Data-driven spatio-temporal dynamic brain connectivity analysis using fALFF: Application to sensorimotor task data
| dc.contributor.author | Hossain, Khondoker Murad | |
| dc.contributor.author | Bhinge, Suchita | |
| dc.contributor.author | Long, Qunfang | |
| dc.contributor.author | Calhoun, Vince D. | |
| dc.contributor.author | Adali, Tulay | |
| dc.date.accessioned | 2022-06-21T21:34:27Z | |
| dc.date.available | 2022-06-21T21:34:27Z | |
| dc.date.issued | 2022-04-14 | |
| dc.description | 2022 56th Annual Conference on Information Sciences and Systems (CISS) Princeton, NJ, USA | en_US |
| dc.description.abstract | Dynamic functional connectivity (dFC) analysis enables us to capture the time-varying interactions between brain regions and can lead to powerful biomarkers. Most dFC studies are focused on the study of temporal dynamics and require significant post-processing to summarize the results of the dynamics analysis. In this paper, we introduce an effective framework that makes use of independent vector analysis (IVA) with fractional amplitude of low frequency fluctuation (fALFF) features extracted from task functional magnetic resonance imaging (fMRI) data. Our approach, which is based on IVA with fALLF features as input, (IVA-fALLF) produces an effective summary of the dynamics also greatly facilitating the study of both spatial and temporal dynamics in a more concise manner. IVA-fALLF captures the spatial and temporal dynamics of sensorimotor task data and identifies a component with significant difference in dynamic behavior between healthy controls (HC) and patients with schizophrenia (SZ). Finally, our post analysis using behavioral scores finds significant correlation between brain imaging data and the associated behavioral scores, increasing confidence on our results. Our results are consistent with the previous data-driven dFC analysis as we find similar brain networks showing abnormal behavior in patients with SZ. Moreover, our analysis identifies component behavior in task and rest windows separately and provides additional confirmation of results through correlation with behavioral scores. | en_US |
| dc.description.sponsorship | 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, CNS1228778, and OAC-1726023) and the SCREMS program (grant no. DMS-0821311), with additional substantial support from the University of Maryland, Baltimore County (UMBC). See http://hpcf.umbc.edu for more information on HPCF and the projects using its resources. | en_US |
| dc.description.uri | https://ieeexplore.ieee.org/document/9751190 | en_US |
| dc.format.extent | 6 pages | en_US |
| dc.genre | conference papers and proceedings | en_US |
| dc.identifier | doi:10.13016/m2cip4-jqhk | |
| dc.identifier.citation | K. M. Hossain, S. Bhinge, Q. Long, V. D. Calhoun and T. Adali, "Data-driven spatio-temporal dynamic brain connectivity analysis using fALFF: Application to sensorimotor task data," 2022 56th Annual Conference on Information Sciences and Systems (CISS), 2022, pp. 200-205, doi: 10.1109/CISS53076.2022.9751190. | en_US |
| dc.identifier.uri | https://doi.org/10.1109/CISS53076.2022.9751190 | |
| dc.identifier.uri | http://hdl.handle.net/11603/25011 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | IEEE | 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 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.subject | UMBC High Performance Computing Facility (HPCF) | |
| dc.title | Data-driven spatio-temporal dynamic brain connectivity analysis using fALFF: Application to sensorimotor task data | en_US |
| dc.type | Text | en_US |
| dcterms.creator | https://orcid.org/0000-0002-6323-6366 | en_US |
| dcterms.creator | https://orcid.org/0000-0003-0594-2796 | en_US |