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: Dr. Ali Ezzati
Project Title: Predicting clinical outcomes using biomarkers and data-driven approaches
Date: [153]
Request ID: D2114
Aim 1: To assess the improvement of the classification of memory impairment in older adults provided by the SOMI over the usual three-stage classification that transitions from normal cognition (CDR=0) to mild cognitive decline (CDR=0.5), and finally to clinical dementia (CDR=1).
Aim 2: To examine clinical and in-vivo-AD-biomarker changes in SOMI longitudinally, and evaluate the effect of baseline and longitudinal AD-biomarkers change on transitions from one SOMI group to another.
Aim 3: To use clustering methods and gaussian mixture models to identify homogeneous subgroups of population based on a combination of biomarkers.
Aim 4: To develop predictive risk scores using ML that can practically estimate risk of conversion to dementia in non-demented older adults over 2-5 years of follow up.
Investigator: Duygu Tosun
Project Title: Autopsy-informed integrated clinical and imaging models for prediction of comorbid non-AD pathology in AD
Date: [153]
Request ID: D2113
Aim 1: Determine the value of ante-mortem MRI for predicting non-AD pathologies
Aim 2: Determine the extent to which multidomain neuropsychological assessments predict presence of non-AD pathologies
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Investigator: Arthi Venkatesan
Project Title: Replication of an Inverse Association between the Hippocampus and Caudate
Date: [153]
Request ID: D2112
Aim 1: Determine whether we can replicate the inverse association between hippocampal and caudate volume in a larger sample of clinically normal older adults.
Aim 2: Determine whether the inverse association extends to other brain regions in the networks supporting the two navigation/memory systems.
Aim 3: Examine the inverse association between hippocampal and caudate volumes across AD stages, particularly the preclinical AD stages.
Aim 4: Assess whether there are differential associations of cognitive tasks with hippocampal and caudate volume.
Investigator: Fuhai Li
Project Title: To uncover neuron-inflammation and immune signaling pathways in AD microenvironment using computational models and single cell omics data
Date: [153]
Request ID: D2111
Aim 1: Aim 1: To develop novel computational models to uncover the activated core signaling networks within individual cell types; and identify the neuron-niche cell signaling interaction networks; and their associations with neuro-inflammation and immune signaling using single cell RNA-seq and proteomics
Aim 2: Aim 2: To develop novel computational models to predict drugs and drug combinations that can perturb the neuro-niche cell signaling communications to inhibit neuro-inflammation and immune response.
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Investigator: John C. Morris
Project Title: Brain Aging associated with Sleep and Heart Health (BASH)
Date: [153]
Request ID: D2110
Aim 1: Determine whether cardiovascular health risk factors mediate effects of sleep on cognition
Aim 2: Determine whether cardiovascular health risk factors mediate effects of sleep on brain structure
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Investigator: Francesca I. De Simone, PhD
Project Title: Knight Alzheimer Disease Research Center LUMIPULSE dataset (CSF Aβ40, Aβ42, tTau and pTau)
Date: [153]
Request ID: D2109
Aim 1: Racial differences between AA and Non-Hispanic whites
Aim 2: Long-term stability data on AB-40 and AB-42 analytes
Aim 3: Age stratification of represented cohort (overall)
Aim 4: Age stratification of represented cohort based on racial stratification
Investigator: Xiaoqin Cheng
Project Title: Attention-based deep learning approaches for Alzheimer’s disease diagnostic classification and prognostic prediction
Date: [153]
Request ID: D2108
Aim 1: To achieve a state-of-the-art machine learning approach providing high accuracy, sensibility and specificity on the diagnosis and prognosis of AD, which would assist clinicians to recognize and treat AD earlier and promote better quality of life.
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Investigator: Andrew Aschenbrenner
Project Title: Emerging CSF biomarkers and their relationship to cognitive decline in late onset Alzheimer disease
Date: [153]
Request ID: D2107
Aim 1: Determine which (if any) emerging fluid biomarkers predict rates of cognitive change, above and beyond the �gold standard� of A�42/A�40.
Aim 2: Determine if longitudinal rates of change in emerging fluid biomarkers correlates with rates of change in a global cognitive composite score.
Aim 3: Determine if different emerging fluid biomarkers are differentially associated with specific cognitive domains.
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Investigator: Tommaso Volpi
Project Title: Understanding the biological basis of human functional connectivity
Date: [153]
Request ID: D2106
Aim 1: Relating CMRO2 and CBF maps to FDG SUVR and fMRI FC
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Investigator: Hongyu An
Project Title: AI Companion Brain Morphometry
Date: [153]
Request ID: D2105
Aim 1: Test the efficacy of Siemens brain morphometry software on different brain imaging cases
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