Brain-Gut Signals May Predict Antidepressant Response Within 7 to 10 Days, Study Finds
New Delhi — A combination of brain activity, gastric electrical signals and clinical symptoms may help predict whether patients will respond to antidepressant treatment within seven to 10 days, according to a study by researchers in India.
The research, conducted by the Indian Institute of Technology Kanpur and Ganesh Shankar Vidyarthi Memorial Medical College, suggests treatment response could potentially be assessed much earlier than the four to six weeks typically required.
Researchers said earlier identification of likely nonresponders could reduce the trial-and-error process often involved in treating depression.
“Our study showed 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,” said Pragathi Priyadharsini Balasubramani, Assistant Professor in IIT Kanpur’s Department of Cognitive Science and corresponding author of the study.
Amal Jude Ashwin Francis, a Ph.D. scholar at IIT Kanpur and the study’s first author, said researchers found that different symptom profiles were associated with distinct patterns of brain and gut activity linked to treatment outcomes.
The study examined electrical activity in the brain using electroencephalography, or EEG, and activity in the stomach using electrogastrography, or EGG. Researchers combined those measurements with clinical symptom data.
The study involved 206 participants, including 144 patients with depression who had not previously received treatment.
EEG and EGG measurements were taken at the start of treatment and again about one week later. Researchers then examined whether those early signals could predict outcomes measured four to six weeks after antidepressant treatment began.
During model evaluation, the predictive system identified patients unlikely to respond to treatment with 84% sensitivity and 78% specificity, according to IIT Kanpur.
When tested on an independent group of patients, the model achieved an overall accuracy of 77.3%, with 80% specificity and 71.4% sensitivity for identifying nonresponders.
Researchers said the findings could eventually support more personalized treatment strategies by helping clinicians identify patients who may benefit from an earlier change in therapy. (Source: IANS)
