Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis
National University of Defense Technology · China Medical University · +1 more institution
Abstract
Recent resting-state functional connectivity magnetic resonance imaging studies have shown significant group differences in several regions and networks between patients with major depressive disorder and healthy controls. The objective of the present study was to investigate the whole-brain resting-state functional connectivity patterns of depressed patients, which can be used to test the feasibility of identifying major depressive individuals from healthy controls. Multivariate pattern analysis was employed to classify 24 depressed patients from 29 demographically matched healthy volunteers. Permutation tests were used to assess classifier performance. The experimental results demonstrate that 94.3% (P
Citation impact
- FWCI
- 26.63
- Percentile
- 100%
- References
- 99
Authors
9- LZLing‐Li ZengCorresponding
National University of Defense Technology
- HSHui Shen
National University of Defense Technology
- LLLi Liu
China Medical University, First Hospital of China Medical University
- LWLubin Wang
National University of Defense Technology
- BLBaojuan Li
National University of Defense Technology
Topics & keywords
- Resting state fMRI
- Parahippocampal gyrus
- Functional magnetic resonance imaging
- Default mode network
- Psychology
- Anterior cingulate cortex
- Neuroscience
- Major depressive disorder
- Reduced inequalities