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 295


Title:Amyloid Chronicity in Autosomal Dominant Alzheimer’s Disease

Date of Request:05/23/2024

Status:pending approval


Aim 1:Examine associations between estimation of amyloid chronicity and Estimated Year of Symptom Onset (EYO) in Autosomal Dominant Alzheimer Disease

Aim 2:Compare longitudinal rate of amyloid accumulation across autosomal dominant Alzheimer Disease, sporadic Alzheimer Disease, and Down Syndrome

Aim 3:Determine the average length of time between becoming amyloid positive and cognitively impaired in different forms of Alzheimer Disease (autosomal dominant Alzheimer Disease, sporadic Alzheimer Disease, and Down Syndrome)

Investigator:David Cash

Title:Sample size estimates for biomarkers in prevention trials

Date of Request:04/17/2024


Aim 1:Assess the rate and variance of serial imaging measures in the Dominantly Inherited Alzheimer Network (DIAN) study

Aim 2:Compare sample size estimates from imaging outcomes with biofluid and cognitive measures

Aim 3:Assess the different power and design requirements for studies using different potential trial inclusion criteria (including but not limited to estimated years to onset, amyloid and tau positivity, cogntiive status) and suitable target treatment effects (reduced rate of atrophy/cognitive decline or reduction of absolute levels of AD-related pathology by end of study)

Aim 4Assess and compare the requirements for classical parallel arm trial design with more novel approaches (run-in designs, common close, staggered starts)

Investigator:Stephanie Schultz

Title:Influence of tau on neurodegeneration in ADAD

Date of Request:04/04/2024

Status:pending approval


Aim 1:Our overall objective is to compare the cross-sectional and longitudinal trajectories of CSF Tau (including pTau181, NT1, pTau205, pTau217, MTBR-tau) and regional Tau-PET with the neurodegenerative trajectories using of CSF NfL and grey-matter volumes in pathogenic variant carriers in DIAN.

Investigator:Jessica Alber

Title:Development of retinal biomarkers in autosomal dominant Alzheimer’s disease: A pilot study

Date of Request:03/29/2024

Status:pending approval


Aim 1:Identify retinal biomarker differences between ADAD mutation carriers and non-carriers. H1: ADAD mutation carriers will have a higher number and surface area of retinal Ab deposits (retinal inclusion bodies) than non-carriers, as shown by FAF imaging. H2: ADAD mutation carriers will have volume and thickness differences in the mRNFL and GC-IPL relative to non-carriers, as measured using SD-OCT imaging.

Aim 2:Project aim 2: Determine whether retinal biomarker alterations predict cerebral biomarker status in ADAD. H3: Number and surface area of retinal inclusion bodies will be able to predict cerebral Ab accumulation (measured via PET and/or CSF as part of the DIAN-Obs protocol) in ADAD.

Investigator:Sabiha Naznin

Title:“Correlating between Sporadic and Autosomal Dominant Alzheimer's Disease: Longitudinal Analysis of Clinical and Neuroimaging Dynamics with a Focus on Sporadic GAP-43 Measurements”

Date of Request:03/28/2024

Status:pending approval


Aim 1:1. Investigate longitudinal patterns of clinical decline and neuroimaging changes in both sporadic and Autosomal Dominant Alzheimer's Disease (ADAD).

Aim 2:2. Specifically, explore the correlation between clinical parameters, and neuroimaging measures to shed light on potential shared mechanisms and differences between sporadic and ADAD considering GAP-43 measurements.

Investigator:Mithilesh Prakash

Title:Simulation of AD progression enabled by multivariate data anlysis

Date of Request:03/26/2024

Status:pending approval


Aim 1:To determine the AD trajectory in relation to mutisite mutivariate variables.

Investigator:Stefan J. Teipel


Date of Request:03/01/2024


Aim 1:test the new hypothesis that, different from sporadic AD, basal forebrain volume and functional connectivity are preserved in ADAD

Aim 2:investigate the moderating and/or mediating effects of Amyloid-PET positivity on measures of basal forebrain atrophy and functional connectivity reduction in preclinical versus clinical cases, based on previous findings in sporadic AD

Aim 3:investigate the effect of carrier status on measures of thalamus and cerebellum volumes and functional connectivity

Investigator:Eric McDade, DO

Title:Variant of Uncertain Significance (VUS) review

Date of Request:02/26/2024



Aim 1:Data will be used to evaluate pathogenicity of variant

Investigator:Agneta Nordberg

Title:Longitudinal subcortical structural changes and their correlation with multi-PET tracers and biofluid AD biomarkers in autosomal dominant Alzheimer’s disease

Date of Request:02/21/2024

Status:pending approval


Aim 1:To investigate longitudinal subcortical volume and white matter diffusion change differences between non-carriers and mutation-carriers from the Dominantly Inherited Alzheimer Network (DIAN)

Aim 2:To explore relationships between longitudinal subcortical volume and white matter diffusion changes and multi-PET tracers including Amyloid/FDG/Tau-PET and biofluid AD markers

Aim 3:To compare these results with those of Karolinska Institutet Nordberg Translational Molecular Imaging Lab

Investigator:Joon-Kyung Seong

Title:A pretrained foundation model for learning representation of regional tau accumulation pattern

Date of Request:02/15/2024

Status:pending approval


Aim 1:The aim of this study is to develop a model trained on large-scale datasets to recognize patterns in brain images.

Aim 2:The versatility of this model allows for fine-tuning with smaller datasets, making it suitable for implementation in medical institutions with limited data.