Spatial Dynamic Functional Connectivity Analysis Identifies Distinctive Biomarkers in Schizophrenia
| dc.contributor.author | Bhinge, Suchita | |
| dc.contributor.author | Long, Qunfang | |
| dc.contributor.author | Calhoun, Vince D | |
| dc.contributor.author | Adali, Tulay | |
| dc.date.accessioned | 2019-10-04T14:30:04Z | |
| dc.date.available | 2019-10-04T14:30:04Z | |
| dc.date.issued | 2019-09-24 | |
| dc.description.abstract | Dynamic functional network connectivity (dFNC) analysis is a widely-used to study associations between dynamic functional correlations and cognitive abilities. Traditional methods analyze time-varying association of different spatial networks while assuming that the spatial network itself is stationary. However, there has been very little work focused on voxelwise spatial variability. Exploiting the variability across both the temporal and spatial domains provide a more promising direction to obtain reliable dynamic functional patterns. However, methods for extracting time-varying spatio-temporal patterns from large-scale functional magnetic resonance imaging (fMRI) data present some challenges, such as degradation in performance with respect to increase in size of the data, estimation of the number of dynamic components, and the potential sensitivity of the resulting dFNCs to selection of the networks. In this work, we implement subsequent extraction of exemplars and dynamics using a constrained independent vector analysis, a data-driven method that efficiently estimates spatial and temporal dynamics from large-scale resting-state fMRI data. We explore the benefits of analyzing spatial dFNC (sdFNC) patterns over temporal dFNC (tdFNC) patterns in the context of differentiating healthy controls and patients with schizophrenia. Our results indicate that for resting-state fMRI data, sdFNC patterns were able to better classify patients and controls, and yield more distinguishing features compared with tdFNC patterns. We also estimate structured patterns of connectivity/states using sdFNC patterns, an area that has not been studied so far, and observe that sdFNC was able to successfully capture distinct information from healthy controls and patients with schizophrenia. In addition, sdFNC patterns were also able to identify functional patterns that associate with signs of paranoia and abnormalities in the patients group. We also observe that patients with schizophrenia tend to switch to or stay in a state corresponding to a hyperconnected brain network. | en_US |
| dc.description.sponsorship | This work was supported in parts by the National Institute of Biomedical Imaging and Bioengineering under Grant R01EB020407, National Science Foundation under Grant 1631838, and National Science Foundation-Computing and Communication Foundations under Grant 1618551. | en_US |
| dc.description.uri | https://www.frontiersin.org/articles/10.3389/fnins.2019.01006/full | |
| dc.format.extent | 13 pages | en_US |
| dc.genre | Journal Articles | en_US |
| dc.identifier | doi:10.13016/m2sflj-x9w1 | |
| dc.identifier.citation | Bhinge S, Long Q, Calhoun VD and Adali T (2019) Spatial Dynamic Functional Connectivity Analysis Identifies Distinctive Biomarkers in Schizophrenia. Front. Neurosci. 13:1006. doi: 10.3389/fnins.2019.01006 | en_US |
| dc.identifier.uri | https://doi.org/10.3389/fnins.2019.01006 | |
| dc.identifier.uri | http://hdl.handle.net/11603/14973 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Frontiers in Neuroscience | 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 | 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 | Attribution 4.0 International (CC BY 4.0) | * |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | * |
| dc.subject | UMBC Machine Learning for Signal Processing Lab | |
| dc.title | Spatial Dynamic Functional Connectivity Analysis Identifies Distinctive Biomarkers in Schizophrenia | en_US |
| dc.type | Text | en_US |