Characteristics of Waveform Shape in Parkinson's Disease Detected with Scalp Electroencephalography.
Nicko Jackson,Scott R. Cole,Bradley Voytek,Nicole C. Swann +3 more
- Vol. 6, Iss: 3
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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.read more
Citations
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Cycle-by-cycle analysis of neural oscillations
Scott R. Cole,Bradley Voytek +1 more
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.
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Linear predictive coding distinguishes spectral EEG features of Parkinson's disease.
Fahim Anjum,Soura Dasgupta,Raghuraman Mudumbai,Arun Singh,James F. Cavanagh,Nandakumar S. Narayanan +5 more
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.
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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.
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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.
Ruxue Gong,Ruxue Gong,Mirko Wegscheider,Christoph Mühlberg,Richard Gast,Christopher Fricke,Jost-Julian Rumpf,Vadim V. Nikulin,Thomas R. Knösche,Joseph Classen +9 more
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.
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