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David A. Wolk

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YNICL Journal 2025 Journal Article

Posterior hippocampal sparing in Lewy body disorders with Alzheimer’s copathology: An in vivo MRI study

  • Jesse S. Cohen
  • Jeffrey Phillips
  • Sandhitsu R. Das
  • Christopher A. Olm
  • Hamsanandini Radhakrishnan
  • Emma Rhodes
  • Katheryn A.Q. Cousins
  • Sharon X. Xie

BACKGROUND: Lewy body disorders (LBD), encompassing Parkinson disease (PD), PD dementia (PDD), and dementia with Lewy bodies (DLB), are characterized by alpha-synuclein pathology but often are accompanied by Alzheimer's disease (AD) neuropathological change (ADNC). The medial temporal lobe (MTL) is a primary locus of tau accumulation and associated neurodegeneration in AD. However, it is unclear the extent to which AD copathology in LBD (LBD/AD+) contributes to MTL-specific patterns of degeneration. We employ a MTL subregional segmentation strategy of T1-weighted (T1w) MRI in biomarker-supported or autopsy-confirmed LBD and LBD/AD+ to investigate the anatomic consequences of co-occurring LBD/AD+ pathology on neurodegeneration. METHODS: < 185.7 pg/mL n = 126 (75.4 %)). The T1 Automated Segmentation of Hippocampal Subfields (ASHS) pipeline was used to compute volume and thickness measurements of MTL subregions in LBD/AD- and LBD/AD+. Linear regression tested the association of AD copathology and subregion volume/thickness, covarying for age and sex, and intracranial volume for volume measurements. Secondary analyses correlated MTL subregional volume/thickness with cognition and neuropathology. RESULTS: LBD/AD+ had decreased volume/thickness compared to LBD/AD- in all MTL subregions except posterior hippocampus. The greatest effect sizes were seen in Brodmann Area 35 (BA35) (Cohen's d = 0.62, p = 0.002, β = 0.107 ± 0.034), and entorhinal cortex (ERC) (Cohen's d = 0.56, p = 0.006, β = 0.088 ± 0.031). Smaller differences were seen in the parahippocampal cortex (PHC) (Cohen's d = 0.5, p = 0.012, β = 0.082 ± 0.033), BA36 (Cohen's d = 0.47, p = 0.021, β = 0.090 ± 0.039) and anterior hippocampus (Cohen's d = 0.45, p = 0.029, β = 111.790 ± 50.595). Verbal memory scores positively correlated with volume/thickness in anterior and posterior hippocampus, BA35, ERC and PHC, while visuospatial memory positively correlated only in BA35. In the subset of participants with autopsy, lower ERC volume was associated with a higher tau load in ERC (adjusted odds ratio 0.013, 95 % CI [0.0002, 0.841], uncorrected p = 0.041). CONCLUSIONS: Relative to LBD/AD-, LBD/AD+ has greater T1w MRI evidence of atrophy in multiple MTL subregions. Atrophy in MTL subregions associates with memory performance and tau pathological load. The observed pattern of atrophy largely follows expectation from AD Braak stages, except for posterior hippocampus. Longitudinal studies are needed to validate the hypothesized spread of neurodegeneration.

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YNIMG Journal 2023 Journal Article

Multiscale functional connectivity patterns of the aging brain learned from harmonized rsfMRI data of the multi-cohort iSTAGING study

  • Zhen Zhou
  • Hongming Li
  • Dhivya Srinivasan
  • Ahmed Abdulkadir
  • Ilya M. Nasrallah
  • Junhao Wen
  • Jimit Doshi
  • Guray Erus

To learn multiscale functional connectivity patterns of the aging brain, we built a brain age prediction model of functional connectivity measures at seven scales on a large fMRI dataset, consisting of resting-state fMRI scans of 4186 individuals with a wide age range (22 to 97 years, with an average of 63) from five cohorts. We computed multiscale functional connectivity measures of individual subjects using a personalized functional network computational method, harmonized the functional connectivity measures of subjects from multiple datasets in order to build a functional brain age model, and finally evaluated how functional brain age gap correlated with cognitive measures of individual subjects. Our study has revealed that functional connectivity measures at multiple scales were more informative than those at any single scale for the brain age prediction, the data harmonization significantly improved the brain age prediction performance, and the data harmonization in the functional connectivity measures' tangent space worked better than in their original space. Moreover, brain age gap scores of individual subjects derived from the brain age prediction model were significantly correlated with clinical and cognitive measures. Overall, these results demonstrated that multiscale functional connectivity patterns learned from a large-scale multi-site rsfMRI dataset were informative for characterizing the aging brain and the derived brain age gap was associated with cognitive and clinical measures.

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YNICL Journal 2022 Journal Article

Ex vivo MRI and histopathology detect novel iron-rich cortical inflammation in frontotemporal lobar degeneration with tau versus TDP-43 pathology

  • M. Dylan Tisdall
  • Daniel T. Ohm
  • Rebecca Lobrovich
  • Sandhitsu R. Das
  • Gabor Mizsei
  • Karthik Prabhakaran
  • Ranjit Ittyerah
  • Sydney Lim

Frontotemporal lobar degeneration (FTLD) is a heterogeneous spectrum of age-associated neurodegenerative diseases that include two main pathologic categories of tau (FTLD-Tau) and TDP-43 (FTLD-TDP) proteinopathies. These distinct proteinopathies are often clinically indistinguishable during life, posing a major obstacle for diagnosis and emerging therapeutic trials tailored to disease-specific mechanisms. Moreover, MRI-derived measures have had limited success to date discriminating between FTLD-Tau or FTLD-TDP. T2*-weighted (T2*w) ex vivo MRI has previously been shown to be sensitive to non-heme iron in healthy intracortical lamination and myelin, and to pathological iron deposits in amyloid-beta plaques and activated microglia in Alzheimer’s disease neuropathologic change (ADNC). However, an integrated, ex vivo MRI and histopathology approach is understudied in FTLD. We apply joint, whole-hemisphere ex vivo MRI at 7 T and histopathology to the study autopsy-confirmed FTLD-Tau (n = 4) and FTLD-TDP (n = 3), relative to ADNC disease-control brains with antemortem clinical symptoms of frontotemporal dementia (n = 2), and an age-matched healthy control. We detect distinct laminar patterns of novel iron-laden glial pathology in both FTLD-Tau and FTLD-TDP brains. We find iron-positive ameboid and hypertrophic microglia and astrocytes largely in deeper GM and adjacent WM in FTLD-Tau. In contrast, FTLD-TDP presents prominent superficial cortical layer iron reactivity in astrocytic processes enveloping small blood vessels with limited involvement of adjacent WM, as well as more diffuse distribution of punctate iron-rich dystrophic microglial processes across all GM lamina. This integrated MRI/histopathology approach reveals ex vivo MRI features that are consistent with these pathological observations distinguishing FTLD-Tau and FTLD-TDP subtypes, including prominent irregular hypointense signal in deeper cortex in FTLD-Tau whereas FTLD-TDP showed upper cortical layer hypointense bands and diffuse cortical speckling. Moreover, differences in adjacent WM degeneration and iron-rich gliosis on histology between FTLD-Tau and FTLD-TDP were also readily apparent on MRI as hyperintense signal and irregular areas of hypointensity, respectively that were more prominent in FTLD-Tau compared to FTLD-TDP. These unique histopathological and radiographic features were distinct from healthy control and ADNC brains, suggesting that iron-sensitive T2*w MRI, adapted to in vivo application at sufficient resolution, may eventually offer an opportunity to improve antemortem diagnosis of FTLD proteinopathies using tissue-validated methods.

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YNIMG Journal 2021 Journal Article

DeepAtrophy: Teaching a neural network to detect progressive changes in longitudinal MRI of the hippocampal region in Alzheimer's disease

  • Mengjin Dong
  • Long Xie
  • Sandhitsu R. Das
  • Jiancong Wang
  • Laura E.M. Wisse
  • Robin deFlores
  • David A. Wolk
  • Paul A. Yushkevich

Measures of change in hippocampal volume derived from longitudinal MRI are a well-studied biomarker of disease progression in Alzheimer's disease (AD) and are used in clinical trials to track therapeutic efficacy of disease-modifying treatments. However, longitudinal MRI change measures based on deformable registration can be confounded by MRI artifacts, resulting in over-estimation or underestimation of hippocampal atrophy. For example, the deformation-based-morphometry method ALOHA (Das et al., 2012) finds an increase in hippocampal volume in a substantial proportion of longitudinal scan pairs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, unexpected, given that the hippocampal gray matter is lost with age and disease progression. We propose an alternative approach to quantify disease progression in the hippocampal region: to train a deep learning network (called DeepAtrophy) to infer temporal information from longitudinal scan pairs. The underlying assumption is that by learning to derive time-related information from scan pairs, the network implicitly learns to detect progressive changes that are related to aging and disease progression. Our network is trained using two categorical loss functions: one that measures the network's ability to correctly order two scans from the same subject, input in arbitrary order; and another that measures the ability to correctly infer the ratio of inter-scan intervals between two pairs of same-subject input scans. When applied to longitudinal MRI scan pairs from subjects unseen during training, DeepAtrophy achieves greater accuracy in scan temporal ordering and interscan interval inference tasks than ALOHA (88.5% vs. 75.5% and 81.1% vs. 75.0%, respectively). A scalar measure of time-related change in a subject level derived from DeepAtrophy is then examined as a biomarker of disease progression in the context of AD clinical trials. We find that this measure performs on par with ALOHA in discriminating groups of individuals at different stages of the AD continuum. Overall, our results suggest that using deep learning to infer temporal information from longitudinal MRI of the hippocampal region has good potential as a biomarker of disease progression, and hints that combining this approach with conventional deformation-based morphometry algorithms may lead to improved biomarkers in the future.

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YNICL Journal 2021 Journal Article

Oh brother, where art tau? Amyloid, neurodegeneration, and cognitive decline without elevated tau

  • Lauren E. McCollum
  • Sandhitsu R. Das
  • Long Xie
  • Robin de Flores
  • Jieqiong Wang
  • Sharon X. Xie
  • Laura E.M. Wisse
  • Paul A. Yushkevich

Mild cognitive impairment (MCI) can be an early manifestation of Alzheimer's disease (AD) pathology, other pathologic entities [e.g., cerebrovascular disease, Lewy body disease, LATE (limbic-predominant age-related TDP-43 encephalopathy)], or mixed pathologies, with concomitant AD- and non-AD pathology being particularly common, albeit difficult to identify, in living MCI patients. The National Institute on Aging and Alzheimer's Association (NIA-AA) A/T/(N) [β-Amyloid/Tau/(Neurodegeneration)] AD research framework, which classifies research participants according to three binary biomarkers [β-amyloid (A+/A-), tau (T+/T-), and neurodegeneration (N+/N-)], provides an indirect means of identifying such cases. Individuals with A+T-(N+) MCI are thought to have both AD pathologic change, given the presence of β-amyloid, and non-AD pathophysiology, given neurodegeneration without tau, because in typical AD it is tau accumulation that is most tightly linked to neuronal injury and cognitive decline. Thus, in A+T-(N+) MCI (hereafter referred to as "mismatch MCI" for the tau-neurodegeneration mismatch), non-AD pathology is hypothesized to drive neurodegeneration and symptoms, because β-amyloid, in the absence of tau, likely reflects a preclinical stage of AD. We compared a group of individuals with mismatch MCI to groups with A+T+(N+) MCI (or "prodromal AD") and A-T-(N+) MCI (or "neurodegeneration-only MCI") on cross-sectional and longitudinal cognition and neuroimaging characteristics. β-amyloid and tau status were determined by CSF assays, while neurodegeneration status was based on hippocampal volume on MRI. Overall, mismatch MCI was less "AD-like" than prodromal AD and generally, with some exceptions, more closely resembled the neurodegeneration-only group. At baseline, mismatch MCI had less episodic memory loss compared to prodromal AD. Longitudinally, mismatch MCI declined more slowly than prodromal AD across all included cognitive domains, while mismatch MCI and neurodegeneration-only MCI declined at comparable rates. Prodromal AD had smaller baseline posterior hippocampal volume than mismatch MCI, and whole brain analyses demonstrated cortical thinning that was widespread in prodromal AD but largely restricted to the medial temporal lobes (MTLs) for the mismatch and neurodegeneration-only MCI groups. Longitudinally, mismatch MCI had slower rates of volume loss than prodromal AD throughout the MTLs. Differences in cross-sectional and longitudinal cognitive and neuroimaging measures between mismatch MCI and prodromal AD may reflect disparate underlying pathologic processes, with the mismatch group potentially being driven by non-AD pathologies on a background of largely preclinical AD. These findings suggest that β-amyloid status alone in MCI may not reveal the underlying driver of symptoms with important implications for enrollment in clinical trials and prognosis.

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YNIMG Journal 2020 Journal Article

A comparison of Freesurfer and multi-atlas MUSE for brain anatomy segmentation: Findings about size and age bias, and inter-scanner stability in multi-site aging studies

  • Dhivya Srinivasan
  • Guray Erus
  • Jimit Doshi
  • David A. Wolk
  • Haochang Shou
  • Mohamad Habes
  • Christos Davatzikos

Automatic segmentation of brain anatomy has been a key processing step in quantitative neuroimaging analyses. An extensive body of literature has relied on Freesurfer segmentations. Yet, in recent years, the multi-atlas segmentation framework has consistently obtained results with superior accuracy in various evaluations. We compared brain anatomy segmentations from Freesurfer, which uses a single probabilistic atlas strategy, against segmentations from Multi-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters and locally optimal atlas selection (MUSE), one of the leading ensemble-based methods that calculates a consensus segmentation through fusion of anatomical labels from multiple atlases and registrations. The focus of our evaluation was twofold. First, using manual ground-truth hippocampus segmentations, we found that Freesurfer segmentations showed a bias towards over-segmentation of larger hippocampi, and under-segmentation in older age. This bias was more pronounced in Freesurfer-v5.3, which has been used in multiple previous studies of aging, while the effect was mitigated in more recent Freesurfer-v6.0, albeit still present. Second, we evaluated inter-scanner segmentation stability using same day scan pairs from ADNI acquired on 1.5T and 3T scanners. We also found that MUSE obtains more consistent segmentations across scanners compared to Freesurfer, particularly in the deep structures.

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YNICL Journal 2020 Journal Article

Arterial spin labeling versus 18F-FDG-PET to identify mild cognitive impairment

  • Sudipto Dolui
  • Zhengjun Li
  • Ilya M. Nasrallah
  • John A. Detre
  • David A. Wolk

Neurodegenerative biomarkers support diagnosis and measurement of disease progression in the Alzheimer's disease (AD) continuum. 18F-Fluorodeoxyglucose Positron Emission Tomography (18F-FDG-PET), which measures glucose metabolism, is one of the most commonly used biomarkers of neurodegeneration, but is expensive and requires exposure to ionizing radiation. Arterial Spin Labeled (ASL) perfusion Magnetic Resonance Imaging (MRI) provides non invasive quantification of cerebral blood flow (CBF), which is believed to be tightly coupled to glucose metabolism. Here we aimed to compare the performances of ASL derived CBF and 18F-FDG-PET derived standardized uptake value ratio (SUVR) in discriminating patients with mild cognitive impairment (MCI) from older Controls. 2D pseudo continuous ASL and 18F-FDG-PET data with adequate scan quality from 50 MCI study participants (age=73. 0 ± 7. 0 years, 16 female) and 35 older controls (age=70. 2 ± 6. 9 years, 20 female), acquired in close temporal proximity, usually on the same day, were considered for this study. We assessed Control-patient group differences both at voxel level and within a priori regions of interest (ROIs). We also compared their area under receiver operating characteristic curves (AUC) with mean CBF or SUVR in a priori selected posterior cingulate cortex (PCC). CBF and 18F-FDG-PET showed abnormalities in similar areas, particularly in medial temporoparietal regions, consistent with the typically observed pattern of prodromal AD. The hypoperfusion pattern with relative CBF (obtained by normalizing voxel CBF values with mean CBF in putamen) was more localized than with absolute CBF. Pearson's correlation coefficients between the T-scores corresponding to the group-differences obtained with 18F-FDG-PET SUVR and absolute and relative ASL CBF were 0. 46 and 0. 43 (p<0. 001), respectively. ROI analyses were also consistent, with the strongest differences observed in PCC (p<0. 01). 18F-FDG-PET SUVR, absolute and relative CBF in the PCC ROI demonstrated moderate and similar discriminatory power in predicting MCI status with AUC of 0. 71 ± 0. 12, 0. 77 ± 0. 12 and 0. 74 ± 0. 13, respectively. In conclusion, ASL CBF may be a reasonable, less expensive and safer substitute for 18F-FDG-PET in clinical research.

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YNIMG Journal 2020 Journal Article

Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan

  • Raymond Pomponio
  • Guray Erus
  • Mohamad Habes
  • Jimit Doshi
  • Dhivya Srinivasan
  • Elizabeth Mamourian
  • Vishnu Bashyam
  • Ilya M. Nasrallah

As medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10, 477 structural brain MRI scans from participants without a known neurological or psychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive age trends of brain structure through the lifespan (3–96 years old). Critically, we present and validate a methodology for harmonizing this pooled dataset in the presence of nonlinear age trends. We provide a web-based visualization interface to generate and present the resulting age trends, enabling future studies of brain structure to compare their data with this reference of brain development and aging, and to examine deviations from ranges, potentially related to disease.

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YNICL Journal 2019 Journal Article

Characterizing a perfusion-based periventricular small vessel region of interest

  • Sudipto Dolui
  • Dylan Tisdall
  • Marta Vidorreta
  • David R. Jacobs
  • Ilya M. Nasrallah
  • R. Nick Bryan
  • David A. Wolk
  • John A. Detre

The periventricular white matter (PVWM) is supplied by terminal distributions of small vessels and is particularly susceptible to developing white matter lesions (WML) associated with cerebral small vessel disease (CSVD). We obtained group-averaged cerebral blood flow (CBF) maps from Arterial Spin Labeled (ASL) perfusion MRI data obtained in 436 middle-aged (50.4 ± 3.5 years) subjects in the NHLBI CARDIA study and in 61 elderly (73.3 ± 6.9 years) cognitively normal subjects recruited from the Penn Alzheimer's Disease Center (ADC) and found that the lowest perfused brain voxels are located within the PVWM. We constructed a white matter periventricular small vessel (PSV) region of interest (ROI) by empirically thresholding the group averaged CARDIA CBF map at CBF < 15 ml/100 g/min. Thereafter we compared CBF in the PSV ROI and in the remaining white matter (RWM) with the location and volume of WML measured with Fluid Attenuated Inversion Recovery (FLAIR) MRI. WM CBF was lower within WML than outside WML voxels (p < <0.0001) in both the PSV and RWM ROIs, however this difference was much smaller (p < <0.0001) in the PSV ROI than in the RWM suggesting a more homogenous reduction of CBF in the PSV region. Normalized WML volumes were significantly higher in the PSV ROI than in the RWM and in the elderly cohort as compared to the middle-aged cohort (p < <0.0001). Additionally, the PSV ROI showed a significantly (p = .001) greater increase in lesion volume than the RWM in the elderly ADC cohort than the younger CARDIA cohort. Considerable intersubject variability in PSV CBF observed in both study cohorts likely represents biological variability that may be predictive of future WML and/or cognitive decline. In conclusion, a data-driven PSV ROI defined by voxels with low perfusion in middle age defines a region with homogeneously reduced CBF that is particularly susceptible to progressive ischemic injury in elderly controls. PSV CBF may provide a mechanistically specific biomarker of CSVD.

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YNIMG Journal 2017 Journal Article

Multi-template analysis of human perirhinal cortex in brain MRI: Explicitly accounting for anatomical variability

  • Long Xie
  • John B. Pluta
  • Sandhitsu R. Das
  • Laura E.M. Wisse
  • Hongzhi Wang
  • Lauren Mancuso
  • Dasha Kliot
  • Brian B. Avants

Rational The human perirhinal cortex (PRC) plays critical roles in episodic and semantic memory and visual perception. The PRC consists of Brodmann areas 35 and 36 (BA35, BA36). In Alzheimer's disease (AD), BA35 is the first cortical site affected by neurofibrillary tangle pathology, which is closely linked to neural injury in AD. Large anatomical variability, manifested in the form of different cortical folding and branching patterns, makes it difficult to segment the PRC in MRI scans. Pathology studies have found that in ~97% of specimens, the PRC falls into one of three discrete anatomical variants. However, current methods for PRC segmentation and morphometry in MRI are based on single-template approaches, which may not be able to accurately model these discrete variants Methods A multi-template analysis pipeline that explicitly accounts for anatomical variability is used to automatically label the PRC and measure its thickness in T2-weighted MRI scans. The pipeline uses multi-atlas segmentation to automatically label medial temporal lobe cortices including entorhinal cortex, PRC and the parahippocampal cortex. Pairwise registration between label maps and clustering based on residual dissimilarity after registration are used to construct separate templates for the anatomical variants of the PRC. An optimal path of deformations linking these templates is used to establish correspondences between all the subjects. Experimental evaluation focuses on the ability of single-template and multi-template analyses to detect differences in the thickness of medial temporal lobe cortices between patients with amnestic mild cognitive impairment (aMCI, n=41) and age-matched controls (n=44). Results The proposed technique is able to generate templates that recover the three dominant discrete variants of PRC and establish more meaningful correspondences between subjects than a single-template approach. The largest reduction in thickness associated with aMCI, in absolute terms, was found in left BA35 using both regional and summary thickness measures. Further, statistical maps of regional thickness difference between aMCI and controls revealed different patterns for the three anatomical variants.

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YNICL Journal 2016 Journal Article

A brain stress test: Cerebral perfusion during memory encoding in mild cognitive impairment

  • Long Xie
  • Sudipto Dolui
  • Sandhitsu R. Das
  • Grace E. Stockbower
  • Molly Daffner
  • Hengyi Rao
  • Paul A. Yushkevich
  • John A. Detre

Arterial spin labeled perfusion magnetic resonance imaging (ASL MRI) provides non-invasive quantification of cerebral blood flow, which can be used as a biomarker of brain function due to the tight coupling between cerebral blood flow (CBF) and brain metabolism. A growing body of literature suggests that regional CBF is altered in neurodegenerative diseases. Here we examined ASL MRI CBF in subjects with amnestic mild cognitive impairment (n = 65) and cognitively normal healthy controls (n = 62), both at rest and during performance of a memory-encoding task. As compared to rest, task-enhanced ASL MRI improved group discrimination, which supports the notion that physiologic measures during a cognitive challenge, or "stress test", may increase the ability to detect subtle functional changes in early disease stages. Further, logistic regression analysis demonstrated that ASL MRI and concomitantly acquired structural MRI provide complementary information of disease status. The current findings support the potential utility of task-enhanced ASL MRI as a biomarker in early Alzheimer's disease.

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YNICL Journal 2014 Journal Article

Integration and relative value of biomarkers for prediction of MCI to AD progression: Spatial patterns of brain atrophy, cognitive scores, APOE genotype and CSF biomarkers

  • Xiao Da
  • Jon B. Toledo
  • Jarcy Zee
  • David A. Wolk
  • Sharon X. Xie
  • Yangming Ou
  • Amanda Shacklett
  • Paraskevi Parmpi

This study evaluates the individual, as well as relative and joint value of indices obtained from magnetic resonance imaging (MRI) patterns of brain atrophy (quantified by the SPARE-AD index), cerebrospinal fluid (CSF) biomarkers, APOE genotype, and cognitive performance (ADAS-Cog) in progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) within a variable follow-up period up to 6 years, using data from the Alzheimer's Disease Neuroimaging Initiative-1 (ADNI-1). SPARE-AD was first established as a highly sensitive and specific MRI-marker of AD vs. cognitively normal (CN) subjects (AUC = 0.98). Baseline predictive values of all aforementioned indices were then compared using survival analysis on 381 MCI subjects. SPARE-AD and ADAS-Cog were found to have similar predictive value, and their combination was significantly better than their individual performance. APOE genotype did not significantly improve prediction, although the combination of SPARE-AD, ADAS-Cog and APOE ε4 provided the highest hazard ratio estimates of 17.8 (last vs. first quartile). In a subset of 192 MCI patients who also had CSF biomarkers, the addition of Aβ1-42, t-tau, and p-tau181p to the previous model did not improve predictive value significantly over SPARE-AD and ADAS-Cog combined. Importantly, in amyloid-negative patients with MCI, SPARE-AD had high predictive power of clinical progression. Our findings suggest that SPARE-AD and ADAS-Cog in combination offer the highest predictive power of conversion from MCI to AD, which is improved, albeit not significantly, by APOE genotype. The finding that SPARE-AD in amyloid-negative MCI patients was predictive of clinical progression is not expected under the amyloid hypothesis and merits further investigation.

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YNIMG Journal 2014 Journal Article

Subject-specific functional parcellation via Prior Based Eigenanatomy

  • Paramveer S. Dhillon
  • David A. Wolk
  • Sandhitsu R. Das
  • Lyle H. Ungar
  • James C. Gee
  • Brian B. Avants

We present a new framework for prior-constrained sparse decomposition of matrices derived from the neuroimaging data and apply this method to functional network analysis of a clinically relevant population. Matrix decomposition methods are powerful dimensionality reduction tools that have found widespread use in neuroimaging. However, the unconstrained nature of these totally data-driven techniques makes it difficult to interpret the results in a domain where network-specific hypotheses may exist. We propose a novel approach, Prior Based Eigenanatomy (p-Eigen), which seeks to identify a data-driven matrix decomposition but at the same time constrains the individual components by spatial anatomical priors (probabilistic ROIs). We formulate our novel solution in terms of prior-constrained ℓ 1 penalized (sparse) principal component analysis. p-Eigen starts with a common functional parcellation for all the subjects and refines it with subject-specific information. This enables modeling of the inter-subject variability in the functional parcel boundaries and allows us to construct subject-specific networks with reduced sensitivity to ROI placement. We show that while still maintaining correspondence across subjects, p-Eigen extracts biologically-relevant and patient-specific functional parcels that facilitate hypothesis-driven network analysis. We construct default mode network (DMN) connectivity graphs using p-Eigen refined ROIs and use them in a classification paradigm. Our results show that the functional connectivity graphs derived from p-Eigen significantly aid classification of mild cognitive impairment (MCI) as well as the prediction of scores in a Delayed Recall memory task when compared to graph metrics derived from 1) standard registration-based seed ROI definitions, 2) totally data-driven ROIs, 3) a model based on standard demographics plus hippocampal volume as covariates, and 4) Ward Clustering based data-driven ROIs. In summary, p-Eigen incarnates a new class of prior-constrained dimensionality reduction tools that may improve our understanding of the relationship between MCI and functional connectivity.

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YNICL Journal 2013 Journal Article

Arterial spin labeled MRI in prodromal Alzheimer's disease: A multi-site study

  • Ze Wang
  • Sandhitsu R. Das
  • Sharon X. Xie
  • Steven E. Arnold
  • John A. Detre
  • David A. Wolk

We examined differences in cerebral blood flow (CBF) measured by Arterial Spin Labeled perfusion MRI (ASL MRI) across the continuum from cognitively normal (CN) older adults to mild Alzheimer's Disease (AD) using data from the multi-site Alzheimer's Disease Neuroimaging Initiative (ADNI). Measures of CBF, in a predetermined set of regions (meta-ROI), and hippocampal volume were compared between CN (n = 47), patients with early and late Mild Cognitive Impairment [EMCI (n = 32), LMCI (n = 35)], and AD (n = 15). Associations between these measures and disease severity, assessed by Clinical Dementia Rating scale sum of boxes (CDR SB), were also assessed. Mean meta-ROI CBF was associated with group status and significant hypoperfusion was observed in LMCI and AD relative to CN. Hippocampal volume was associated with group status, but only AD patients had significantly smaller volumes than the CN. When examining the relationship between these measures and disease severity, both were significantly associated with CDR SB and appeared to provide independent prediction of status. In light of the tight link between CBF and metabolism, ASL MRI represents a promising functional biomarker for early diagnosis and disease tracking in AD and this study is the first to demonstrate the feasibility in a multi-site context in this population. Combining functional and structural measures, which can be acquired in the same scanning session, appears to provide additional information about disease severity relative to either measure alone.

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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.

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YNIMG Journal 2011 Journal Article

Effects of task-set adoption on ERP correlates of controlled and automatic recognition memory

  • Kristine A. Wilckens
  • Joshua J. Tremel
  • David A. Wolk
  • Mark E. Wheeler

Successful memory retrieval depends not only on memory fidelity but also on the mental preparedness on the part of the subject. ERP studies of recognition memory have identified two topographically distinct ERP components, the FN400 old/new effect and the late posterior component (LPC) old/new effect, commonly associated with familiarity and recollection, respectively. Here we used a task-switching paradigm to examine the extent to which adoption of a retrieval task-set influences FN400 and LPC old/new effects, in light of the presumption that recollection, as a control process, relies on the adoption of a retrieval task-set, but that familiarity-based retrieval does not. Behavioral accuracy indicated that source memory (experiment 2), but not item recognition (experiment 1), improved with task-set adoption. ERP data demonstrated a larger LPC on stay trials when a task-set had been adopted even with a simple recognition memory judgment. We conclude that adopting a retrieval task-set impacts recollection memory but not familiarity. These data indicate that attentional state immediately prior to retrieval can influence objective measures of recollection memory.

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YNIMG Journal 2011 Journal Article

Fractionating verbal episodic memory in Alzheimer's disease

  • David A. Wolk
  • Bradford C. Dickerson

The aim of this study was to determine the neural correlates of different stages of episodic memory function and their modulation by Alzheimer's disease (AD). Several decades of work has supported the role of the medial temporal lobes (MTL) in episodic memory function. However, a more recent work, derived in part from functional neuroimaging studies, has suggested that other brain structures make up a large-scale network that appears to support successful encoding and retrieval of episodic memories. Furthermore, controversy exists as to whether dissociable MTL regions support qualitatively different aspects of memory (hippocampus: contextual memory or ‘recollection’; perirhinal/lateral entorhinal cortex: item memory or ‘familiarity’). There is limited neuropsychological support for these models and most work in AD only has examined free recall memory measures. We studied the relationship between performance on different stages of the Rey Auditory Verbal Learning Test (AVLT), a 15-item word list learning task, and structural MRI measures in mild AD patients. Structural measures included hippocampal volume and cortical thickness of several ROIs known to undergo atrophy in AD. Correlation and multiple regression analyses, controlling for age, education, and gender, were performed in 146 mild AD patients (MMSE 23. 3±2. 0). To evaluate the robustness of these relationships, similar analyses were performed with additional standardized verbal memory measures. Early immediate recall trials (e. g. Trial 1 of the AVLT) were not associated with the size of MTL regions, but correlated most strongly with inferior parietal, middle frontal gyrus, and temporal pole ROIs. After repeated exposure (e. g. Trial 5 of the AVLT), immediate recall was correlated with both MTL and a similar distribution of isocortical structures, but most strongly the temporal pole. For delayed recall, only the hippocampus correlated with performance. In contrast, for delayed recognition discrimination, the perirhinal/entorhinal cortex correlated more strongly than the hippocampus; no other isocortical regions were strongly associated with performance. Convergent results were found for immediate and delayed trials of other memory tests. The current results suggest that a richer understanding of the memory deficits in AD can be gained by examining multiple measures, which tap different aspects of memory function. Furthermore, the present findings are consistent with models hypothesizing different stages of verbal list learning map onto dissociable brain regions. These data have implications for understanding the anatomic basis of processes underlying episodic memory, particularly related to a division of labor within the medial temporal lobes and within the large-scale MTL-cortical memory network.

Details DOI

YNIMG Journal 2009 Journal Article

Left posterior parietal cortex participates in both task preparation and episodic retrieval

  • Jeffrey S. Phillips
  • Katerina Velanova
  • David A. Wolk
  • Mark E. Wheeler

Optimal memory retrieval depends not only on the fidelity of stored information, but also on the attentional state of the subject. Factors such as mental preparedness to engage in stimulus processing can facilitate or hinder memory retrieval. The current study used functional magnetic resonance imaging (fMRI) to distinguish preparatory brain activity before episodic and semantic retrieval tasks from activity associated with retrieval itself. A catch-trial imaging paradigm permitted separation of neural responses to preparatory task cues and memory probes. Episodic and semantic task preparation engaged a common set of brain regions, including the bilateral intraparietal sulcus (IPS), left fusiform gyrus (FG), and the pre-supplementary motor area (pre-SMA). In the subsequent retrieval phase, the left IPS was among a set of frontoparietal regions that responded differently to old and new stimuli. In contrast, the right IPS responded to preparatory cues with little modulation during memory retrieval. The findings support a strong left-lateralization of retrieval success effects in left parietal cortex, and further indicate that left IPS performs operations that are common to both task preparation and memory retrieval. Such operations may be related to attentional control, monitoring of stimulus relevance, or retrieval.

Details DOI

YNIMG Journal 2008 Journal Article

Compensatory neural activity distinguishes different patterns of normal cognitive aging

  • Jenna L. Riis
  • Hyemi Chong
  • Katherine K. Ryan
  • David A. Wolk
  • Dorene M. Rentz
  • Phillip J. Holcomb
  • Kirk R. Daffner

Most cognitive neuroscientific research exploring the nature of age-associated compensatory mechanisms has compared old adults (high vs. average performers) to young adults (not split by performance), leaving ambiguous whether findings are truly age-related or reflect differences between high and average performers throughout the life span. Here, we examined differences in neural activity (as measured by ERPs) that were generated by high vs. average performing old, middle-age, and young adults while processing novel and target events to investigate the following three questions: (1) Are differences between cognitively high and average performing subjects in the allocation of processing resources (as indexed by P3 amplitude) specific to old subjects, or found throughout the adult life span? (2) Are differences between cognitively high and average performing subjects in speed of processing (as indexed by target P3 latency) of similar magnitude throughout the adult life span? (3) Where along the information processing stream does the compensatory neural activity attributed to cognitively high performing old subjects begin to take place? Our results suggest that high performing old adults successfully manage the task by a compensatory neural mechanism associated with the modulation of controlled processing and the allocation of more resources, whereas high performing younger subjects execute the task more efficiently with fewer resources. Differences between cognitively high and average performers in processing speed increase with age. Middle-age seems to be a critical stage in which substantial differences in neural activity between high and average performers emerge. These findings provide strong evidence for different patterns of age-related changes in the processing of salient environmental stimuli, with cognitive status serving as a key mediating variable.

Details DOI
v2026.09.13