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AUEB-Stats Seminars 27/9/2024: "Modeling longitudinal trajectories of neuropsychological and neuroimaging brain changes", by John Kornak (Professor, University of California, San Francisco)
Thu 19 Sep 2024 - 21:19
AUEB STATS SEMINARS 2024
John Kornak
(Professor, University of California, San Francisco)
Title: Modeling longitudinal trajectories of neuropsychological and neuroimaging brain changes
Friday, 27/9/2024
13:15
Room: Ε609, Evelpidon Building
https://www.dept.aueb.gr/en/stat/events/seminar-modeling-longitudinal-trajectories-neuropsychological-and-neuroimaging-brain
ABSTRACT
The hypothetical 2010 'Jack' model describes the timeline for which different biomarkers change in Alzheimer's Disease and has sparked much discussion and subsequent research into disease trajectory modeling. Understanding this temporal ordering of effects in dementia and other neurological diseases/illnesses would benefit individual-level prediction and clinical trial design. I will present nonlinear trajectory modeling approaches to estimate normalized cognitive test scores for individuals that appropriately account for demographic and other factors. These normalized scores are subsequently used in Bayesian disease progression modeling of brain biomarker trajectories (cognition, imaging, and otherwise) in frontotemporal dementia, the goal of which is to determine potential differences in disease progression across genetic subtypes. This is collaborative work with the UCSF Memory and Aging Center and the Berry Consultants group.
John Kornak
(Professor, University of California, San Francisco)
Title: Modeling longitudinal trajectories of neuropsychological and neuroimaging brain changes
Friday, 27/9/2024
13:15
Room: Ε609, Evelpidon Building
https://www.dept.aueb.gr/en/stat/events/seminar-modeling-longitudinal-trajectories-neuropsychological-and-neuroimaging-brain
ABSTRACT
The hypothetical 2010 'Jack' model describes the timeline for which different biomarkers change in Alzheimer's Disease and has sparked much discussion and subsequent research into disease trajectory modeling. Understanding this temporal ordering of effects in dementia and other neurological diseases/illnesses would benefit individual-level prediction and clinical trial design. I will present nonlinear trajectory modeling approaches to estimate normalized cognitive test scores for individuals that appropriately account for demographic and other factors. These normalized scores are subsequently used in Bayesian disease progression modeling of brain biomarker trajectories (cognition, imaging, and otherwise) in frontotemporal dementia, the goal of which is to determine potential differences in disease progression across genetic subtypes. This is collaborative work with the UCSF Memory and Aging Center and the Berry Consultants group.
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