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 and Andres M. Lozano ()
Additional contact information
Alexandre Boutet: University of Toronto
Radhika Madhavan: GE Global Research Center
Gavin J. B. Elias: University Health Network and University of Toronto
Suresh E. Joel: GE Healthcare
Robert Gramer: University Health Network and University of Toronto
Manish Ranjan: University Health Network and University of Toronto
Vijayashankar Paramanandam: University of Toronto
David Xu: University Health Network and University of Toronto
Jurgen Germann: University Health Network and University of Toronto
Aaron Loh: University Health Network and University of Toronto
Suneil K. Kalia: University Health Network and University of Toronto
Mojgan Hodaie: University Health Network and University of Toronto
Bryan Li: University of Toronto
Sreeram Prasad: University of Toronto
Ailish Coblentz: University of Toronto
Renato P. Munhoz: University of Toronto
Jeffrey Ashe: GE Global Research Center
Walter Kucharczyk: University of Toronto
Alfonso Fasano: University of Toronto
Andres M. Lozano: University Health Network and University of Toronto

Nature Communications, 2021, vol. 12, issue 1, 1-13

Abstract: Abstract Commonly used for Parkinson’s disease (PD), deep brain stimulation (DBS) produces marked clinical benefits when optimized. However, assessing the large number of possible stimulation settings (i.e., programming) requires numerous clinic visits. Here, we examine whether functional magnetic resonance imaging (fMRI) can be used to predict optimal stimulation settings for individual patients. We analyze 3 T fMRI data prospectively acquired as part of an observational trial in 67 PD patients using optimal and non-optimal stimulation settings. Clinically optimal stimulation produces a characteristic fMRI brain response pattern marked by preferential engagement of the motor circuit. Then, we build a machine learning model predicting optimal vs. non-optimal settings using the fMRI patterns of 39 PD patients with a priori clinically optimized DBS (88% accuracy). The model predicts optimal stimulation settings in unseen datasets: a priori clinically optimized and stimulation-naïve PD patients. We propose that fMRI brain responses to DBS stimulation in PD patients could represent an objective biomarker of clinical response. Upon further validation with additional studies, these findings may open the door to functional imaging-assisted DBS programming.

Date: 2021
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DOI: 10.1038/s41467-021-23311-9

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