Pulsed Facilitation of Corticospinal Excitability by the Sensorimotor μ-Alpha Rhythm

The Brain Electrophysiological recording & STimulation (BEST) toolbox

Non-invasive brain stimulation (NIBS) experiments involve many recurring procedures that are not sufficiently standardized in the community. Given the diversity in experimental design and experience of the investigators, automated but yet flexible data collection and analysis tools are needed to increase objectivity, reliability, and reproducibility of NIBS experiments. The Brain Electrophysiological recording and STimulation (BEST) Toolbox is a MATLAB-based, open-source software with graphical user interface that allows users to design, run, and share freely configurable multi-protocol, multi-session NIBS studies, including transcranial magnetic, electric, and ultrasound stimulation (TMS, tES, TUS). Interfacing with a variety of recording and stimulation devices, the BEST toolbox analyzes EMG and EEG data, and configures stimulation parameters on-the-fly to facilitate closed-loop protocols and real-time applications. Its functionality is continuously expanded and includes e.g., TMS motor hotspot search, threshold estimation, motor evoked potential (MEP) and TMS-evoked EEG potential (TEP) measurements, dose-response curves, paired-pulse and dual-coil TMS, rTMS interventions.

Completing the puzzle: Why studies in non-human primates are needed to better understand the effects of non-invasive brain stimulation

Brain stimulation is a core method in neuroscience. Numerous non-invasive brain stimulation (NIBS) techniques are currently in use in basic and clinical research, and recent advances promise the ability to non-invasively access deep brain structures. While encouraging, there is a surprising gap in our understanding of precisely how NIBS perturbs neural activity throughout an interconnected network, and how such perturbed neural activity ultimately links to behaviour. In this review, we will consider why non-human primate (NHP) models of NIBS are ideally situated to address this gap in knowledge, and why the oculomotor network that moves our line of sight offers a particularly valuable platform in which to empirically test hypothesis regarding NIBS-induced changes in brain and behaviour. NHP models of NIBS will enable investigation of the complex, dynamic effects of brain stimulation across multiple hierarchically interconnected brain areas, networks, and effectors. By establishing such links between brain and behavioural output, work in NHPs can help optimize experimental and therapeutic approaches, improve NIBS efficacy, and reduce side-effects of NIBS.

Efficient high-resolution TMS mapping of the human motor cortex by nonlinear regression

Transcranial magnetic stimulation (TMS) is a powerful tool to investigate causal structure-function relationships in the human brain. However, a precise delineation of the effectively stimulated neuronal populations is notoriously impeded by the widespread and complex distribution of the induced electric field. Here, we propose a method that allows rapid and feasible cortical localization at the individual subject level. The functional relationship between electric field and behavioral effect is quantified by combining experimental data with numerically modeled fields to identify the cortical origin of the modulated effect. Motor evoked potentials (MEPs) from three finger muscles were recorded for a set of random stimulations around the primary motor area. All induced electric fields were nonlinearly regressed against the elicited MEPs to identify their cortical origin. We could distinguish cortical muscle representation with high spatial resolution and localized them primarily on the crowns and rims of the precentral gyrus. A post-hoc analysis revealed exponential convergence of the method with the number of stimulations, yielding a minimum of about 180 random stimulations to obtain stable results. Establishing a functional link between the modulated effect and the underlying mode of action, the induced electric field, is a fundamental step to fully exploit the potential of TMS. In contrast to previous approaches, the presented protocol is particularly easy to implement, fast to apply, and very robust due to the random coil positioning and therefore is suitable for practical and clinical applications.

Phase and power modulations on the amplitude of TMS-induced motor evoked potentials

The evaluation of transcranial magnetic stimulation (TMS)-induced motor evoked potentials (MEPs) promises valuable information about fundamental brain related mechanisms and may serve as a diagnostic tool for clinical monitoring of therapeutic progress or surgery procedures. However, reports about spontaneous fluctuations of MEP amplitudes causing high intra-individual variability have led to increased concerns about the reliability of this measure. One possible cause for high variability of MEPs could be neuronal oscillatory activity, which reflects fluctuations of membrane potentials that systematically increase and decrease the excitability of neuronal networks. Here, we investigate the dependence of MEP amplitude on oscillation power and phase by combining the application of single pulse TMS over the primary motor cortex with concurrent recordings of electromyography and electroencephalography. Our results show that MEP amplitude is correlated to alpha phase, alpha power as well as beta phase. These findings may help explain corticospinal excitability fluctuations by highlighting the modulatory effect of alpha and beta phase on MEPs. In the future, controlling for such a causal relationship may allow for the development of new protocols, improve this method as a (diagnostic) tool and increase the specificity and efficacy of general TMS applications.

The Corticospinal Excitability Can Be Predicted by Spontaneous Electroencephalography Oscillations

Transcranial magnetic stimulation (TMS) has a wide range of clinical applications, and there is growing interest in neural oscillations and corticospinal excitability determined by TMS. Previous studies have shown that corticospinal excitability is influenced by fluctuations of brain oscillations in the sensorimotor region, but it is unclear whether brain network activity modulates corticospinal excitability. Here, we addressed this question by recording electroencephalography (EEG) and TMS measurements in 32 healthy individuals. The resting motor threshold (RMT) and active motor threshold (AMT) were determined as markers of corticospinal excitability. The least absolute shrinkage and selection operator (LASSO) was used to identify significant EEG metrics and then correlation analysis was performed. The analysis revealed that alpha2 power in the sensorimotor region was inversely correlated with RMT and AMT. Innovatively, graph theory was used to construct a brain network, and the relationship between the brain network and corticospinal excitability was explored. It was found that the global efficiency in the theta band was positively correlated with RMT. Additionally, the global efficiency in the alpha2 band was negatively correlated with RMT and AMT. These findings indicated that corticospinal excitability can be modulated by the power spectrum in sensorimotor regions and the global efficiency of functional networks. EEG network analysis can provide a useful supplement for studying the association between EEG oscillations and corticospinal excitability.

The Effect of Sound and Stimulus Expectation on Transcranial Magnetic Stimulation-Elicited Motor Evoked Potentials

The amplitude of motor-evoked potentials (MEPs) elicited by transcranial magnetic stimulation (TMS) over the motor cortex is influenced by multiple factors. TMS delivery is accompanied by an abrupt clicking noise which can induce a startle response. This study investigated how masking/attenuating the sound produced by the TMS system discharging influences MEP amplitudes. In addition, the effects of increasing the time between consecutive stimuli and of making participants aware of the time at which they would be stimulated were studied. MEPs were recorded from the Flexor Carpi Radialis (FCR) muscle at rest by stimulation at motor threshold (MT), 120% MT and 140% MT intensity. Participants (N = 23) received stimulation under normal (NORMAL) conditions and while: wearing sound-attenuating earmuffs (EAR); listening to white noise (NOISE); the interval between stimuli were prolonged (LONG); stimulation timing was presented on a screen (READY). The results showed that masking (p = 0.020) and attenuating (p = 0.004) the incoming sound significantly reduced the amplitude of MEPs recorded across the intensities of stimulation. Increasing the interval between pulses had no effect on the recorded traces if a jitter was introduced (p = 1), but making participants aware of stimulation timing decreased MEP amplitudes (p = 0.049). These findings suggest that the sound produced by TMS at discharging increases MEP amplitudes and that MEP amplitudes are influenced by stimulus expectation. These confounding factors need to be considered when using TMS to assess corticospinal excitability.

Phase-dependent offline enhancement of human motor memory

BACKGROUND

Skill learning engages offline activity in the primary motor cortex (M1). Sensorimotor cortical activity oscillates between excitatory trough and inhibitory peak phases of the mu (8-12 Hz) rhythm. We recently showed that these mu phases influence the magnitude and direction of neuroplasticity induction within M1. However, the contribution of M1 activity during mu peak and trough phases to human skill learning has not been investigated.

OBJECTIVE

To evaluate the effects of phase-dependent TMS during mu peak and trough phases on offline learning of a newly-acquired motor skill.

METHODS

On Day 1, three groups of healthy adults practiced an explicit motor sequence learning task with their non-dominant left hand. After practice, phase-dependent TMS was applied to the right M1 during either mu peak or mu trough phases. The third group received sham TMS during random mu phases. On Day 2, all subjects were re-tested on the same task to evaluate offline learning.

RESULTS

Subjects who received phase-dependent TMS during mu trough phases showed increased offline skill learning compared to those who received phase-dependent TMS during mu peak phases or sham TMS during random mu phases. Additionally, phase-dependent TMS during mu trough phases elicited stronger whole-brain broadband oscillatory power responses than phase-dependent TMS during mu peak phases.

CONCLUSIONS

We conclude that sensorimotor mu trough phases reflect brief windows of opportunity during which TMS can strengthen newly-acquired skill memories.

Towards a mechanistic approach for the development of non-invasive brain-computer interfaces for motor rehabilitation

Brain-computer interfaces (BCIs) designed for motor rehabilitation use brain signals associated with motor-processing states to guide neuroplastic changes in a state-dependent manner. These technologies are uniquely positioned to induce targeted and functionally relevant plastic changes in the human motor nervous system. However, while several studies have shown that BCI-based neuromodulation interventions may improve motor function in patients with lesions in the central nervous system, the neurophysiological structures and processes targeted with the BCI interventions have not been identified. In this review, we first summarize current knowledge of the changes in the central nervous system associated with learning new motor skills. Then, we propose a classification of current BCI paradigms for plasticity induction and motor rehabilitation based on the expected neural plastic changes promoted. This classification proposes four paradigms based on two criteria: the plasticity induction methods and the brain states targeted. The existing evidence regarding the brain circuits and processes targeted with these different BCIs is discussed in detail. The proposed classification aims to serve as a starting point for future studies trying to elucidate the underlying plastic changes following BCI interventions.

Does pericentral mu-rhythm "power" corticomotor excitability? - A matter of EEG perspective

BACKGROUND

Electroencephalography (EEG) and single-pulse transcranial magnetic stimulation (spTMS) of the primary motor hand area (M1-HAND) have been combined to explore whether the instantaneous expression of pericentral mu-rhythm drives fluctuations in corticomotor excitability, but this line of research has yielded diverging results.

OBJECTIVES

To re-assess the relationship between the mu-rhythm power expressed in left pericentral cortex and the amplitude of motor potentials (MEP) evoked with spTMS in left M1-HAND.

METHODS

15 non-preselected healthy young participants received spTMS to the motor hot spot of left M1-HAND. Regional expression of mu-rhythm was estimated online based on a radial source at motor hotspot and informed the timing of spTMS which was applied either during epochs belonging to the highest or lowest quartile of regionally expressed mu-power. Using MEP amplitude as dependent variable, we computed a linear mixed-effects model, which included mu-power and mu-phase at the time of stimulation and the inter-stimulus interval (ISI) as fixed effects and subject as a random effect. Mu-phase was estimated by post-hoc sorting of trials into four discrete phase bins. We performed a follow-up analysis on the same EEG-triggered MEP data set in which we isolated mu-power at the sensor level using a Laplacian montage centered on the electrode above the M1-HAND.

RESULTS

Pericentral mu-power traced as radial source at motor hot spot did not significantly modulate the MEP, but mu-power determined by the surface Laplacian did, showing a positive relation between mu-power and MEP amplitude. In neither case, there was an effect of mu-phase on MEP amplitude.

CONCLUSION

The relationship between cortical oscillatory activity and cortical excitability is complex and minor differences in the methodological choices may critically affect sensitivity.

Examining motor evoked potential amplitude and short-interval intracortical inhibition on the up-going and down-going phases of a transcranial alternating current stimulation (tacs) imposed alpha oscillation

Many brain regions exhibit rhythmical activity thought to reflect the summed behaviour of large populations of neurons. The endogenous alpha rhythm has been associated with phase-dependent modulation of corticospinal excitability. However, whether exogenous alpha rhythm, induced using transcranial alternating current stimulation (tACS) also has a phase-dependent effect on corticospinal excitability remains unknown. Here, we triggered transcranial magnetic stimuli (TMS) on the up- or down-going phase of a tACS-imposed alpha oscillation and measured motor evoked potential (MEP) amplitude and short-interval intracortical inhibition (SICI). There was no significant difference in MEP amplitude or SICI when TMS was triggered on the up- or down-going phase of the tACS-imposed alpha oscillation. The current study provides no evidence of differences in corticospinal excitability or GABAergic inhibition when targeting the up-going (peak) and down-going (trough) phase of the tACS-imposed oscillation.

Closed-Loop Phase-Dependent Vibration Stimulation Improves Motor Imagery-Based Brain-Computer Interface Performance

The motor imagery (MI) paradigm has been wildly used in brain-computer interface (BCI), but the difficulties in performing imagery tasks limit its application. Mechanical vibration stimulus has been increasingly used to enhance the MI performance, but its improvement consistence is still under debate. To develop more effective vibration stimulus methods for consistently enhancing MI, this study proposes an EEG phase-dependent closed-loop mechanical vibration stimulation method. The subject's index finger of the non-dominant hand was given 4 different vibration stimulation conditions (i.e., continuous open-loop vibration stimulus, two different phase-dependent closed-loop vibration stimuli and no stimulus) when performing two tasks of imagining movement and rest of the index finger from his/her dominant hand. We compared MI performance and brain oscillatory patterns under different conditions to verify the effectiveness of this method. The subjects performed 80 trials of each type in a random order, and the average phase-lock value of closed-loop stimulus conditions was 0.71. It was found that the closed-loop vibration stimulus applied in the falling phase helped the subjects to produce stronger event-related desynchronization (ERD) and sustain longer. Moreover, the classification accuracy was improved by about 9% compared with MI without any vibration stimulation (p = 0.012, paired t-test). This method helps to modulate the mu rhythm and make subjects more concentrated on the imagery and without negative enhancement during rest tasks, ultimately improves MI-based BCI performance. Participants reported that the tactile fatigue under closed-loop stimulation conditions was significantly less than continuous stimulation. This novel method is an improvement to the traditional vibration stimulation enhancement research and helps to make stimulation more precise and efficient.

Discrete sampling in perception via neuronal oscillations-Evidence from rhythmic, non-invasive brain stimulation

A variety of perceptual phenomena suggest that, in contrast to our everyday experience, our perception may be discrete rather than continuous. The possibility of such discrete sampling processes inevitably prompts the question of how such discretization is implemented in the brain. Evidence from neurophysiological measurements suggest that neural oscillations, particularly in the lower frequencies, may provide a mechanism by which such discretization can be implemented. It is hypothesized that cortical excitability is rhythmically enhanced or reduced along the positive and negative half-cycle of such oscillations. In recent years, rhythmic non-invasive brain stimulation approaches such as rhythmic transcranial magnetic stimulation (rTMS) and transcranial alternating current stimulation (tACS) are increasingly used to test this hypothesis. Both methods are thought to entrain endogenous brain oscillations, allowing them to alter their power, frequency, and phase in order to study their roles in perception. After a brief introduction to the core mechanisms of both methods, we will provide an overview of rTMS and tACS studies probing the role of brain oscillations for discretized perception in different domains and will contrast these results with unsuccessful attempts. Further, we will discuss methodological pitfalls and challenges associated with the methods.

Induction of LTD-like corticospinal plasticity by low-frequency rTMS depends on pre-stimulus phase of sensorimotor μ-rhythm

Background

Neural oscillations reflect rapidly changing brain excitability states. We have demonstrated previously with EEG-triggered transcranial magnetic stimulation (TMS) of human motor cortex that the positive vs. negative peak of the sensorimotor μ-oscillation reflect corticospinal low-vs. high-excitability states. In vitro experiments showed that induction of long-term depression (LTD) by low-frequency stimulation depends on the postsynaptic excitability state.

Objective/Hypothesis: We tested the hypothesis that induction of LTD-like corticospinal plasticity in humans by 1 Hz repetitive TMS (rTMS) is enhanced when rTMS is synchronized with the low-excitability state, but decreased or even shifted towards long-term (LTP)-like plasticity when synchronized with the high-excitability state.

Methods

We applied real-time EEG-triggered 1-Hz-rTMS (900 pulses) to the hand area of motor cortex in healthy subjects. In a randomized double-blind three-condition crossover design, pulses were synchronized to either the positive or negative peak of the sensorimotor μ-oscillation, or were applied at random phase (control). The amplitude of motor evoked potentials was recorded as an index of corticospinal excitability before and after 1-Hz-rTMS.

Results

1-Hz-rTMS at random phase resulted in a trend towards LTD-like corticospinal plasticity. RTMS in the positive peak condition (i.e., the low-excitability state) induced significant LTD-like plasticity. RTMS in the negative peak condition (i.e., the high-excitability state) showed a trend towards LTP-like plasticity, which was significantly different from the other two conditions.

Conclusion

The level of corticospinal depolarization reflected by phase of the μ-oscillation determines the degree of corticospinal plasticity induced by low-frequency rTMS, a finding that may guide future personalized therapeutic stimulation.

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Positive vs. negative phase of μ-rhythm are states of low vs. high excitability.

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1-Hz-rTMS coupled to positive but not negative phase results in LTD-like plasticity.

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Phase of μ-rhythm determines effect size of 1-Hz-rTMS induced plasticity.

Alertness fluctuations when performing a task modulate cortical evoked responses to transcranial magnetic stimulation

Transcranial magnetic stimulation (TMS) has been widely used in human cognitive neuroscience to examine the causal role of distinct cortical areas in perceptual, cognitive and motor functions. However, it is widely acknowledged that the effects of focal cortical stimulation can vary substantially between participants and even from trial to trial within individuals. Recent work from resting state functional magnetic resonance imaging (fMRI) studies has suggested that spontaneous fluctuations in alertness over a testing session can modulate the neural dynamics of cortical processing, even when participants remain awake and responsive to the task at hand. Here we investigated the extent to which spontaneous fluctuations in alertness during wake-to-sleep transition can account for the variability in neurophysiological responses to TMS. We combined single-pulse TMS with neural recording via electroencephalography (EEG) to quantify changes in motor and cortical reactivity with fluctuating levels of alertness defined objectively on the basis of ongoing brain activity. We observed rapid, non-linear changes in TMS-evoked responses with decreasing levels of alertness, even while participants remained responsive in the behavioural task. Specifically, we found that the amplitude of motor evoked potentials peaked during periods of EEG flattening, whereas TMS-evoked potentials increased and remained stable during EEG flattening and the subsequent occurrence of theta ripples that indicate the onset of NREM stage 1 sleep. Our findings suggest a rapid and complex reorganization of active neural networks in response to spontaneous fluctuations of alertness over relatively short periods of behavioural testing during wake-to-sleep transition.

Inferring Causality from Noninvasive Brain Stimulation in Cognitive Neuroscience

Noninvasive brain stimulation (NIBS) techniques, such as transcranial magnetic stimulation or transcranial direct and alternating current stimulation, are advocated as measures to enable causal inference in cognitive neuroscience experiments. Transcending the limitations of purely correlative neuroimaging measures and experimental sensory stimulation, they allow to experimentally manipulate brain activity and study its consequences for perception, cognition, and eventually, behavior. Although this is true in principle, particular caution is advised when interpreting brain stimulation experiments in a causal manner. Research hypotheses are often oversimplified, disregarding the underlying (implicitly assumed) complex chain of causation, namely, that the stimulation technique has to generate an electric field in the brain tissue, which then evokes or modulates neuronal activity both locally in the target region and in connected remote sites of the network, which in consequence affects the cognitive function of interest and eventually results in a change of the behavioral measure. Importantly, every link in this causal chain of effects can be confounded by several factors that have to be experimentally eliminated or controlled to attribute the observed results to their assumed cause. This is complicated by the fact that many of the mediating and confounding variables are not directly observable and dose-response relationships are often nonlinear. We will walk the reader through the chain of causation for a generic cognitive neuroscience NIBS study, discuss possible confounds, and advise appropriate control conditions. If crucial assumptions are explicitly tested (where possible) and confounds are experimentally well controlled, NIBS can indeed reveal cause-effect relationships in cognitive neuroscience studies.