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

Subtyping Depression using Brain-Gut Electrophysiology for Early Prediction of Antidepressant Response | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Subtyping Depression using Brain-Gut Electrophysiology for Early Prediction of Antidepressant Response Amal Jude Ashwin Francis, Alok Bajpai, Hari Prakash Tiwari, Nandini Priyanka Balasubramani, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7345538/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 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. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Medical research Health sciences/Neurology Biological sciences/Neuroscience Electrogastrography Electroencephalography gut-brain coupling longitudinal assessments early prediction treatment outcome Depression Full Text Additional Declarations No competing interests reported. Supplementary Files SUPPLEMENTARYMATERIALS.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7345538","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":509263961,"identity":"fb08fbbd-2300-4aef-9bad-489215594de6","order_by":0,"name":"Amal Jude Ashwin Francis","email":"","orcid":"","institution":"Indian Institute of Technology Kanpur","correspondingAuthor":false,"prefix":"","firstName":"Amal","middleName":"Jude Ashwin","lastName":"Francis","suffix":""},{"id":509263962,"identity":"cf1dc3b4-587f-4812-a5c1-ff947f993bbb","order_by":1,"name":"Alok Bajpai","email":"","orcid":"","institution":"Indian Institute of Technology 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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\u0026mdash;such as frontal theta power and alpha asymmetry\u0026mdash;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.\u003c/p\u003e\u003cp\u003eOur 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-na\u0026iuml;ve 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\u0026ndash;10 days). 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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.\u003c/p\u003e\u003cp\u003eOur findings suggest that combining brain and gut electrophysiological markers with clinical phenotyping offers a promising, scalable approach to personalize depression treatment. 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