Nature Communications (May 2021)
Predicting optimal deep brain stimulation parameters for Parkinson’s disease using functional MRI and machine learning
- 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
Affiliations
- Alexandre Boutet
- Joint Department of Medical Imaging, University of Toronto
- Radhika Madhavan
- GE Global Research Center
- Gavin J. B. Elias
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto
- Suresh E. Joel
- GE Healthcare
- Robert Gramer
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto
- Manish Ranjan
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto
- Vijayashankar Paramanandam
- Edmond J. Safra Program in Parkinson’s Disease, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, UHN, Division of Neurology, University of Toronto
- David Xu
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto
- Jurgen Germann
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto
- Aaron Loh
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto
- Suneil K. Kalia
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto
- Mojgan Hodaie
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto
- Bryan Li
- Joint Department of Medical Imaging, University of Toronto
- Sreeram Prasad
- Edmond J. Safra Program in Parkinson’s Disease, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, UHN, Division of Neurology, University of Toronto
- Ailish Coblentz
- Joint Department of Medical Imaging, University of Toronto
- Renato P. Munhoz
- Edmond J. Safra Program in Parkinson’s Disease, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, UHN, Division of Neurology, University of Toronto
- Jeffrey Ashe
- GE Global Research Center
- Walter Kucharczyk
- Joint Department of Medical Imaging, University of Toronto
- Alfonso Fasano
- Edmond J. Safra Program in Parkinson’s Disease, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, UHN, Division of Neurology, University of Toronto
- Andres M. Lozano
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto
- DOI
- https://doi.org/10.1038/s41467-021-23311-9
- Journal volume & issue
-
Vol. 12,
no. 1
pp. 1 – 13
Abstract
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.
