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 138

Investigator:Carlos Cruchaga/Celeste Karch


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

Date of Request:12/21/2017

Status:Approved

ID:DIAN-D1729




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

Status:Approved

ID:DIAN-D1728




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

Status:Approved

ID:DIAN-D1727




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

Status:Approved

ID:DIAN-D1726




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







Investigator:David Cash and Nick Fox


Title:Sample size estimates for imaging biomarkers in prevention trials: exploring different modalities, enrichment strategies and trial designs

Date of Request:10/02/2017

Status:Approved

ID:DIAN-D1725




Aim 1:Assess and optimise imaging measures of preclinical AD for use as trial inclusion, staging criteria and outcome measures




Aim 2:Assess the rate and variance of serial imaging measures in the Dominantly Inherited Alzheimer Network (DIAN) study




Aim 3:Assess the different power and design requirements for studies using different potential trial inclusion criteria – including, but not limited to a) estimated years to onset (based on from parental age in familial AD); b) amyloid PET imaging; c) amyloid PET positivity plus hippocampal volume




Aim 4Assess and compare the requirements for classical parallel arm trial design with a) run-in designs that include a pre-treatment assessment period and b) staggered start designs

Investigator:Mikael Simons


Title:Lipidomics study of preclinical plasma biomarkers for Alzheimer´s disease

Date of Request:09/28/2017

Status:Incorrect submission-resubmit as sample request

ID:DIAN-D1724




Aim 1:Establish a preclinical biomarker for AD




Aim 2:Determine lipid profiles in preclinical AD




Aim 3:Compare lipid profiles in DIAN and Framingham




Aim 4Determine longitudinal changes of lipid profiles during disease

Investigator:Hamied Haroon


Title:“Quantima – Diagnostics Imaging Biomarkers

Date of Request:09/13/2017

Status:Approved

ID:DIAN-D1723




Aim 1:Generate diffusion atlas for healthy controls and participants diagnosed with Alzheimer's disease.










Investigator:Anne Fagan


Title:Evaluation of longitudinal trajectories of novel CSF markers of neuronal injury/neuroinflammation

Date of Request:09/13/2017

Status:Approved

ID:DIAN-D1722




Aim 1:Perform cross-sectional and longitudinal analyses of clinical, cognitive, structural imaging and biochemical changes according to estimated age of symptomatic onset for standard CSF biomarkers (tau, ptau, and Aβ42) as well as novel ones (VILIP-1, Ng, SNAP-25, YKL-40) in order to evaluate the rate of change of the novel CSF biomarkers as a function of baseline estimated age of symptomatic onset




Aim 2:Compare their rates of change with those of the standard biomarkers




Aim 3:Evaluate the association of cross-sectional and longitudinal patterns of all CSF biomarkers with regional brain atrophy measures.




Investigator:Randall Bateman


Title:Preliminary data to assess viability of potential CSF biomarker study (Pfizer collaboration)

Date of Request:03/23/2017

Status:Approved

ID:DIAN-D1710




Aim 1:Identify mutations by site to assess feasibility of possibly conducting this study.










Investigator:Benoit Lehallier


Title:Large-scale screening of preclinical biomarkers of Alzheimer�s disease

Date of Request:03/23/2017

Status:Pending

ID:DIAN-D1711




Aim 1:To develop new algorithms to identify reliable biomarkers of Alzheimer's disease (AD)




Aim 2:To accurately predict time to MCI or to AD




Aim 3:To investigate the relative interest of imaging, proteomics, clinical and demographics




Aim 4to validate the best models in independent data sets such as the NACC and the ADNI cohorts.