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Neural activity during inhibitory control predicts suicidal ideation with machine learning

Medicine and Health

Neural activity during inhibitory control predicts suicidal ideation with machine learning

J. Nan, G. Grennan, et al.

This groundbreaking research harnesses machine learning to differentiate individuals with and without suicidal ideation using EEG data. With a model boasting 89% sensitivity and 98% specificity, the study illuminates key brain regions, enhancing our understanding of mental health. Conducted by Jason Nan, Gillian Grennan, Soumya Ravichandran, Dhakshin Ramanathan, and Jyoti Mishra, this work paves the way for innovative assessments in psychological health.

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Playback language: English
Abstract
This study uses machine learning (ML) to classify individuals with and without suicidal ideation (SI) based on electroencephalography (EEG) data recorded during an inhibitory control task. The best-performing model, using beta band power, achieved high sensitivity (89%) and specificity (98%). Key brain regions driving the model were identified using Shapley explainer plots. External validation in an independent sample showed moderate performance.
Publisher
NPP - Digital Psychiatry and Neuroscience
Published On
Jul 08, 2024
Authors
Jason Nan, Gillian Grennan, Soumya Ravichandran, Dhakshin Ramanathan, Jyoti Mishra
Tags
machine learning
suicidal ideation
EEG
beta band power
mental health
inhibitory control
brain regions

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