Subtyping Depression using Brain-Gut Electrophysiology for Early Prediction of Antidepressant Response: a multicentric clinical study

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. Our study aims to achieve three key objectives: first, to assess the predictive ability of previously identified biomarkers such as frontal theta power and alpha asymmetry, in explaining the response to interventions within our sample; second, to evaluate the utility of whole-person research approach, focusing the gut-brain interactions, in predicting responses; and third, to identify reliable early biomarkers that can predict responses across various phenotypic subtypes. Across two sites, a total of 161 (+45) participants, including 99 (45) treatment-naive patients, enrolled in our study from site 1 (+site 2) which spanned three visits. We aimed to predict antidepressant outcomes at the third visit (4-6 weeks) using data collected from visits one (baseline) and two (7-10 days), and the data from site 2 was solely used for testing the predictive utility.

Our predictive models, which incorporated electrophysiological data from both the brain and gut along with clinical information, achieved an cross validation (independent test) performance of 78% (80%) specificity and 84% (71.43%) sensitivity in identifying non-responders to antidepressant treatment administered as per Clinical Practice Guidelines of India. 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.