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

Author

Listed:
  • Alexandre Boutet

    (University of Toronto
    University Health Network and 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
    Krembil Brain Institute
    Center for Advancing Neurotechnological Innovation to Application (CRANIA))

  • Andres M. Lozano

    (University Health Network and University of Toronto
    Krembil Brain Institute
    Center for Advancing Neurotechnological Innovation to Application (CRANIA))

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.

Suggested Citation

  • 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, 2021. "Predicting optimal deep brain stimulation parameters for Parkinson’s disease using functional MRI and machine learning," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-23311-9
    DOI: 10.1038/s41467-021-23311-9
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    Cited by:

    1. Wei Jiang & Qing Li & Ruofei Zhang & Jianru Li & Qianyu Lin & Jingyun Li & Xinyao Zhou & Xiyun Yan & Kelong Fan, 2023. "Chiral metal-organic frameworks incorporating nanozymes as neuroinflammation inhibitors for managing Parkinson’s disease," Nature Communications, Nature, vol. 14(1), pages 1-18, December.

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