Turning markers into targets – scoping neural circuits for motor neurofeedback training in Parkinson’s disease

Purpose Motor symptoms of patients suffering from Parkinson’s disease (PD) are currently mainly treated with dopaminergic pharmacology, and where indicated, with deep brain stimulation. In the last decades, a substantial body of literature has described neurophysiological correlates related to both...

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Main Author: David M. A. Mehler
Format: Article
Language:English
Published: Taylor & Francis Group 2022-12-01
Series:Brain-Apparatus Communication
Subjects:
Online Access:http://dx.doi.org/10.1080/27706710.2022.2061300
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author David M. A. Mehler
author_facet David M. A. Mehler
author_sort David M. A. Mehler
collection DOAJ
description Purpose Motor symptoms of patients suffering from Parkinson’s disease (PD) are currently mainly treated with dopaminergic pharmacology, and where indicated, with deep brain stimulation. In the last decades, a substantial body of literature has described neurophysiological correlates related to both motor symptoms and treatment effects. These mechanistic insights allow, at least theoretically, for precise targeting of neural processes responsible for PD motor symptoms. Materials and methods Literature search was conducted to identify electrophysiological and hemodynamic signals that may serve as neural targets for future neurofeedback training protocols. Results In particular alpha, beta and gamma oscillations over the motor cortex show high potential as neural targets for electrophysiological neurofeedback training. Hemodynamic functional magnetic resonance imaging (fMRI) with higher spatial resolution provides additional insights about network activity between cortical and subcortical brain regions in response to established treatments. fMRI based neurofeedback training (NFT) further allows targeting involved networks. Hemodynamic functional near infrared spectroscopy (fNIRS) may be a suitable transfer technology for more and cost-efficient hemodynamic NFT. Conclusions This scoping review presents summarises neural markers that may be promising for NFT interventions that are informed by validated neural circuit models. Recommendations for best practice in study design and reporting are provided, highlighting the importance of adequate control conditions and statistical power.
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spelling doaj.art-ba1d3f6b60bc453fa9df61dd6bf342962023-09-14T13:24:41ZengTaylor & Francis GroupBrain-Apparatus Communication2770-67102022-12-011112710.1080/27706710.2022.20613002061300Turning markers into targets – scoping neural circuits for motor neurofeedback training in Parkinson’s diseaseDavid M. A. Mehler0Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen UniversityPurpose Motor symptoms of patients suffering from Parkinson’s disease (PD) are currently mainly treated with dopaminergic pharmacology, and where indicated, with deep brain stimulation. In the last decades, a substantial body of literature has described neurophysiological correlates related to both motor symptoms and treatment effects. These mechanistic insights allow, at least theoretically, for precise targeting of neural processes responsible for PD motor symptoms. Materials and methods Literature search was conducted to identify electrophysiological and hemodynamic signals that may serve as neural targets for future neurofeedback training protocols. Results In particular alpha, beta and gamma oscillations over the motor cortex show high potential as neural targets for electrophysiological neurofeedback training. Hemodynamic functional magnetic resonance imaging (fMRI) with higher spatial resolution provides additional insights about network activity between cortical and subcortical brain regions in response to established treatments. fMRI based neurofeedback training (NFT) further allows targeting involved networks. Hemodynamic functional near infrared spectroscopy (fNIRS) may be a suitable transfer technology for more and cost-efficient hemodynamic NFT. Conclusions This scoping review presents summarises neural markers that may be promising for NFT interventions that are informed by validated neural circuit models. Recommendations for best practice in study design and reporting are provided, highlighting the importance of adequate control conditions and statistical power.http://dx.doi.org/10.1080/27706710.2022.2061300parkinson’s diseaseneurofeedback trainingdeep brain stimulationelectroencephalography
spellingShingle David M. A. Mehler
Turning markers into targets – scoping neural circuits for motor neurofeedback training in Parkinson’s disease
Brain-Apparatus Communication
parkinson’s disease
neurofeedback training
deep brain stimulation
electroencephalography
title Turning markers into targets – scoping neural circuits for motor neurofeedback training in Parkinson’s disease
title_full Turning markers into targets – scoping neural circuits for motor neurofeedback training in Parkinson’s disease
title_fullStr Turning markers into targets – scoping neural circuits for motor neurofeedback training in Parkinson’s disease
title_full_unstemmed Turning markers into targets – scoping neural circuits for motor neurofeedback training in Parkinson’s disease
title_short Turning markers into targets – scoping neural circuits for motor neurofeedback training in Parkinson’s disease
title_sort turning markers into targets scoping neural circuits for motor neurofeedback training in parkinson s disease
topic parkinson’s disease
neurofeedback training
deep brain stimulation
electroencephalography
url http://dx.doi.org/10.1080/27706710.2022.2061300
work_keys_str_mv AT davidmamehler turningmarkersintotargetsscopingneuralcircuitsformotorneurofeedbacktraininginparkinsonsdisease