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GRStats2- Posts : 63
Join date : 2022-11-19
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-brainABSTRACT
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.
- AUEB STATS SEMINARS 12/12/2018: A new approach to Bayesian image analysis by John Kornak
- AUEB-Stats Seminars 12/6/2024: "Modelling longitudinal cognitive test data with ceiling effects and left skewness", by Denitsa Grigorova (Faculty of Mathematics and Informatics, Sofia University, Sofia, Bulgaria)
- AUEB-Stats Seminars 12/4/2024: Bayesian inference with variable-memory models for times series by Ioannis Kontoyiannis (Professor | Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, UK)
- AUEB-Stats Seminars 19/4/2024: "Regional effect plots for the interpretation of black box machine learning models", by Christos Diou (Assistant Professor | Department of Informatics and Telematics, Harokopio University of Athens)
- AUEB-Stats Seminars 22/3/2024: Leveraging Convex Hulls for Advanced Statistical Monitoring by Polychronis Economou (Associate Professor of Statistics, University of Patras, School of Engineering, Department of Civil Engineering)
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