Unveiling diverse clinical symptom patterns and neural activity profiles in major depressive disorder subtypes

Background The heterogeneity of major depressive disorder (MDD) significantly hinders its effective and optimal clinical outcomes. This study aimed to identify MDD subtypes by adopting a data-driven approach and assessing validity based on symptomatology and neuroimaging. Methods A total of 259 patients with MDD and 92 healthy controls were enrolled in this cross-sectional study. Latent profile analysis (LPA) was used to identify MDD subtypes based on validated clinical symptoms. To examine whether there were differences between these identified MDD subtypes, network analysis was used to test any differences in symptom patterns between these subtypes. We also compared neural activity between these identified MDD subtypes and tested whether certain neural activities were related to individual subtypes. This MDD subtyping was further tested in an independent dataset that contains 86 patients with MDD. Findings Five MDD subtypes with distinct depressive symptom patterns were identified using the LPA model, with the 5-class model selected as the optimal classification solution based on its superior fit indices (AIC = 6656.296, aBIC = 6681.030, entropy = 0.917, LMR p = 0.3267, BLRT p < 0.001). The identified subtypes include atypical-like depression, two melancholic depression (moderate and severe) subtypes with distinct patterns on feeling anxious, and two anhedonic depression subtypes (moderate and severe) with different manifestations on weight/appetite loss. The reproducibility of the classification was also confirmed. Significant differences in symptom structures between melancholic and two anhedonic subtypes, and between anhedonic and atypical subtypes were observed (all p < 0.05). Furthermore, these identified subtypes had differential neural activities in both regional spontaneous neural activity (pFWE < 0.005) and functional connectivity between different brain regions (pFDR < 0.005), linked to different clinical symptoms (FDR q < 0.05). Interpretation The network analysis and neuroimaging tests support the existence and validity of the identified MDD subtypes, each exhibiting unique clinical manifestations and neural activity patterns. The categorisation of these subtypes sheds light on the heterogeneity of depression and suggest that personalised treatment and management strategies tailored to specific subtypes may enhance intervention strategies in clinical settings.