Predicting optimal deep brain stimulation parameters for Parkinson’s disease using functional MRI and machine learning

Predicting optimal deep brain stimulation parameters for Parkinson’s disease using functional MRI and machine learning

Deep brain stimulation programming for Parkinson’s disease entails the assessment of a large number of possible simulation settings, requiring numerous clinic visits after surgery. Here, the authors show that patterns of functional MRI can predict the optimal stimulation settings.

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Detalles Bibliográficos
Autores principales: Alexandre Boutet, Radhika Madhavan, Gavin J. B. Elias, Suresh E. Joel, Robert Gramer, Manish Ranjan, Vijayashankar Paramanandam, David Xu, Jurgen Germann, Aaron Loh, Suneil K. Kalia, Mojgan Hodaie, Bryan Li, Sreeram Prasad, Ailish Coblentz, Renato P. Munhoz, Jeffrey Ashe, Walter Kucharczyk, Alfonso Fasano, Andres M. Lozano
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
Materias:
Science
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Acceso en línea:https://doaj.org/article/58ee235dc4e54cd085cabad46031c324
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