Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/C64C4759-BD00-45F8-A37C-382F587255B4 | ||
| Year | 2020 | ||
| Type of Item | Conference Publication | ||
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| Bibliographic Citation | P. A. Karakasis, A. P. Liavas, N. D. Sidiropoulos, P. G. Simos and E. Papadaki, "Multi-subject task-related fMRI data analysis via generalized canonical correlation analysis," in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2020), Montreal, Canada, 2020, pp. 1497-1502, doi: 10.1109/EMBC44109.2020.9175371. https://doi.org/10.1109/EMBC44109.2020.9175371 | ||
| Appears in Collections |
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. We test our theoretical results using real-world fMRI data. Our experimental findings corroborate our theoretical results, rendering our approach a very good candidate for multi-subject task-related fMRI processing.Clinical Relevance—This work provides a set of methods for amplifying and recovering commonalities across subjects that appear in data from multi-subject task-related fMRI experiments.
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