Multisubject task-related fMRI data processing via a two-stage generalized canonical correlation analysisMultisubject task-related fMRI data processing via a two-stage generalized canonical correlation analysis Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2023-12-012022enThe work of Paris A. Karakasis, Athanasios P. Liavas, Panagiotis G. Simos, and Efrosini Papadaki was supported in part by the European Regional Development Fund of the European Union and Greek National Funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH–CREATE–INNOVATE under Project T1EΔK-03360.Functional magnetic resonance imaging (fMRI) is one of the most popular methods for studying the human brain. Task-related fMRI data processing aims to determine which brain areas are activated when a specific task is performed and is usually based on the Blood Oxygen Level Dependent (BOLD) signal. The background BOLD signal also reflects systematic fluctuations in regional brain activity which are attributed to the existence of resting-state brain networks. We propose a new fMRI data generating model which takes into consideration the existence of common task-related and resting-state components. We first estimate the common task-related temporal component, via two successive stages of generalized canonical correlation analysis and, then, we estimate the common task-related spatial component, leading to a task-related activation map. The experimental tests of our method with synthetic data reveal that we are able to obtain very accurate temporal and spatial estimates even at very low Signal to Noise Ratio (SNR), which is usually the case in fMRI data processing. The tests with real-world fMRI data show significant advantages over standard procedures based on General Linear Models (GLMs).http://creativecommons.org/licenses/by/4.0/IEEE Transactions on Image Processing314011-4022 Karakasis Paris Καρακασης Παρις Liavas Athanasios Λιαβας Αθανασιος Sidiropoulos Nikos Σιδηροπουλος Νικος Simos Panagiotis G. Papadaki Efrosini Institute of Electrical and Electronics Engineers fMRI Generalized CCA MAX-VAR