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Shindy, A.

Publications and source records attributed to Shindy, A..

2 recordsLinked to original sources

Individual Stability of Pain- and Touch-Related Neuronal Gamma Oscillations

BackgroundThe processing of brief pain and touch stimuli has been associated with an increase of neuronal oscillations in the gamma range (40-90 Hz). However, some studies report divergent gamma effects across single participants. MethodsIn two repeated sessions we recorded gamma responses to pain and touch stimuli using EEG. Individual gamma responses were extracted from EEG channels and from ICA components that contain a strong gamma amplitude. ResultsWe observed gamma responses in the majority of the participants. If present, gamma synchronisation was always bound to a component that contained a laser-evoked response. We found a broad variety of individual cortical processing: some participants showed a clear gamma effect, others did not exhibit any gamma. For both modalities, the effect was reproducible between sessions. In addition, participants with a strong gamma response showed a similar time-frequency pattern across sessions. ConclusionsOur results indicate that current measures of reproducibility of research results do not reflect the complex reality of the diverse individual processing pattern of applied pain and touch. The present findings raise the question of whether we would find similar quantitatively different processing patterns in other domains in neuroscience: group results would be replicable but the overall effect is driven by a subgroup of the participants.

neuroscience↗

A Novel Tool for the Removal of Muscle Artefacts from EEG: Improving Data Quality in the Gamma Frequency Range

BackgroundThe past two decades have seen a particular focus towards high-frequency neural activity in the gamma band (>30Hz). However, gamma band activity shares frequency range with unwanted artefacts from muscular activity. New MethodWe developed a novel approach to remove muscle artefacts from neurophysiological data. We re-analysed existing EEG data that were decomposed by a blind source separation method (independent component analysis, ICA), which helped to better spatially and temporally separate single muscle spikes. We then applied an adapting algorithm that detects these singled-out muscle spikes. ResultsWe obtained data almost free from muscle artefacts; we needed to remove significantly fewer artefact components from the ICA and we included more trials for the statistical analysis compared to standard ICA artefact removal. All pain-related cortical effects in the gamma band have been preserved, which underlines the high efficacy and precision of this algorithm. ConclusionsOur results show a significant improvement of data quality by preserving task-relevant gamma oscillations of cortical origin. We were able to precisely detect, gauge, and carve out single muscle spikes from the time course of neurophysiological measures. We advocate the application of the tool for studies investigating gamma activity that contain a rather low number of trials, as well as for data that are highly contaminated with muscle artefacts. This validation of our tool allows for the application on event-free continuous EEG, for which the artefact removal is more challenging.

neuroscience↗