Researchers at the Indian Institute of Technology Kanpur (IIT Kanpur), in collaboration with GSVM Medical College, Kanpur, have found that brain and gastric electrical signals, combined with clinical symptoms, could help predict antidepressant treatment outcomes within 7–10 days of starting therapy.
The findings could enable earlier identification of treatment response than the conventional 4–6 week period typically used to assess antidepressant effectiveness. The study examined electrical activity in the brain and stomach using electroencephalography (EEG) and electrogastrography (EGG), alongside clinical symptom data.
The study included 206 participants, including 144 treatment-naive patients with depression. EEG and EGG signals were recorded at treatment initiation and approximately one week later. The researchers assessed whether these early biological signals could predict treatment outcomes evaluated 4–6 weeks after therapy began.
The predictive model identified patients unlikely to respond to treatment with 84% sensitivity and 78% specificity during model evaluation. On an independent patient cohort, it achieved 77.3% overall accuracy, with 80% specificity and 71.4% sensitivity for identifying nonresponders.
Dr. Pragathi Priyadharsini Balasubramani, Assistant Professor, Department of Cognitive Science, IIT Kanpur, and corresponding author, said: “Our study shows that objective non-invasive brain and gut electrophysiological signals collected in about the first week of treatment already contain valuable information about treatment response to precisely guide the intervention.”
Amal Jude Ashwin Francis, PhD Scholar, Department of Cognitive Science, IIT Kanpur, and first author, said: “We found that different symptom profiles were associated with distinct patterns of brain and gut physiology linked to treatment outcomes. Recognizing these biological subtypes helps explain why patients respond differently to the same medication and facilitates personalized treatment strategies.”
The researchers noted that larger studies involving more diverse patient groups will be needed to validate the approach.
