclinical AD was opposite to the expected direction, and may reflect a biphasic response to preclinical AD pathology or may be driven by inconsistency between age-related vs. AD-related networks. Overall, FC-predicted brain age may be a sensitive AD biomarker.

YNICL Journal 2021 Journal Article

Lack of association between acute stroke, post-stroke dementia, race, and β-amyloid status

• Lauren N. Koenig
• Lena M. McCue
• Elizabeth Grant
• Parinaz Massoumzadeh
• Catherine M. Roe
• Chengjie Xiong
• Krista L. Moulder
• Liang Wang

INTRODUCTION: Stroke and Alzheimer disease share risk factors and often co-occur, and both have been reported to have a higher prevalence in African Americans as compared to non-Hispanic whites. However, their interaction has not been established. The objective of this study was to determine if preclinical Alzheimer disease is a risk factor for stroke and post-stroke dementia and whether racial differences moderate this relationship. METHODS: This case-control study was analyzed in 2019 using retrospective data from 2007 to 2013. Participants were adults age 65 and older with and without acute ischemic stroke. Recruitment included word of mouth and referrals in Saint Louis, MO, with stroke participants recruited from acutely hospitalized patients and non-stroke participants from community living older adults who were research volunteers. Our assessment included radiologic reads of infarcts, microbleeds, and white matter hyperintensitites (WMH); a Pittsburgh Compound B PET measure of cortical β-amyloid binding; quantitative measures of hippocampal and WMH volume; longitudinal Mini Mental State Examination (MMSE) scores; and Clinical Dementia Rating (CDR) 1 year post-stroke. RESULTS: A total of 243 participants were enrolled, 81 of which had a recent ischemic stroke. Participants had a mean age of 75, 57% were women, and 52% were African American. Cortical amyloid did not differ significantly by race, stroke status, or CDR post-stroke. There were racial differences in MMSE scores at baseline (mean 26.8 for African Americans, 27.9 for non-Hispanic whites, p = 0.03), but not longitudinally. African Americans were more likely to have microbleeds (32.8% vs 22.6%, p = 0.04), and within the acute stroke group, African Americans were more likely to have small infarcts (75.6% vs 56.8%, p = 0.049). CONCLUSION: Preclinical Alzheimer disease did not show evidence of being a risk factor for stroke nor predictive of post-stroke dementia. We did not observe racial differences in β-amyloid levels. However, even after controlling for several vascular risk factors, African Americans with clinical stroke presentations had greater levels of vascular pathology on MRI.

YNICL Journal 2020 Journal Article

Comparing cortical signatures of atrophy between late-onset and autosomal dominant Alzheimer disease

• Aylin Dincer
• Brian A. Gordon
• Amrita Hari-Raj
• Sarah J. Keefe
• Shaney Flores
• Nicole S. McKay
• Angela M. Paulick
• Kristine E. Shady Lewis

F-florbetapir. To generate cortical signature maps of cortical thickness, we performed a vertex-wise analysis between the cognitively normal controls and impaired groups within each cohort using six increasingly conservative statistical thresholds to determine significance. The optimal cortical map among the six statistical thresholds was determined from a receiver operating characteristic analysis testing the performance of each map in discriminating between the cognitively normal controls and preclinical groups. We then performed within-cohort and cross-cohort (e.g. ADAD maps evaluated in the Knight ADRC cohort) analyses to examine the sensitivity of the optimal cortical signature maps to the amyloid levels using only the cognitively normal individuals (cognitively normal controls and preclinical groups) in comparison to hippocampal volume. We found the optimal cortical signature maps were sensitive to early increases in amyloid for the asymptomatic individuals within their respective cohorts and were significant beyond the inclusion of hippocampus volume, but the cortical signature maps performed poorly when analyzing across cohorts. These results suggest the cortical signature maps are a useful MRI biomarker of early AD-related neurodegeneration in preclinical individuals and the pattern of decline differs between LOAD and ADAD.

YNICL Journal 2020 Journal Article

Select Atrophied Regions in Alzheimer disease (SARA): An improved volumetric model for identifying Alzheimer disease dementia

• Lauren N. Koenig
• Gregory S. Day
• Amber Salter
• Sarah Keefe
• Laura M. Marple
• Justin Long
• Pamela LaMontagne
• Parinaz Massoumzadeh

INTRODUCTION: Volumetric biomarkers for Alzheimer disease (AD) are attractive due to their wide availability and ease of administration, but have traditionally shown lower diagnostic accuracy than measures of neuropathological contributors to AD. Our purpose was to optimize the diagnostic specificity of structural MRIs for AD using quantitative, data-driven techniques. METHODS: This retrospective study assembled several non-overlapping cohorts (total n = 1287) with publicly available data and clinical patients from Barnes-Jewish Hospital (data gathered 1990-2018). The Normal Aging Cohort (n = 383) contained amyloid biomarker negative, cognitively normal (CN) participants, and provided a basis for determining age-related atrophy in other cohorts. The Training (n = 216) and Test (n = 109) Cohorts contained participants with symptomatic AD and CN controls. Classification models were developed in the Training Cohort and compared in the Test Cohort using the receiver operating characteristics areas under curve (AUCs). Additional model comparisons were done in the Clinical Cohort (n = 579), which contained patients who were diagnosed with dementia due to various etiologies in a tertiary care outpatient memory clinic. RESULTS: While the Normal Aging Cohort showed regional age-related atrophy, classification models were not improved by including age as a predictor or by using volumetrics adjusted for age-related atrophy. The optimal model used multiple regions (hippocampal volume, inferior lateral ventricle volume, amygdala volume, entorhinal thickness, and inferior parietal thickness) and was able to separate AD and CN controls in the Test Cohort with an AUC of 0.961. In the Clinical Cohort, this model separated AD from non-AD diagnoses with an AUC 0.820, an incrementally greater separation of the cohort than by hippocampal volume alone (AUC of 0.801, p = 0.06). Greatest separation was seen for AD vs. frontotemporal dementia and for AD vs. non-neurodegenerative diagnoses. CONCLUSIONS: Volumetric biomarkers distinguished individuals with symptomatic AD from CN controls and other dementia types but were not improved by controlling for normal aging.

YNICL Journal 2019 Journal Article

Quantification of white matter cellularity and damage in preclinical and early symptomatic Alzheimer's disease

• Qing Wang
• Yong Wang
• Jingxia Liu
• Courtney L. Sutphen
• Carlos Cruchaga
• Tyler Blazey
• Brian A. Gordon
• Yi Su

Interest in understanding the roles of white matter (WM) inflammation and damage in the pathophysiology of Alzheimer disease (AD) has been growing significantly in recent years. However, in vivo magnetic resonance imaging (MRI) techniques for imaging inflammation are still lacking. An advanced diffusion-based MRI method, neuro-inflammation imaging (NII), has been developed to clinically image and quantify WM inflammation and damage in AD. Here, we employed NII measures in conjunction with cerebrospinal fluid (CSF) biomarker classification (for β-amyloid (Aβ) and neurodegeneration) to evaluate 200 participants in an ongoing study of memory and aging. Elevated NII-derived cellular diffusivity was observed in both preclinical and early symptomatic phases of AD, while disruption of WM integrity, as detected by decreased fractional anisotropy (FA) and increased radial diffusivity (RD), was only observed in the symptomatic phase of AD. This may suggest that WM inflammation occurs earlier than WM damage following abnormal Aβ accumulation in AD. The negative correlation between NII-derived cellular diffusivity and CSF Aβ42 level (a marker of amyloidosis) may indicate that WM inflammation is associated with increasing Aβ burden. NII-derived FA also negatively correlated with CSF t-tau level (a marker of neurodegeneration), suggesting that disruption of WM integrity is associated with increasing neurodegeneration. Our findings demonstrated the capability of NII to simultaneously image and quantify WM cellularity changes and damage in preclinical and early symptomatic AD. NII may serve as a clinically feasible imaging tool to study the individual and composite roles of WM inflammation and damage in AD.

YNICL Journal 2018 Journal Article

Utilizing the Centiloid scale in cross-sectional and longitudinal PiB PET studies

• Yi Su
• Shaney Flores
• Russ C. Hornbeck
• Benjamin Speidel
• Andrei G. Vlassenko
• Brian A. Gordon
• Robert A. Koeppe
• William E. Klunk

Amyloid imaging is a valuable tool for research and diagnosis in dementing disorders. Successful use of this tool is limited by the lack of a common standard in the quantification of amyloid imaging data. The Centiloid approach was recently proposed to address this problem and in this work, we report our implementation of this approach and evaluate the impact of differences in underlying image analysis methodologies using both cross-sectional and longitudinal datasets. The Centiloid approach successfully converts quantitative amyloid burden measurements into a common Centiloid scale (CL) and comparable dynamic range. As expected, the Centiloid values derived from different analytical approaches inherit some of the inherent benefits and drawbacks of the underlying approaches, and these differences result in statistically significant (p < 0. 05) differences in the variability and group mean values. Because of these differences, even after expression in CL, the 95% specificity amyloid positivity thresholds derived from different analytic approaches varied from 5. 7 CL to 11. 9 CL, and the reliable worsening threshold varied from −2. 0 CL to 11. 0 CL. Although this difference is in part due to the dependency of the threshold determination methodology on the statistical characteristics of the measurements. When amyloid measurements obtained from different centers are combined for analysis, one should not expect Centiloid conversion to eliminate all the differences in amyloid burden measurements due to variabilities in underlying acquisition protocols and analysis techniques.

YNIMG Journal 2017 Journal Article

AV-1451 PET imaging of tau pathology in preclinical Alzheimer disease: Defining a summary measure

• Shruti Mishra
• Brian A. Gordon
• Yi Su
• Jon Christensen
• Karl Friedrichsen
• Kelley Jackson
• Russ Hornbeck
• David A. Balota

Utilizing [18F]-AV-1451 tau positron emission tomography (PET) as an Alzheimer disease (AD) biomarker will require identification of brain regions that are most important in detecting elevated tau pathology in preclinical AD. Here, we utilized an unsupervised learning, data-driven approach to identify brain regions whose tau PET is most informative in discriminating low and high levels of [18F]-AV-1451 binding. 84 cognitively normal participants who had undergone AV-1451 PET imaging were used in a sparse k-means clustering with resampling analysis to identify the regions most informative in dividing a cognitively normal population into high tau and low tau groups. The highest-weighted FreeSurfer regions of interest (ROIs) separating these groups were the entorhinal cortex, amygdala, lateral occipital cortex, and inferior temporal cortex, and an average SUVR in these four ROIs was used as a summary metric for AV-1451 uptake. We propose an AV-1451 SUVR cut-off of 1. 25 to define high tau as described by imaging. This spatial distribution of tau PET is a more widespread pattern than that predicted by pathological staging schemes. Our data-derived metric was validated first in this cognitively normal cohort by correlating with early measures of cognitive dysfunction, and with disease progression as measured by β-amyloid PET imaging. We additionally validated this summary metric in a cohort of 13 Alzheimer disease patients, and showed that this measure correlates with cognitive dysfunction and β-amyloid PET imaging in a diseased population.

YNIMG Journal 2017 Journal Article

In vivo detection of microstructural correlates of brain pathology in preclinical and early Alzheimer Disease with magnetic resonance imaging

• Yue Zhao
• Marcus E. Raichle
• Jie Wen
• Tammie L. Benzinger
• Anne M. Fagan
• Jason Hassenstab
• Andrei G. Vlassenko
• Jie Luo

Background Alzheimer disease (AD) affects at least 5 million individuals in the USA alone stimulating an intense search for disease prevention and treatment therapies as well as for diagnostic techniques allowing early identification of AD during a long pre-symptomatic period that can be used for the initiation of prevention trials of disease-modifying therapies in asymptomatic individuals. Methods Our approach to developing such techniques is based on the Gradient Echo Plural Contrast Imaging (GEPCI) technique that provides quantitative in vivo measurements of several brain-tissue-specific characteristics of the gradient echo MRI signal (GEPCI metrics) that depend on the integrity of brain tissue cellular structure. Preliminary data were obtained from 34 participants selected from the studies of aging and dementia at the Knight Alzheimer's Disease Research Center at Washington University in St. Louis. Cognitive status was operationalized with the Clinical Dementia Rating (CDR) scale. The participants, assessed as cognitively normal (CDR=0; n=23) or with mild AD dementia (CDR=0. 5 or 1; n=11) underwent GEPCI MRI, a collection of cognitive performance tests and CSF amyloid (Aβ) biomarker Aβ42. A subset of 19 participants also underwent PET PiB studies to assess their brain Aβ burden. According to the Aβ status, cognitively normal participants were divided into normal (Aβ negative; n=13) and preclinical (Aβ positive; n=10) groups. Results GEPCI quantitative measurements demonstrated significant differences between all the groups: normal and preclinical, normal and mild AD, and preclinical and mild AD. GEPCI quantitative metrics characterizing tissue cellular integrity in the hippocampus demonstrated much stronger correlations with psychometric tests than the hippocampal atrophy. Importantly, GEPCI-determined changes in the hippocampal tissue cellular integrity were detected even in the hippocampal areas not affected by the atrophy. Our studies also uncovered strong correlations between GEPCI brain tissue metrics and beta-amyloid (Aβ) burden defined by positron emission tomography (PET) – the current in vivo gold standard for detection of cortical Aβ, thus supporting GEPCI as a potential surrogate marker for Aβ imaging – a known biomarker of early AD. Remarkably, the data show significant correlations not only in the areas of high Aβ accumulation (e. g. precuneus) but also in some areas of medial temporal lobe (e. g. parahippocampal cortex), where Aβ accumulation is relatively low. Conclusion We have demonstrated that GEPCI provides a new approach for the in vivo evaluation of AD-related tissue pathology in the preclinical and early symptomatic stages of AD. Since MRI is a widely available technology, the GEPCI surrogate markers of AD pathology have a potential for improving the quality of AD diagnostic, and the evaluation of new disease-modifying therapies.

YNIMG Journal 2015 Journal Article

Partial volume correction in quantitative amyloid imaging

• Yi Su
• Tyler M. Blazey
• Abraham Z. Snyder
• Marcus E. Raichle
• Daniel S. Marcus
• Beau M. Ances
• Randall J. Bateman
• Nigel J. Cairns

Amyloid imaging is a valuable tool for research and diagnosis in dementing disorders. As positron emission tomography (PET) scanners have limited spatial resolution, measured signals are distorted by partial volume effects. Various techniques have been proposed for correcting partial volume effects, but there is no consensus as to whether these techniques are necessary in amyloid imaging, and, if so, how they should be implemented. We evaluated a two-component partial volume correction technique and a regional spread function technique using both simulated and human Pittsburgh compound B (PiB) PET imaging data. Both correction techniques compensated for partial volume effects and yielded improved detection of subtle changes in PiB retention. However, the regional spread function technique was more accurate in application to simulated data. Because PiB retention estimates depend on the correction technique, standardization is necessary to compare results across groups. Partial volume correction has sometimes been avoided because it increases the sensitivity to inaccuracy in image registration and segmentation. However, our results indicate that appropriate PVC may enhance our ability to detect changes in amyloid deposition.

YNICL Journal 2015 Journal Article

The effects of white matter hyperintensities and amyloid deposition on Alzheimer dementia

• Brian A. Gordon
• Safa Najmi
• Phillip Hsu
• Catherine M. Roe
• John C. Morris
• Tammie L.S. Benzinger

BACKGROUND AND PURPOSE: Elevated levels of amyloid deposition as well as white matter damage are thought to be risk factors for Alzheimer Disease (AD). Here we examined whether qualitative ratings of white matter damage predicted cognitive impairment beyond measures of amyloid. MATERIALS AND METHODS: The study examined 397 cognitively normal, 51 very mildly demented, and 11 mildly demented individuals aged 42-90 (mean 68.5). Participants obtained a T2-weighted scan as well as a positron emission tomography scan using (11)[C] Pittsburgh Compound B. Periventricular white matter hyperintensities (PVWMHs) and deep white matter hyperintensities (DWMHs) were measured on each T2 scan using the Fazekas rating scale. The effects of amyloid deposition and white matter damage were assessed using logistic regressions. RESULTS: Levels of amyloid deposition (ps < 0.01), as well as ratings of PVWMH (p < 0.01) and DWMH (p < 0.05) discriminated between cognitively normal and demented individuals. CONCLUSIONS: The amount of amyloid deposition and white matter damage independently predicts cognitive impairment. This suggests a diagnostic utility of qualitative white matter scales in addition to measuring amyloid levels.

YNIMG Journal 2013 Journal Article

The effects of aging and Alzheimer's disease on cerebral cortical anatomy: Specificity and differential relationships with cognition

• Akram Bakkour
• John C. Morris
• David A. Wolk
• Bradford C. Dickerson

Although both normal aging and Alzheimer's disease (AD) are associated with regional cortical atrophy, few studies have directly compared the spatial patterns and magnitude of effects of these two processes. The extant literature has not addressed two important questions: 1) Is the pattern of age-related cortical atrophy different if cognitively intact elderly individuals with silent AD pathology are excluded? and 2) Does the age- or AD-related atrophy relate to cognitive function? Here we studied 142 young controls, 87 older controls, and 28 mild AD patients. In addition, we studied 35 older controls with neuroimaging data indicating the absence of brain amyloid. Whole-cortex analyses identified regions of interest (ROIs) of cortical atrophy in aging and in AD. Results showed that some regions are predominantly affected by age with relatively little additional atrophy in patients with AD, e. g. , calcarine cortex; other regions are predominantly affected by AD with much less of an effect of age, e. g. , medial temporal cortex. Finally, other regions are affected by both aging and AD, e. g. , dorsolateral prefrontal cortex and inferior parietal lobule. Thus, the processes of aging and AD have both differential and partially overlapping effects on specific regions of the cerebral cortex. In particular, some frontoparietal regions are affected by both processes, most temporal lobe regions are affected much more prominently by AD than aging, while sensorimotor and some prefrontal regions are affected specifically by aging and minimally more by AD. Within normal older adults, atrophy in aging-specific cortical regions relates to cognitive performance, while in AD patients atrophy in AD-specific regions relates to cognitive performance. Further work is warranted to investigate the behavioral and clinical relevance of these findings in additional detail, as well as their histological basis; ROIs generated from the present study could be used strategically in such investigations.

YNIMG Journal 2012 Journal Article

Bootstrapping GEE models for fMRI regional connectivity

• Gina M. D'Angelo
• Nicole A. Lazar
• Gongfu Zhou
• William F. Eddy
• John C. Morris
• Yvette I. Sheline

An Alzheimer's fMRI study has motivated us to evaluate inter-regional correlations during rest between groups. We apply generalized estimating equation (GEE) models to test for differences in regional correlations across groups. Both the GEE marginal model and GEE transition model are evaluated and compared to the standard pooling Fisher-z approach using simulation studies. Standard errors of all methods are estimated both theoretically (model-based) and empirically (bootstrap). Of all the methods, we find that the transition models have the best statistical properties. Overall, the model-based standard errors and bootstrap standard errors perform about the same. We also demonstrate the methods with a functional connectivity study in a healthy cognitively normal population of ApoE4+ participants and ApoE4− participants who are recruited from the Adult Children's Study conducted at the Washington University Knight Alzheimer's Disease Research Center.

YNIMG Journal 2012 Journal Article

Cognitively normal individuals with AD parents may be at risk for developing aging-related cortical thinning patterns characteristic of AD

• Katherine Reiter
• Kathryn I. Alpert
• Derin J. Cobia
• Mary J. Kwasny
• John C. Morris
• John C. Csernansky
• Lei Wang

Children of Alzheimer's disease (AD) patients are at heightened risk of developing AD due to genetic influences, including the apolipoprotein E4 (ApoE4) allele. In this study, we assessed the earliest cortical changes associated with AD in 71 cognitively healthy, adult children of AD patients (AD offspring) as compared with 69 with no family history of AD (non-AD offspring). Cortical thickness measures were obtained using FreeSurfer from 1. 5T magnetic resonance (MR) scans. ApoE genotyping was obtained. Primary analyses examined family history and ApoeE4 effects on cortical thickness. Secondary analyses examined age effects within groups. All comparisons were adjusted using False Discovery Rate at a significance threshold of p <0. 05. There were no statistically significant differences between family history and ApoE4 groups. Within AD offspring, increasing age was related to reduced cortical thickness (atrophy) over large areas of the precuneus, superior frontal and superior temporal gyri, starting at around age 60. Further, these patterns existed within female and maternal AD offspring, but were absent in male and paternal AD offspring. Within non-AD offspring, negative correlations existed over small regions of the superior temporal, insula and lingual cortices. These results suggest that as AD offspring age, cortical atrophy is more prominent, particularly if the parent with AD is mother or if the AD offspring is female.

YNIMG Journal 2006 Journal Article

Abnormalities of hippocampal surface structure in very mild dementia of the Alzheimer type

• Lei Wang
• J. Philp Miller
• Mokhtar H. Gado
• Daniel W. McKeel
• Marcus Rothermich
• Michael I. Miller
• John C. Morris
• John G. Csernansky

To better define the pattern of hippocampal deformity early in the course of Alzheimer's disease, we compared the pattern of hippocampal surface variation in subjects with very mild dementia of the Alzheimer type (DAT) and nondemented subjects. The surface of the hippocampus was divided a priori on a neuroanatomical template into three zones approximating the locations of underlying subfields [Csernansky, J. G. , Wang, L. , Swank, J. , Miller, J. P. , Gado, M. , McKeel, D. , Miller, M. I. , Morris, J. C. , 2005. Preclinical detection of Alzheimer's disease: hippocampal shape and volume predict dementia onset in the elderly. NeuroImage 25, 783–792]; i. e. , a lateral zone (LZ) approximating the CA1 subfield, a superior zone (SZ) approximating the combined CA2, CA3, CA4 subfields and the gyrus dentatus (GD), and an inferior-medial zone (IMZ) approximating the subiculum. Large-deformation high-dimensional brain mapping (HDBM-LD) was used to generate the hippocampal surfaces of all subjects and to register the surface zones across subjects. Average variations within each zone were calculated for the subjects with very mild DAT as compared to the average of the nondemented subjects. After correcting for multiple comparisons, the very mild DAT subjects showed significant inward variation in the left and right LZ, the left and right IMZ, but not in the left and right SZ as compared to nondemented subjects. In logistic regression analyses, inward variation of the left and right LZ or IMZ by 0. 1 mm relative to the average of the nondemented subjects increased the odds of the subject being a very mild DAT subject (range—1. 18 to 1. 57) rather than being a nondemented subject. The odds ratios for the left and right SZ were not significant. These results represent a replication of our previous findings [Csernansky, J. G. , Wang, L. , Joshi, S. , Miller, J. P. , Gado, M. , Kido, D. , McKeel, D. , Morris, J. C. , Miller, M. I. , 2000. Early DAT is distinguished from aging by high-dimensional mapping of the hippocampus. Neurology 55, 1636–1643. ] and suggest that inward deformities of the hippocampal surface in proximity to the CA1 subfield and subiculum can be used to distinguish subjects with very mild DAT from nondemented subjects.

YNIMG Journal 2004 Journal Article

A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume

• Randy L. Buckner
• Denise Head
• Jamie Parker
• Anthony F. Fotenos
• Daniel Marcus
• John C. Morris
• Abraham Z. Snyder

Atlas normalization, as commonly used by functional data analysis, provides an automated solution to the widely encountered problem of correcting for head size variation in regional and whole-brain morphometric analyses, so long as an age- and population-appropriate target atlas is used. In the present article, we develop and validate an atlas normalization procedure for head size correction using manual total intracranial volume (TIV) measurement as a reference. The target image used for atlas transformation consisted of a merged young and old-adult template specifically created for cross age-span normalization. Automated atlas transformation generated the Atlas Scaling Factor (ASF) defined as the volume-scaling factor required to match each individual to the atlas target. Because atlas normalization equates head size, the ASF should be proportional to TIV. A validation analysis was performed on 147 subjects to evaluate ASF as a proxy for manual TIV measurement. In addition, 19 subjects were imaged on multiple days to assess test–retest reliability. Results indicated that the ASF was (1) equivalent to manual TIV normalization (r = 0. 93), (2) reliable across multiple imaging sessions (r = 1. 00; mean absolute percentage of difference = 0. 51%), (3) able to connect between-gender head size differences, and (4) minimally biased in demented older adults with marked atrophy. Hippocampal volume differences between nondemented (n = 49) and demented (n = 50) older adults (measured manually) were equivalent whether corrected using manual TIV or automated ASF (effect sizes of 1. 29 and 1. 46, respectively). To provide normative values, ASF was used to automatically derive estimated TIV (eTIV) in 335 subjects aged 15–96 including both clinically characterized nondemented (n = 77) and demented (n = 90) older adults. Differences in eTIV between nondemented and demented groups were negligible, thus failing to support the hypothesis that large premorbid brain size moderates Alzheimer's disease. Gender was the only robust factor that influenced eTIV. Men showed an approximately ∼12% larger eTIV than women. These results demonstrate that atlas normalization using appropriate template images provides a robust, automated method for head size correction that is equivalent to manual TIV correction in studies of aging and dementia. Thus, atlas normalization provides a common framework for both morphometric and functional data analysis.

YNIMG Journal 2003 Journal Article

Changes in hippocampal volume and shape across time distinguish dementia of the Alzheimer type from healthy aging☆

• Lei Wang
• Jeffrey S. Swank
• Irena E. Glick
• Mokhtar H. Gado
• Michael I. Miller
• John C. Morris
• John G. Csernansky

Rates of hippocampal volume loss have been shown to distinguish subjects with dementia of the Alzheimer type (DAT) from nondemented controls (Jack et al. , 2000). In this study, we obtained magnetic resonance scans in 18 subjects with very mild DAT (CDR 0. 5) and 26 age-matched nondemented controls (CDR 0) 2 years apart. Large-deformation high-dimensional brain mapping was used to quantify and compare changes in hippocampal shape as well as volume in the two groups of subjects. Hippocampal volume loss over time was significantly greater in the CDR 0. 5 subjects (left = 8. 3%, right = 10. 2%) than in the CDR 0 subjects (left = 4. 0%, right = 5. 5%) (ANOVA, F = 7. 81, P = 0. 0078). We used singular-value decomposition and logistic regression models to quantify hippocampal shape change across time within individuals, and this shape change in the CDR 0. 5 and CDR 0 subjects was found to be significantly different (Wilks's λ, P = 0. 014). Further, at baseline, CDR 0. 5 subjects, in comparison to CDR 0 subjects, showed inward deformation over 38% of the hippocampal surface; after 2 years this difference grew to 47%. Also, within the CDR 0 subjects, shape change between baseline and follow-up was largely confined to the head of the hippocampus and subiculum, while in the CDR 0. 5 subjects, shape change involved the lateral body of the hippocampus as well as the head region and subiculum. These results suggest that different patterns of hippocampal shape change in time as well as different rates of hippocampal volume loss distinguish very mild DAT from healthy aging.

YNIMG Journal 2001 Journal Article

Regional cortical thinning of the aging brain

• David H. Salat
• Randy L. Buckner
• Abraham Z. Snyder
• Rahul Desikan
• John C. Morris
• Andres M. Dale
• Bruce Fischl