Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson’s disease

Merk, Timon; Peterson, Victoria; Lipski, Witold; Blankertz, Benjamin; Turner, Robert S.; Li, Ningfei; Horn, Andreas; Kühn, Andrea A.; Richardson, R. Mark; Neumann, Wolf‐Julian

doi:10.1101/2021.04.24.441207

Cited by 11 publications

(9 citation statements)

References 112 publications

(250 reference statements)

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“…It appears to be particularly well suited for electrophysiological datasets [30], which are typically small, structured and noisy. In a recent study by Merk et al [31], XGBoost outperformed linear regression and artificial neural networks in the prediction of grip force based on STN and cortical oscillations.…”

Section: Machine Learning Modelmentioning

confidence: 99%

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

et al. 2022

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Section: Machine Learning Modelmentioning

confidence: 99%

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

et al. 2022

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“…For instance, suppressing beta while minimizing the suppression of gamma during movement might result in improved motor performance, and less stimulation induced adverse events, such as dysarthria, gait disturbances and dyskinesia. Several previous studies have demonstrated the feasibility of detecting movement state based on bioelectrical signals recorded from the cortical-basal ganglia-thalamic circuit in people PD or essential tremor (21,28,(49)(50)(51). However, extracting gamma power in real-time using chronically implanted devices might still be challenging considering stimulation artefacts.…”

Section: Remaining Challenges For the Development Of Adbs Systems For Pdmentioning

confidence: 99%

Beta-triggered adaptive deep brain stimulation during reaching movement in Parkinson’s disease

Baig

Merla

et al. 2022

Preprint

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Subthalamic nucleus (STN) beta-triggered adaptive deep brain stimulation (ADBS) has been shown to provide clinical improvement comparable to conventional continuous DBS (CDBS) in people with Parkinson's disease (PD) with less energy delivered to the brain and less stimulation induced side-effects. However, several questions remain unanswered. First, there is a normal physiological reduction of STN beta band power just prior to and during voluntary movement. ADBS systems will therefore reduce or cease stimulation during movement and could therefore compromise motor performance compared to CDBS. Second, beta power was smoothed and estimated over time periods of 400ms or longer in most previous ADBS studies. A shorter smoothing period could have the advantage of being more sensitive to changes in beta power which could enhance motor performance. In this study, we addressed these two questions by evaluating the effectiveness of STN beta-triggered ADBS using a standard 400ms and a shorter 200ms smoothing window during reaching movements. Results from 13 people with PD showed that STN beta-triggered ADBS is effective in improving motor performance during reaching movements as it better preserves gamma oscillation than CDBS in people with PD, and that shortening the smoothing window does not result in any additional behavioural benefit. ADBS significantly improved tremor compared with no DBS but was not as effective as CDBS. When developing ADBS systems for PD, it might not be necessary to track very fast beta dynamics; combining beta, gamma, and motor decoding might be more beneficial with additional biomarkers needed for optimal treatment of tremor.

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“…Using a comprehensive set of EEG features time-locked to reaching the maximum force, we could predict MV with an accuracy of around 75% in HC and around 81% in PD. These are comparable to the prediction based on features from deep brain stimulation recordings ( 29 ), but the EEG may actually be superior to subthalamic local field potentials for movement decoding in PD ( 30 ). This suggests that, under normal conditions, cortically based EEG signals may provide sufficient information to create an MV biomarker.…”

Section: Discussionmentioning

confidence: 85%

Galvanic Vestibular Stimulation Effects on EEG Biomarkers of Motor Vigor in Parkinson's Disease

et al. 2021

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Background: Impaired motor vigor (MV) is a critical aspect of Parkinson's disease (PD) pathophysiology. While MV is predominantly encoded in the basal ganglia, deriving (cortical) EEG measures of MV may provide valuable targets for modulation via galvanic vestibular stimulation (GVS).Objective: To find EEG features predictive of MV and examine the effects of high-frequency GVS.Methods: Data were collected from 20 healthy control (HC) and 18 PD adults performing 30 trials total of a squeeze bulb task with sham or multi-sine (50–100 Hz “GVS1” or 100–150 Hz “GVS2”) stimuli. For each trial, we determined the time to reach maximum force after a “Go” signal, defined MV as the inverse of this time, and used the EEG data 1-sec prior to this time for prediction. We utilized 53 standard EEG features, including relative spectral power, harmonic parameters, and amplitude and phase of bispectrum corresponding to standard EEG bands from each of 27 EEG channels. We then used LASSO regression to select a sparse set of features to predict MV. The regression weights were examined, and separate band-specific models were developed by including only band-specific features (Delta, Theta, Alpha-low, Alpha-high, Beta, Gamma). The correlation between MV prediction and measured MV was used to assess model performance.Results: Models utilizing broadband EEG features were capable of accurately predicting MV (controls: 75%, PD: 81% of the variance). In controls, all EEG bands performed roughly equally in predicting MV, while in the PD group, the model using only beta band features did not predict MV well compared to other bands. Despite having minimal effects on the EEG feature values themselves, both GVS stimuli had significant effects on MV and profound effects on MV predictability via the EEG. With the GVS1 stimulus, beta-band activity in PD subjects became more closely associated with MV compared to the sham condition. With GVS2 stimulus, MV could no longer be accurately predicted from the EEG.Conclusions: EEG features can be a proxy for MV. However, GVS stimuli have profound effects on the relationship between EEG and MV, possibly via direct vestibulo-basal ganglia connections not measurable by the EEG.

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Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson’s disease

Cited by 11 publications

References 112 publications

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

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

Beta-triggered adaptive deep brain stimulation during reaching movement in Parkinson’s disease

Galvanic Vestibular Stimulation Effects on EEG Biomarkers of Motor Vigor in Parkinson's Disease

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