Assessing the utility of brain and gut cognitive electrophysiology for early prediction of treatment outcome in major depressive disorder

According to World Health Organization, about 5% of adults globally suffer from clinical depression, and in India it is about 4.5% of people. Oral medication is a common treatment against depression. However, more than half of those treated do not respond to the pharmacological treatment strategy in the first trial and may require switching or augmentation with other medications. There is a strong need for precise models for arriving at a personalized treatment strategy in a sooner timeline. Some earlier models using clinical information along with electroencephalogram (EEG) data showed good performance for early predicting treatment outcome in depression. However, the critical features identified by those studies including the presence of differential frontal theta power and frontal alpha asymmetry in depression patients has been challenged in the recent times due to contradictions in interpretability and robustness: when the theta and alpha frequency signals were teased apart from their aperiodic component, the resulting periodic components were not robust for prediction. On the other hand, gut abnormality in depression has been reported by many earlier studies but have not been used for predictive or prognosis purposes in depression. Our study aims are twofold: first to identify the features that can early predict treatment outcome, and interpret them for different patient subgroups, and second to understand the utility of longitudinal data collection and gut-brain interactions to predicting treatment outcome. About 161 participants (naïve patients = 99) registered for our longitudinal study spanning three visits, and our aim was to investigate whether visits 1 (baseline) and visit 2 (in 7-10 days) could predict the antidepressant treatment outcome in visit 3 (after 30 days). After attrition, electroencephalography and electrogastrography data from 89 participants were collected in visit 2 (patients = 42), and 61 in visit 3 (patients = 21). We used electrophysiological features in the brain and the gut along with clinical data to train simple predictive models, and it was able to reliably predict non-response to depression medications with specificity 78% and sensitivity 84%. The significant features explaining the treatment outcome were ranked, altogether offering a scalable, whole body cognition tool for clinicians for guiding their medication strategy.