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 298

Investigator:Eric McDade, DO

Title:Variant of Uncertain Significance (VUS) review

Date of Request:02/26/2024



Aim 1:Data will be used to evaluate pathogenicity of variant

Investigator:Agneta Nordberg

Title:Longitudinal subcortical structural changes and their correlation with multi-PET tracers and biofluid AD biomarkers in autosomal dominant Alzheimer’s disease

Date of Request:02/21/2024

Status:pending approval


Aim 1:To investigate longitudinal subcortical volume and white matter diffusion change differences between non-carriers and mutation-carriers from the Dominantly Inherited Alzheimer Network (DIAN)

Aim 2:To explore relationships between longitudinal subcortical volume and white matter diffusion changes and multi-PET tracers including Amyloid/FDG/Tau-PET and biofluid AD markers

Aim 3:To compare these results with those of Karolinska Institutet Nordberg Translational Molecular Imaging Lab

Investigator:Joon-Kyung Seong

Title:A pretrained foundation model for learning representation of regional tau accumulation pattern

Date of Request:02/15/2024

Status:pending approval


Aim 1:The aim of this study is to develop a model trained on large-scale datasets to recognize patterns in brain images.

Aim 2:The versatility of this model allows for fine-tuning with smaller datasets, making it suitable for implementation in medical institutions with limited data.

Investigator:Yujia Zhou

Title:Researching Alzheimer's disease

Date of Request:02/02/2024

Status:pending approval


Aim 1:Image-based diagnosis

Aim 2:Biomarker-based diagnosis

Investigator:Prashant Upadhyay

Title:Deep Learning based ensembled method for Alzheimer's Disease Classification

Date of Request:01/15/2024

Aim 1:My Grandmother suffered from the same disease. Since then I am working on this Disease. That's why I have chosen this as my research topic in my PhD. I have experience working with similar datasets and am committed to upholding ethical data practices and ensuring data security.


Title:PET/MRI-based Alzheimer's Disease Research Using Deep Learning

Date of Request:12/26/2023

Status:pending approval


Aim 1:Longitudinal imaging markers describing changes in AD over time

Aim 2:Explore the relationship with AB deposition, related genes and cognition to obtain the most relevant imaging and biological indicators

Aim 3:Multiple features were extracted and machine learning and deep learning were used to explore their biological markers

Investigator:Qunxi Dong

Title:Classification based on hippocampal multivariate morphometry statistics

Date of Request:11/19/2023

Status:pending approval


Aim 1:High-dimensional morphological features of the hippocampus were extracted to analyze group differences among different populations

Aim 2:The dimension of high-dimensional morphological features is reduced for subsequent classification

Investigator:Qunxi Dong

Title:Classification based on hippocampal multivariate morphometry statistics

Date of Request:11/19/2023

Aim 1:Extracting the high-dimensional morphological features of the hippocampus and cerebral ventricle of ADAD

Aim 2:Analyzing high-dimensional features to identify inter-group morphological differences in the hippocampus.

Aim 3:Using high-dimensional features for classification.

Investigator:Philippe Ravassard

Title:Organoid-based lncRNA discovery platform for Alzheimer's Disease.

Date of Request:11/14/2023



Aim 1:Generate assembloid cultures (assembled organoids) from familial and sporadic Alzheimer's Disease induced pluripotent stem cell (iPSC) lines, as well as their Crispr-Cas9 generated isogenic controls.

Aim 2:Extract cell-type specific long non-coding RNA (lncRNA) repertoires by using a combination of lineage purification, deep RNA sequencing and ATAC sequencing.

Aim 3:Annotate novel lncRNA and identify transcripts that are dysregulated in patient vs control cells.

Aim 4Use bulk and single-cell transcriptomes from the DIAN cohorts to validate newly identified lncRNA that may be associated with the disease.

Investigator:Prof Daniel Alexander

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

Date of Request:09/29/2023



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.