Subtyping Depression using Brain-Gut Electrophysiology for Early Prediction of Antidepressant Response

Depression affects approximately 5% of adults worldwide, with India reporting a prevalence of 4.5%. Oral medication is a common treatment, but over 50% of patients fail to respond to the first-line antidepressants and often require medication adjustments or augmentation. This highlights the urgent need for predictive models that can guide personalized treatment strategies more quickly. Previous studies have used clinical data and electroencephalogram (EEG) features to predict treatment outcomes in depression, but the robustness of critical biomarkers—such as frontal theta power and alpha asymmetry—has been questioned due to inconsistencies in their interpretability and predictive value. Additionally, while gut abnormalities in depression have been documented, their role in predicting treatment outcomes has not been explored in depth.Our study aims to address two key objectives: first, to identify reliable early biomarkers for predicting antidepressant response and interpret them across depression subtypes; and second, to explore the role of gut-brain interactions, particularly through longitudinal data, in predicting treatment success. A total of 161 participants, including 99 treatment-naïve patients, enrolled in our study, which spanned three visits. We aimed to predict antidepressant outcomes at the third visit (30 days after baseline) using data collected from visits one (baseline) and two (7–10 days). After attrition, we obtained EEG and electrogastrography (EGG) data from 89 participants at visit two (42 patients) and 61 at visit three (21 patients).Our predictive models, which incorporated electrophysiological data from both the brain and gut, along with clinical information, achieved an accuracy of 78% specificity and 84% sensitivity in identifying non-responders to antidepressant treatment. We found that certain electrophysiological features were strongly predictive of treatment outcomes for specific depression subtypes. For example, increased excitation-inhibition ratios in the fronto-central brain regions were predictive for patients with dominant anxiety and sleep symptoms. Similarly, decreased tachygastric gut coupling with the sensory-motor brain region predicted treatment non-response in patients with high levels of negative self-thoughts. Increased connectivity in the right fronto-central region was associated with better outcomes in patients with significant appetite issues. Additionally, higher fronto-central theta power and beta asymmetry were predictive of responses in patients with a composite set of symptoms.Our findings suggest that combining brain and gut electrophysiological markers with clinical phenotyping offers a promising, scalable approach to personalize depression treatment. This approach could guide clinicians in developing more effective and tailored medication strategies, ultimately improving patient outcomes.