Resolving heterogeneity in first-episode and drug-naive major depressive disorder based on individualized structural covariance network: evidence from the REST-meta-MDD consortium

Abstract Background Major depressive disorder (MDD) is a complex and heterogeneous disorder, and this heterogeneity poses a significant challenge for advancing precision medicine in patients with MDD. MRI-based subtyping analysis has been widely employed to address the heterogeneity of MDD patients. In this study, we investigated the subtypes of first-episode and drug-naive (FEDN) MDD patients based on the individualized structural covariance network (IDSCN). Methods In this study, we used T1-weighted anatomical images of 164 FEDN MDD patients and 164 healthy controls from the REST-meta-MDD consortium. The IDSCN of participants was obtained using the network template perturbation method. Subtypes of FEDN MDD were identified using k-means clustering analysis, and differences in neuroimaging findings and clinical symptoms between the identified subtypes were compared using two-sample t -tests. Results This study identified two subtypes of FEDN MDD: subtype 1 ( n = 117) and subtype 2 ( n = 47) by characterizing 10 edges that were significantly altered in at least 5% of patients (i.e., 8 patients) in the IDSCN. Compared with subtype 2, subtype 1 had significantly higher anxiety symptom scores, stronger structural covariance edges in 9 edges within the thalamus, and a significantly reduced gray matter volume (GMV) in the frontal and parietal regions, and in the thalamus. Conclusions Our results suggest that patients with FEDN MDD can be classified into two different subtypes based on their IDSCN, providing an important reference for personalized treatment and precision medicine for patients with FEDN MDD.