DIAN Data Resource Requests

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

Date of Request:01/27/2018

Status:Approved

ID:DIAN-D1802

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

Status:Approved

ID:DIAN-D1801

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)

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

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.

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.

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

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

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

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.

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.