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Identifying Neural Biomarkers of Risk-Taking from Intracranial EEG Recordings

Guo, Y.; Merkley, A.; Jaffee, S.; Whiting, A. C.; Grover, P.

2025-10-09 neuroscience
10.1101/2025.10.08.681279 bioRxiv
Show abstract

Risk-taking behavior is associated with neuropsychiatric disorders such as addiction. In this study, we present a data-driven approach to identify neural biomarkers of risk-taking from intracranial stereo-electroencephalography (sEEG) recordings. Using time and frequency domain features, we train a classifier to distinguish between risk-taking and risk-avoidance behaviors. Based on data from a single patient, the model achieves an accuracy of 68.5%, which is significantly above chance. These results highlight the potential of identifying risk-taking states from invasive recordings. Clinical SignificanceThis work demonstrates the feasibility of identifying risk-related biomarkers from intracranial recordings, highlighting a promising direction for closed-loop neuromodulation for neuropsychiatric conditions.

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