In Journal of neural engineering ; h5-index 52.0
OBJECTIVE : Deep brain stimulation is a treatment option for patients with refractory obsessive-compulsive disorder. A new generation of stimulators hold promise for closed loop stimulation, with adaptive stimulation in response to biologic signals. Here we aimed to discover a suitable biomarker in the ventral striatum in patients with obsessive compulsive disorder using local field potentials.
APPROACH : We induced obsessions and compulsions in 11 patients undergoing deep brain stimulation treatment using a symptom provocation task. Then we trained machine learning models to predict symptoms using the recorded intracranial signal from the deep brain stimulation electrodes.
MAIN RESULTS : Average areas under the receiver operating characteristics curve were 62.1% for obsessions and 78.2% for compulsions for patient specific models. For obsessions it reached over 85% in one patient, whereas performance was near chance level when the model was trained across patients. Optimal performances for obsessions and compulsions was obtained at different recording sites.
SIGNIFICANCE : The results from this study suggest that closed loop stimulation may be a viable option for obsessive-compulsive disorder, but that intracranial biomarkers are patient and not disorder specific.
CLINICAL TRIAL : Netherlands trial registry NL7486.
Fridgeirsson Egill Axfjord, Bais Melisse N, Eijsker Nadine, Thomas Rajat M, Smit Dirk J A, Bergfeld Isidoor O, Richard Schuurman P, van den Munckhof Pepijn, de Koning Pelle, Vulink Nienke, Figee Martijn, Mazaheri Ali, van Wingen Guido A, Denys Damiaan
2023-Feb-24
Deep brain stimulation, Deep learning, Obsessive compulsive disorder, closed loop stimulation, intracranial biomarker, local field potentials
