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Multiple linear regression to estimate time-frequency electrophysiological responses in single trials.

Hu L, Zhang ZG, Mouraux A, Iannetti GD - Neuroimage (2015)

Bottom Line: Transient sensory, motor or cognitive event elicit not only phase-locked event-related potentials (ERPs) in the ongoing electroencephalogram (EEG), but also induce non-phase-locked modulations of ongoing EEG oscillations.ERD and ERS reflect changes in the parameters that control oscillations in neuronal networks and, depending on the frequency at which they occur, represent neuronal mechanisms involved in cortical activation, inhibition and binding.This permits within-subject statistical comparisons, correlation with pre-stimulus features, and integration of simultaneously-recorded EEG and fMRI.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Cognition and Personality (Ministry of Education) and Faculty of Psychology, Southwest University, Chongqing, China; Department of Neuroscience, Physiology and Pharmacology, University College London, UK. Electronic address: huli@swu.edu.cn.

No MeSH data available.


Related in: MedlinePlus

Correlation between different single-trial estimates, as well as correlation between single-trial estimates and the corresponding single-trial subjective pain intensity.Top panel: All possible correlations between different single-trial estimates, measured using TF-MLR (left) and TF-MLRd (right), as well as between these single-trial estimates and the corresponding subjective pain intensity. Red and blue dots represent positive and negative correlations. When a correlation was significant, the corresponding box was marked in yellow. Note that the correlations were markedly similar between TF-MLR and TF-MLRd.Bottom panel: Representative correlations between different single-trial estimates, measured using TF-MLRd. The single-trial ERP latencies showed a significant negative correlation with the corresponding ERP frequencies (mean R = − 0.31 ± 0.07, p < 0.0001; left). The single-trial ERD latencies showed a significant negative correlation with the corresponding ERD frequencies (mean R = − 0.36 ± 0.09, p < 0.0001; middle). The single-trial ERP magnitudes showed a significant positive correlation with the corresponding pain perception intensity (mean R = 0.51 ± 0.15, p < 0.0001; right).
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f0035: Correlation between different single-trial estimates, as well as correlation between single-trial estimates and the corresponding single-trial subjective pain intensity.Top panel: All possible correlations between different single-trial estimates, measured using TF-MLR (left) and TF-MLRd (right), as well as between these single-trial estimates and the corresponding subjective pain intensity. Red and blue dots represent positive and negative correlations. When a correlation was significant, the corresponding box was marked in yellow. Note that the correlations were markedly similar between TF-MLR and TF-MLRd.Bottom panel: Representative correlations between different single-trial estimates, measured using TF-MLRd. The single-trial ERP latencies showed a significant negative correlation with the corresponding ERP frequencies (mean R = − 0.31 ± 0.07, p < 0.0001; left). The single-trial ERD latencies showed a significant negative correlation with the corresponding ERD frequencies (mean R = − 0.36 ± 0.09, p < 0.0001; middle). The single-trial ERP magnitudes showed a significant positive correlation with the corresponding pain perception intensity (mean R = 0.51 ± 0.15, p < 0.0001; right).

Mentions: Correlation between different single-trial estimates, as well as correlation between single-trial estimates and corresponding single-trial subjective pain intensity.Fig. 7 shows all possible correlations between single-trial parameters estimated using TF-MLR and TF-MLRd. Overall, correlations were markedly similar in the data estimated using TF-MLR and TF-MLRd. We observed significant negative correlations between single-trial ERP latencies and the corresponding ERP frequencies (mean R = − 0.31 ± 0.07, p < 0.0001, obtained from TF-MLRd, hereinafter), and between the single-trial ERD latencies and the corresponding ERD frequencies (mean R = − 0.36 ± 0.09, p < 0.0001). In addition, we observed significant positive correlations between the single-trial ERP and ERS magnitudes and the corresponding subjective pain intensity (ERP: mean R = 0.51 ± 0.15, p < 0.0001; ERS: mean R = 0.15 ± 0.19, p = 0.03).


Multiple linear regression to estimate time-frequency electrophysiological responses in single trials.

Hu L, Zhang ZG, Mouraux A, Iannetti GD - Neuroimage (2015)

Correlation between different single-trial estimates, as well as correlation between single-trial estimates and the corresponding single-trial subjective pain intensity.Top panel: All possible correlations between different single-trial estimates, measured using TF-MLR (left) and TF-MLRd (right), as well as between these single-trial estimates and the corresponding subjective pain intensity. Red and blue dots represent positive and negative correlations. When a correlation was significant, the corresponding box was marked in yellow. Note that the correlations were markedly similar between TF-MLR and TF-MLRd.Bottom panel: Representative correlations between different single-trial estimates, measured using TF-MLRd. The single-trial ERP latencies showed a significant negative correlation with the corresponding ERP frequencies (mean R = − 0.31 ± 0.07, p < 0.0001; left). The single-trial ERD latencies showed a significant negative correlation with the corresponding ERD frequencies (mean R = − 0.36 ± 0.09, p < 0.0001; middle). The single-trial ERP magnitudes showed a significant positive correlation with the corresponding pain perception intensity (mean R = 0.51 ± 0.15, p < 0.0001; right).
© Copyright Policy - CC BY
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4401443&req=5

f0035: Correlation between different single-trial estimates, as well as correlation between single-trial estimates and the corresponding single-trial subjective pain intensity.Top panel: All possible correlations between different single-trial estimates, measured using TF-MLR (left) and TF-MLRd (right), as well as between these single-trial estimates and the corresponding subjective pain intensity. Red and blue dots represent positive and negative correlations. When a correlation was significant, the corresponding box was marked in yellow. Note that the correlations were markedly similar between TF-MLR and TF-MLRd.Bottom panel: Representative correlations between different single-trial estimates, measured using TF-MLRd. The single-trial ERP latencies showed a significant negative correlation with the corresponding ERP frequencies (mean R = − 0.31 ± 0.07, p < 0.0001; left). The single-trial ERD latencies showed a significant negative correlation with the corresponding ERD frequencies (mean R = − 0.36 ± 0.09, p < 0.0001; middle). The single-trial ERP magnitudes showed a significant positive correlation with the corresponding pain perception intensity (mean R = 0.51 ± 0.15, p < 0.0001; right).
Mentions: Correlation between different single-trial estimates, as well as correlation between single-trial estimates and corresponding single-trial subjective pain intensity.Fig. 7 shows all possible correlations between single-trial parameters estimated using TF-MLR and TF-MLRd. Overall, correlations were markedly similar in the data estimated using TF-MLR and TF-MLRd. We observed significant negative correlations between single-trial ERP latencies and the corresponding ERP frequencies (mean R = − 0.31 ± 0.07, p < 0.0001, obtained from TF-MLRd, hereinafter), and between the single-trial ERD latencies and the corresponding ERD frequencies (mean R = − 0.36 ± 0.09, p < 0.0001). In addition, we observed significant positive correlations between the single-trial ERP and ERS magnitudes and the corresponding subjective pain intensity (ERP: mean R = 0.51 ± 0.15, p < 0.0001; ERS: mean R = 0.15 ± 0.19, p = 0.03).

Bottom Line: Transient sensory, motor or cognitive event elicit not only phase-locked event-related potentials (ERPs) in the ongoing electroencephalogram (EEG), but also induce non-phase-locked modulations of ongoing EEG oscillations.ERD and ERS reflect changes in the parameters that control oscillations in neuronal networks and, depending on the frequency at which they occur, represent neuronal mechanisms involved in cortical activation, inhibition and binding.This permits within-subject statistical comparisons, correlation with pre-stimulus features, and integration of simultaneously-recorded EEG and fMRI.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Cognition and Personality (Ministry of Education) and Faculty of Psychology, Southwest University, Chongqing, China; Department of Neuroscience, Physiology and Pharmacology, University College London, UK. Electronic address: huli@swu.edu.cn.

No MeSH data available.


Related in: MedlinePlus