Institutional Repository [SANDBOX]
Technical University of Crete
EN  |  EL

Search

Browse

My Space

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

Simple record


URIhttp://purl.tuc.gr/dl/dias/653089D5-88DB-41D8-B939-07EF11D9BF2F-
Identifierhttps://doi.org/10.1109/TIP.2022.3159125-
Identifierhttps://ieeexplore.ieee.org/document/9778969-
Languageen-
Extent12 pagesen
TitleMultisubject task-related fMRI data processing via a two-stage generalized canonical correlation analysisen
CreatorKarakasis Parisen
CreatorΚαρακασης Παριςel
CreatorLiavas Athanasiosen
CreatorΛιαβας Αθανασιοςel
CreatorSidiropoulos Nikosen
CreatorΣιδηροπουλος Νικοςel
CreatorSimos Panagiotis G.en
CreatorPapadaki Efrosinien
PublisherInstitute of Electrical and Electronics Engineersen
Content SummaryFunctional 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
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2023-12-01-
Date of Publication2022-
SubjectfMRIen
SubjectGeneralized CCAen
SubjectMAX-VARen
Bibliographic CitationP. 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

Services

Statistics