Search DIAN Data Resource Requests

In order to avoid the situation where two investigators study the same research question, please search our database to determine if your topic has already been studied. If you find that your topic or a related topic has already been submitted, you may wish to contact the investigator to inquire about his/her 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 # (e.g. DIAN-D1004), the full request has been submitted and is either approved, disapproved or in process.

Displaying 11 - 20 of 289

Investigator:Prof Daniel Alexander

Title:Computational Modelling and Inference of Neurodegenerative Disease Propagation (CU-MONDAI)

Date of Request:09/29/2023

Status:pending approval


Aim 1:To provide new insights into how pathology spread is linked to the brain’s connectivity architecture in autosomal dominant Alzheimer’s disease and validate new computational models of pathology propagation in neurodegenerative diseases.

Investigator:Nelly Joseph-Mathurin

Title:Examining the Predictive Value of Synaptic Dysfunction and Neuronal Injury Measures on Imaging Markers of Disease Presentation and Progression in Alzheimer's Disease

Date of Request:09/26/2023

Status:pending approval


Aim 1:Evaluate association between rates of longitudinal change in CSF levels of Ng, SNAP-25, VILIP-1 and imaging brain changes and cognition in a DIAD cohort.

Aim 2:Evaluate association between rates of longitudinal change in CSF levels of Ng, SNAP-25, VILIP-1 and imaging brain changes and cognition in aged adults LOAD cohort.

Investigator:Seonjoo Lee

Title:Evaluating neural correlates of apathy in Alzheimer’s disease

Date of Request:09/18/2023

Status:pending approval


Aim 1:Aim1. We will seek to determine if apathy is embedded in a larger network of NPS and functional and cognitive impairment using clustering analysis. In the subset of data with neuropathology information, we will identify the association between apathy clusters and neuropathology.

Aim 2:Aim2. We will examine brain morphometry, structural connectivity, metabolism, amyloid PET in reward-sensitive areas, and effort valuation processing areas in apathy and/or its networks.

Aim 3:Aim3. We will evaluate the association between the intrinsic time scale (fMRI) and apathy and apathy networks in the course of disease.


Title:Prediction of Alzheimer's Disease through multimodal approach by machine learning

Date of Request:09/07/2023

Status:pending approval


Aim 1:Develop a multimodal machine learning model to accurately predict Alzheimer's disease progression using a combination of imaging, genetic, and clinical data.

Aim 2:Investigate the potential biomarkers and features from various modalities that contribute significantly to the early detection and progression tracking of Alzheimer's disease.

Aim 3:Evaluate the model's performance in predicting Alzheimer's risk and progression in a diverse patient population, including individuals with different genetic backgrounds and demographics.

Aim 4Explore the interpretability of the multimodal model to gain insights into the underlying mechanisms of Alzheimer's disease, aiding in the development of targeted interventions.

Investigator:Jee Hoon Roh/Jae-Hong Lee

Title:Epigenetic analyses to assess the resilience among DIAN mutation carriers

Date of Request:09/05/2023

Status:pending approval


Aim 1:To investigate the potential epigenetic causes of resilience among DIAN mutation carriers who are discordant in disease courses measured by biomarkers

Investigator:Dr Shahid Zaman

Title:Knowledge Guided Machine Learning for Prognosis Forecast ing of Alzheimer’s Disease in People with Down Syndrome

Date of Request:08/17/2023

Status:pending approval


Aim 1:explore different methods, such as data augmentation, transfer learning, and knowledge-guided generative adversarial networks (GANs), to address the insufficient training data issue thus improve the quality of data representations

Aim 2:the fusion of the valuable domain-specific knowledge of AD and designs of graph neural networks will be explored to improve the robustness and interpretability in diagnosis and prognosis of AD

Aim 3:develop multimodal machine learning frameworks for early diagnosis and prognosis of AD

Investigator:Le Shi

Title:The association of sleep disturbance and β-amyloid pathology among cognitively normal older adults

Date of Request:08/09/2023

Status:pending approval


Aim 1:Using the data obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Database and DIAN Observational Database.

Aim 2:The sample consisted of CN individuals aged between 55 and 90 years with Aβ positron emission tomography scan, APOE genotype, and sleep behaviors.

Investigator:Ruichen Han

Title:Functional connections in the hippocampus subregion of familial Alzheimer's disease before symptoms as biomarkers for predicting disease status

Date of Request:08/05/2023

Status:pending approval


Aim 1:To investigate whether there are differences in the damage patterns of brain functional network connections between familial Alzheimer's disease and sporadic Alzheimer's disease

Aim 2:To explore can functional connections serve as a biomarker to predict cognitive state

Investigator:Karin Meeker

Title:Tau Phosphorylation in Preclinical and Symptomatic Autosomal Dominant Alzheimer Disease

Date of Request:08/05/2023

Status:pending approval


Aim 1:Temporal progression of tauopathy. Tau sites (e.g., pT181, pT202, pT205, pT217) will be assessed in relation to preclinical AD biomarkers (e.g., CSF Ab42) and biomarkers of symptom onset (e.g., CSF total tau, RS-FC, cognitive performance), and EYO. Longitudinal changes in variables will also be plotted against EYO to determine temporal trajectories and to assess how biomarkers change relative to one another across disease progression. It is hypothesized that elevation of each site will occur at varying stages of the disease process and will follow distinct individual trajectories over time. Specifically, it is expected that relative to other tau sites, elevation in CSF pT217 and pT181 levels will arise first and will most strongly associate with preclinical biomarkers, such as CSF Ab42, while other tau sites will become elevated later in the disease process and will most strongly associate with biomarkers of symptom onset.

Aim 2:Spatial progression of tauopathy in ADAD. Compared to established biomarkers (e.g., amyloid and total tau), it is unknown how phosphorylated tau sites differentially relate to and drive alterations in resting state brain network dynamics during the preclinical and clinical phases of ADAD. To determine whether specific tau sites are associated with alterations in brain network RS-FC organization, tau sites will be cross-sectionally correlated with within-network and between-network RS-FC. Longitudinal changes in tau sites and RS-FC will additionally be correlated and plotted against EYO to determine whether changes in tau sites and RS-FC arise in a specific pattern, and more specifically to elucidate the spatial progression of tauopathy. It is hypothesized that elevated levels of CSF tau sites will be associated with a local to diffuse pattern of decreases in RS-FC, and that associations between tau sites and RS-FC will be greatest in regions where tau deposition occurs, as measured by tau positron emission tomography (PET).

Investigator:Wencai Ding, Huan He.

Title:Association of cortical and subcortical microstructure with disease severity: impact on cognitive decline and language impairments in Dominantly Inherited Alzheimer’s disease.

Date of Request:07/25/2023

Status:pending approval


Aim 1:We aimed to study the cortical and subcortical microstructural variations using surface-based analysis (cMD and cortical fractional anisotropy (cFA)) and TBSS (MD and FA)

Aim 2:We also explored the relationships between multimodal macrostructural/ microstructural measures and the clinical scale scores representing language abilities (verbal fluency test (VFT) and Boston naming test (BNT)).