Multi-modal data fusion using source separation: Application to medical imaging
| dc.contributor.author | Adali, Tulay | |
| dc.contributor.author | Levin-Schwartz, Yuri | |
| dc.contributor.author | Calhoun, Vince D. | |
| dc.date.accessioned | 2019-02-25T14:55:01Z | |
| dc.date.available | 2019-02-25T14:55:01Z | |
| dc.date.issued | 2015-08-17 | |
| dc.description.abstract | The Joint ICA (jICA) and the Transposed IVA (tIVA) models are two effective solutions based on blind source separation that enable fusion of data from multiple modalities in a symmetric and fully multivariate manner. In [1], their properties and the major issues in their implementation are discussed in detail. In this accompanying paper, we consider the application of these two models to fusion of multi-modal medical imaging data—functional magnetic resonance imaging (fMRI), structural MRI (sMRI), and electroencephalography (EEG) data collected from a group of healthy controls and patients with schizophrenia performing an auditory oddball task. We show how both models can be used to identify a set of components that report on differences between the two groups, jointly, for all the modalities used in the study. We discuss the importance of algorithm and order selection as well as trade-offs involved in the selection of one model over another. We note that for the selected dataset, especially given the limited number of subjects available for the study, jICA provides a more desirable solution, however the use of an ICA algorithm that uses flexible density matching provides advantages over the most widely used algorithm, Infomax, for the problem. | en_US |
| dc.description.sponsorship | This work was supported by the NSF-IIS under Grant 1017718, NSF-CCF under Grant 1117056, NIH under Grant 2R01EB000840, and NIH COBRE under Grant P20GM103472. | en_US |
| dc.description.uri | https://ieeexplore.ieee.org/document/7206517?arnumber=7206517 | en_US |
| dc.format.extent | 10 pages | en_US |
| dc.genre | conference papers and proceedings postprints | en_US |
| dc.identifier | doi:10.13016/m2odvu-zimf | |
| dc.identifier.citation | Tülay Adali , Yuri Levin-Schwartz, Vince D. Calhoun, Multimodal Data Fusion Using Source Separation: Two Effective Models Based on ICA and IVA and Their Properties, Proceedings of the IEEE ,Volume: 103 , Issue: 9 , Sept. 2015, DOI: 10.1109/JPROC.2015.2461601 | en_US |
| dc.identifier.uri | https://doi.org/10.1109/JPROC.2015.2461601 | |
| dc.identifier.uri | http://hdl.handle.net/11603/12853 | |
| 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.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 | © 2015 IEEE | |
| dc.subject | data fusion | en_US |
| dc.subject | multimodality | en_US |
| dc.subject | independent component analysis (ICA) | en_US |
| dc.subject | independent vector analysis (IVA) | en_US |
| dc.subject | MRI | |
| dc.subject | functional magnetic resonance imaging (fMRI) | |
| dc.subject | electroencephalography (EEG) | |
| dc.subject | medical imaging | |
| dc.subject | source separation | |
| dc.title | Multi-modal data fusion using source separation: Application to medical imaging | en_US |
| dc.type | Text | en_US |