Search DIAN Data Resource Requests

Title:Computational Subtypes of familial AD progression

Date of Request:02/26/2021

Aim 1:Identify and characterize data-driven subtypes of ADAD from cross-sectional multimodal data. Validate the data-driven subtypes using longitudinal follow-up data to demonstrate stability of subtype assignments and assess longitudinal disease progression within each subtype.

Aim 2:Determine what relationship, if any, that these subtypes have with genotype (APP vs PSEN1 vs PSEN2) and mutation site, e.g. pre and post-codon 200 in PSEN1.

Aim 3:Construct and compare different versions of model within the SuStaIn algorithm, e.g, comparing the event-based model [Fonteijn 2012; Huang 2012; Young 2014] ] with the linear z-score model introduced in [Young 2018]. This will be used to determine which modelling limitations are important to address, which elements are essential, which variants (and parameters/settings) are feasible, and how much complexity can be supported.

Aim 4Explore detailed neuroimaging-based models that investigate the temporal progression/accumulation of pathology/dysfunction [Garbarino 2019; Raj 2012] across the brain, e.g., from tau/amyloid/FDG PET, and structural/diffusion/functional MRI. Characterize and understand these neuroimaging subtypes through associations with genetics, cognition/neuropsychology, non-imaging biomarkers, and EYO.

Title:A population model for the analysis of ADAD data

Date of Request:02/23/2021

Aim 1:Improve on the DIAN-TU Disease Progression Model

Aim 2:Generate research hypotheses about the causal relationship between biomarkers and cognitive measures

Aim 3:Improve EYO estimates or identify the stage of disease progression for a newly observed subject without EYO assigned

Title:High-resolution multiplex localization of Alzheimer's disease risk and resilience factors

Date of Request:02/09/2021

Aim 1:Determine the expression levels, localization, and co-localization patterns of proteins en-coded by the m109 and BIN1 protein clusters in relation to synaptic markers. Synaptic changes have long been recognized to be tightly linked to AD severity (9). We will determine the expression patterns of the two protein clusters relative to excitatory and inhibitory synapses using antibodies specific for excitatory synapses (e.g., AMPA- and NMDA-type receptors and PSD-95), inhibitory synapses (e.g., vesicular GABA transporter/VGAT and gephyrin), and presynaptic terminals (e.g., synapsin).

Aim 2:Determine the expression levels, localization, and co-localization patterns of proteins en-coded by the m109 and BIN1 protein clusters in relation to AD pathology. Probable AD is confirmed at au-topsy by the presence of amyloid plaques and tangles (10). We will determine the expression patterns of the two protein clusters as a function of proximity to regions with amyloid plaques (using a 6E10 anti-body), neurofibrillary tangles (using an AT8 antibody), astrocytes (using a GFAP antibody), and microglia (using an Iba1 antibody).

Title:Quantitative autoradiography, genetics, and biochemistry studies

Date of Request:02/09/2021

Aim 1:Specific Aims: Specific Aim 1: To biochemically quantify the amount of amyloid-beta and tau species in specific regions of the frozen brain regions by mass spectrometry in DIAN-TU, DIAN-obs, and non-AD age-matched controls. Additional biochemical measures of other biomolecules will also be explored: alpha-synuclein, APP, ApoE, inflammatory markers, as well as various synaptic & neuronal markers.

Aim 2:Specific Aim 2: Compare the amount of soluble and insoluble amyloid-beta and tau species between active gantenerumab, active sola, placebo, and control cases including DIAN-obs and non-AD controls to determine the effects of drug treatment on amyloid-beta and tau species.

Aim 3:Specific Aim 3: To quantify by immunohistochemistry the amount of amyloid-beta and tau pathology and astrocytic, glial, alpha-synuclein, and TDP-43 in specific areas of the fixed brain regions in DIAN-TU, DIAN-obs, and non-AD age-matched controls. Additional immunohistochemistry measures of other biomolecules will also be explored, for example, APP, ApoE, inflammatory markers, as well as various synaptic & neuronal markers.

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

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.

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

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.

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

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.