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 164

Investigator:Ting Ma


Title:Association of morphology and network connectivity of degenerative disease

Date of Request:02/21/2019




Aim 1:Morphological change mechanism of Alzheimer’s continuum




Aim 2:Structural connectivity change mechanism of Alzheimer’s continuum




Aim 3:Brain function connectivity variation of Alzheimer’s continuum




Aim 4Association of morphology and network connectivity in AD development

Investigator:Suman Jayadev


Title:MicroRNA regulation of central nervous system and systemic inflammation in AD

Date of Request:5/25/2017

ID:DIAN-D1716




Aim 1:Determine if FAD and SAD brain develop distinct patterns of myeloid cell heterogeneity.










Investigator:Adam L. Boxer, MD, PhD


Title:Individualized prediction of symptom onset in familial AD using structural MRI

Date of Request:02/11/2019




Aim 1:create individualized atrophy maps in patients with fAD




Aim 2:investigate the utility of these maps for predicting the likelihood of developing symptoms of dementia on longitudinal follow-up







Investigator:Randall Bateman MD


Title:Dominantly Inherited Alzheimer's Disease Protective Factor Study

Date of Request:01/29/2019




Aim 1:The overall objective is to identify factors that protect mutation-carriers from AD symptoms.




Aim 2:1. Obtaining the clinical, cognitive, biomarker and genetic results from the DIAN research study .




Aim 3:2. Administering a number of questionnaires assessing health and lifestyle factors to include the following: Current Health Status, Medical Conditions, Physical Functioning, Exposure History Form, Occupational and Environmental Exposure History.




Investigator:na


Title:Advanced analytics on DIAN imaging data

Date of Request:01/28/2019




Aim 1:Use advanced analytics to predict mutation carrier status of DIAN patients by vMRI data only




Aim 2:Use advanced analytics to predict mutation carrier status of DIAN patients by amyloid PET data only







Investigator:Randall Bateman. MD


Title:Relationship between literacy, socio-economic status, Alzheimer pathology and cognitive decline in autosomal dominant Alzheimer’s disease.

Date of Request:12/27/2018




Aim 1:Aim 1: To examine the extent to which environmental risk factors like level of education and SES influence age of onset in ADAD. Hypothesis 1a: Age at symptom onset will be earlier in those with a lower level of education or lower SES.




Aim 2:Aim 2: To determine the influence of educational level (EL) and SES on cognitive decline, disease progression and mortality rate. Hypothesis 2a: Educational Level and SES has a significant effect on cognitive decline and mortality rate after diagnosis, so cognitive decline rate and mortality rate will be faster in those with less than 9 years of education and lower SES.







Investigator:Randall Bateman. MD


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

Date of Request:12/12/2018

Status:Pending

ID:DIAN-D1821




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

Status:Pending

ID:DIAN-D1820




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.