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 144

Investigator:Simone Wahl

Title:DIAN Elecsys CSF and Amyloid Imaging Concordance

Date of Request:02/21/2018



Aim 1:Determine the concordance between CSF measures obtained via the Elecsys platform

Aim 2:Assess Elecsys CSF measure relative to other clinical and cognitive variables

Aim 3:Assess how well Elecsys measures predict clinical and cognitive progression

Investigator:Viktoria Andreeva

Title:DIAN data analysis in a tranSMART platform

Date of Request:02/16/2018



Aim 1:Curate the DIAN data and prepare for tranSMART loading

Aim 2:Perform investigation of data in tranSMART platform

Investigator:Dr. Alan C. Evans

Title:Comparing Multifactorial Pathologic Trajectories in Asymptomatic Autosomal Dominant Alzheimer’s Disease Mutation Carriers and Late-Onset Alzheimer’s Disease patients

Date of Request:02/04/2018



Aim 1:Identify disease triggering events in each DIAN subject, using all the available multimodal neuroimaging data

Aim 2:Subdivide and stage the DIAN subjects based on the identified individual causal pathological mechanisms (from Aim 1)

Aim 3:Compare both the obtained potential triggering events and the subjects’ subdivision/staging (from Aims 1 and 2 respectively) with the equivalent results previously obtained for LOAD patients from ADNI database.

Investigator:Howard Rosen, Brad Boeve, Adam Boxer

Title:Quantification of Longitudinal Volume Change in Familial FTD (fFTD)

Date of Request:01/28/2018



Aim 1:To quantify rates of change in structural (T1w) imaging in symptomatic fFTD

Aim 2:To quantify rates of change in structural (T1w) imaging in presymptomatic fFTD

Aim 3:To derive subject-specific maps of volumetric change in fFTD

Investigator:Betty Tijms

Title:Linking brain connectivity changes to development of symptoms in autosomal dominant AD

Date of Request:01/27/2018



Aim 1:To study the association of grey matter connectivity in autosomal dominant AD with estimated year of onset and cognitive decline.

Aim 2:To study associations between grey matter connectivity alterations and AD and injury markers in CSF (aβ42, aβ40, p-tau, t-tau, neurogranin, VLIP-1, SNAP-25, NRGN, YKL-40) and PET (aβ, FDG, and tau) cross-sectionally and longitudinally.

Aim 3:Compare cross-sectional findings in grey matter connectivity with longitudinal changes.


Title:Updated analyses of Dominantly Inherited Alzheimer Disease (AD) neuroimaging data using new biomarker, clinical, genetic, and psychometric data

Date of Request:01/26/2018



Aim 1:Cross-sectional evaluation of the temporal ordering of imaging biomarkers in asymptomatic ADAD with current biomarker, clinical, genetic, and psychometric data.

Aim 2:Harmonization of refined ADAD imaging processed values with the ADNI sporadic AD studies (ADNI)

Investigator:Carlos Cruchaga/Celeste Karch

Title:Clinical and Molecular Characterization of PSEN1 and PSEN2 Variants of Unknown Pathogenicity

Date of Request:12/21/2017



Aim 1:To describe imaging and fluid biomarkers for PSEN1 and PSEN2 variants of unknown pathogenicity

Investigator:Michael Ewers

Title:Developing a machine-learning based biomarker model to predict Alzheimer’s disease progression

Date of Request:11/30/2017



Aim 1:1. To train a machine learning algorithm that sensitively predicts ADAD stage (i.e. EYO) based on cross-sectional biomarker and imaging data in the DIAN cohort. The trained algorithm will be cross-validated in sporadic AD (ADNI) as a predictor of a. baseline cognition b. longitudinal disease progression

Aim 2:1. To train a machine learning algorithm that discriminates between mutation carrier (MC) and non-mutation carriers (NC) based on cross-sectional biomarker and imaging data in the DIAN cohort. Cross-validation of the trained model for diagnostic classification will be applied to cases with sporadic AD and controls from the ADNI data set.

Investigator:Juan Manuel Górriz Sáez

Title:Time course of imaging markers of neurodegeneration in autosomal dominantly inherited Alzheimer’s disease assessed by Linear mixed effects models

Date of Request:11/29/2017



Aim 1:The specific aim for the proposed work with the DIAN dataset is to make use of the machine learning paradigm for classification in combination with feature extraction methods such as partial least squares (PLS) algorithm [4] on a combined set of different imaging modalities. PLS is a very well-known algorithm in the neuroimaging field.

Aim 2:Additionally we aim for the examination of some other covariates which are known to affect neurodegeneration such as gender or genetic status by a LME approach, which represents a model of a response variable with fixed and random effects. These models comprise fitted coefficients, covariance parameters, design matrices, residuals and other diagnostic information.

Aim 3:Furthermore we are planning to follow an enhanced approach by querying the evolution of the selected features and by proposing predictive models based on supervised learning applied to the reference controls.

Investigator:Joseph Therriault

Title:Determination of Voxel-based Receiver Operating Characteristic thresholds of amyloid- deposition

Date of Request:11/07/2017



Aim 1:Determine the extent to which the patterns of amyloid burden that optimally differentiate between sporadic AD patients and controls are observed in ADAD.

Aim 2:Determine whether amyloid burden in structures that optimally differentiate between controls and AD patients, determined by the results of the voxel-wise ROC curve, can provide additional information about disease severity as measured by neuropsychological test scores and [18F]FDG-PET