Subthalamic beta targeted neurofeedback speeds up movement initiation but increases tremor in Parkinsonian patients
- University of Oxford, United Kingdom
- St George's University of London, United Kingdom
- Nuffield Dep of Clinical Neurosciences and MRC BNDU, United Kingdom
- King's College Hospital NHS Foundation Trust, United Kingdom
- South China University of Technology, China
- Kings College London, United Kingdom
Abstract
Previous studies have explored neurofeedback training for Parkinsonian patients to suppress beta oscillations in the subthalamic nucleus (STN). However, its impacts on movements and Parkinsonian tremor are unclear. We developed a neurofeedback paradigm targeting STN beta bursts and investigated whether neurofeedback training could improve motor initiation in Parkinson's disease compared to passive observation. Our task additionally allowed us to test which endogenous changes in oscillatory STN activities are associated with trial-to-trial motor performance. Neurofeedback training reduced beta synchrony and increased gamma activity within the STN, and reduced beta band coupling between the STN and motor cortex. These changes were accompanied by reduced reaction times in subsequently cued movements. However, in Parkinsonian patients with pre-existing symptoms of tremor, successful volitional beta suppression was associated with an amplification of tremor which correlated with theta band activity in STN LFPs, suggesting an additional cross-frequency interaction between STN beta and theta activities.
Data availability
Source data and codes for generating Figures 2-7, all supplement figures, and Table II have been provided.
Article and author information
Author details
Funding
Medical Research Council (MR/P012272/1)
- Shenghong He
- Huiling Tan
Medical Research Council (MC_UU_12024/1)
- Flavie Torrecillos
- Gerd Tinkhauser
- Petra Fischer
- Alek Pogosyan
- Peter Brown
National Institute for Health Research (Oxford Biomedical Research Centre)
- Shenghong He
- Abteen Mostofi
- Emilie Syed
- Flavie Torrecillos
- Gerd Tinkhauser
- Petra Fischer
- Alek Pogosyan
- Peter Brown
- Huiling Tan
Rosetrees Trust
- Shenghong He
- Huiling Tan
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Preeya Khanna, University of California, Berkeley, United States
Ethics
Human subjects: Informed consent and consent to publish was obtained from patients before they took part in the study, which was approved by Oxfordshire Research Ethics Committee, reference number 18/SC/0006.
Version history
- Received: July 12, 2020
- Accepted: November 16, 2020
- Accepted Manuscript published: November 18, 2020 (version 1)
- Version of Record published: November 27, 2020 (version 2)
Copyright
© 2020, He et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Subthalamic beta targeted neurofeedback speeds up movement initiation but increases tremor in Parkinsonian patients
eLife 9:e60979.
https://doi.org/10.7554/eLife.60979
Further reading
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Artificial neural networks (ANNs) are a powerful class of computational models for unravelling neural mechanisms of brain function. However, for neural control of movement, they currently must be integrated with software simulating biomechanical effectors, leading to limiting impracticalities: (1) researchers must rely on two different platforms and (2) biomechanical effectors are not generally differentiable, constraining researchers to reinforcement learning algorithms despite the existence and potential biological relevance of faster training methods. To address these limitations, we developed MotorNet, an open-source Python toolbox for creating arbitrarily complex, differentiable, and biomechanically realistic effectors that can be trained on user-defined motor tasks using ANNs. MotorNet is designed to meet several goals: ease of installation, ease of use, a high-level user-friendly application programming interface, and a modular architecture to allow for flexibility in model building. MotorNet requires no dependencies outside Python, making it easy to get started with. For instance, it allows training ANNs on typically used motor control models such as a two joint, six muscle, planar arm within minutes on a typical desktop computer. MotorNet is built on PyTorch and therefore can implement any network architecture that is possible using the PyTorch framework. Consequently, it will immediately benefit from advances in artificial intelligence through PyTorch updates. Finally, it is open source, enabling users to create and share their own improvements, such as new effector and network architectures or custom task designs. MotorNet’s focus on higher-order model and task design will alleviate overhead cost to initiate computational projects for new researchers by providing a standalone, ready-to-go framework, and speed up efforts of established computational teams by enabling a focus on concepts and ideas over implementation.
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The cerebellum contributes to a diverse array of motor conditions, including ataxia, dystonia, and tremor. The neural substrates that encode this diversity are unclear. Here, we tested whether the neural spike activity of cerebellar output neurons is distinct between movement disorders with different impairments, generalizable across movement disorders with similar impairments, and capable of causing distinct movement impairments. Using in vivo awake recordings as input data, we trained a supervised classifier model to differentiate the spike parameters between mouse models for ataxia, dystonia, and tremor. The classifier model correctly assigned mouse phenotypes based on single-neuron signatures. Spike signatures were shared across etiologically distinct but phenotypically similar disease models. Mimicking these pathophysiological spike signatures with optogenetics induced the predicted motor impairments in otherwise healthy mice. These data show that distinct spike signatures promote the behavioral presentation of cerebellar diseases.
