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.
Search Terms:
Investigator: Kaanan Shah
Project Title: Predicting phenotypes in Alzheimer’s Disease using machine learning-enabled polygenic risk models
Date: [153]
Request ID: D2005
Aim 1: Optimize AmyloidGB for predicting amyloid beta and validate in an external post-mortem cohort
Aim 2: Optimize and validate assays for predicting AD co-morbidities
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Investigator: Bruna Bellaver
Project Title: Meta-analysis on glial biomarkers for AD
Date: [153]
Request ID: D2004
Aim 1: Summarize the evidence for the diagnostic value of glial biomarkers in AD through a systematic review and meta-analysis
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Investigator: Prof. Dr. Martin Hofmann-Apitius
Project Title: Modeling Predictive Therapeutic Interventions with Scientific Machine Learning
Date: [153]
Request ID: D2003
Aim 1: Paired intimitely with prior knowledge established from scientific literature curated by researchers, the data will be used to fit remaining unknowns via deep learning (neural networks).
Aim 2: The final model will be used to attempt to predict the effect of certain interventions, rankring their capacity to address protein aggregation in Alzheimer’s Disease.
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Investigator: John C. Morris
Project Title: Knight ADRC Genetics and High Throughput -Omics Core
Date: [153]
Request ID: D2002
Aim 1: Obtain pedigree and MMSE data for spouse control participants.
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Investigator: Rajendra Apte
Project Title: Neurofilament Light Chain (NfL) as a Biomarker for Neurodegeneration in Glaucoma
Date: [153]
Request ID: D2001
Aim 1: To determine whether there is an association between serum or cerebrospinal fluid (CSF) NfL and glaucoma in the Memory & Aging Project (MAP) cohort
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Investigator: Ze Wang
Project Title: brain entropy mapping in aging and early AD
Date: [153]
Request ID: D1918
Aim 1: To find brain entropy difference between normal aging and early AD
Aim 2: To use brain entropy to assess AD progression
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Investigator: Chengjie Xiong
Project Title: Statistical Modeling of Aging and Risk of Transition: Update
Date: [153]
Request ID: D1917
Aim 1: Update and extensively revise the existing SMART project database with additional participant data; obtain and integrate linked Medicare claims data.
Aim 2: Investigate the relationship of HTN, T2DM, and multimorbidity in advanced old age with AD and non-AD neuropathologies
Aim 3: Investigate the relationship of HTN and T2DM in advanced old age with cognitive states including subjective memory complaints, mild cognitive impairment, and dementia
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Investigator: Aristeidis Sotiras
Project Title: Machine learning to quantify spatial patterns of neuroimaging biomarkers
Date: [153]
Request ID: D1916
Aim 1: Quantify patterns of structural covariance at the individual level and investigate their relationship with neuropsych performance measures, clinical, and laboratory values
Aim 2: Quantify patterns of coordinated amyloid deposition at the individual level and investigate their relationship with neuropsych performance measures, clinical, and laboratory values
Aim 3: Quantify patterns of coordinated tau deposition at the individual level and investigate their relationship with neuropsych performance measures, clinical, and laboratory values
Aim 4: Quantify patterns of coordinated dti metric covariation at the individual level and investigate their relationship with neuropsych performance measures, clinical, and laboratory values
Investigator: John Morris
Project Title: Hierarchical Clustering of AD Biomarkers
Date: [153]
Request ID: D1915
Aim 1: Examine clustering of ADRC biomarkers
Aim 2: Compare clustering as a function of disease stage
Aim 3: Comparing clustering to that seen in ADAD
Aim 4: Generate interactive GUI to plot biomarkers against one another
Investigator: Sheng Luo, PhD
Project Title: Integrative modeling and dynamic prediction of Alzheimer�s disease
Date: [153]
Request ID: D1914
Aim 1: To develop a novel integrative modeling framework for clinical studies of AD that collect longitudinal clinical data.
Aim 2: To generalize the integrative modeling framework for the joint analysis of longitudinal clinical data and high-dimensional MRI data.
Aim 3: To advance the integrative modeling framework to incorporate relevant genetic markers from genome-wide association studies.
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