DIAN Data Resource Requests

Title:Relationship between literacy, socio-economic status, Alzheimer pathology and cognitive decline in autosomal dominant Alzheimer’s disease.

Date of Request:12/27/2018

Status:Approved

ID:DIAN-D1822

Aim 1:Aim 1: To examine the extent to which environmental risk factors like level of education and SES influence age of onset in ADAD. Hypothesis 1a: Age at symptom onset will be earlier in those with a lower level of education or lower SES.

Aim 2:Aim 2: To determine the influence of educational level (EL) and SES on cognitive decline, disease progression and mortality rate. Hypothesis 2a: Educational Level and SES has a significant effect on cognitive decline and mortality rate after diagnosis, so cognitive decline rate and mortality rate will be faster in those with less than 9 years of education and lower SES.

Title:Relationship between clinical heterogeneity and neuroanatomical variability in Autosomal dominant familial Alzheimer disease.

Date of Request:12/12/2018

Status:Approved

ID:DIAN-D1821

Aim 1:Examine the extent to which factors like atrophy pattern, regional brain metabolism and underlying pathology (regional AB and TAU) influence clinical heterogeneity in ADAD. Hypothesis 1a: Regional brain atrophy, hypometabolism, and underlying pathology will correlate with distinct clinical phenotypes in ADAD.

Aim 2:To determine the influence of underlying pathology (regional AB and TAU) on cognitive decline and neuropsychological profiles. Hypothesis 2a: the amount and distribution of 18F-AV1451 retention will correlate with cognitive decline rate and neuropsychological performance.

Title:Predictive analytics in DIAN study based on biomarker data.

Date of Request:12/10/2018

Status:Approved

ID:DIAN-D1820

Aim 1:To tests the hypothesis that in comparison with traditional MRI biomarkers, subgroups identified by latent class analysis based on multidimensional biomarker data, will have higher accuracy for predicting longitudinal cognitive trajectories and cognitive events.

Aim 2:2) To develop a machine learning framework for prediction of time to conversion to AD (or MCI) in preclinical AD stage in participants with and without AD mutations and comparing effect of individual features (indicators) in the computational models.

Title:Relationship between functional connectivity and cognition in sporadic and autosomal dominant Alzheimer disease

Date of Request:10/22/2018

Status:Approved

ID:DIAN-D1819

Aim 1:evaluate how resting state functional connectivity relates to cognition

Aim 2:compare the connectivity - cognition relationship across LOAD and DIAN

Title:Tractometry informed dementia predictor

Date of Request:10/17/2018

Status:Pending

ID:DIAN-D1818

Aim 1:Look at longitudinal values of white matter tractometry to find which metrics or combination of metrics predicts best incoming dementia symptoms. Our tractometry algorithm will look at 12 metrics (DTI, HARDI, Freewater and microstructure) inside 33 white matter bundles each split in multiple subsections.

Title:Characterizing disease propagation in autosomal dominant Alzheimer's disease

Date of Request:10/03/2018

Status:Pending

ID:DIAN-D1817

Aim 1:To utilize a non-Euclidean wavelet transform for connectivity signature (WaCS) to characterize structural connectivity alterations in autosomal AD.

Aim 2:To create subject specific propagation maps utilizing PET data in order to predict the spread of AD pathophysiologic markers over time.

Aim 3:To predict longitudinal cognitive decline or end-stage disease (EYO) using markers of AD pathology (CSF and PET based).

Title:Amyloid Imagıng in Familial and Non-Familial AD

Date of Request:09/29/2018

Status:Pending

ID:DIAN-D1816

Aim 1:Detection of similarities in terms of Amyloid Imaging

Aim 2:Detection of differences in terms of Amyloid Imaging

Title:Neuroinflammation in Obesity

Date of Request:09/13/2018

Status:Pending

ID:DIAN-D1815

Aim 1:The first aim is to replicate in the DIAN controls cohort our finding that DTI-based indicators of neuroinflammation are greater in obese individuals compared to normal-weight individuals.

Title:Build a predictive model to estimate EYO for sporadic Alzheimer disease

Date of Request:09/05/2018

Status:Approved

ID:DIAN-D1814

Aim 1:Validate the generalizability of the EYO estimation models using the ACS study

Aim 2:Evaluate the goodness-of-estimation of these statistical models by comparing the model estimate EYO with the mutation/parent EYO (calculated using mutation mean age of onset or parent age of onset) using the DIAN observational study

Aim 3:Improve the accuracy of the EYO for the ADAD population by modifying the statistical model to estimate a shift in the EYO using the biomarker, imaging, and cognition information.

Aim 4Build a predictive model for sporadic EYO by optimizing/modifying the statistical models to select the most appropriate model for each outcome (e.g. with/without monotonic assumption) and to select the most sensitive outcomes.

Title:How the brain breaks: Non-linear network level changes in neurodegenerative diseases.

Date of Request:08/23/2018

Status:Pending

ID:DIAN-D1813

Aim 1:To see how resting state networks change as a function of estimated years to symptom onset

Aim 2:To explore the idea of "cascading network failure" in DIAN

Aim 3:To relate changes in resting state functional connectivity to other AD biomarkers