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.

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Title:• Comparison of different CSF biomarkers in asymptomatic DIAD population

Date of Request:01/03/2021

Aim 1:Comparison of different CSF biomarkers to conclude CSF ptau217/tau217 ratio is an appropriate endpoint for asymptomatic DIAD population

Aim 2:• Characterization of CSF ptau217/tau217 ratio change over time

Investigator:Joana B. Pereira

Title:Characterizing the Progression of Familial Alzheimer’s Disease with Deep Learning

Date of Request:12/11/2020

Aim 1:Characterize the progression of autosomal dominant Alzheimer’s disease using deep learning approaches.

Aim 2:Define a new biomarker-based model for autosomal dominant Alzheimer’s disease.

Investigator:Hamid Sohrabi and Ralph Martins

Title:Five Factor Model of Personality factors, cognition, brain and genetics and brain

Date of Request:12/03/2020

Aim 1:Examine the relationship between personality factors and cognition over time

Aim 2:Examine the relationship between personality factors and genetic risk factors for dementia

Aim 3:Examine the relationship between personality factors and brain structural, volumetric and functional measures

Aim 4Examine the change in biological markers and personality factors

Investigator:Pete Millar

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

Date of Request:11/24/2020

Aim 1:Generate machine learning based model predictions of brain age from multimodal structural and functional neuroimaging data.

Aim 2:Test whether brain-predicted age is elevated in autosomal dominant AD and if it is associated with years to expected onset in mutation carriers.

Investigator:Zahinoor Ismail

Title:Neuropsychiatric Symptoms in Dementia

Date of Request:11/24/2020

Aim 1:Explore and detect associations between neuropsychiatric symptoms and ATN biomarkers in dementia paitents

Investigator:Yasamin Salimi

Title:Exploring the Dataset Landscape in Alzheimer's Disease and Building Progression Models

Date of Request:10/28/2020

Aim 1:Providing an overview on all accessible AD/dementia clinical cohort datasets. Systematically compare them by looking at the actual data, not only metadata. Assess the state of data sharing in the dementia field.

Aim 2:Multi-study comparative modeling of longitudinal patient trajectories. Using machine learning and artificial intelligence

Aim 3:Include longitudinal models into The Virtual Brain platform to allow for individual patient brain simulations.


Title:The role of soluble β-amyloid peptide-42 in cognition and brain tropism: sub-analysis of the Dominantly Inherited Alzheimer Network

Date of Request:10/23/2020

Aim 1:To determine the extent to which the levels of soluble CSFAβ42 are higher in subjects with normal cognition compared with amyloid-positive subjects with cognitive impairment or dementia.

Aim 2:To evaluate whether the conversion from normal cognition to cognitive impairment is associated with a specific threshold of CSF Aβ42 and whether that threshold is dependent or independent from the levels of brain amyloid burden.

Aim 3:To evaluate whether levels of soluble CSFAβ42 are associated with hippocampal volume and PET-FDG uptake, and whether that association is dependent or independently from the brain amyloid burden.

Aim 4To examine whether the severity of cognitive impairment is affected to a greater extent by changes in soluble Aβ42 or amyloid burden.

Investigator:Carlos Cruchaga

Title:Using quantitative traits to identify new genes, biomarkers and drug targets for ADAD and LOAD

Date of Request:09/08/2020

Aim 1:To identify genetic variants associated with biomarker levels and quantitative traits in ADAD

Aim 2:To identify novel variants and genes associated with onset and progression by using multi-omics QTL data

Aim 3:To identify novel molecular biomarkers and drug targets by combining multi-omic QTL and Mendelian randomization analysis

Investigator:Patrick Lao

Title:Imaging White matter hyperintensities and Tau in Autosomal Dominant Alzheimer’s Disease

Date of Request:09/04/2020

Aim 1:To determine the difference in the relationship among tau and WMH, independent or dependent on amyloid, between mutation carriers and controls.

Aim 2:To characterize the timing of the relationship among tau and WMH in mutation carriers compared to controls.

Aim 3:To characterize any added benefit to incorporating spatial information from tau PET in Aims 1 and 2.

Aim 4To determine the difference in the relationship among baseline WMH and tau accumulation between mutation carriers and controls.

Investigator:Anna Dieffenbacher

Title:Specific facets of Personality traits in Autosomal Dominant Alzheimer disease

Date of Request:08/30/2020

Aim 1:1) to determine the different baseline differences between mutation carriers and non-mutation carriers in respect to the facets within each Personality Factor (OCEAN: openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism). 2) to analyze longitudinal changes of facets within mutation-carriers and non-carriers.