- Higher-order interactions capture abnormal brain network topology in MDD, with significant structural and functional alterations in DMN, visual and frontoparietal networks.
- Fusion of DTI structural metrics and persistent homology functional signatures enabled SVM classification of MDD with excellent performance (AUC 88.2%).
- Multimodal topological features robustly predicted suicide risk in MDD, achieving a significant correlation of r = 0.604 between predicted and actual scores.
Front Psychiatry. 2026 Oct 20;17:1964608. doi: 10.3389/fpsyt.2026.1964608. eCollection 2026.
ABSTRACT
BACKGROUND: Conventional neuroimaging analyses for major depressive disorder (MDD) predominantly construct brain networks based solely on pairwise regional functional couplings. Accumulating microscale neuroscientific evidence highlights the essential role of higher-order interactions (HOIs, interactions among three or more brain nodes) in capturing the intricate spatiotemporal dynamics of the human brain. This study aims to integrate machine learning techniques with neuroimaging topological features to systematically investigate the impact of non-pairwise interaction patterns in the brains of MDD patients on system dynamics, and to develop diagnostic and predictive models for MDD clinical manifestations.
METHODS: Firstly, diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from 154 MDD patients with first-episode, drug-naïve and 153 age and gender matched healthy controls (HCs). Graph-theoretical analysis was applied to DTI-derived tractography data to quantify structural topology metrics, whereas persistent homology was adopted to extract higher-order functional topological signatures from rs-fMRI time series. Second, feature-level fusion was performed, and then a classification model was constructed using support vector machine (SVM) for MDD diagnosis. A repeated nested validation strategy with 50 repeated stratified 70/30 hold-out splits was adopted. Classification performance was evaluated using the area under the receiver operating characteristic curve (AUC). Subsequently, a prediction model was built based on the selected fused features using ridge regression, and the prediction performance was evaluated based on the correlation between the predicted values and the actual scale scores.
RESULTS: Compared with HC, MDD patients exhibited significant alterations in structural and functional topological metrics within key brain networks, including DMN, VN and FPN. The classification performance of the SVM model integrating multimodal neuroimaging features achieved excellent classification performance, with an AUC of 88.2%. Additionally, multimodal imaging features demonstrated robust predictive capacity for suicide risk level in MDD patients (r=0.604).
CONCLUSION: By integrating structural and higher-order functional topological features, we are able to effectively diagnose and predict MDD, highlighting the crucial role of higher-order interactions in the pathological mechanisms of brain networks in depression.
PMID:42978927 | PMC:PMC13727736 | DOI:10.3389/fpsyt.2026.1964608
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