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 1 - 10 of 159

Investigator:Randall Bateman. MD


Title:Relationship between clinical heterogeneity and neuroanatomical variability in Autosomal dominant familial Alzheimer disease.

Date of Request:12/12/2018




Aim 1:Examine the extent to which factors like atrophy pattern, regional brain metabolism and underlying pathology (regional AB and TAU) influence clinical heterogeneity in ADAD. Hypothesis 1a: Regional brain atrophy, hypometabolism, and underlying pathology will correlate with distinct clinical phenotypes in ADAD.




Aim 2:To determine the influence of underlying pathology (regional AB and TAU) on cognitive decline and neuropsychological profiles. Hypothesis 2a: the amount and distribution of 18F-AV1451 retention will correlate with cognitive decline rate and neuropsychological performance.







Investigator:Ali Ezzati, MD


Title:Predictive analytics in DIAN study based on biomarker data.

Date of Request:12/10/2018




Aim 1:To tests the hypothesis that in comparison with traditional MRI biomarkers, subgroups identified by latent class analysis based on multidimensional biomarker data, will have higher accuracy for predicting longitudinal cognitive trajectories and cognitive events.




Aim 2:2) To develop a machine learning framework for prediction of time to conversion to AD (or MCI) in preclinical AD stage in participants with and without AD mutations and comparing effect of individual features (indicators) in the computational models.







Investigator:NA


Title:Relationship between functional connectivity and cognition in sporadic and autosomal dominant Alzheimer disease

Date of Request:10/22/2018

Status:Pending

ID:DIAN-D1819




Aim 1:evaluate how resting state functional connectivity relates to cognition




Aim 2:compare the connectivity - cognition relationship across LOAD and DIAN







Investigator:Maxime Descoteaux


Title:Tractometry informed dementia predictor

Date of Request:10/17/2018

Status:Pending

ID:DIAN-D1818




Aim 1:Look at longitudinal values of white matter tractometry to find which metrics or combination of metrics predicts best incoming dementia symptoms. Our tractometry algorithm will look at 12 metrics (DTI, HARDI, Freewater and microstructure) inside 33 white matter bundles each split in multiple subsections.










Investigator:Barbara Bendlin/Vikas Singh


Title:Characterizing disease propagation in autosomal dominant Alzheimer's disease

Date of Request:10/03/2018

Status:Pending

ID:DIAN-D1817




Aim 1:To utilize a non-Euclidean wavelet transform for connectivity signature (WaCS) to characterize structural connectivity alterations in autosomal AD.




Aim 2:To create subject specific propagation maps utilizing PET data in order to predict the spread of AD pathophysiologic markers over time.




Aim 3:To predict longitudinal cognitive decline or end-stage disease (EYO) using markers of AD pathology (CSF and PET based).




Investigator:Prof Dr Baris Topcular


Title:Amyloid Imagıng in Familial and Non-Familial AD

Date of Request:09/29/2018

Status:Pending

ID:DIAN-D1816




Aim 1:Detection of similarities in terms of Amyloid Imaging




Aim 2:Detection of differences in terms of Amyloid Imaging







Investigator:Sarah Eisenstein


Title:Neuroinflammation in Obesity

Date of Request:09/13/2018

Status:Pending

ID:DIAN-D1815




Aim 1:The first aim is to replicate in the DIAN controls cohort our finding that DTI-based indicators of neuroinflammation are greater in obese individuals compared to normal-weight individuals.










Investigator:Guoqiao Wang


Title:Build a predictive model to estimate EYO for sporadic Alzheimer disease

Date of Request:09/05/2018

Status:Pending

ID:DIAN-D1814




Aim 1:Validate the generalizability of the EYO estimation models using the ACS study




Aim 2:Evaluate the goodness-of-estimation of these statistical models by comparing the model estimate EYO with the mutation/parent EYO (calculated using mutation mean age of onset or parent age of onset) using the DIAN observational study




Aim 3:Improve the accuracy of the EYO for the ADAD population by modifying the statistical model to estimate a shift in the EYO using the biomarker, imaging, and cognition information.




Aim 4Build a predictive model for sporadic EYO by optimizing/modifying the statistical models to select the most appropriate model for each outcome (e.g. with/without monotonic assumption) and to select the most sensitive outcomes.

Investigator:Steve Petersen


Title:How the brain breaks: Non-linear network level changes in neurodegenerative diseases.

Date of Request:08/23/2018

Status:Pending

ID:DIAN-D1813




Aim 1:To see how resting state networks change as a function of estimated years to symptom onset




Aim 2:To explore the idea of "cascading network failure" in DIAN




Aim 3:To relate changes in resting state functional connectivity to other AD biomarkers




Investigator:N/A


Title:QMENTA Data Request

Date of Request:07/06/2018

Status:Pending

ID:DIAN-D1812




Aim 1:run data analysis




Aim 2:internal R&D purposes