Brain stimulation · 2025
From subthalamic local field potentials to the selection of chronic deep brain stimulation contacts in Parkinson's disease - A systematic review.
СтатьяСистематический обзорПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Muller Marjolein, van Leeuwen Mark F C, Hoffmann Carel F, van der Gaag Niels A, Zutt Rodi, van der Gaag Saskia, Schouten Alfred C, Contarino M Fiorella
Аннотация
BACKGROUND: Programming deep brain stimulation (DBS) of the subthalamic nucleus for optimal symptom control in Parkinson's Disease (PD) requires time and trained personnel. Novel implantable neurostimulators allow local field potentials (LFP) recording, which could be used to identify the optimal (chronic) stimulation contact. However, literature is inconclusive on which LFP features and prediction techniques are most effective.
OBJECTIVE: To evaluate the performance of different LFP-based physiomarkers for predicting the optimal (chronic) stimulation contacts.
METHODS: A literature search was conducted across nine databases, resulting in 418 individual papers. Two independent reviewers screened the articles based on title, abstract, and full text. The quality of included studies was assessed using a modified Joanna Briggs Institute Critical Appraisal Checklist for Case Series. Results were categorised in four classes based on the predictive performance with respect to the a priori chance.
RESULTS: Twenty-five studies were included. Single-feature beta-band predictions demonstrated positive performance scores in 94 % of the outcomes. Predictions based on single non-beta-frequency features yielded positive scores in only 25 % of the outcomes, with positive results mainly for high frequency oscillations. Multi-feature predictions (e.g. machine learning) achieved accuracy scores within the two highest performance classes more often than single beta-based predictions (100 % versus 39 %).
CONCLUSION: Predicting the optimal stimulation contact based on LFP recordings is feasible and can improve DBS programming efficiency in PD. Single beta-band predictions show more promising results than non-beta-frequency features alone, but are outperformed by multi-feature predictions. Future research should further explore multi-feature predictions for optimal contact identification.
OBJECTIVE: To evaluate the performance of different LFP-based physiomarkers for predicting the optimal (chronic) stimulation contacts.
METHODS: A literature search was conducted across nine databases, resulting in 418 individual papers. Two independent reviewers screened the articles based on title, abstract, and full text. The quality of included studies was assessed using a modified Joanna Briggs Institute Critical Appraisal Checklist for Case Series. Results were categorised in four classes based on the predictive performance with respect to the a priori chance.
RESULTS: Twenty-five studies were included. Single-feature beta-band predictions demonstrated positive performance scores in 94 % of the outcomes. Predictions based on single non-beta-frequency features yielded positive scores in only 25 % of the outcomes, with positive results mainly for high frequency oscillations. Multi-feature predictions (e.g. machine learning) achieved accuracy scores within the two highest performance classes more often than single beta-based predictions (100 % versus 39 %).
CONCLUSION: Predicting the optimal stimulation contact based on LFP recordings is feasible and can improve DBS programming efficiency in PD. Single beta-band predictions show more promising results than non-beta-frequency features alone, but are outperformed by multi-feature predictions. Future research should further explore multi-feature predictions for optimal contact identification.