David A Wolk
VerifiedUniversity of Pennsylvania · Rehabilitation Medicine
Active 2002–2025
About
David A Wolk, MD, is a Professor of Neurology at the University of Pennsylvania and an Attending Neurologist at the Hospital of the University of Pennsylvania. He is also a faculty member at the Center for Cognitive Neuroscience and a Fellow at the Institute on Aging at the University of Pennsylvania. Dr. Wolk serves as the Co-Director of the Penn Memory Center and the Penn Alzheimer's Disease Research Center, and he is the Division Chief of Cognitive Neurology. His research expertise includes episodic memory, event-related potentials, transcranial direct current stimulation, MRI, Alzheimer's Disease, dementia, memory disorders, cognitive neurology, and aging. Dr. Wolk's clinical focus is on Alzheimer's Disease, dementia, and memory disorders. He has contributed to the understanding of neuroimaging biomarkers, memory function in aging and early Alzheimer’s disease, and the neural mechanisms underlying cognitive decline. His work has been published in numerous scientific journals, and he is recognized for his contributions to the field of cognitive neurology and Alzheimer's research.
Research topics
- Medicine
- Neuroscience
- Internal medicine
- Pathology
- Psychology
- Biology
- Artificial Intelligence
- Computer Science
- Psychiatry
- Radiology
- Oncology
- Machine Learning
- Political Science
- Theoretical computer science
- Cardiology
- Data science
- Mathematics
- Nuclear medicine
- Chemistry
- Intensive care medicine
- Econometrics
Selected publications
Predicting cognitive decline in amyloid‐negative individuals with amnestic mild cognitive impairment
Alzheimer s & Dementia · 2025-12-01
articleOpen accessBACKGROUND: A considerable portion of patients with amnestic mild cognitive impairment (aMCI) have negative amyloid-β (Aβ) biomarkers and are therefore unlikely to have Alzheimer's disease (AD). Potential causes of cognitive decline in this heterogeneous group include limbic-predominant age-related TDP-43 encephalopathy (LATE), cardio/cerebrovascular diseases, primary age-related tauopathy (PART), and subthreshold Aβ. The prognosis of Aβ-negative (Aβ-) aMCI patients, putatively more benign, is unclear. We aim to investigate which predictors - including demographics, baseline cognition, fluid and imaging biomarkers - can best predict cognitive decline and progression to dementia in Aβ- aMCI. METHOD: We included 140 Aβ- aMCI patients (Aβ status based on cerebrospinal fluid (CSF) and positron emission tomography, when available; 'amnestic' based on norm scores for AD Assessment Scale delayed word recall) from BioFINDER-1/2 with longitudinal Mini-Mental State Examination (MMSE), and subsets with longitudinal data on Clinical Dementia Rating Sum of Boxes (CDR-SB; n = 67) and progression to dementia (n = 134, 43% progressors; Table 1). Predictors included global and regional atrophy measures, specific for LATE and PART, CSF Aβ42/40 and p-tau181, hypertension, white matter hyperintensities and global cognition. Individual MMSE and CDR-SB slopes were estimated with linear mixed-effects models. Associations of predictors with MMSE/CDR-SB slopes and progression to dementia were tested. Significant predictors and demographic variables were included in the model selection process using R package MuMIn, which tests linear combinations of variables and ranks models by the Akaike information criterion (AIC). RESULTS: Figure 1 shows individual associations for the identification of significant predictors for the model selection process. For MMSE (AIC: 350.57; Figure 2a), the selected most parsimonious model included baseline MMSE and whole-brain cortical thickness. For CDR-SB (AIC: 176.73; Figure 2b), baseline CDR-SB, amygdala volume, and middle frontal gyrus cortical thickness were included. For progression to dementia (AIC: 97.65; Figure 2c), the selected model included MMSE, lateral ventricles volume, entorhinal and whole-brain cortical thickness, sex and CSF Aβ42/40. CONCLUSIONS: Baseline cognition, global and regional atrophy measures are valuable predictors of cognitive decline in Aβ- aMCI, with regional brain measures hinting at specific pathologies. These prediction models are relevant for the new LATE clinical criteria. We aim to validate our findings in ADNI.
Alzheimer s & Dementia · 2025-12-01
articleOpen accessSenior authorBACKGROUND: The heterogeneity of Alzheimer's disease (AD) and lack of well-validated markers of non-AD factors (e.g. TDP-43) present a substantial challenge for therapeutics. Our prior work showed discordance between tau (T) and neurodegeneration (N) identified non-AD factors in AD through multi-modality imaging. Here we tried a simplified approach using plasma ptau217 and medial temporal lobe (MTL) morphometry, given this region's common association with co-pathologies, particularly LATE-NC. METHOD: We included 349 ADNI participants (188 cognitively normal, 161 MCI/dementia) with paired T1-MRI and plasma ptau217. The MTL was segmented into subregions and further parcellated into 100 bilateral super-points within regional boundaries. T1-MRI-derived thickness and amygdala volume represented N, and plasma p-Tau217 represented T. T-N residuals, calculated through regression across super-points and amygdala, were used for weighted clustering. RESULT: P-Tau217 showed strong association with MTL atrophy (Figure 1A). Three distinct data-driven T-N groups were identified based on mismatch patterns (Figure 1B), including a canonical group (N∼T), a vulnerable group (N>T) with negative residuals primarily in anterior hippocampal and extrahippocampal areas, and a resilient group (N<T) with positive residuals. After clustering, group comparisons were restricted to the AD continuum (i.e. A+). While groups differed in regional volumes (e.g., amygdala), tau severity did not vary (Table 1), suggesting these patterns were not driven by AD pathology. The vulnerable group, displayed greater anterior MTL atrophy aligning with their T-N residual patterns, while the resilient group had less atrophy in anterior extrahippocampal area (Figure 2A). Outside the MTL, the vulnerable group showed greater anterior limbic atrophy whereas the resilient group showed less (Figure 2B). The T-N groups differed in Clinical Dementia Rating (CDR) with the vulnerable group having the worst ratings and the resilient group the best (Table 1). Notably, the vulnerable group demonstrated greater baseline memory impairment. Longitudinally, the vulnerable group also declined more severely across multiple cognitive domains while resilient group remained most stable (Figure 2C). CONCLUSION: T-N mismatch within MTL using MRI and plasma biomarkers revealed groups with varying vulnerability/resilience, with the vulnerable group displaying patterns of atrophy and cognition suggestive of LATE-NC. It offers a less invasive, cost-effective method for stratifying individuals for therapeutic interventions.
Alzheimer s & Dementia · 2025-12-01
articleOpen accessAbstract Background Postmortem MRI has opened‐up avenues to study brain structure at ultra high‐resolution revealing details not possible to observe with in vivo MRI. Here, we present a novel package (purple‐mri) which performs tissue segmentation, anatomical parcellation and spatial normalization of postmortem MRI. Additionally, we provide a framework to perform point‐wise surface‐based group‐level studies linking morphometry/histopathology in common coordinate system for postmortem MRI. Method We developed a joint voxel‐ and surface‐based pipeline combining deep learning with classical techniques for topology correction, cortical modeling, inflation, and registration for accurate parcellation of postmortem cerebral hemispheres (Figure 1 Khandelwal et al. 2024). Furthermore, using the GM/WM segmentations derived from postmortem hemisphere and FreeSurfer‐processed antemortem MRI, we performed deformable image registration between the ante‐ and postmortem MRI for each brain specimen. To demonstrate the utility of purple‐mri, point‐wise analysis was performed to correlate thickness (mm) with tau and neuronal loss distribution in corresponding specimens ( N = 49) of postmortem (7T at 0.3mm 3 ) and antemortem (3T at 0.8mm 3 ) MRI (Table 1) within the AD continuum diagnosis. An additional 26 postmortem 7T scans without corresponding antemortem scans were included in some analyses. The semi‐quantitative average tau and neuronal loss ratings were derived from histopathological examination across the brain. All analyses include age, sex, and postmortem (or antemortem) interval as covariates. Result Our method parcellates postmortem brain hemisphere using a variety of brain atlases even in areas with low contrast (anterior/posterior regions), profound imaging artifacts and severely atrophied brains (Figure 1). Our registration pipeline provides one‐to‐one correspondence between the two modalities. For thickness/pathology associations (Figure 2), small sparse significant clusters only in superior temporal cortex and precuneus in antemortem MRI ( N = 49) were observed. However, postmortem MRI showed much stronger associations across large clusters in temporal, entorhinal cortex, and cingulate for both the matched cases ( N = 49) and the full cohort ( N = 75), regions implicated in ADRD. Conclusion Purple‐mri paves the way for large‐scale postmortem image analysis. Stronger associations between thickness and average tau burden/neuronal loss than antemortem MRI suggests that our pipeline could inform the development of more precise and sensitive invivo biomarkers by mapping information from postmortem to antemortem MRI in a common reference coordinate system.
Alzheimer s & Dementia · 2025-12-01
articleOpen accessSenior authorBACKGROUND: Current cognitive analysis methods in neurodegenerative disease rely on the use of individual neuropsychological assessments, which can be subject to variation and bias. A potentially more robust way to represent cognitive status is to use a cognitive age model to predict an individual's age from their cognitive assessment scores and demographics. This modeling allows for identification of individuals exhibiting resilience or vulnerability to normal aging processes across diverse datasets. METHOD: Psychometric and demographic data from cognitively unimpaired individuals from NACC UDS 3 (n = 11,752) were obtained. Neuropsychological assessment and demographic data were used to build a random forest model to predict a "cognitive" age. The distributions of predicted cognitive ages for each biological age were used to calculate a percentile rank for individuals in an independent dataset, with higher and lower percentile rank indicating vulnerability (cognitive > chronologic age) and resilience (cognitive < chronologic age), respectively. We assessed this model both with and without self-identified race (white, black) as a predictor variable and related percentile rank to Area of Deprivation Index (ADI). RESULT: We applied the model to cognitively unimpaired individuals in the Penn Aging Brain Cohort (n = 424). The model without race as a predictive feature resulted in a stronger relationship between "cognitive" age centile rank and ADI, suggesting that covarying for race may partially mask the effects of ADI. Removing race from the model also resulted in higher percentile ranks for Black participants, indicating a higher level of vulnerability in these individuals. Percentile rank can also be used to place individuals into resilient, normal, and vulnerable groups to understand what factors contribute to resilience and vulnerability. Preliminary analyses of structural MRI data comparing resilient and vulnerable groups yielded moderately higher thickness in the anterior cingulate cortex and medial frontal cortex of resilient individuals, necessitating further research in a larger population. CONCLUSION: This machine learning model is a valuable tool for analyzing cognition and identifying abnormal aging patterns in large-scale datasets, including identification of vulnerable and resilient individuals. Understanding the influence of demographic variables in generating these predictions allows us to better understand their role in vulnerable and resilient aging.
Alzheimer s & Dementia · 2025-12-01
articleOpen accessSenior authorAbstract Background 18 F‐Flortaucipir is widely used for positron emission tomography (PET) imaging of Alzheimer’s disease (AD)‐type tau, but its sensitivity in early Braak stages has been questioned, and hippocampal uptake is at least partially confounded by off‐target binding. We investigated associations between antemortem PET uptake and digitally‐quantified tau and TDP‐43 neuropathology. Methods Participants ( n = 14, Figure 1) included 5 people with no/low AD neuropathologic change (ADNC) at autopsy, 2 intermediate, and 7 high. Clinical diagnoses included normal cognition ( n = 2), AD ( n = 5), dementia with Lewy bodies ( n = 2), Parkinson’s disease dementia ( n = 1), corticobasal syndrome ( n = 2), and posterior cortical atrophy ( n = 2). We used the Automated Segmentation of Hippocampal Subfields T1 MRI pipeline to segment anterior and posterior hippocampus, Brodmann’s areas (BA) 35 (transentorhinal cortex) & 36, and entorhinal cortex. 18 F‐Flortaucipir standardized uptake value ratios (SUVRs) were computed relative to inferior cerebellar grey matter and averaged across hemispheres. Postmortem sampling comprised hippocampal subiculum, CA1, CA2, CA3, and dentate gyrus; BA35 and BA36; and entorhinal cortex. FFPE‐brain tissue was immunostained using PHF1 and phospho‐specific TDP‐43 antibodies and digitally imaged. Two different weakly supervised learning algorithms, Wildcat, were trained to identify tau tangles and threads; or somatic and neuritic phosphorylated TDP‐43 (pTDP‐43) inclusions. Each pathology type was quantified by summary statistics on Wildcat heatmaps, averaged over hippocampal subfields; and over BA35/entorhinal cortex (Denning et al., 2024). We computed non‐parametric correlations between SUVRs and pathology measures at a=0.05 with false discovery rate correction. Results Tangles were associated with SUVRs in BA35/entorhinal cortex (Spearman’s r=0.59, p = 0.029; Figure 2A). The mean hippocampal tangle measure was associated with SUVRs in both anterior (r=0.77, p = 0.002) and posterior (r=0.77, p = 0.002) hippocampus (Figure 2B‐C). Associations between SUVR and tau threads were marginally significant (Figure 2D‐F). In BA35/entorhinal cortex, 9/9 intermediate‐high ADNC cases had SUVRs above an established positivity cutoff of 1.23 (Figure 3). PET SUVRs were not associated with somatic or neuritic pTDP‐43 measures. Conclusion Results suggest 18 F‐flortaucipir is sensitive to tau burden in early Braak‐stage regions and primarily reflects neurofibrillary tangles rather than thread‐like tau inclusions.
Alzheimer s & Dementia · 2025-12-01
articleOpen accessAbstract Background Morphometry of medial temporal lobe (MTL) subregions in brain MRI is sensitive biomarker to Alzheimer's Disease and other related conditions. While T2‐weighted (T2w) MRI with high in‐plane resolution is widely used to segment hippocampal subfields due to its higher contrast in hippocampus, its lower out‐of‐plane resolution reduces the accuracy of subregion thickness measurements. To address this issue, we developed a nearly isotropic segmentation pipeline that incorporates image and label upsampling and high‐resolution segmentation in T2w MRI. Method First, a high‐resolution atlas was created based on an existing anisotropic atlas derived from 29 individuals. Both T1‐weighted and T2w images in the atlas were upsampled from their original resolution to a nearly isotropic resolution using a non‐local means approach. Manual segmentations within the atlas were also upsampled to match this resolution using a UNet‐based neural network, which was trained on a cohort consisting of both high‐resolution ex vivo and low‐resolution anisotropic in vivo MRI with manual segmentations (Figure 1a). Second, a multi‐modality deep learning‐based segmentation model was trained within this nearly isotropic atlas (Figure 1b). This method was evaluated on independent sets, including cross‐sectional ( N = 196) and longitudinal ( N = 31) MRI scans, which were used for the group difference analysis (Amyloid+ mild cognitive impairment (A+MCI) vs. Amyloid‐ cognitively normal (A‐CN)) and longitudinal consistency analysis, respectively (Figure 1c). Result Table 1(a) displays the group differences of cross‐sectional median thickness between A+MCI and A‐CN with age as covariate. The T2w segmentation in isotropic space achieved larger effect sizes in the predicted direction (A+MCI < A‐CN) and outperformed T2w anisotropic segmentation over most subregions. Table 1(b) shows the consistency analysis of longitudinal median thickness. When measured as the sum of absolute median thickness differences, the consistency of isotropic T2w segmentation outperformed that of anisotropic T2w segmentation over most subregions. Figure 2 shows the visualization of the segmentation and point‐wise group difference analysis at different resolutions, with isotropic T2w segmentation demonstrating a smoother surface and larger effect sizes than anisotropic T2w segmentation. Conclusion Nearly isotropic subregion segmentation improved the accuracy of cortical thickness as an imaging biomarker for neurodegeneration in T2w MRI.
Alzheimer s & Dementia · 2025-12-01
articleOpen accessAbstract Background Postmortem MRI has opened‐up avenues to study brain structure at ultra high‐resolution revealing details not possible to observe with in vivo MRI. Here, we present a novel package (purple‐mri) which performs tissue segmentation, anatomical parcellation and spatial normalization of postmortem MRI. Additionally, we provide a framework to perform point‐wise surface‐based group‐level studies linking morphometry/histopathology in common coordinate system for postmortem MRI. Method We developed a joint voxel‐ and surface‐based pipeline combining deep learning with classical techniques for topology correction, cortical modeling, inflation, and registration for accurate parcellation of postmortem cerebral hemispheres (Figure 1 Khandelwal et al. 2024). Furthermore, using the GM/WM segmentations derived from postmortem hemisphere and FreeSurfer‐processed antemortem MRI, we performed deformable image registration between the ante‐ and postmortem MRI for each brain specimen. To demonstrate the utility of purple‐mri, point‐wise analysis was performed to correlate thickness (mm) with tau and neuronal loss distribution in corresponding specimens ( N = 49) of postmortem (7T at 0.3mm 3 ) and antemortem (3T at 0.8mm 3 ) MRI (Table 1) within the AD continuum diagnosis. An additional 26 postmortem 7T scans without corresponding antemortem scans were included in some analyses. The semi‐quantitative average tau and neuronal loss ratings were derived from histopathological examination across the brain. All analyses include age, sex, and postmortem (or antemortem) interval as covariates. Result Our method parcellates postmortem brain hemisphere using a variety of brain atlases even in areas with low contrast (anterior/posterior regions), profound imaging artifacts and severely atrophied brains (Figure 1). Our registration pipeline provides one‐to‐one correspondence between the two modalities. For thickness/pathology associations (Figure 2), small sparse significant clusters only in superior temporal cortex and precuneus in antemortem MRI ( N = 49) were observed. However, postmortem MRI showed much stronger associations across large clusters in temporal, entorhinal cortex, and cingulate for both the matched cases ( N = 49) and the full cohort ( N = 75), regions implicated in ADRD. Conclusion Purple‐mri paves the way for large‐scale postmortem image analysis. Stronger associations between thickness and average tau burden/neuronal loss than antemortem MRI suggests that our pipeline could inform the development of more precise and sensitive invivo biomarkers by mapping information from postmortem to antemortem MRI in a common reference coordinate system.
Alzheimer s & Dementia · 2025-12-01
articleOpen accessSenior authorAbstract Background Perivascular spaces (PVS) are small fluid‐filled structures that are of major interest in both cerebral small vessel disease (cSVD) and neurodegeneration, particularly in the pathophysiology of AD and cerebral amyloid angiopathy (CAA). Current approaches to measure and quantify PVS rely on multi‐contrast combination or high‐field MRI to enhance visualization. We propose here an optimized imaging sequence for selective imaging of intracranial fluids with high resolution and clinically practical scan time capable of detecting AD‐related PVS enlargement. Method 19 patients with MCI or mild Dementia due to AD scanned prior to initiation of anti‐amyloid therapy (73±7yo) and 19 cognitively unimpaired (CU) older adults (71±7yo) were enrolled and scanned on a Siemens Prisma 3T MRI at the University of Pennsylvania. We acquired a 3D ultra‐long‐TE T2‐weighted sequence (ulTE‐T2, TR/TE=5000/876ms, FA=75deg, BW=681Hz/pix, 1mm isotropic, GRAPPA=4) in 3min 40sec. Motion‐corrupted scans (4 AD, 2 CU) were excluded. Images were co‐registered with a T1‐weighted volume and warped into MNI space. A 3D‐Frangi filter was used to automatically segment PVS within WM‐masked regions‐of‐interest (ROI). PVS volume fractions (PVS vf = V pvs /V ROI ) were computed in various lobal (parietal/occipital/frontal/temporal) and subcortical regions of interests and whole brain PVS probability maps were also produced by averaging individual PVS segmentations. A lobar/subcortical PVS vf ratio was calculated and then compared between AD and CU. Result Figure 1 shows the sensitivity towards PVS of the ulTE‐T2 sequence and automatic segmentation results. Group probability maps show high PVSvf in the basal ganglia and in posterior > anterior WM (Figure 2). PVSvf lobar/subcortical ratio was higher in AD compared to CU (Figure 3, β =0.68 [0.11,1.26], t (35)=2.42, p =0.021) after controlling for age, which was negatively associated with PVSvf ratio ( β =‐0.41 [‐0.70, ‐0.12], t (35)=‐2.84, p =0.007). Conclusion Fluid‐specific PVS imaging can be performed within clinically feasible scan times. Early results show strong visual and quantitative sensitivity towards PVS. Furthermore, individuals with AD show increased lobar to subcortical PVS volume ratio compared to CU both on visual assessment and with quantitative evaluation. Ongoing future directions include implementing variant sequences with improved motion robustness and longitudinal follow‐up to evaluate the effects of anti‐amyloid therapy on PVS.
Alzheimer s & Dementia · 2025-12-01
articleOpen accessSenior authorBACKGROUND: Paradoxical reductions in brain volume in individuals with Alzheimer's disease treated with anti-amyloid therapy have been reported in clinical trials. It has been hypothesized that these changes could partly be attributed to processes related to the clearance of amyloid plaques. Further, the presence of amyloid-related imaging abnormalities (ARIA) has been associated with greater ventricular expansion in clinical trials of anti-amyloid immunotherapy, and may also be an important factor driving brain volume loss. Here we measure longitudinal changes from structural MRI during the course of anti-amyloid treatment in brain regions known to accumulate amyloid plaques and ask whether these regions show differences in observed changes depending on presence of ARIA. METHOD: We analyzed 125 MRI images from 45 patients undergoing surveillance scanning during the course of lecanemab therapy. Average duration between the earliest and latest scans was 181 days. For each patient, images were included if from an identical MRI protocol and scanner. Measures of gray matter thickness in 100 brain regions were obtained using an unbiased longitudinal pipeline. Regions were nominally dichotomized into amyloid-accumulating and non-amyloid-accumulating based on composite regions used for determining amyloid positivity from PET imaging. Thickness was analyzed using linear mixed effects model that included time and presence of ARIA (ARIA-E or ARIA-H) as explanatory variables and age and sex as nuisance covariates. RESULT: Both amyloid-accumulating and non-amyloid-accumulating regions showed thinning during the treatment period. However, there was a significant interaction between time and ARIA status only in amyloid-accumulating regions such that patients with ARIA showed greater observed thinning. Hippocampal volume also showed a decrease over time, but there was no effect of presence of ARIA. CONCLUSION: These data show that presence of ARIA may play a role in observed brain parenchymal changes during anti-amyloid treatment. The specificity of the effects in amyloid-accumulating brain regions point to an interaction between amyloid clearance and the emergence of ARIA as confounding factors in measuring longitudinal change. While the underlying mechanism remains unclear, better characterization of such effects can help disentangle their influence on MRI-based measures of change and ultimately improve the usability of MRI as a marker for tracking potential treatment effects.
Annals of Neurology · 2025-12-25 · 2 citations
articleOpen accessSenior authorObjective Plasma biomarkers of Alzheimer's disease (AD) pathology are frequently tested in specialized research settings, which limits the generalizability of findings. Using electronic health records and banked plasma, we evaluated plasma biomarkers—phosphorylated tau 217 (p‐tau 217 ), β‐amyloid 1–42/1–40 (Aβ 42 /Aβ 40 ) and p‐tau 217 /Aβ 42 —in a real‐world, diverse clinical population with multimorbidities. Methods Participants (n = 617; 44% Black/African American; 41% female) were selected from the University of Pennsylvania Medicine BioBank with plasma assayed using Fujirebio Lumipulse. International Classification of Diseases (ICD) Ninth and Tenth Revision codes determined AD dementia (ADD) (n = 43), mild‐cognitive impairment (MCI) (n = 140), unspecified/non‐AD cognitive impairment (CI) (n = 106), and cognitively normal cases (n = 328), and other medical histories. APOE ε4, body mass index (BMI), metrics of kidney function (eg, estimated glomerular filtration rate [eGFR]), and liver disease were derived from electronic health records. Multivariable models identified factors related to plasma levels. Previously established cutpoints classified AD status (“AD+,” “AD−,” or “Intermediate”). Results Plasma p‐tau 217 /Aβ 42 had the strongest association with known AD‐related factors—MCI, ADD, future progression to MCI/ADD, age, and APOE ε4—compared to p‐tau 217 and Aβ 42 /Aβ 40 . Plasma p‐tau 217 /Aβ 42 was also associated with eGFR, diabetes, and history of hearing loss. Importantly, AD‐related factors were most frequent/severe for AD+ classification by p‐tau 217 /Aβ 42 , whereas medical morbidities were most frequent/severe for Intermediate classification. Exploratory analyses test p‐tau 217 /Aβ 42 adjusted for eGFR to eliminate its influence on plasma levels. Interpretation In this real‐world dataset, we identified effects of multimorbidities on plasma biomarkers, especially kidney function. The p‐tau 217 /Aβ 42 ratio had low rates of Intermediate classification and may help to account for multimorbidity effects on plasma levels. ANN NEUROL 2026;99:1030–1045
Recent grants
NIH · $132k · 2006
NIH · $7.7M · 2023
NIH · $4.0M · 2022–2027
NIH · $766k · 2011
Modulators of Medial Temporal Lobe Subregion Structure and Function in Normal and Pathological Aging
NIH · $3.3M · 2017–2023
Frequent coauthors
- 820 shared
Keith A. Johnson
Massachusetts General Hospital
- 567 shared
D. Cheng
- 548 shared
Steven E. Arnold
Harvard University
- 486 shared
Joseph C. Wu
- 405 shared
Carl K. Hoh
University of California, San Diego
- 405 shared
Monte S. Buchsbaum
University of California, Irvine
- 405 shared
Marcelo F. Di Carli
Harvard University
- 356 shared
John Q. Trojanowski
University of Pennsylvania
Awards & honors
- Fellow, Institute on Aging, University of Pennsylvania
- Co-Director, Penn Memory Center
- Director, Penn Alzheimer's Disease Research Center
- Division Chief, Cognitive Neurology, University of Pennsylva…
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