Researchers at the Indian Institute of Technology Kanpur (IIT Kanpur), in collaboration with Ganesh Shankar Vidyarthi Memorial (GSVM) Medical College, have discovered that analyzing a combination of brain and gastric electrical signals, alongside clinical symptoms, can predict the effectiveness of antidepressant treatments within 7 to 10 days of initiation. This significant finding could lead to earlier assessments of treatment responses, reducing the conventional evaluation period of 4 to 6 weeks.
Researchers found that these signals, recorded within the first 7–10 days of treatment, could help identify patients unlikely to respond to an antidepressant. The study included 206 participants, including 144 treatment-naive patients with depression. Researchers recorded EEG and EGG signals at the start of treatment and again about a week later, examining whether these early biological signals, together with clinical information, could predict treatment outcomes assessed 4–6 weeks after antidepressant therapy began. The predictive model identified patients unlikely to respond to treatment with 84% sensitivity and 78% specificity during model evaluation. When tested on an independent patient cohort, the model achieved 77.3% overall accuracy, with 80% specificity and 71.4% sensitivity for identifying non-responders.
“Our study shows that objective, non-invasive brain and gut electrophysiological signals collected within the first week of treatment already contain valuable information about treatment response and can help guide interventions more precisely,” said Dr Pragathi Priyadharsini Balasubramani, assistant professor, Department of Cognitive Science, IIT Kanpur, and corresponding author of the study. Recognising these biological subtypes helps explain why patients respond differently to the same medication and facilitates personalised treatment strategies,” said Amal Jude Ashwin Francis, PhD scholar, Department of Cognitive Science, IIT Kanpur, and first author of the study. The study examined electrical activity in the brain and stomach using electroencephalography (EEG) and electrogastrography (EGG), respectively, along with clinical symptom data.
Advancements in Depression Treatment Identification
An estimated 5% of adults globally and 4.5% of India’s population suffer from depression. A significant challenge in managing this condition is that over half of patients do not respond adequately to their first antidepressant, often leading to prolonged periods of trial and error before finding effective treatment. Early identification of potential treatment responses could improve care outcomes.
The tools utilized in this research are available through Neuroclinical Innovative Solutions (NCIS) Private Limited, which has received funding from the Biotechnology Industry Research Assistance Council (BIRAC) to support this translational project. This backing is aimed at advancing research towards practical clinical applications, facilitating earlier and more personalized treatment decisions for patients with depression.