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 227

Investigator:Matteo Vestrucci


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




Investigator:Dan Nicholson


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).







Investigator:Jinbin Xu


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.




Investigator:NA


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







Investigator:Joana B. Pereira


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.







Investigator:Hamid Sohrabi and Ralph Martins


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

Investigator:Pete Millar


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.







Investigator:Zahinoor Ismail


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










Investigator:Yasamin Salimi


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.




Investigator:NA


Title:The role of soluble β-amyloid peptide-42 in cognition and brain tropism: sub-analysis of the Dominantly Inherited Alzheimer Network

Date of Request:10/23/2020




Aim 1:To determine the extent to which the levels of soluble CSFAβ42 are higher in subjects with normal cognition compared with amyloid-positive subjects with cognitive impairment or dementia.




Aim 2:To evaluate whether the conversion from normal cognition to cognitive impairment is associated with a specific threshold of CSF Aβ42 and whether that threshold is dependent or independent from the levels of brain amyloid burden.




Aim 3:To evaluate whether levels of soluble CSFAβ42 are associated with hippocampal volume and PET-FDG uptake, and whether that association is dependent or independently from the brain amyloid burden.




Aim 4To examine whether the severity of cognitive impairment is affected to a greater extent by changes in soluble Aβ42 or amyloid burden.