Dictionary Learning-Based fMRI Data Analysis for Capturing Common and Individual Neural Activation Maps
| dc.contributor.author | Jin, Rui | |
| dc.contributor.author | Dontaraju, Krishna | |
| dc.contributor.author | Kim, Seung-Jun | |
| dc.contributor.author | Akhonda, Siddique | |
| dc.contributor.author | Adali, Tulay | |
| dc.date.accessioned | 2020-07-01T17:54:50Z | |
| dc.date.available | 2020-07-01T17:54:50Z | |
| dc.date.issued | 2020-05-04 | |
| dc.description.abstract | A novel dictionary learning (DL) method is proposed to estimate sparse neural activations from multi-subject fMRI data sets. By exploiting the label information such as the patient and the normal healthy groups, the activation maps that are commonly shared across the groups as well as those that can explain the group differences are both captured. The proposed method was tested using real fMRI data sets consisting of schizophrenic subjects and healthy controls. The DL approach not only reproduced most of the maps obtained from the conventional independent component analysis (ICA), but also identified more maps that are significantly group-different, including a number of novel ones that were not revealed by ICA. The stability analysis of the DL method and the correlation analysis with separate neuropsychological test scores further strengthen the validity of our analysis. | en_US |
| dc.description.sponsorship | The authors would like to thank Dr. Vince Calhoun for providing the feature dataset, and Qunfang Long and Suchita Bhinge for their helpful feedback on the analysis results. | en_US |
| dc.description.uri | https://ieeexplore.ieee.org/document/9086064 | en_US |
| dc.format.extent | 15 pages | en_US |
| dc.genre | journal articles postprints | en_US |
| dc.identifier | doi:10.13016/m2v4w5-istc | |
| dc.identifier.citation | R. Jin, K. Dontaraju, S. Kim, S. Akhonda and T. Adali, "Dictionary Learning-Based fMRI Data Analysis for Capturing Common and Individual Neural Activation Maps," in IEEE Journal of Selected Topics in Signal Processing, doi: 10.1109/JSTSP.2020.2992430. | en_US |
| dc.identifier.uri | 10.1109/JSTSP.2020.2992430 | |
| dc.identifier.uri | http://hdl.handle.net/11603/19052 | |
| 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 | 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 | © 2020 IEEE | |
| dc.title | Dictionary Learning-Based fMRI Data Analysis for Capturing Common and Individual Neural Activation Maps | en_US |
| dc.type | Text | en_US |