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

Nature Communications - Tập 12 Số 1
Alexandre Boutet1, Radhika Madhavan2, Gavin J.B. Elias3, Suresh E. Joel4, Robert Gramer3, Manish Ranjan3, Vijayashankar Paramanandam5, David S. Xu3, Jürgen Germann3, Aaron Loh3, Suneil K. Kalia3, Mojgan Hodaie3, Bryan Li1, Sreeram Prasad5, Ailish Coblentz1, Renato P. Munhoz5, Jeffrey Ashe2, Walter Kucharczyk1, Alfonso Fasano5, Andrés M. Lozano6
1Joint Department of Medical Imaging, University of Toronto, Toronto, Canada
2GE Global Research Center, Niskayuna, NY, USA
3Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, ON, Canada
4GE Healthcare, Bangalore, India
5Edmond J. Safra Program in Parkinson’s Disease, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, UHN, Division of Neurology, University of Toronto, Toronto, ON, Canada
6Krembil Brain Institute, Toronto, ON, Canada

Tóm tắt

AbstractCommonly 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.

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