Coupled CP tensor decompostion with shared and distinct components for multi-task fMRI data fusion
| dc.contributor.author | Borsoi, R. A. | |
| dc.contributor.author | Lehmann, I. | |
| dc.contributor.author | Akhonda, Mohammad Abu Baker Siddique | |
| dc.contributor.author | Calhoun, V. D. | |
| dc.contributor.author | Usevich, K. | |
| dc.contributor.author | Brie, D. | |
| dc.contributor.author | Adali, Tulay | |
| dc.date.accessioned | 2023-07-06T18:21:13Z | |
| dc.date.available | 2023-07-06T18:21:13Z | |
| 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 | en_US |
| dc.description.abstract | Discovering components that are shared in multiple datasets, next to dataset-specific features, has great potential for studying the relationships between different subjects or tasks in functional Magnetic Resonance Imaging (fMRI) data. Coupled matrix and tensor factorization approaches have been useful for flexible data fusion, or decomposition to extract features that can be used in multiple ways. However, existing methods do not directly recover shared and dataset-specific components, which requires post-processing steps involving additional hyperparameter selection. In this paper, we propose a tensor-based framework for multi-task fMRI data fusion, using a partially constrained canonical polyadic (CP) decomposition model. Differently from previous approaches, the proposed method directly recovers shared and dataset-specific components, leading to results that are directly interpretable. A strategy to select a highly reproducible solution to the decomposition is also proposed. We evaluate the proposed methodology on real fMRI data of three tasks, and show that the proposed method finds meaningful components that clearly identify group differences between patients with schizophrenia and healthy controls. | en_US |
| dc.description.sponsorship | This work was supported in part by NSF-NCS 1631838, NSF 2112455, and NIH grants R01 MH118695, R01 MH123610, and R01 AG073949, and in part by the German Research Foundation under grant SCHR 1384/3-2. T. Adali’s visit to CRAN was supported in part by the Université de Lorraine. | en_US |
| dc.description.uri | https://ieeexplore.ieee.org/document/10096241 | 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/m20ybi-olbj | |
| dc.identifier.citation | R. A. Borsoi et al., "Coupled CP Tensor Decomposition with Shared and Distinct Components for Multi-Task Fmri Data Fusion," ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023, pp. 1-5, doi: 10.1109/ICASSP49357.2023.10096241. | en_US |
| dc.identifier.uri | https://doi.org/10.1109/ICASSP49357.2023.10096241 | |
| dc.identifier.uri | http://hdl.handle.net/11603/28411 | |
| 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 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.title | Coupled CP tensor decompostion with shared and distinct components for multi-task fMRI data fusion | en_US |
| dc.type | Text | en_US |
| dcterms.creator | https://orcid.org/0000-0003-0826-453X | en_US |
| dcterms.creator | https://orcid.org/0000-0003-0594-2796 | en_US |