dc.contributor.author	Belyaeva, Irina
dc.contributor.author	Gabrielson, Ben
dc.contributor.author	Wang, Yu-Ping
dc.contributor.author	Wilson, Tony W.
dc.contributor.author	Calhoun, Vince D.
dc.contributor.author	Stephen, Julia M.
dc.contributor.author	Adali, Tulay
dc.date.accessioned	2023-01-06T18:54:12Z
dc.date.available	2023-01-06T18:54:12Z
dc.date.issued	2022-08-24
dc.description.abstract	Identification of informative signatures from electrophysiological signals is important for understanding brain developmental patterns, where techniques such as magnetoencephalography (MEG) are particularly useful. However, less attention has been given to fully utilizing the multidimensional nature of MEG data for extracting components that describe these patterns. Methods: Tensor factorizations of MEG yield components that encapsulate the data’s multidimensional nature, providing parsimonious models identifying latent brain patterns for meaningful summarization of neural processes. To address the need for meaningful MEG signatures for studies of pediatric cohorts, we propose a tensor-based approach for extracting developmental signatures of multi-subject MEG data. We employ the canonical polyadic (CP) decomposition for estimating latent spatiotemporal components of the data, and use these components for group level statistical inference. Results: Using CP decomposition along with hierarchical clustering, we were able to extract typical early and late latency event-related field (ERF) components that were discriminative of high and low performance groups (p < 0.05) and significantly correlated with major cognitive domains such as attention, episodic memory, executive function, and language comprehension. Conclusion: We demonstrate that tensor-based group level statistical inference of MEG can produce signatures descriptive of the multidimensional MEG data. Furthermore, these features can be used to study group differences in brain patterns and cognitive function of healthy children. Significance: We provide an effective tool that may be useful for assessing child developmental status and brain function directly from electrophysiological measurements and facilitate the prospective assessment of cognitive processes.	en_US
dc.description.sponsorship	This work was supported in part by NSF-NCS 1631838, NSF 2112455, NIH R01 MH118695, NIH R01MH123610, NIH R01AG073949, NIH R01 MH121101, and NIH P20 GM144641.	en_US
dc.description.uri	https://link.springer.com/article/10.1007/s12021-022-09599-y	en_US
dc.format.extent	35 pages	en_US
dc.genre	journal articles	en_US
dc.genre	postprints	en_US
dc.identifier	doi:10.13016/m2naju-fh4n
dc.identifier.citation	Belyaeva, I., Gabrielson, B., Wang, YP. et al. Multi-Subject Analysis for Brain Developmental Patterns Discovery via Tensor Decomposition of MEG Data. Neuroinform (2022). https://doi.org/10.1007/s12021-022-09599-y	en_US
dc.identifier.uri	https://doi.org/10.1007/s12021-022-09599-y
dc.identifier.uri	http://hdl.handle.net/11603/26584
dc.language.iso	en_US	en_US
dc.publisher	Springer	en_US
dc.relation.isAvailableAt	The University of Maryland, Baltimore County (UMBC)
dc.relation.ispartof	UMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartof	UMBC Student Collection
dc.relation.ispartof	UMBC Information Systems Department
dc.rights	This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s12021-022-09599-y	en_US
dc.rights	Access to this item will begin on 08/24/2023
dc.title	Multi-Subject Analysis for Brain Developmental Patterns Discovery via Tensor Decomposition of MEG Data	en_US
dc.type	Text	en_US
dcterms.creator	https://orcid.org/0000-0003-0594-2796	en_US

Multi-Subject Analysis for Brain Developmental Patterns Discovery via Tensor Decomposition of MEG Data

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