Symptom-based depression subtypes: brain dynamic specificity and its association with gene expression profiles.

Abstract

At least 227 combinations of symptoms meet the criteria for Major Depressive Disorder (MDD). However, in clinical practice, patients consistently present symptoms in a regular rather than random manner, and the neural basis underlying the MDD subtypes remains unclear. To help clarify the neural basis, patients with MDD were clustered by symptom combinations to investigate the neural underpinning of each subtype using functional resonance imaging (fMRI). Four symptom-based subtypes of MDD were identified using latent profile analysis according to the clinical scales. Subsequently, brain dynamics were evaluated using fMRI, and the dysregulations in attention and limbic network were observed among the subtypes. Correlation between brain dynamics and symptom combinations was then assessed via canonical correlation analysis (CCA). The brain-symptom correlation was higher when evaluated in subtypes (r = 0.77 to 0.92) compared to the entire group (r = 0.5). The loading weight in CCA showed that dynamics in transmodal networks contributed the most to the correlation in the subtypes characterized by typical depression symptoms, whereas unimodal networks contributed the most to subtypes characterized by anxiety and insomnia. Finally, gene expression underlying the CCA model, along with its biological encoding process, performed using a postmortem gene expression atlas revealed distinct gene enrichments for different subtypes. These findings highlight that distinct symptom clusters in MDD have specific neural correlates, providing insights into depression's heterogeneous diagnosis and precision medicine opportunities.