Wang X, Xue L, Shao J, Dai Z, Hua L, Yan R, Yao Z, Lu Q. Distinct MRI-based functional and structural connectivity for antidepressant response prediction in major depressive disorder.
Clin Neurophysiol 2024;
160:19-27. [PMID:
38367310 DOI:
10.1016/j.clinph.2024.02.004]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 12/28/2023] [Accepted: 02/06/2024] [Indexed: 02/19/2024]
Abstract
OBJECTIVE
Emerging studies have identified treatment-related connectome predictors in major depressive disorder (MDD). However, quantifying treatment-responsive patterns in structural connectivity (SC) and functional connectivity (FC) simultaneously remains underexplored. We aimed to evaluate whether spatial distributions of FC and SC associated treatment responses are shared or unique.
METHODS
Diffusion tensor imaging and resting-state functional magnetic resonance imaging were collected from 210 patients with MDD at baseline. We separately developed connectome-based prediction models (CPM) to predict reduction of depressive severity after 6-week monotherapy based on structural, functional, and combined connectomes, then validated them on the external dataset. We identified the predictive SC and FC from CPM with high occurrence frequencies during the cross-validation.
RESULTS
Structural connectomes (r = 0.2857, p < 0.0001), functional connectomes (r = 0.2057, p = 0.0025), and their combined CPM (r = 0.4, p < 0.0001) can significantly predict a reduction of depressive severity. We didn't find shared connectivity between predictive FC and SC. Specifically, the most predictive FC stemmed from the default mode network, while predictive SC was mainly characterized by within-network SC of fronto-limbic networks.
CONCLUSIONS
These distinct patterns suggest that SC and FC capture unique connectivity concerning the antidepressant response.
SIGNIFICANCE
Our findings provide comprehensive insights into the neurophysiology of antidepressants response.
Collapse