Multi-subject Task-related fMRI Data Analysis via Generalized Canonical Correlation Analysis
Functional magnetic resonance imaging (fMRI) is one of the most popular methods for studying the human brain. It measures brain activity, by detecting local changes of Blood Oxygen Level Dependent (BOLD) signal in the brain, over time, and can be used in both task-related and resting-state studies.
In task-related studies, our aim is to determine which brain areas are activated when a specific task is performed. Various unsupervised multivariate statistical methods are being increasingly employed in fMRI data analysis. Their main goal is to extract information from a dataset, often with no prior knowledge of the experimental conditions.
Generalized canonical correlation analysis (gCCA) is a well known statistical method that can be considered as a way to estimate a linear subspace, which is “common” to multiple random linear subspaces. We propose a new fMRI data generating model which takes into consideration the existence of common task-related and resting-state components.
We estimate the common spatial task-related component via a two-stage gCCA. Our experimental findings corroborate our theoretical results, rendering our approach a very good candidate for multi-subject task-related fMRI processing.
About the Speaker
Paris Karakasis earned his Diploma and M.Sc. degree in Electrical and Computer Engineering from the Technical University of Crete, Greece, in 2017 and 2019, respectively.
Since January 2020 he is a Ph.D. student in University of Virginia, under the supervision of Nikos Sidiropoulos.
His research interests include signal processing for biomedical applications, optimization, tensor decomposition, and factor analysis.