Search Existing Data Requests

The Knight ADRC has supported many investigators at Washington University and at other institutions over the years. We wish to avoid the situation where two investigators study the same research question to avoid duplication of effort and potential conflict. To determine if your topic has already been studied with our resources, please search our database. If you find that your topic or a related topic has been submitted, you may wish to contact the investigator to inquire about their findings to determine how you might proceed. You may wish to collaborate or modify your request to avoid overlap. The results below reflect requests made since online requests have been accepted. As such, not all fields will have data as certain information, such as aims, were not collected until recently. If an entry has been assigned an ID number (e.g. T1004), the full request has been submitted and is either approved, disapproved or in process. If an entry has no ID number, then it represents a submission that has not yet been reviewed. Search terms are applied across an entire requests application including variables not displayed below. A more specific, detailed search may yield better results depending upon your needs.


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Investigator: Parinaz Massoumzadeh

Project Title: Clinical-Biological Stage Concordance and Discordance in Alzheimer’s Disease

Date: September 25, 2025 at 9:02 pm

Request ID: D2542

Aim 1: Implementing the revised two dimensional biological-clinical staging framework to the Knight-ADRC cohort to classify participants and provide an empirical distribution of staging categories.

Aim 2: Analyzing baseline characteristics of participants in each category, and identifying factors associated with compliant, resilient divergent, and co-morbid divergent behaviors.

Aim 3: Comparing longitudinal trajectories of validated biological and clinical markers within each category.

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Investigator: Lars Lau Raket

Project Title: Longitudinal Item Response Theory Modelling on Clinical Dementia Rating (CDR)

Date: September 14, 2025 at 9:38 am

Request ID: D2541

Aim 1: Develop and validate a novel longitudinal item-level statistical framework for CDR data that models the latent disease trajectory with both global structure and local stochastic deviations, and identifies items most predictive of treatment effect and future decline.

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Investigator: Brice McConnell

Project Title: Determining the Connections Between Sleep-Based Digital Biomarker Signals and Brain Aging, Cognition, Molecular Biomarkers, and Neuroimaging

Date: September 9, 2025 at 6:02 pm

Request ID: D2540

Aim 1: Aim 1: Elucidate the oscillatory event features of sleep EEG that best predict brain age and determine the performance of these features in assessing whether an individual is experiencing more “youthful” or “accelerated” brain aging.

Aim 2: Aim 2: Examine the relationship between sleep’s memory-processing oscillatory circuit integrity and cognitive decline.

Aim 3: Aim 3: Delineate the relationships between memory-processing oscillatory circuity integrity and Alzheimer’s disease-related neuroimaging and molecular biomarkers.

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Investigator: Richard Head

Project Title: Identification of molecular signatures associated with combinatorial AD pathologies.

Date: August 27, 2025 at 2:29 pm

Request ID: D2539

Aim 1: Identification of molecular signatures associated with combinatorial AD pathologies:We will use a model of cognitive decline, based on plaque and tangle burden along with AD pathologies and identify how these combinatorial effects result in cognitive decline

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Investigator: Gemma Salvadó

Project Title: Independent contribution of aggregated amyloid and tau pathologies on plasma p-tau217 levels

Date: August 22, 2025 at 2:11 am

Request ID: D2538

Aim 1: To investigate the contributions of aggregated amyloid and tau pathologies on plasma p-tau217 levels at different stages of the disease

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Investigator: Donghoon Kim

Project Title: Deep Learning-Based Prediction of Brain Tau Burden Using MRI

Date: August 20, 2025 at 3:40 pm

Request ID: D2537

Aim 1: Develop a non-invasive MRI-based tau burden prediction model.

Aim 2: Develop a minimally invasive multimodal MRI-based tau burden prediction model.

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Investigator: Olivier Keunen

Project Title: Predi-PET

Date: August 20, 2025 at 8:28 am

Request ID: D2536

Aim 1: Predict PET outcomes form multi protocol MRI by feature extraction

Aim 2: Correlate OASIS-3 MRI set to OASIS-3 longitudinal Tau (OASIS3-AV1451L)

Aim 3: Create a fountational model that can use multiple MRI protocols and predict multiple PET tracers signals

Aim 4: Generation of PET volumes from MRI and evaluate metrics (PNSR,SSIM, SUVr,…)


Investigator: Kaleigh Roberts

Project Title: Comparative Deep Learning Analysis of Familial and Sporadic Alzheimer’s Disease

Date: August 15, 2025 at 2:37 pm

Request ID: D2535

Aim 1: Develop a foundation model (merging with the other data generated by the Crary lab).

Aim 2: Use supervised convolutional neural networks (CNNs) to classify images based on disease group and genotype, using regions of interest enriched for known pathological features (e.g., amyloid plaques, neurofibrillary tangles).

Aim 3: Apply multiple instance learning (e.g., CLAM) to detect latent features that distinguish familial and sporadic AD without relying on annotations (unsupervised), and interpret attention maps to identify novel morphologic signals.

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Investigator: Giovanni Frisoni

Project Title: Evaluation of the Alzheimer’s Association Workgroup 2024 (AA-2024) diagnostic criteria staging matrix: Influence of APOE4 genotype on disease trajectory and longitudinal progression

Date: August 14, 2025 at 2:14 am

Request ID: D2534

Aim 1: Investigate how the APOE4 genotype influences the distribution across the AA-2024 staging matrix at baseline.

Aim 2: Examine longitudinal transitions within the matrix among different APOE4 groups, assessing individual progression through biological and clinical stages over follow-up periods.

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Investigator: Manuel Menéndez González

Project Title: Probabilistic Diagnosis of Neurodegenerative Diseases: Correlation Between Clinical Diagnoses and Neuropathological Confirmation

Date: August 7, 2025 at 6:51 am

Request ID: D2533

Aim 1: To validate a new clinical diagnostic paradigm based on a three-dimensional framework that integrates the clinical and biological complexity of neurodegenerative diseases (NDDs).

Aim 2: To develop a probabilistic inference model that integrates multiple variables to estimate, during life, the likelihood of various neurodegenerative disease (NDD) diagnoses, including the possibility of co-pathologies.

Aim 3: To compare the model’s estimations with post-mortem neuropathological findings, assessing its reliability, validity, and clinical utility.

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