Constrained Independent Component Analysis Based on Entropy Bound Minimization for Subgroup Identification from Multi-subject fMRI Data

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dc.contributor.authorYang, H.
dc.contributor.authorGhayem, F.
dc.contributor.authorGabrielson, Ben
dc.contributor.authorAkhonda, Mohammad Abu Baker Siddique
dc.contributor.authorCalhoun, Vince D.
dc.contributor.authorAdali, Tulay
dc.date.accessioned2023-05-23T17:49:11Z
dc.date.available2023-05-23T17:49:11Z
dc.date.issued2023-05-05
dc.descriptionICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 04-10 June 2023en_US
dc.description.abstractIdentification of subgroups of subjects homogeneous functional networks is a key step for precision medicine. Independent vector analysis (IVA) is shown to be effective for this task, however, it has a substantial computing cost. We propose a constrained independent component analysis algorithm based on minimizing the entropy bound (c-EBM) to overcome the computational complexity limitation of IVA. A set of spatial maps used as constraints provides a connection across the datasets, provides alignment across subject-wise ICA analyses and serves as a foundation for subgroup identification. The approach makes use of the available prior knowledge while allowing flexible density modeling without an orthogonality requirement for the demixing matrix. Synthetic data and large scale multi-subject resting state fMRI data have both been used to evaluate the performance of the new algorithm, c-EBM. The findings demonstrate that c-EBM is adaptable in terms of various settings for the constraint parameter on the synthetic data. With multi-subject resting state fMRI data, c-EBM can effectively identify subgroups and discover meaningful brain networks that show significant group differences between subgroups.en_US
dc.description.sponsorshipThis work was supported in part by NSF-NCS 1631838, NSF 2112455, and NIH grants R01 MH118695, R01 MH123610, R01 AG073949. The hardware used in the computational studies is part of the UMBC High Performance Computing Facility (HPCF): hpcf.umbc.edu.en_US
dc.description.urihttps://ieeexplore.ieee.org/abstract/document/10095816en_US
dc.format.extent5 pagesen_US
dc.genreconference papers and proceedingsen_US
dc.genrepostprints
dc.identifierdoi:10.13016/m2xvl0-gahz
dc.identifier.citationH. Yang, F. Ghayem, B. Gabrielson, M. A. B. S. Akhonda, V. D. Calhoun and T. Adali, "Constrained Independent Component Analysis Based on Entropy Bound Minimization for Subgroup Identification from Multi-subject fMRI Data," ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023, pp. 1-5, doi: 10.1109/ICASSP49357.2023.10095816.en_US
dc.identifier.urihttps://doi.org/10.1109/ICASSP49357.2023.10095816
dc.identifier.urihttp://hdl.handle.net/11603/28060
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.rights© 2023 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.subjectUMBC High Performance Computing Facility (HPCF)en_US
dc.subjectsubgroup identificationen_US
dc.subjectconstrained ICAen_US
dc.subjectmultisubject dataen_US
dc.subjectresting state fMRIen_US
dc.subjectprecision medicineen_US
dc.titleConstrained Independent Component Analysis Based on Entropy Bound Minimization for Subgroup Identification from Multi-subject fMRI Dataen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0001-9217-6641en_US
dcterms.creatorhttps://orcid.org/0000-0003-0826-453Xen_US
dcterms.creatorhttps://orcid.org/0000-0003-0594-2796en_US

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