New interpretable patterns and discriminative features from brain functional network connectivity using dictionary learning
| dc.contributor.author | Ghayem, F. | |
| dc.contributor.author | Yang, H. | |
| dc.contributor.author | Kantar, F. | |
| dc.contributor.author | Kim, Seung-Jun | |
| dc.contributor.author | Calhoun, V. D. | |
| dc.contributor.author | Adali, Tulay | |
| dc.date.accessioned | 2022-12-14T15:56:41Z | |
| dc.date.available | 2022-12-14T15:56:41Z | |
| dc.date.issued | 2023-05-05 | |
| dc.description | 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 04-10 June 2023 | |
| dc.description.abstract | Independent component analysis (ICA) of multi-subject functional magnetic resonance imaging (fMRI) data has proven useful in providing a fully multivariate summary that can be used for multiple purposes. ICA can identify patterns that can discriminate between healthy controls (HC) and patients with various mental disorders such as schizophrenia (Sz). Temporal functional network connectivity (tFNC) obtained from ICA can effectively explain the interactions between brain networks. On the other hand, dictionary learning (DL) enables the discovery of hidden information in data using learnable basis signals through the use of sparsity. In this paper, we present a new method that leverages ICA and DL for the identification of directly interpretable patterns to discriminate between the HC and Sz groups. We use multi-subject resting-state fMRI data from 358 subjects and form subject-specific tFNC feature vectors from ICA results. Then, we learn sparse representations of the tFNCs and introduce a new set of sparse features as well as new interpretable patterns from the learned atoms. Our experimental results show that the new representation not only leads to effective classification between HC and Sz groups using sparse features, but can also identify new interpretable patterns from the learned atoms that can help understand the complexities of mental diseases such as schizophrenia. | en_US |
| dc.description.sponsorship | This work was supported in part by NSF-NCS 1631838, 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). | en_US |
| dc.description.uri | https://ieeexplore.ieee.org/document/10096473 | en_US |
| dc.format.extent | 5 pages | en_US |
| dc.genre | conference papers and proceedings | en_US |
| dc.genre | postprints | en_US |
| dc.identifier | doi:10.13016/m2vgdd-gcpc | |
| dc.identifier.citation | F. Ghayem, H. Yang, F. Kantar, S.-J. Kim, V. D. Calhoun and T. Adali, "New Interpretable Patterns and Discriminative Features from Brain Functional Network Connectivity using Dictionary Learning," ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023, pp. 1-5, doi: 10.1109/ICASSP49357.2023.10096473. | |
| dc.identifier.uri | https://doi.org/10.1109/ICASSP49357.2023.10096473 | |
| dc.identifier.uri | http://hdl.handle.net/11603/26446 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | IEEE | |
| 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 | © 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.subject | UMBC High Performance Computing Facility (HPCF) | |
| dc.title | New interpretable patterns and discriminative features from brain functional network connectivity using dictionary learning | en_US |
| dc.type | Text | en_US |
| dcterms.creator | https://orcid.org/0000-0002-5504-4997 | en_US |
| dcterms.creator | https://orcid.org/0000-0003-0594-2796 |