Independent vector analysis for common subspace analysis: Application to multi-subject fMRI data yields meaningful subgroups of schizophrenia
| dc.contributor.author | Long, Qunfang | |
| dc.contributor.author | Bhinge, Suchita | |
| dc.contributor.author | Calhoun, Vince D. | |
| dc.contributor.author | Adali, Tulay | |
| dc.date.accessioned | 2020-06-08T17:07:32Z | |
| dc.date.available | 2020-06-08T17:07:32Z | |
| dc.date.issued | 2020-04-28 | |
| dc.description.abstract | The extraction of common and distinct biomedical signatures among different populations allows for a more detailed study of the group-specific as well as distinct information of different populations. A number of subspace analysis algorithms have been developed and successfully applied to data fusion, however they are limited to joint analysis of only a couple of datasets. Since subspace analysis is very promising for analysis of multi-subject medical imaging data as well, we focus on this problem and propose a new method based on independent vector analysis (IVA) for common subspace extraction (IVA-CS) for multi-subject data analysis. IVA-CS leverages the strength of IVA in identification of a complete subspace structure across multiple datasets along with an efficient solution that uses only second-order statistics. We propose a subset analysis approach within IVA-CS to mitigate issues in estimation in IVA due to high dimensionality, both in terms of components estimated and the number of datasets. We introduce a scheme to determine a desirable size for the subset that is high enough to exploit the dependence across datasets and is not affected by the high dimensionality issue. We demonstrate the success of IVA-CS in extracting complex subset structures and apply the method to analysis of functional magnetic resonance imaging data from 179 subjects and show that it successfully identifies shared and complementary brain patterns from patients with schizophrenia (SZ) and healthy controls group. Two components with linked resting-state networks are identified to be unique to the SZ group providing evidence of functional dysconnectivity. IVA-CS also identifies subgroups of SZs that show significant differences in terms of their brain networks and clinical symptoms. | en_US |
| dc.description.sponsorship | This work was supported by NSF-CCF 1618551, NSF-NCS 1631838, NSF 1539067 and NIH grants R01 MH118695, R01EB020407, R01EB006841, P20GM103472, and P30GM12 2734. The authors thank the research staff from the Mind Research Network COBRE study who collected, preprocessed and shared the data. The authors appreciate the valuable feedback provided by the members of Machine Learning for Signal Processing Laboratory in University of Maryland, Baltimore County. The hardware used in the computational studies is part of the UMBC High Performance Computing Facility (HPCF). The facility is supported by the U.S. National Science Foundation through the MRI program (grant nos. CNS-0821258, CNS-1228778, and OAC-1726023) and the SCREMS program (grant no. DMS-0821311), with additional substantial support from the University of Maryland, Baltimore County (UMBC). See hpcf.umbc.edu for more information on HPCF and the projects using its resources. | en_US |
| dc.description.uri | https://www.sciencedirect.com/science/article/pii/S105381192030358X | en_US |
| dc.format.extent | 16 pages | en_US |
| dc.genre | journal articles postprints | en_US |
| dc.identifier | doi:10.13016/m2joxg-6eem | |
| dc.identifier.citation | Qunfang Long et al., Independent vector analysis for common subspace analysis: Application to multi-subject fMRI data yields meaningful subgroups of schizophrenia, NeuroImage Volume 216, 1 August 2020, 116872, https://doi.org/10.1016/j.neuroimage.2020.116872 | en_US |
| dc.identifier.uri | https://doi.org/10.1016/j.neuroimage.2020.116872 | |
| dc.identifier.uri | http://hdl.handle.net/11603/18838 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Elsevier | 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 Faculty Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) | * |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.subject | UMBC Machine Learning for Signal Processing Lab | |
| dc.subject | UMBC High Performance Computing Facility (HPCF) | |
| dc.title | Independent vector analysis for common subspace analysis: Application to multi-subject fMRI data yields meaningful subgroups of schizophrenia | en_US |
| dc.type | Text | en_US |