Contribution of resting-state functional connectivity of the subgenual anterior cingulate to prediction of antidepressant efficacy in patients with major depressive disorder

Abstract

This study investigated how resting-state functional connectivity (rsFC) of the subgenual anterior cingulate cortex (sgACC) predicts antidepressant response in patients with major depressive disorder (MDD). Eighty-seven medication-free MDD patients underwent baseline resting-state functional MRI scans. After 12 weeks of escitalopram treatment, patients were classified into remission depression (RD, n = 42) and nonremission depression (NRD, n = 45) groups. We conducted two analyses: a voxel-wise rsFC analysis using sgACC as a seed to identify group differences, and a prediction model based on the sgACC rsFC map to predict treatment efficacy. Haufe transformation was used to interpret the predictive rsFC features. The RD group showed significantly higher rsFC between the sgACC and regions in the fronto-parietal network (FPN), including the bilateral dorsolateral prefrontal cortex (DLPFC) and bilateral inferior parietal lobule (IPL), compared to the NRD group. These sgACC rsFC measures correlated positively with symptom improvement. Baseline sgACC rsFC also significantly predicted treatment response after 12 weeks, with a mean accuracy of 72.64% (p < 0.001), mean area under the curve of 0.74 (p < 0.001), mean specificity of 0.82, and mean sensitivity of 0.70 in 10-fold cross-validation. The predictive voxels were mainly within the FPN. The rsFC between the sgACC and FPN is a valuable predictor of antidepressant response in MDD patients. These findings enhance our understanding of the neurobiological mechanisms underlying treatment response and could help inform personalized treatment strategies for MDD.

Fig. 1. Results of rsFC analysis between RD and NRD.

Brain regions (left panel) and bar graphs (right panel) showing significant differences for the rsFC of the right sgACC between patients in the RD and NRD groups. RD remission depression, NRD nonremission depression, DLPFC dorsolateral prefrontal cortex, IPL inferior parietal lobule.

Fig. 2. Results of Pearson correlation analyses.

Scatter plots showing positive correlations between the percent improvement of HAMD-17 scores and rsFC of the sgACC with the bilateral DLPFC and the bilateral IPL separately. HAMD-17 17-item Hamilton Depression Rating Scale, DLPFC dorsolateral prefrontal cortex, IPL inferior parietal lobule.

Fig. 3. Result of the efficacy prediction model analysis.

Mean ROC curves over 10 folds for predicting the escitalopram treatment outcomes. ROC receiver operating characteristic.

Fig. 4. The association between predictive features and ROIs differentiating RD and NRD groups.

A The surface mapping visualization presents the K-means clusters 1 and 2 based on the predictive features. B The surface mapping visualization shows the 4 ROIs identified in the between-group comparation analysis of the sgACC rsFC. C The surface mapping visualization presents the K-means clusters, the ROIs obtained from the between-group comparation analysis of the sgACC rsFC, and the FPN brain network from Yeo et al. [35]. D The Venn diagram illustrates a substantial overlap between the predictive voxels (n = 832) in K-means cluster 1 and the ROIs obtained from the between-group comparation analysis of the sgACC rsFC. E The bar plot describes that the distribution of the cluster voxels across different functional networks and the majority of identified important voxels clearly locate in the FPN. RD remission depression, NRD nonremission depression, ROI region of interest, FPN fronto-parietal network.