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Multisubject task-related fMRI data processing via a two-stage generalized canonical correlation analysis

Karakasis Paris, Liavas Athanasios, Sidiropoulos Nikos, Simos Panagiotis G., Papadaki Efrosini

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/653089D5-88DB-41D8-B939-07EF11D9BF2F-
Αναγνωριστικόhttps://doi.org/10.1109/TIP.2022.3159125-
Αναγνωριστικόhttps://ieeexplore.ieee.org/document/9778969-
Γλώσσαen-
Μέγεθος12 pagesen
ΤίτλοςMultisubject task-related fMRI data processing via a two-stage generalized canonical correlation analysisen
ΔημιουργόςKarakasis Parisen
ΔημιουργόςΚαρακασης Παριςel
ΔημιουργόςLiavas Athanasiosen
ΔημιουργόςΛιαβας Αθανασιοςel
ΔημιουργόςSidiropoulos Nikosen
ΔημιουργόςΣιδηροπουλος Νικοςel
ΔημιουργόςSimos Panagiotis G.en
ΔημιουργόςPapadaki Efrosinien
ΕκδότηςInstitute of Electrical and Electronics Engineersen
ΠερίληψηFunctional 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).en
ΤύποςPeer-Reviewed Journal Publicationen
ΤύποςΔημοσίευση σε Περιοδικό με Κριτέςel
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2023-12-01-
Ημερομηνία Δημοσίευσης2022-
Θεματική ΚατηγορίαfMRIen
Θεματική ΚατηγορίαGeneralized CCAen
Θεματική ΚατηγορίαMAX-VARen
Βιβλιογραφική ΑναφοράP. A. Karakasis, A. P. Liavas, N. D. Sidiropoulos, P. G. Simos and E. Papadaki, "Multisubject task-related fMRI data processing via a two-stage generalized canonical correlation analysis," IEEE Trans. Image Process., vol. 31, pp. 4011-4022, doi: 10.1109/TIP.2022.3159125.en

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