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Characteristics of Waveform Shape in Parkinson's Disease Detected with Scalp Electroencephalography.

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TLDR
It is shown that non-sinusoidal features of β oscillation shape also distinguish PD patients on and off medication using non-invasive recordings in a dataset of 15 PD patients with resting scalp EEG, and that β oscillations over sensorimotor electrodes most often had a canonical shape.
Abstract
Neural activity in the β frequency range (13-30 Hz) is excessively synchronized in Parkinson's disease (PD). Previous work using invasive intracranial recordings and non-invasive scalp electroencephalography (EEG) has shown that correlations between β phase and broad-band γ (>50 Hz) amplitude [i.e., phase amplitude coupling (PAC)] are elevated in PD, perhaps a reflection of this synchrony. Recently, it has also been shown, in invasive human recordings, that non-sinusoidal features of β oscillation shape also characterize PD. Here, we show that these features of β waveform shape also distinguish PD patients on and off medication using non-invasive recordings in a dataset of 15 PD patients with resting scalp EEG. Specifically, β oscillations over sensorimotor electrodes in PD patients off medication had greater sharpness asymmetry and steepness asymmetry than on medication (sign rank, p < 0.02, corrected). We also showed that β oscillations over sensorimotor cortex most often had a canonical shape, and that using this prototypical shape as an inclusion criteria increased the effect size of our findings. Together, our findings suggest that novel ways of measuring β synchrony that incorporate waveform shape could improve detection of PD pathophysiology in non-invasive recordings. Moreover, they motivate the consideration of waveform shape in future EEG studies.

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Journal ArticleDOI

Cycle-by-cycle analysis of neural oscillations

TL;DR: A new analysis framework is presented that is complementary to existing Fourier- and Hilbert-transform based approaches that quantifies oscillatory features in the time domain, on a cycle-by-cycle basis and is validated in simulation and against experimental recordings of patients with Parkinson's disease.
Journal ArticleDOI

Linear predictive coding distinguishes spectral EEG features of Parkinson's disease.

TL;DR: LEAPD is described, an efficient algorithm that is suitable for real time application and captures spectral EEG features using few parameters and reliably differentiates PD patients from demographically-matched controls.
Journal ArticleDOI

Primary motor cortex in Parkinson’s disease: Functional changes and opportunities for neurostimulation

TL;DR: It is argued that the therapeutic profile of M1 neurostimulation is likely to be greatly enhanced with alternative technologies that permit cell-type specific control and incorporate feedback from electrophysiological biomarkers measured locally.
Journal ArticleDOI

PDCNNet: An Automatic Framework for the Detection of Parkinson’s Disease Using EEG Signals

TL;DR: Parkinson's disease detection using smoothed pseudo-Wigner Ville distribution (SPWVD) coupled with convolutional neural networks (CNN) called Parkinson's disease CNN (PDCNNet) is proposed in this paper.
Journal ArticleDOI

Spatiotemporal features of β-γ phase-amplitude coupling in Parkinson's disease derived from scalp EEG.

TL;DR: Enhanced phase-amplitude coupling of Parkinson's disease patients was enhanced in dorsolateral prefrontal cortex, premotor cortex, primary motor cortex and somatosensory cortex, the difference being statistically significant in the hemisphere contralateral to the clinically more affected side.
References
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TL;DR: The concepts of power analysis are discussed in this paper, where Chi-square Tests for Goodness of Fit and Contingency Tables, t-Test for Means, and Sign Test are used.
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Controlling the false discovery rate: a practical and powerful approach to multiple testing

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TL;DR: EELAB as mentioned in this paper is a toolbox and graphic user interface for processing collections of single-trial and/or averaged EEG data of any number of channels, including EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), Independent Component Analysis (ICA) and time/frequency decomposition including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling.
Journal ArticleDOI

Adaptive deep brain stimulation in advanced Parkinson disease

TL;DR: This work uses a BCI to interpret pathological brain activity in patients with advanced Parkinson disease and to use this feedback to control when therapeutic deep brain stimulation (DBS) is delivered to improve on both the efficacy and efficiency of conventional continuous DBS.
Journal ArticleDOI

Broadband Shifts in Local Field Potential Power Spectra Are Correlated with Single-Neuron Spiking in Humans

TL;DR: It is found that firing rates were positively correlated with broadband (2–150 Hz) shifts in the LFP power spectrum and narrowband oscillations correlated both positively and negatively with firing rates at different recording sites.
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