The Knight ADRC has supported many investigators at Washington University and at other institutions over the years. We wish to avoid the situation where two investigators study the same research question to avoid duplication of effort and potential conflict. To determine if your topic has already been studied with our resources, please search our database. If you find that your topic or a related topic has been submitted, you may wish to contact the investigator to inquire about their 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 number (e.g. T1004), the full request has been submitted and is either approved, disapproved or in process. If an entry has no ID number, then it represents a submission that has not yet been reviewed. Search terms are applied across an entire requests application including variables not displayed below. A more specific, detailed search may yield better results depending upon your needs.
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Investigator: Oskar Hansson
Project Title: Head-to-head comparison of different plasma p-tau217 assays
Date: April 22, 2024
Request ID: D2415
Aim 1: Replicate key findings (mass-spec p-tau217 performing better than immunoassay) to support generalizability to broader population.
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Investigator: Behnaz Jafari
Project Title: Enhancing Alzheimer’s Disease Detection: Fusion of EEG and fMRI Data Using Artificial Neural Networks
Date: April 22, 2024
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Aim 1: To develop a graph neural network encoder using non-linear Granger causality methods to model effective connectivity in fMRI data, focusing on individual brain regions in both healthy controls and individuals with Alzheimer’s disease.
Aim 2: To construct a recurrent neural network (RNN), such as Long Short-Term Memory (LSTM), encoder for EEG data across different frequency bands, for brain regions, targeting both healthy controls and Alzheimer’s disease cases.
Aim 3: To create a neural network-based decoder framework to integrate features extracted from MEG/EEG and fMRI time series data, leveraging the outputs from the RNN and GNN encoders, respectively.
Aim 4: To apply the developed framework to a datasets obtained from healthy controls and individuals diagnosed with Alzheimer’s disease for comprehensive analysis and validation
Investigator: Sheng Chih (Peter) Jin
Project Title: Investigating the impact of somatic mosaicism on Alzheimer’s disease
Date: April 11, 2024
Request ID: D2414
Aim 1: Along with the Cruchaga lab, we will identify various types of somatic mosaicism associated with AD, including clonal hematopoiesis of indeterminate potential (Aim 1a), mosaic chromosomal alterations (Aim 1b), mosaic loss of chromosome Y (Aim 1c), and heteroplasmic mitochondrial mutations (Aim 1d).
Aim 2: Once putative functional somatic variants are identified, we will conduct differential expression analyses using bulk or single-cell RNA-sequencing data, along with brain, cerebrospinal fluid or plasma proteomic data by comparing somatic mutation carriers to matched controls.
Aim 3: To validate somatic variants, we will use relevant assays on brain tissues from mutation carriers. Along with investigators at the ADRC, we will conduct in vitro and/or in vivo functional experiments to determine the pathophysiological mechanisms underlying the identified somatic variants.
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Investigator: Oskar Hansson
Project Title: Screening for early Alzheimer disease by plasma p-tau217 versus CSF or PET methods
Date: April 2, 2024
Request ID: D2413
Aim 1: Characterize asymptomatic AD individuals identified by p-tau217 versus those who are not identified by plasma p-tau217
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Investigator: Ying Hwey Nai
Project Title: Brain Segmentation of PET images without Structural Images (BSPwSI)
Date: March 26, 2024
Request ID: D2412
Aim 1: Use DL to segment ROIs in brain PET images of both human and nonhuman primates (NHP)
Aim 2: To develop a PET-based image processing pipeline in MIR
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Investigator: Marta Stojanovic
Project Title: Associations between resting state functional connectivity and depression in Alzheimer disease
Date: March 19, 2024
Request ID: D2411
Aim 1: Examine whether resting state functional connectivity networks are associated with depression, as indicated by depressive symptoms and depression diagnosis.
Aim 2: Examine whether AD pathology moderates the association between resting state functional connectivity networks and depression.
Aim 3: Investigate whether use of antidepressants (e.g., SSRIs) moderates the relationship between resting state functional connectivity and depression.
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Investigator: Tammie L. S. Benzinger
Project Title: Identifying the Social Determinants of Individuals Undergoing Amyloid PET without a Structural MRI
Date: March 11, 2024
Request ID: D2410
Aim 1: We aim to validate a MR-free PET quantification pipeline against an established MR-dependent PET quantification method (i.e., FreeSurfer-based PUP) on a pooled cohort from SEABIRD, the Knight ADRC, and the SORTOUT-AB studies.
Aim 2: We aim to identify the common demographic and socioeconomic characteristics of those with missing or unusable MR scans in the above-mentioned cohort.
Aim 3: We aim to ascertain the percentage of previously unusable data points recovered by using the MR-free PET pipeline, as well as the estimated cost-savings on data collection.
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Investigator: Armentrout
Project Title: Evaluation of the CTX Mobile Cognitive Health Application
Date: March 7, 2024
Request ID: D2408
Aim 1: Develop new and engaging cognitive tests and pipelines for assessing visual search and targeting, expressive and receptive language, motor movement and episodic memory
Aim 2: Validate measures in cognitively normal and impaired cohorts; and (3) Analyze and prepare data for publication and premarket submissions to the FDA.
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Investigator: Arash Nazeri
Project Title: Understanding the Links between CSF Circulation and Alzheimer’s Disease Biomarkers
Date: March 7, 2024
Request ID: D2409
Aim 1: To determine the correlation between CSF flow patterns and the CSF and PET biomarkers of Alzheimer’s disease.
Aim 2: To investigate the relationship between CSF flow patterns and CSF biomarker kinetics using SILK.
Aim 3: To examine how CSF flow patterns modulate the interrelationship between plasma and CSF proteome.
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Investigator: Esther Lu & Qing Wang
Project Title: Change-point analysis of Alzheimer disease
Date: March 6, 2024
Request ID: D2407
Aim 1: to identify change-points of DBSI using a new algorithm of pruned exact linear time with a cost based on the empirical distribution (ED-PELT).
Aim 2: to examine the trends in the DBSI and other biomarkers on the AD diagnosis using a generalized linear change-point model (GLCPM).
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