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 21 - 30 of 302

Investigator:Qunxi Dong


Title:Classification based on hippocampal multivariate morphometry statistics

Date of Request:11/19/2023

Status:pending approval

ID:DIAN-D2326




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

Status:approved

ID:DIAN-D2325




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

Status:approved

ID:DIAN-D2324




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:approved

ID:DIAN-D2323




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

ID:DIAN-D2322




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.




Investigator:nil


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

Date of Request:09/07/2023

Status:pending approval

ID:DIAN-D2321




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:approved

ID:DIAN-D2320




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:approved

ID:DIAN-D2319




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

ID:DIAN-D2318




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.