Neuronal oscillations predict deep brain stimulation outcome in Parkinson's disease

Jan Hirschmann; Alexandra Steina; Jan Vesper; Esther Florin; Alfons Schnitzler

doi:10.1016/j.brs.2022.05.008

Neuronal oscillations predict deep brain stimulation outcome in Parkinson's disease

Brain Stimul. 2022 May-Jun;15(3):792-802. doi: 10.1016/j.brs.2022.05.008. Epub 2022 May 11.

Authors

Jan Hirschmann¹, Alexandra Steina², Jan Vesper³, Esther Florin², Alfons Schnitzler⁴

Affiliations

¹ Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, 40225, Düsseldorf, Germany. Electronic address: Jan.Hirschmann@med.uni-duesseldorf.de.
² Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, 40225, Düsseldorf, Germany.
³ Functional Neurosurgery and Stereotaxy, Department of Neurosurgery, Medical Faculty, Heinrich Heine University, 40225, Düsseldorf, Germany.
⁴ Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, 40225, Düsseldorf, Germany; Center for Movement Disorders and Neuromodulation, Department of Neurology, Medical Faculty, Heinrich Heine University, 40225, Düsseldorf, Germany.

PMID: 35568311
DOI: 10.1016/j.brs.2022.05.008

Abstract

Background: Neuronal oscillations are linked to symptoms of Parkinson's disease. This relation can be exploited for optimizing deep brain stimulation (DBS), e.g. by informing a device or human about the optimal location, time and intensity of stimulation. Whether oscillations predict individual DBS outcome is not clear so far.

Objective: To predict motor symptom improvement from subthalamic power and subthalamo-cortical coherence.

Methods: We applied machine learning techniques to simultaneously recorded magnetoencephalography and local field potential data from 36 patients with Parkinson's disease. Gradient-boosted tree learning was applied in combination with feature importance analysis to generate and understand out-of-sample predictions.

Results: A few features sufficed for making accurate predictions. A model operating on five coherence features, for example, achieved correlations of r > 0.8 between actual and predicted outcomes. Coherence comprised more information in less features than subthalamic power, although in general their information content was comparable. Both signals predicted akinesia/rigidity reduction best. The most important local feature was subthalamic high-beta power (20-35 Hz). The most important connectivity features were subthalamo-parietal coherence in the very high frequency band (>200 Hz) and subthalamo-parietal coherence in low-gamma band (36-60 Hz). Successful prediction was not due to the model inferring distance to target or symptom severity from neuronal oscillations.

Conclusion: This study demonstrates for the first time that neuronal oscillations are predictive of DBS outcome. Coherence between subthalamic and parietal oscillations are particularly informative. These results highlight the clinical relevance of inter-areal synchrony in basal ganglia-cortex loops and might facilitate further improvements of DBS in the future.

Keywords: Deep brain stimulation; Machine learning; Neuronal oscillations; Parkinson's disease; Subthalamic nucleus.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Basal Ganglia
Deep Brain Stimulation* / methods
Humans
Magnetoencephalography
Parkinson Disease* / therapy
Subthalamic Nucleus* / physiology