Multimodal fusion of multiple rest fMRI networks and MRI gray matter via multilink joint ICA reveals highly significant function/structure coupling in Alzheimer’s disease
| dc.contributor.author | Khalilullah, K M Ibrahim | |
| dc.contributor.author | Agcaoglu, Oktay | |
| dc.contributor.author | Sui, Jing | |
| dc.contributor.author | Adali, Tulay | |
| dc.contributor.author | Duda, Marlena | |
| dc.contributor.author | Calhoun, Vince D. | |
| dc.date.accessioned | 2023-04-03T19:41:24Z | |
| dc.date.available | 2023-04-03T19:41:24Z | |
| dc.date.issued | 2023-03-01 | |
| dc.description.abstract | In this paper we focus on estimating the joint relationship between structural MRI (sMRI) gray matter (GM) and multiple functional MRI (fMRI) intrinsic connectivity networks (ICN) using a novel approach called multi-link joint independent component analysis (ml-jICA). The proposed model offers several improvements over the existing joint independent component analysis (jICA) model. We assume a shared mixing matrix for both the sMRI and fMRI modalities, while allowing for different mixing matrices linking the sMRI data to the different ICNs. We introduce the model and then apply this approach to study the differences in resting fMRI and sMRI data from patients with Alzheimer’s disease (AD) versus controls. The results yield significant differences with large effect sizes that include regions in overlapping portions of default mode network, and also hippocampus and thalamus. Importantly, we identify two joint components with partially overlapping regions which show opposite effects for Alzheimer’s disease versus controls, but were able to be separated due to being linked to distinct functional and structural patterns. This highlights the unique strength of our approach and multimodal fusion approaches generally in revealing potentially biomarkers of brain disorders that would likely be missed by a unimodal approach. These results represent the first work linking multiple fMRI ICNs to gray matter components within a multimodal data fusion m | en_US |
| dc.description.sponsorship | This study was supported by NSF 2112455 and NIH RF1AG063153 | en_US |
| dc.description.uri | https://www.biorxiv.org/content/10.1101/2023.02.28.530458v1 | en_US |
| dc.format.extent | 21 pages | en_US |
| dc.genre | journal articles | en_US |
| dc.genre | preprints | en_US |
| dc.identifier | doi:10.13016/m2fkgl-ysdy | |
| dc.identifier.uri | https://doi.org/10.1101/2023.02.28.530458 | |
| dc.identifier.uri | http://hdl.handle.net/11603/27239 | |
| dc.language.iso | en_US | 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. | en_US |
| dc.title | Multimodal fusion of multiple rest fMRI networks and MRI gray matter via multilink joint ICA reveals highly significant function/structure coupling in Alzheimer’s disease | en_US |
| dc.type | Text | en_US |
| dcterms.creator | https://orcid.org/0000-0003-0594-2796 | en_US |