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: Maysam Abbod
Project Title: Deep learning assisted MR images segmentation for PET attenuation correction
Date: [153]
Request ID: D2104
Aim 1: Develop a robust segmentation algorithm for brain MR images using deep learning
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Investigator: Chengjie Xiong
Project Title: Predictive Modeling of Neuropathological Diagnosis by CSF and Imaging Biomarkers using Statistical and Machine Learning Models
Date: [153]
Request ID: D2103
Aim 1: Predict neuropathological diagnosis with statistical and machine learning models only using baseline CSF and imaging biomarkers
Aim 2: Evaluate data preprocessing methods in dealing with missing value problem
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Investigator: Beau M Ances
Project Title: Plasma neurofilament light chain as an amyloid-independent marker for cognitive decline and white matter integrity
Date: [153]
Request ID: D2102
Aim 1: Generate cross-sectional models of plasma neurofilament light chain (NfL) in older individuals as a function of age, sex, amyloid status, genetic risk factors (including APOE genotype, polygenic risk score (PRS)), medical comorbidities, race, white matter hyperintensity (WMH) volume and neurodegen.
Aim 2: Model the intra-individual annual rate of change in plasma and CSF NfL as function of baseline plasma and CSF NfL, age, sex, amyloid status, genetic risk factors (including APOE genotype, polygenic risk score (PRS)), medical comorbidities, race, WMH volume, neurodegeneration, and cognitive decline
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Investigator: Jin-Moo Lee
Project Title: Plasma NfL in participants with cerebral small vessel disease
Date: [153]
Request ID: D2101
Aim 1: To correlate plasma NfL levels with white matter hyperintensity volumes in older individuals
Aim 2: To determine the trajectory of plasma NfL change in older individuals
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Investigator: Atif Hassan
Project Title: A novel feature selection algorithm for identifying a new set of cerebrospinal fluid biomarkers to guide the earlier diagnosis of Alzheimer’s Disease (AD)
Date: [153]
Request ID: D2021
Aim 1: Developing a novel feature selection algorithm
Aim 2: Screening a new set of CSF protein biomarkers from high-dimensional OMICs data
Aim 3: Building a clinical decision support system for the earlier diagnosis of Alzheimer’s Disease
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Investigator: Xianbo Wu
Project Title: The association of the distribution of fat with cognitive decline
Date: [153]
Request ID: D2020
Aim 1: What are the associations of regional adipose tissue depots and cognitive decline?
Aim 2: Does the expected inverse association between the distribution of fat and health risks differ among different weight status?
Aim 3: 3)Are there any biomarkers that may reflect or mediate the associations between regional body fat and cognitive decline?
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Investigator: Pete Millar
Project Title: Modeling brain-predicted age in preclinical and early symptomatic Alzheimer disease
Date: [153]
Request ID: D2019
Aim 1: Generate machine learning based model predictions of brain age from multimodal structural and functional neuroimaging data.
Aim 2: Replicate previous findings of �elevated� brain-predicted age in symptomatic AD using multimodal neuroimaging model.
Aim 3: Test whether brain-predicted age is elevated in preclinical AD and if it is associated with AD biomarkers.
Aim 4: Test associations between brain-predicted age and cognition using multimodal neuroimaging model.
Investigator: Shea Andrews
Project Title: Association of mitochondrial DNA copy number with AD neuropathological change
Date: [153]
Request ID: D2018
Aim 1: Evaluate association of mitochondrial DNA copy number with neuropathological diagnosis of AD
Aim 2: Evaluate association of mitochondrial DNA copy number with AD neuropathology
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Investigator: Jason Hassenstab
Project Title: Does sundowning occur in preclinical and early stage Alzheimer disease?
Date: [153]
Request ID: D2017
Aim 1: Assessing whether increasing AD pathology is associated with mild sundowning behaviors, manifesting in short term decline in cognition even in the asymptomatic preclinical and early symptomatic stages of Alzheimer disease.
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Investigator: Nure Alom Nobel
Project Title: Alzheimer’s Disease Detection Using Deep Learning.
Date: [153]
Request ID: D2016
Aim 1: How Alzheimer’s Disease affects the human brain and what changes are made in the brain.
Aim 2: We can know whether a patient has AD or not.
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