INDEPENDENT VECTOR ANALYSIS BASED SUBGROUP IDENTIFICATION FROM MULTISUBJECT FMRI DATA

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dc.contributor.authorYang, H.
dc.contributor.authorAkhonda, Mohammad Abu Baker Siddique
dc.contributor.authorGhayem, F.
dc.contributor.authorLong, Qunfang
dc.contributor.authorCalhoun, V. D.
dc.contributor.authorAdali, Tulay
dc.date.accessioned2022-06-21T21:34:14Z
dc.date.available2022-06-21T21:34:14Z
dc.date.issued2022-04-27
dc.descriptionICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Singapore, Singaporeen_US
dc.description.abstractIdentification of homogeneous subgroups of subjects plays a key role in the study of precision medicine. While there are a number of approaches based on the clustering of low-level features such as behavioral variables, work that makes use of fully multivariate nature of medical imaging data is very limited. Given that the individual variability in brain functional networks obtained from functional magnetic resonance imaging (fMRI) data is noted as being both significant and consistent like fingerprints, its use provides a particularly appealing approach to this challenging problem. We present a completely data-driven approach, subgroup identification using independent vector analysis (SI-IVA), which leverages the desirable properties of IVA to uncover the relationship across subjects along with the discovery of subgroup structures revealed by Gershgorin disc theorem. We show that SI-IVA outperforms an eigenanalysis-based approach by simulations. We then apply the method to real fMRI data obtained from patients of during resting state to identify group differences in multiple relevant brain regions including primary somatosensory and motor cortex, which demonstrates that SI-IVA provides interpretable and meaningful results.en_US
dc.description.sponsorshipThis work was supported in part by NSF grants CCF 1618551, NCS 1631838 and HRD 2112455, and NIH grants R01MH123610 and R01MH118695. The hardware used in the computational studies is part of the UMBC High Performance Computing Facility (HPCF).en_US
dc.description.urihttps://ieeexplore.ieee.org/document/9747224en_US
dc.format.extent5 pagesen_US
dc.genreconference papers and proceedingsen_US
dc.identifierdoi:10.13016/m2ekf5-kwfu
dc.identifier.citationH. Yang, M. A. B. S. Akhonda, F. Ghayem, Q. Long, V. D. Calhoun and T. Adali, "Independent Vector Analysis Based Subgroup Identification from Multisubject fMRI Data," ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 1471-1475, doi: 10.1109/ICASSP43922.2022.9747224.en_US
dc.identifier.urihttps://doi.org/10.1109/ICASSP43922.2022.9747224
dc.identifier.urihttp://hdl.handle.net/11603/25010
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 Student Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rights© 2022 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)
dc.titleINDEPENDENT VECTOR ANALYSIS BASED SUBGROUP IDENTIFICATION FROM MULTISUBJECT FMRI DATAen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0003-0826-453Xen_US
dcterms.creatorhttps://orcid.org/0000-0002-6323-6366en_US
dcterms.creatorhttps://orcid.org/0000-0003-0594-2796en_US

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