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 253

Investigator:Sun Lin


Title:Exploration for risk factors of cognitive impairment in non-dementia elderly

Date of Request:06/24/2022




Aim 1:The correlation between psychiatry symptoms and cognitive impairment in non-dementia elderly




Aim 2:The effects of daily habits on cognitive impairment in non-dementia elderly




Aim 3:The predictive biomarkers for cognitive impairment in non-dementia elderly




Aim 4The genetic factors of cognitive impairment in non-dementia elderly

Investigator:Xiaohu Zhao


Title:Multimodal Magnetic Resonance Imaging Study of Abnormal Regulation Machanism of Default Mode Network in AD

Date of Request:06/04/2022




Aim 1:To explore the abnormal DMN regulation mechanism in AD patients and its clinical relevance










Investigator:Kamil A. Grajski, PhD


Title:Medial temporal lobe (MTL) – default-mode network (DMN) functional connectivity disruption in Autosomal Dominant Alzheimer’s Disease (ADAD) progression in a Dominantly Inherited Alzheimer Network (DIAN) cohort

Date of Request:05/01/2022




Aim 1:Reprocess DIAN MRI data with FreeSurfer 7 and confirm progression of cortical thickness and subcortical volume results reported in McKay, N. S., et al. (2022).




Aim 2:Process rsfMRI data to characterize the progression of disruption in functional connectivity in the Default-Mode Network and related structures using an hybrid a priori and data-driven identification of ROI pairs of interest using a combination of AFNI and “home-grown” analytics.




Aim 3:Identify the subpopulation of DIAN patients for whom both MRI and rsfMRI data pass stringent quality criteria for inclusion. Establish the temporal correspondence of morphological and functional connectivity changes. For example, Grajski & Bressler (2019) showed in an ADNI cohort that functional connectivity changes may be detected prior to detection of morphological changes. Will the same, similar, or other hold for DIAN cohort(s)?




Aim 4Explore the clinical applicability of rsfMRI as a biomarker of Alzheimer's Disease progression. McKay, N. S., et al. (2022), noted a key benefit of the DIAN population is its relative absence of age-related and other comorbidities such as observed with sporadic Alzheimer's Disease. Explore viability of rsfMRI as a biomarker through machine learning-based approach to classification of MRI and rsfMRI data by DIAN clinical group.

Investigator:NA


Title:Develop an Item Response Theory Based Score for the Clinical Dementia Rating (CDR®)

Date of Request:04/10/2022




Aim 1:Develop an item response theory (IRT) based score for the CDR based on the item level data collected for the CDR in DIAN Obs




Aim 2:Demonstrate the superiority of the IRT based score compared to the CDR sum of box in terms of detecting cognitive change and association with other cognitive/clinical, imaging and fluid biomarkers







Investigator:Jonathan Wagg


Title:: Leverage DIAN data to derive and validate EYO estimation models applicable to Downs Syndrome (DS) populations

Date of Request:04/07/2022




Aim 1:Review and analyze DIAN longitudinal clinical endpoint and biomarker measure changes over time that are: (i) of potential utility for development of EYO estimation models; (ii) comparable to measures available in longitudinal DS cohort datasets such as the ABC-DS cohort.




Aim 2:Use measures identified in (1.) to derive alternative model structures for EYO estimation in the DIAN population.




Aim 3:Compare the predictive performance of candidate model structures in the DIAN population.




Aim 4Assess the best performing model structures in a DS population.

Investigator:Brian Gordon


Title:Comparing biomarkers between Down Syndrome, Autosomal Dominant, and Late onset Alzheimer Disease

Date of Request:03/25/2022




Aim 1:How do measures of tau pathology measured using PET compare among DS, ADAD, and LOAD?




Aim 2:Does the spread of tau pathology over time differ between cohorts?




Aim 3:Are white matter lesions a common finding in all three forms of AD?




Aim 4How do biofluid profiles compare between DS, ADAD, and LOAD?

Investigator:kun zhao


Title:Alzheimer's disease

Date of Request:03/18/2022




Aim 1:e want to provide a reference point for each individual during aging to provide a reference at each time of the lifespan.




Aim 2:we also provide a frame for the intersection of multi neurodegenerative disease and mental disorder.







Investigator:Randy Buckner


Title:Exploration of multimodal MRI Biomarkers in preclinical stage of Alzheimer's Disease

Date of Request:03/16/2022




Aim 1:The goal of this study is to combine multimodal structural imaging biomarkers to optimistically distinguish Alzheimer’s disease and normal aging in the early and mild stages of Alzheimer’s disease.










Investigator:Professor John Gallacher


Title:The biopsychosocial determinants of cognitive change and Alzheimer's disease

Date of Request:02/14/2022




Aim 1:The overall aim of the project will be to assess the biopsychosocial determinants of Alzheimer’s disease across modifiable and non-modifiable factors




Aim 2:To profile cognitive change by biopsychosocial construct determinants




Aim 3:To profile cognitive change and AD by biopsychosocial construct determinants and genetic stratification




Aim 4To create a biopsychosocial risk profiling tool for brain health

Investigator:John Ringman


Title:Biomarker Characterization of the A431E mutation in PSEN1

Date of Request:02/11/2022




Aim 1:To assess plasma and CSF biomarkers associated with the A431E mutation in PSEN1.




Aim 2:To assess volumetric changes, white matter hyperintensities, and microhemorrhages on MRI seen in association with the A4341E mutation in PSEN1.




Aim 3:To assess cerebral amyloid deposition measured using PiB PET associated with the A4341E mutation in PSEN1.




Aim 4To assess cerebral tau deposition measured using flortaucipir PET associated with the A4341E mutation in PSEN1.