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 41 - 50 of 295

Investigator:Nicole McKay

Title:Investigating how white matter integrity and tauopathy underpin cognitive decline in autosomal dominant Alzheimer disease.

Date of Request:10/24/2022



Aim 1:Characterize attentional control in autosomal dominant Alzheimer disease

Aim 2:Examine the relationship between attentional control and biomarkers of white matter health in autosomal dominant Alzheimer disease.

Aim 3:Consider the relationship between attentional control and tau in autosomal dominant Alzheimer disease.

Investigator:Haiyan Liu

Title:Estimated Year of Symptoms at Onset in DIAD-A Systemic Review and Meta-analysis

Date of Request:09/28/2022



Aim 1:1. To identify newly described highly penetrant DIAD variants using data from DIAN-OBS, DIAN-TU and from a literature review.

Aim 2:2. To update the DIAD mutation AAO using data from DIAN-OBS, DIAN-TU and from a literature review

Aim 3:3. To determine the accuracy of the AAO and EYO model to predict the actual symptom onset.

Aim 44. To explore the relationship of EYO with CSF biomarkers, FDG PET, brain MRI, PiB PET and survival duration

Investigator:Peter Millar

Title:Modeling brain-predicted age in autosomal dominant Alzheimer disease

Date of Request:09/14/2022



Aim 1:Generate machine learning model predictions of brain age from functional connectivity MRI and evaluate as a marker of autosomal dominant AD progression

Aim 2:Generate machine learning model predictions of brain age from volumetric MRI and evaluate as a marker of autosomal dominant AD progression

Aim 3:Compare functional and volumetric brain age estimates to existing MRI features in autosomal dominant AD

Investigator:Randall Bateman

Title:Influence of biological sex and gender identity in the spectrum of Autosomal Dominant Alzheimer’s Disease: An Investigation from the Dominantly Inherited Alzheimer’s Network (DIAN)

Date of Request:08/25/2022



Aim 1:To compare age at onset, clinical presentation, neuropsychological performance and rate of cognitive decline between women and men.

Aim 2:To investigate possible differences in the spatial and temporal development of cerebral tau & amyloid-β, brain glucose metabolism, and structural atrophy, as well as their relationships, in women and men.

Aim 3:To compare fluid biomarker levels and rates of change between women and men.

Investigator:Eileen Crimmins

Title:Race/Ethnicity Demographics of Alzheimers Disease Neuroimaging Studies

Date of Request:08/23/2022

Status:pending review


Aim 1:Summarize race/ethnicity demographics of the DIAN observational study, in addition to other neuroimaging studies of Alzheimers Disease and aging

Investigator:Haiyan Liu

Title:Autophagy-Lysosome network proteins and disease staging in DIAN

Date of Request:08/18/2022



Aim 1:To identify autophagy-lysosome pathway protein changes in DIAD compared with control

Aim 2:To determine the relationship of classic AD biomarkers and relationship with autophagy-lysosome pathway proteins

Aim 3:To compare mitochondria functional protein changes in DIAD with control and their relationships with autophagy-lysosome pathway proteins.

Aim 4To identify the protein changes in endosome, ubiquitin-proteasome system and their relationship with lysosome proteins in DIAN compared with control.

Investigator:Chong Yao Feng

Title:Staging amyloid, tau and WMH co-evolution in Alzheimer's disease

Date of Request:07/28/2022



Aim 1:Apply machine learning algorithm to a dataset of amyloid PET data

Aim 2:Same as Aim 1, but with tau PET and WMH data added

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.