Corticospinal excitability is highest at the early rising phase of sensorimotor µ-rhythm

Zrenner, Christoph; Kozák, Gábor; Schaworonkow, Natalie; Metsomaa, Johanna; Baur, Dirk G.; Vetter, David Michael; Blumberger, Daniel M.; Ziemann, Ulf; Belardinelli, Paolo

doi:10.1016/j.neuroimage.2022.119805

Cited by 21 publications

(18 citation statements)

References 56 publications

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“…This is in line with previous studies showing the importance of PSD and SNR in phase determination [12,56,61], which suggested for instance to measure the phase with multiple filters and looking at the robustness of results [61,62]. Separating periodic and aperiodic components of the power spectra is becoming a new trend in the EEG field [17,35,43,63], and our results confirmed its importance in the context of brain-state-dependent stimulation.…”

Section: Key Role Of Eeg Preprocessing In the Assessment Of Ongoing B...supporting

confidence: 92%

“…The same was seen by [34] and implicitly by [39], which preselected subjects on the basis of power in the alpha band. Focusing on the phase, our results together with the outcomes of figure 3 confirm a literature trend stating that the oscillatory phase of the alpha band in the sensorimotor cortex can explain excitatory states of the corticospinal tract [30,35,39,43]. Trough and rising phases are the most excitable and significantly different from the peak and falling phases; this result can be interpreted following the pulsed-inhibition theory, where the trough phase of alpha is excitatory, and the peak phase is inhibitory [40,64].…”

Section: Mep Variance Explained By Alpha Oscillationssupporting

confidence: 85%

“…Trough and rising phases are the most excitable and significantly different from the peak and falling phases; this result can be interpreted following the pulsed-inhibition theory, where the trough phase of alpha is excitatory, and the peak phase is inhibitory [40,64]. Here, we expand the trough phase to the rising one [35,43]. Importantly, the number of trials per phase label is comparable (i.e.…”

Section: Mep Variance Explained By Alpha Oscillationsmentioning

confidence: 79%

“…The filter was applied after zero-padding the epoch of the same length of the original signal; after filtering, 30% of the end of the signal was cut and the phase at the stimulation point estimated by using as period the double the distance between the last peak and trough found and as starting phase the phase found with the Hilbert function [56]. In addition to the pipeline described above, we have reproduced the methodology of [30,35,39] and [41] to test their results on our dataset and evaluate the importance of EEG processing choices on outcome measures. According to the estimated phase in radians between −π and +π, this was categorized into four groups following a sine wave: trough (−3π/4-−π/4), rising (−π/4-π/4), peak (π/4-3π/4) and falling (>3π/4 or <−3π/4).…”

Section: Eeg Preprocessing and Brain-state Definitionmentioning

confidence: 99%

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MEP and TEP features variability: is it just the brain-state?

Bigoni,

Pagnamenta,

Cadic-Melchior

et al. 2024

J. Neural Eng.

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Objective: The literature investigating the effects of alpha oscillations on corticospinal excitability is divergent. We believe inconsistency in the findings may arise, among others, from the EEG processing for brain-state determination. Here, we provide further insights in the effects of the brain-state on cortical and corticospinal excitability and quantify the impact of different EEG processing. Approach: Corticospinal excitability was measured using motor evoked potential (MEP) peak-to-peak amplitudes elicited with transcranial magnetic stimulation (TMS); cortical responses were studied through TMS-evoked potentials’ (TEPs) features. A TMS-EEG-EMG dataset of 18 young healthy subjects who received 180 single-pulse and 180 paired pulses to determine short-intracortical inhibition was investigated. To study the effect of different EEG processing, we compared the brain-state estimation deriving from three published methods. The influence of presence of neural oscillations was also investigated. To evaluate the effect of the brain-state on MEP and TEP features variability, we defined the brain-state based on specific EEG phase and power combinations, only in trials where neural oscillations were present. The relationship between TEPs and MEPs was further evaluated.Main results: The presence of neural oscillations resulted in more consistent results regardless of the EEG processing approach. Nonetheless, the latter still critically affected the outcomes, making conclusive claims complex. With our approach, the MEP amplitude was positively modulated by the alpha power and phase, with stronger responses during the trough phase and high power. Power and phase also affected TEP features. Importantly, similar effects were observed in both TMS conditions. Significance: These findings support the view that the brain state of alpha oscillations is associated with the variability observed in cortical and corticospinal responses to TMS, with a tight correlation between the two. The results further highlight the importance of closed-loop stimulation approaches while underlining that care is needed in designing experiments and choosing the analytical approaches, which should be based on knowledge from offline studies to control for the heterogeneity originating from different EEG processing strategies.

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Section: Key Role Of Eeg Preprocessing In the Assessment Of Ongoing B...supporting

confidence: 92%

Section: Mep Variance Explained By Alpha Oscillationssupporting

confidence: 85%

Section: Mep Variance Explained By Alpha Oscillationsmentioning

confidence: 79%

Section: Eeg Preprocessing and Brain-state Definitionmentioning

confidence: 99%

See 2 more Smart Citations

MEP and TEP features variability: is it just the brain-state?

Bigoni,

Pagnamenta,

Cadic-Melchior

et al. 2024

J. Neural Eng.

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show abstract

“…Moreover, the relation between stimulation intensity and the level of ongoing neural activity (Ogata et al, 2019), and the interaction between oscillatory phase and power may influence CSE to an extent unexplained by each of these features alone (Hussain et al, 2019). Also, individual post-hoc significance testing for a sinusoidal fit yielded alpha (mu) phase dependency in one third of individuals only (Zrenner et al, 2023). Furthermore, minor differences in the methodological choices may critically affect the sensitivity to detect the complex relationship between oscillatory activity and corticospinal excitability (Karabanov et al, 2021) and even lead to erroneous phase estimations (Khademi et al, 2023).…”

mentioning

confidence: 99%

Phase-specific stimulation reveals consistent sinusoidal modulation of human corticospinal excitability along the oscillatory beta cycle

Keute

Gebuehr

Guggenberger

et al. 2023

Preprint

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The responsiveness of neuronal populations to incoming information fluctuates. Retrospective analyses of randomly applied stimuli reveal a neural input-output relationship along the intrinsic oscillatory cycle. Prospectively harnessing this biological mechanism would necessitate frequency- and phase-specificity, intra- and inter-individual consistency, and instantaneous access to the oscillatory cycle. We used a novel real-time approach to electroencephalography-triggered transcranial magnetic stimulation to precisely target 8 equidistant phases of the oscillatory cycle in the human motor cortex of male and female healthy participants. The phase-dependency of corticospinal excitability was investigated in ten different intrinsic frequencies (4, 8, 12, 16, 20, 24, 28, 32, 36, and 40Hz) and indexed by motor-evoked potentials (MEP) in the corresponding forearm muscle. On both the individual and group level, we detected a consistent sinusoidal MEP modulation along the oscillatory cycle at 24Hz (Chi 22=9.2, p=.01), but not at any other target frequency (all Chi 22<5, all p>.08). Moreover, cross-validations showed also at 24Hz the highest consistency of the optimal phase between prospective (real-time) and retrospective (out-of-sample) testing (r=.605, p<.001), and across experimental sessions on three different days (r greater than or equal to .45). The optimal corticospinal signal transmission was at the transition from the trough to the rising flank of the oscillatory 24Hz cycle. Integrating real-time measurement and brain stimulation revealed that the sinusoidal input-output relationship of corticospinal signal transmission is frequency- and phase specific, and consistent within and across individuals and sessions. In future, this approach allows to selectively and repetitively target windows of increased responsiveness, and to thereby investigate potential cumulative effects on plasticity induction.

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A Novel Real-time Phase Prediction Network in EEG Rhythm

Liu,

Qi,

Wang

et al. 2024

Neurosci. Bull.

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Closed-loop neuromodulation, especially using the phase of the electroencephalography (EEG) rhythm to assess the real-time brain state and optimize the brain stimulation process, is becoming a hot research topic. Because the EEG signal is non-stationary, the commonly used EEG phase-based prediction methods have large variances, which may reduce the accuracy of the phase prediction. In this study, we proposed a machine learning-based EEG phase prediction network, which we call EEG phase prediction network (EPN), to capture the overall rhythm distribution pattern of subjects and map the instantaneous phase directly from the narrow-band EEG data. We verified the performance of EPN on pre-recorded data, simulated EEG data, and a real-time experiment. Compared with widely used state-of-the-art models (optimized multi-layer filter architecture, auto-regress, and educated temporal prediction), EPN achieved the lowest variance and the greatest accuracy. Thus, the EPN model will provide broader applications for EEG phase-based closed-loop neuromodulation.

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Corticospinal excitability is highest at the early rising phase of sensorimotor µ-rhythm

Cited by 21 publications

References 56 publications

MEP and TEP features variability: is it just the brain-state?

MEP and TEP features variability: is it just the brain-state?

Phase-specific stimulation reveals consistent sinusoidal modulation of human corticospinal excitability along the oscillatory beta cycle

A Novel Real-time Phase Prediction Network in EEG Rhythm

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