Yang, H.Akhonda, Mohammad Abu Baker SiddiqueGhayem, F.Long, QunfangCalhoun, V. D.Adali, Tulay2022-06-212022-06-212022-04-27H. 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.https://doi.org/10.1109/ICASSP43922.2022.9747224http://hdl.handle.net/11603/25010ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Singapore, SingaporeIdentification 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.5 pagesen-US© 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.UMBC High Performance Computing Facility (HPCF)Text