MULTI-TASK FMRI DATA FUSION USING IVA AND PARAFAC2
| dc.contributor.author | Lehmann, Isabell | |
| dc.contributor.author | Acar, Evrim | |
| dc.contributor.author | Hasija, Tanuj | |
| dc.contributor.author | Akhonda, Mohammad Abu Baker Siddique | |
| dc.contributor.author | Calhoun, Vince D. | |
| dc.contributor.author | Schreier, Peter J. | |
| dc.contributor.author | Adali, Tulay | |
| dc.date.accessioned | 2022-06-21T21:34:01Z | |
| dc.date.available | 2022-06-21T21:34:01Z | |
| dc.date.issued | 2022-04-27 | |
| dc.description | ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Singapore, Singapore | en_US |
| dc.description.abstract | Data fusion—the joint analysis of multiple datasets—through coupled factorizations has the promise to enable enhanced knowledge discovery, and hence is an active area. Various formulations of coupled matrix factorizations have been proposed, each with its own modeling assumptions. In this paper, we study two such methods, namely Independent Vector Analysis (IVA), i.e., extension of Independent Component Analysis (ICA) to multiple datasets, and PARAFAC2, a tensor factorization approach. We demonstrate the modeling assumptions of IVA and PARAFAC2 using simulations, revealing that both methods can accurately capture the latent components, albeit with certain differences in capturing the corresponding subject scores. By making use of a rich multi-task functional Magnetic Resonance Imaging (fMRI) dataset, we show how the two methods can be used for achieving two important goals at once, namely capturing group differences between patients with schizophrenia and healthy controls with interpretable components, as well as understanding the relationship across multiple tasks. This is achieved through the definition of source component vectors across datasets. | en_US |
| dc.description.sponsorship | This work was supported in part by NSF grants CCF 1618551, NCS 1631838 and NIH grants R01MH123610 and R01MH118695, and in part by the Research Council of Norway through project 300489, and in part by the German Research Foundation (DFG) under grant SCHR 1384/3-2. 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/9747662 | en_US |
| dc.format.extent | 5 pages | en_US |
| dc.genre | conference papers and proceedings | en_US |
| dc.identifier | doi:10.13016/m2cus9-jbfc | |
| dc.identifier.citation | I. Lehmann et al., "Multi-Task fMRI Data Fusion Using IVA and PARAFAC2," ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 1466-1470, doi: 10.1109/ICASSP43922.2022.9747662. | en_US |
| dc.identifier.uri | https://doi.org/10.1109/ICASSP43922.2022.9747662 | |
| dc.identifier.uri | http://hdl.handle.net/11603/25009 | |
| 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 | MULTI-TASK FMRI DATA FUSION USING IVA AND PARAFAC2 | 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 |