Rhythmic oscillatory brain activity at rest is associated with high versus low neuronal responsiveness, or ‘excitability’ of a region (Jensen and Mazaheri, 2010; Jensen et al., 2011). Measuring these momentary fluctuations of neural activity via electro- or magnetoencephalography (EEG/MEG) over human primary motor cortex (M1), it has been demonstrated that frequency, amplitude and phase of the ongoing oscillation cycle systematically modulate responses evoked by transcranial magnetic stimulation (TMS) (Zarkowski et al., 2006; Sauseng et al., 2009; Schaworonkow et al., 2018; Kelly et al., 2009; Mazaheri et al., 2009; Schubert et al., 2006). In particular, it has been shown that corticomotor excitability is significantly higher when the power (amplitude) of sensorimotor rhythms in the alpha band (8–14 Hz, also called the ‘mu’-rhythm), or beta band (15–30 Hz) are low, or when M1 is stimulated during the trough of the oscillatory cycle of these rhythms (Zaehle et al., 2010). This concept has inspired neurofeedback interventions whereby, for example, stroke patients learn to volitionally desynchronize sensorimotor rhythms with the goal of bringing the sensorimotor system into a more excitable state as a precursor for enhanced neural plasticity and accelerated recovery (Buch et al., 2008; Caria et al., 2011; Soekadar et al., 2015a).

Previous research has focussed on interactions between corticomotor excitability and cortical dynamics at rest, but much less is known about whether it is possible to voluntarily control the excitability of sensorimotor circuits while keeping motor output and sensory feedback constant. In the case of stroke rehabilitation, this mechanism may become particularly relevant as patients are unable to move or receive sensory feedback from the paretic limb. Therefore, interventions that optimally harness the residual ability to voluntarily and endogenously activate relevant brain circuits in the days and weeks after the incident, may provide the crucial innervation necessary to promote re-wiring for functional recovery (Maulden et al., 2005).

It is well known that primates (Fetz, 2013; Engelhard et al., 2013), and humans (Buch et al., 2008; Mellinger et al., 2007; Soekadar et al., 2015b; Sitaram et al., 2017; Thompson et al., 2009) can gain volitional control of neural activity by receiving neurofeedback via a brain-computer interface (BCI). Here, we used a BCI-neurofeedback approach as an effective method for training participants to both volitionally upregulate and downregulate corticomotor excitability as reflected by the size of TMS-evoked motor potentials (MEPs), with the aim to modulate their amplitudes over a much larger dynamic range than observed during rest. Using this approach enabled us to investigate the neural mechanisms that underlie volitional up- versus down-regulation of corticospinal excitability in the motor system and the associated oscillatory signatures. By modulating one neural marker, motor evoked potential amplitude, while measuring independent modalities using EEG, or paired-pulse TMS, this approach allows us to causally relate voluntary rather than incidental changes of corticomotor excitability to cortical dynamics.

To achieve this goal we developed a BCI by stimulating the cortex with TMS and providing neurofeedback of MEP amplitudes (Figure 1). The feedback was designed such that participants were rewarded for larger than average MEPs in one condition, and smaller than average in another condition.

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In a within-subject cross-over design, participants performed four training sessions with TMS-neurofeedback of MEP amplitudes in order to learn how to up- and downregulate their corticomotor excitability (two ‘UP’ sessions, two ‘DOWN’ sessions, order counterbalanced across participants). After the training we characterised the neural underpinnings of these two distinct activity states in detail by conducting a series of multimodal experiments using EEG and paired pulse TMS to profile the associated oscillatory and neurophysiological processes. As it has been proposed that dynamic modulation of neuronal activity is realized via synchronization of high frequency rhythms (Fries, 2005) which are tightly coupled to desynchronizing sensorimotor rhythms (Grosse-Wentrup et al., 2011), we hypothesised that Gamma synchronisation (31–80 Hz) and alpha (8–13 Hz)/beta (14–30 Hz) desynchronization play a critical role in actively determining the state of the motor cortex. Specifically, we hypothesised that following UP neurofeedback training, MEP amplitudes would be increased, and this volitional upregulation would be associated with increased gamma synchronisation and alpha/beta desynchronization. Concurrent reductions in TMS-derived measures of inhibition were also hypothesised during upregulation. The reverse pattern was predicted following DOWN neurofeedback training.

Here we aimed to uncover neural activity evoked by voluntarily facilitating or suppressing excitability within sensorimotor circuits, while keeping motor or muscle contraction-related sensory feedback constant. We show that using a bidirectional TMS-neurofeedback approach is critical to gain volitional control over MEP amplitudes, a skill that is retained for at least 6 months without further training. This voluntary state-setting with a large dynamic range was related to variation in a paired-pulse TMS proxy measure of pre-synaptic GABA_B mediated disinhibition, and to a prominent increase of gamma power in sensorimotor cortex for the UP state which was accompanied by a clear reduction of power in the theta, low and high alpha bands.

Previous studies have shown that it is possible to gain voluntary control over activity in the central nervous system if appropriate neurofeedback is embedded in a reinforcement learning task, with food rewards for animals (Fetz, 2013; Engelhard et al., 2013) and visually rewarding stimuli for humans (Thompson et al., 2009; Majid et al., 2015). Here we confirm that this approach is also suitable for learning how corticomotor excitability can be bidirectionally up- or down-regulated. Our participants were initially familiarized with two motor imagery strategies which are known to modulate corticospinal excitability in the required manner (Majid et al., 2015; Stinear et al., 2006; Izumi et al., 1995; Fadiga et al., 1999). Learning, however, indicated by progressively stronger dissociation between the UP and the DOWN state, only took place when direct low-latency feedback regarding the MEP amplitude was provided. After training, participants were able to modulate corticomotor excitability across a large range so that MEP amplitudes were approximately twice as large during the UP than the DOWN condition. UP training, in particular, resulted in an 83.8% increase of MEP amplitudes from baseline, while downregulation of MEP amplitude was possible (e.g. see Majid et al., 2015) but more difficult (30.6% decrease from baseline). Once acquired, volitional control of corticomotor excitability was retained for at least 6 months and could be performed even without online feedback indicating true, long-term learning (Magill, 2010).

Once participants could control their corticomotor excitability, we uncovered the electrophysiological underpinnings by applying measurements that were independent of the feedback modality (single pulse TMS) and investigated whether there were differences between the UP versus DOWN state. This approach cancelled out the effects that were common to both mental strategies, isolating the mechanisms underlying the MEP modulation. This revealed two key novel electrophysiological findings, involving presynaptic GABA_B disinhibition, and gamma oscillations. Additionally, the pronounced changes of cortical physiology despite the absence of motor or muscle contraction-related sensory input suggests that the increase vs decrease of corticomotor excitability was -at least partly- of cortical origin rather than mediated by spinal cord mechanisms.

The UP state was associated with a significant increase of LCD while other measurements probing inhibitory M1 circuits failed to reveal differential effects for the UP versus DOWN state. LCD is thought to represent a read-out of the presynaptic self-inhibition of GABAergic neurons which is thought to be mediated by extrasynaptic GABA_B auto-receptors (Mott and Lewis, 1991; Sanger et al., 2001). This mechanism is hypothesized to result in a net facilitatory effect as observed during the UP condition in our study. Previously, LCD was found to be elevated during motor imagery (MI), but this increase relative to rest was observed irrespective of whether participants imagined voluntarily activating or relaxing hand muscles (Chong and Stinear, 2017). However, this investigation was conducted in a single session, and did not employ neurofeedback, so MEP modulation by these two imagination conditions could be expected to be substantially smaller than observed in our study, particularly for the voluntary relaxation condition which had a similar excitability state as the rest condition. Thus, it is possible that the clear modulation of LCD observed here only manifested after neurofeedback training, that is when the two excitability states became clearly distinct. It is important to note here that in our protocol, LCD was detected only during the UP state and not at rest. In our search procedure to decide upon the optimum conditioning stimulus (CS) intensities, we prioritized SICI and LICI, finding a CS intensity that elicited as close to 50% inhibition of the test MEP as possible. We tested intensities between 106–114% RMT for LICI (and above or below this if no appropriate inhibition was found), and applied these also to LCD (such that the only difference between the LCD and LICI protocols was the ISI). This may have simply been too low to consistently elicit LCD, which appears to be more robustly evoked at higher intensities (Cash et al., 2010; Cash et al., 2016) or during mild contraction (Caux-Dedeystère et al., 2014), and is not observed in all individuals (Cash et al., 2016). It may also be that LCD is more readily observed using triple pulse procedures in which disinhibition can more directly be measured by the reduction of SICI following a priming stimulus (Sanger et al., 2001; Cash et al., 2016; Ni et al., 2007). The increase in MEP amplitude observed in the TMS-feedback induced UP state, may also be related to the increase in I-wave recruitment that has been reported during LCD (Cash et al., 2011) and suggests that further research into this effect is warranted, with parameters more specifically tailored to investigate LCD.

The potential contribution of I-wave mechanisms to the current results can be speculated upon by considering the literature relating disinhibition to short-interval intracortical facilitation (SICF). SICF is mediated by excitatory networks in the motor cortex, which are believed to be responsible for the production of I-waves at a periodicity of around 1.5 ms. When measured in the presence of inhibition (eg. during the LICI time period), the magnitude of SICF remains unchanged, whereas if measured during the period of disinhibition (LCD), SICF is found to be enhanced (Cash et al., 2011). In the study by Cash et al. (2011), it was concluded that disinhibition, rather than inhibition, modulates the excitatory networks of SICF, and therefore of I-wave generation. They further suggest that the increase in SICF in periods of disinhibition supports the view that this is an opportune time period to achieve optimal modulation of synaptic networks while they are in a state that is less constrained by ongoing inhibitory processes. In the context of the current results, it may accordingly be predicted that SICF (and recruitment of I-waves in general) would be increased in magnitude during the UP condition, but this is a speculation that will require further experimental testing.

We observed significant modulation of the alpha and gamma rhythms close to M1 of the trained hemisphere. Focusing on data from the recording electrode closest to each individual’s hotspot revealed a significant association between low alpha and high gamma power for the UP versus DOWN state. Trial-by-trial modulation of these rhythms correlated significantly with MEP amplitude, and a support vector machine (SVM) classifying the two states based on EEG data ranked the high gamma and high alpha band as the two top features characterizing the distinction. Our observation of reciprocal changes in the alpha and gamma band are in line with previous studies using transcranial as well as intracranial recording methods (Jensen and Mazaheri, 2010). The ‘pulsed inhibition’ theory suggests that repeated bursts of inhibitory alpha activity serve to temporarily silence gamma oscillations (Jensen and Mazaheri, 2010). Thus, these two rhythms are seen to exhibit a reciprocal relationship, whereby when alpha is high, gamma is low. In periods of high alpha, gamma may still burst periodically, but only at the troughs of the oscillation cycle, meaning that the gamma ‘duty cycle’ (window for neural processing) is short, and only brief messages can be sent. By contrast, in periods of low alpha power, the gamma duty cycle is longer, and more extensive neuronal processing and inter-regional communication may occur. Our finding of increased gamma activity is also consistent with previous animal literature, showing that the pharmacological removal of GABA_B-mediated inhibition (by receptor blockage) in rats results in increased gamma oscillations (Leung and Shen, 2007) which have been shown to be largest in M1’s layer V (Johnson et al., 2017).

Gamma has often been considered difficult to detect using scalp electrodes because it is highly localised (Buzsaki, 2006) and may also reflect non-cortical sources when recorded with EEG (Yuval-Greenberg et al., 2008; Whitham et al., 2007). However, it is tempting to speculate that, in our experiment, gamma activity was strongly synchronized as a consequence of the neurofeedback training, where participants learned to substantially facilitate corticomotor excitability while keeping EMG activity constant, such that EMG amplitude differed only minimally between the UP and DOWN conditions. This suggestion is in line with previous neurofeedback studies that provided direct feedback of gamma activity, showing that gamma power could be upregulated to a substantial amount which even exceeded power values observed during movement execution (Engelhard et al., 2013; Miller et al., 2010). By keeping the visual feedback for the two conditions identical, we ensured that differences in eye movements between the UP and DOWN states were minor. As we were particularly interested in gamma oscillations, we additionally performed all EEG recordings in an electromagnetically shielded room, using a gel-based electrode system to maximize signal to noise ratio.

Previous studies have taken a correlational approach to investigating the relationships between brain rhythms and corticomotor excitability. These have shown that low alpha (Sauseng et al., 2009; Zrenner et al., 2018) or beta power (Mäki and Ilmoniemi, 2010) as well as high gamma power (Zarkowski et al., 2006) during natural fluctuations at rest are associated with larger MEP amplitudes. We confirm and extend these results by introducing causality to this relationship for the first time, showing that experimentally driving excitability into two distinct states causes specific patterns of neural dynamics in the volitionally controlled cortical area.

While changes in alpha and gamma were specific to the hemisphere from which feedback was provided, theta showed a bilateral pattern of modulation, being higher in the DOWN than the UP state in motor areas in both hemispheres. While mid-frontal theta activity has been linked to error monitoring (Pezzetta et al., 2018) the role of lateralized theta activity close to the sensorimotor hotspot electrode and its symmetric counterpart is less clear. Slower rhythms exert effects over larger distances, and are thought to be involved in long-range communication (Buzsaki, 2006). A similar pattern of upregulation and downregulation was observed in the homologous muscle in the opposite limb, albeit weaker and not statistically significant. This is likely a reflection of the extensive transcallosal structural connectivity and functional coupling of homologous regions of the cortical motor network (Ruddy et al., 2017a; Ruddy et al., 2017b; Ruddy, 2017). It is tempting to speculate that the bilateral theta activity observed in the current study served to regulate the inhibition/facilitation of functional coupling or ‘spillover’ of activation from motor areas in the target hemisphere to their homologous counterparts.

Surprisingly we did not observe differential modulation of the Beta band, which is the predominant oscillatory frequency in sensorimotor cortical regions (Baker et al., 1997; Murthy and Fetz, 1996). It typically desynchronizes (together with alpha) during motor execution and motor imagery (Ramos-Murguialday and Birbaumer, 2015; Pfurtscheller and Neuper, 1997; Pfurtscheller and Berghold, 1989; Alegre et al., 2004) and has been associated with corticomotor excitability at rest (Zarkowski et al., 2006). As our results represent the direct contrast between the UP and DOWN states, the lack of Beta involvement may firstly be due to the fact that both conditions involved a mental strategy targeted at the sensorimotor system and, secondly, that no temporal structure was imposed so that we could not perform analyses which are, for example, time-locked to the potential onset of these mental strategies. However, our data further confirm that the two ‘inhibitory’ rhythms alpha and beta might serve different functions in selecting and activating the appropriate sensorimotor representations (Brinkman et al., 2014).

Here we present an innovative approach to voluntarily and bidirectionally change the state of the motor cortex, by directly targeting MEP amplitudes in a neurofeedback paradigm. This method provided a unique opportunity to reveal the oscillatory and neurochemical underpinnings of the two distinct trained brain states, using concurrent TMS EEG measurements, and mechanistic follow-up investigations using paired-pulse TMS. The results comparing UP and DOWN states indicate that voluntary upregulation of corticomotor excitability causes increased presynaptic GABA_B-mediated disinhibition, elevated neural oscillations in the gamma frequency range, and reduced alpha and theta rhythms.

This paves the way for new technologies that allow the user to regulate aspects of their own brain function in order to reach desired states that are, for example, associated with enhanced motor performance. In the context of stroke rehabilitation, training volitional modulation of corticomotor excitability may hold promise as a rehabilitative therapy early after stroke, that is when patients are deprived of rehabilitation training because they are unable to execute overt movements with the impaired upper limb. As it is known that LCD is recruited during actual movement (Caux-Dedeystère et al., 2015; Hammond and Vallence, 2007; Opie et al., 2015), the elevated LCD we observed in the UP condition may reflect that the neurofeedback had engaged similar mechanisms to those involved in movement execution, using only voluntary endogenous processes. Furthermore, as pathological hyperexcitability of the non-damaged hemisphere has been hypothesized to limit recovery in some patients (Murase et al., 2004), the TMS-neurofeedback protocol can be individually tailored either to upregulate the damaged hemisphere, down-regulate the intact hemisphere, or a combination of both, depending on the patient’s specific needs.

Data are openly available on the ETH Library Research Collection with the DOI: https://doi.org/10.3929/ethz-b-000300799. This contains processed EEG data for all subjects (Figure 5), EMG data and MEPs from the main experiment (Figure 2) and follow-up experiments (Figures 3 and 4). Scripts used for execution of the TMS Neurofeedback experiment are available on GitHub (https://github.com/ncmlabeth/TMS-neurofeedback; copy archived at https://github.com/elifesciences-publications/TMS-neurofeedback).

1. 1. Ruddy KL
   2. Balsters JH
   3. Mantini D
   4. Liu Q
   5. Kassraian-Fard P
   6. Enz N
   7. Mihelj E
   8. Chander B
   9. Soekadar S
   10. Wenderoth N

   (2018) ETH Library research collection

   MEP neurofeedback.

   https://doi.org/10.3929/ethz-b-000300799

1. 1. Alegre M
   2. de Gurtubay IG
   3. Labarga A
   4. Iriarte J
   5. Malanda A
   6. Artieda J

   (2004) Alpha and beta oscillatory activity during a sequence of two movements

   Clinical Neurophysiology 115:124–130.

   https://doi.org/10.1016/S1388-2457(03)00311-0

   • PubMed
   • Google Scholar
2. 1. Baker SN
   2. Olivier E
   3. Lemon RN

   (1997) Coherent oscillations in monkey motor cortex and hand muscle EMG show task-dependent modulation

   The Journal of Physiology 501:225–241.

   https://doi.org/10.1111/j.1469-7793.1997.225bo.x

   • PubMed
   • Google Scholar
3. 1. Brinkman L
   2. Stolk A
   3. Dijkerman HC
   4. de Lange FP
   5. Toni I

   (2014) Distinct roles for alpha- and beta-band oscillations during mental simulation of goal-directed actions

   Journal of Neuroscience 34:14783–14792.

   https://doi.org/10.1523/JNEUROSCI.2039-14.2014

   • PubMed
   • Google Scholar
4. 1. Buch E
   2. Weber C
   3. Cohen LG
   4. Braun C
   5. Dimyan MA
   6. Ard T
   7. Mellinger J
   8. Caria A
   9. Soekadar S
   10. Fourkas A
   11. Birbaumer N

   (2008) Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke

   Stroke 39:910–917.

   https://doi.org/10.1161/STROKEAHA.107.505313

   • PubMed
   • Google Scholar
5. Book

   1. Buzsaki G

   (2006)

   Rhythms of the Brain

   United States: Oxford University Press.

   • Google Scholar
6. 1. Caria A
   2. Weber C
   3. Brötz D
   4. Ramos A
   5. Ticini LF
   6. Gharabaghi A
   7. Braun C
   8. Birbaumer N

   (2011) Chronic stroke recovery after combined BCI training and physiotherapy: a case report

   Psychophysiology 48:578–582.

   https://doi.org/10.1111/j.1469-8986.2010.01117.x

   • PubMed
   • Google Scholar
7. 1. Carson RG
   2. Nelson BD
   3. Buick AR
   4. Carroll TJ
   5. Kennedy NC
   6. Cann RM

   (2013) Characterizing changes in the excitability of corticospinal projections to proximal muscles of the upper limb

   Brain Stimulation 6:760–768.

   https://doi.org/10.1016/j.brs.2013.01.016

   • PubMed
   • Google Scholar
8. Book

   1. Carson RG
   2. Ruddy KL
   3. McNickle E

   (2016)

   Progress in Motor Control; Theories and Translations

   Laczko J, Latash M. L, editors. Springer Science and Business Media.

   • Google Scholar
9. 1. Cash RF
   2. Ziemann U
   3. Murray K
   4. Thickbroom GW

   (2010) Late cortical disinhibition in human motor cortex: a triple-pulse transcranial magnetic stimulation study

   Journal of Neurophysiology 103:511–518.

   https://doi.org/10.1152/jn.00782.2009

   • PubMed
   • Google Scholar
10. 1. Cash RF
    2. Ziemann U
    3. Thickbroom GW

    (2011) Inhibitory and disinhibitory effects on I-wave facilitation in motor cortex

    Journal of Neurophysiology 105:100–106.

    https://doi.org/10.1152/jn.00650.2010

    • PubMed
    • Google Scholar
11. 1. Cash RF
    2. Murakami T
    3. Chen R
    4. Thickbroom GW
    5. Ziemann U

    (2016) Augmenting Plasticity Induction in Human Motor Cortex by Disinhibition Stimulation

    Cerebral Cortex 26:58–69.

    https://doi.org/10.1093/cercor/bhu176

    • PubMed
    • Google Scholar
12. 1. Caux-Dedeystère A
    2. Rambour M
    3. Duhamel A
    4. Cassim F
    5. Derambure P
    6. Devanne H

    (2014) Task-dependent changes in late inhibitory and disinhibitory actions within the primary motor cortex in humans

    European Journal of Neuroscience 39:1485–1490.

    https://doi.org/10.1111/ejn.12505

    • PubMed
    • Google Scholar
13. 1. Caux-Dedeystère A
    2. Derambure P
    3. Devanne H

    (2015) Late cortical disinhibition in relaxed versus active hand muscles

    Neuroscience 298:52–62.

    https://doi.org/10.1016/j.neuroscience.2015.04.018

    • PubMed
    • Google Scholar
14. 1. Chong BW
    2. Stinear CM

    (2017) Modulation of motor cortex inhibition during motor imagery

    Journal of Neurophysiology 117:1776–1784.

    https://doi.org/10.1152/jn.00549.2016

    • PubMed
    • Google Scholar
15. 1. Devanne H
    2. Lavoie BA
    3. Capaday C

    (1997) Input-output properties and gain changes in the human corticospinal pathway

    Experimental Brain Research 114:329–338.

    https://doi.org/10.1007/PL00005641

    • PubMed
    • Google Scholar
16. 1. Engelhard B
    2. Ozeri N
    3. Israel Z
    4. Bergman H
    5. Vaadia E

    (2013) Inducing γ oscillations and precise spike synchrony by operant conditioning via brain-machine interface

    Neuron 77:361–375.

    https://doi.org/10.1016/j.neuron.2012.11.015

    • PubMed
    • Google Scholar
17. 1. Fadiga L
    2. Buccino G
    3. Craighero L
    4. Fogassi L
    5. Gallese V
    6. Pavesi G

    (1999) Corticospinal excitability is specifically modulated by motor imagery: a magnetic stimulation study

    Neuropsychologia 37:147–158.

    https://doi.org/10.1016/S0028-3932(98)00089-X

    • PubMed
    • Google Scholar
18. 1. Fetz EE

    (2013) Volitional control of cortical oscillations and synchrony

    Neuron 77:216–218.

    https://doi.org/10.1016/j.neuron.2013.01.003

    • PubMed
    • Google Scholar
19. 1. Fisher RJ
    2. Nakamura Y
    3. Bestmann S
    4. Rothwell JC
    5. Bostock H

    (2002) Two phases of intracortical inhibition revealed by transcranial magnetic threshold tracking

    Experimental Brain Research 143:240–248.

    https://doi.org/10.1007/s00221-001-0988-2

    • PubMed
    • Google Scholar
20. 1. Fries P

    (2005) A mechanism for cognitive dynamics: neuronal communication through neuronal coherence

    Trends in Cognitive Sciences 9:474–480.

    https://doi.org/10.1016/j.tics.2005.08.011

    • PubMed
    • Google Scholar
21. 1. Grosse-Wentrup M
    2. Schölkopf B
    3. Hill J

    (2011) Causal influence of gamma oscillations on the sensorimotor rhythm

    NeuroImage 56:837–842.

    https://doi.org/10.1016/j.neuroimage.2010.04.265

    • PubMed
    • Google Scholar
22. 1. Gueorguieva R
    2. Krystal JH

    (2004) Move over ANOVA: progress in analyzing repeated-measures data and its reflection in papers published in the Archives of General Psychiatry

    Archives of General Psychiatry 61:310–317.

    https://doi.org/10.1001/archpsyc.61.3.310

    • PubMed
    • Google Scholar
23. 1. Guyon I
    2. Weston J
    3. Barnhill S
    4. Vapnik V

    (2002) Gene Selection for Cancer Classification using Support Vector Machines

    Machine Learning 46:389–422.

    https://doi.org/10.1023/A:1012487302797

    • Google Scholar
24. 1. Hammond G
    2. Vallence AM

    (2007) Modulation of long-interval intracortical inhibition and the silent period by voluntary contraction

    Brain Research 1158:63–70.

    https://doi.org/10.1016/j.brainres.2007.05.014

    • PubMed
    • Google Scholar
25. 1. Hess CW
    2. Mills KR
    3. Murray NM

    (1986) Magnetic stimulation of the human brain: facilitation of motor responses by voluntary contraction of ipsilateral and contralateral muscles with additional observations on an amputee

    Neuroscience Letters 71:235–240.

    https://doi.org/10.1016/0304-3940(86)90565-3

    • PubMed
    • Google Scholar
26. 1. Izumi S
    2. Findley TW
    3. Ikai T
    4. Andrews J
    5. Daum M
    6. Chino N

    (1995) Facilitatory effect of thinking about movement on motor-evoked potentials to transcranial magnetic stimulation of the brain

    American Journal of Physical Medicine & Rehabilitation 74:207–213.

    https://doi.org/10.1097/00002060-199505000-00005

    • PubMed
    • Google Scholar
27. 1. Jensen O
    2. Bahramisharif A
    3. Oostenveld R
    4. Klanke S
    5. Hadjipapas A
    6. Okazaki YO
    7. van Gerven MAJ

    (2011) Using brain–computer interfaces and brain-state dependent stimulation as tools in cognitive neuroscience

    Frontiers in Psychology 2:e00100.

    https://doi.org/10.3389/fpsyg.2011.00100

    • Google Scholar
28. 1. Jensen O
    2. Mazaheri A

    (2010) Shaping functional architecture by oscillatory alpha activity: gating by inhibition

    Frontiers in Human Neuroscience 4:.

    https://doi.org/10.3389/fnhum.2010.00186

    • PubMed
    • Google Scholar
29. 1. Johnson NW
    2. Özkan M
    3. Burgess AP
    4. Prokic EJ
    5. Wafford KA
    6. O'Neill MJ
    7. Greenhill SD
    8. Stanford IM
    9. Woodhall GL

    (2017) Phase-amplitude coupled persistent theta and gamma oscillations in rat primary motor cortex in vitro

    Neuropharmacology 119:141–156.

    https://doi.org/10.1016/j.neuropharm.2017.04.009

    • PubMed
    • Google Scholar
30. 1. Kelly SP
    2. Gomez-Ramirez M
    3. Foxe JJ

    (2009) The strength of anticipatory spatial biasing predicts target discrimination at attended locations: a high-density EEG study

    European Journal of Neuroscience 30:2224–2234.

    https://doi.org/10.1111/j.1460-9568.2009.06980.x

    • PubMed
    • Google Scholar
31. 1. Leung LS
    2. Shen B

    (2007) GABAB receptor blockade enhances theta and gamma rhythms in the hippocampus of behaving rats

    Hippocampus 17:281–291.

    https://doi.org/10.1002/hipo.20267

    • PubMed
    • Google Scholar
32. Book

    1. Magill R

    (2010)

    Motor Learning and Control: Concepts and Applications

    McGraw-Hill Education.

    • Google Scholar
33. 1. Majid DS
    2. Lewis C
    3. Aron AR

    (2015) Training voluntary motor suppression with real-time feedback of motor evoked potentials

    Journal of Neurophysiology 113:3446–3452.

    https://doi.org/10.1152/jn.00992.2014

    • PubMed
    • Google Scholar
34. 1. Mäki H
    2. Ilmoniemi RJ

    (2010) EEG oscillations and magnetically evoked motor potentials reflect motor system excitability in overlapping neuronal populations

    Clinical Neurophysiology 121:492–501.

    https://doi.org/10.1016/j.clinph.2009.11.078

    • PubMed
    • Google Scholar
35. 1. Maulden SA
    2. Gassaway J
    3. Horn SD
    4. Smout RJ
    5. DeJong G

    (2005) Timing of initiation of rehabilitation after stroke

    Archives of Physical Medicine and Rehabilitation 86:34–40.

    https://doi.org/10.1016/j.apmr.2005.08.119

    • PubMed
    • Google Scholar
36. 1. Mazaheri A
    2. Nieuwenhuis IL
    3. van Dijk H
    4. Jensen O

    (2009) Prestimulus alpha and mu activity predicts failure to inhibit motor responses

    Human Brain Mapping 30:1791–1800.

    https://doi.org/10.1002/hbm.20763

    • PubMed
    • Google Scholar
37. 1. McDonnell MN
    2. Orekhov Y
    3. Ziemann U

    (2006) The role of GABA_(B) receptors in intracortical inhibition in the human motor cortex

    Experimental Brain Research 173:86–93.

    https://doi.org/10.1007/s00221-006-0365-2

    • PubMed
    • Google Scholar
38. 1. Mellinger J
    2. Schalk G
    3. Braun C
    4. Preissl H
    5. Rosenstiel W
    6. Birbaumer N
    7. Kübler A

    (2007) An MEG-based brain-computer interface (BCI)

    NeuroImage 36:581–593.

    https://doi.org/10.1016/j.neuroimage.2007.03.019

    • PubMed
    • Google Scholar
39. 1. Miller KJ
    2. Schalk G
    3. Fetz EE
    4. den Nijs M
    5. Ojemann JG
    6. Rao RP

    (2010) Cortical activity during motor execution, motor imagery, and imagery-based online feedback

    PNAS 107:4430–4435.

    https://doi.org/10.1073/pnas.0913697107

    • PubMed
    • Google Scholar
40. 1. Mott DD
    2. Lewis DV

    (1991) Facilitation of the induction of long-term potentiation by GABAB receptors

    Science 252:1718–1720.

    https://doi.org/10.1126/science.1675489

    • PubMed
    • Google Scholar
41. 1. Murase N
    2. Duque J
    3. Mazzocchio R
    4. Cohen LG

    (2004) Influence of interhemispheric interactions on motor function in chronic stroke

    Annals of Neurology 55:400–409.

    https://doi.org/10.1002/ana.10848

    • PubMed
    • Google Scholar
42. 1. Murthy VN
    2. Fetz EE

    (1996) Oscillatory activity in sensorimotor cortex of awake monkeys: synchronization of local field potentials and relation to behavior

    Journal of Neurophysiology 76:3949–3967.

    https://doi.org/10.1152/jn.1996.76.6.3949

    • PubMed
    • Google Scholar
43. 1. Ni Z
    2. Gunraj C
    3. Chen R

    (2007) Short interval intracortical inhibition and facilitation during the silent period in human

    The Journal of Physiology 583:971–982.

    https://doi.org/10.1113/jphysiol.2007.135749

    • PubMed
    • Google Scholar
44. 1. Oldfield RC

    (1971) The assessment and analysis of handedness: the Edinburgh inventory

    Neuropsychologia 9:97–113.

    https://doi.org/10.1016/0028-3932(71)90067-4

    • PubMed
    • Google Scholar
45. 1. Opie GM
    2. Ridding MC
    3. Semmler JG

    (2015) Task-related changes in intracortical inhibition assessed with paired- and triple-pulse transcranial magnetic stimulation

    Journal of Neurophysiology 113:1470–1479.

    https://doi.org/10.1152/jn.00651.2014

    • PubMed
    • Google Scholar
46. 1. Peurala SH
    2. Müller-Dahlhaus JF
    3. Arai N
    4. Ziemann U

    (2008) Interference of short-interval intracortical inhibition (SICI) and short-interval intracortical facilitation (SICF)

    Clinical Neurophysiology 119:2291–2297.

    https://doi.org/10.1016/j.clinph.2008.05.031

    • PubMed
    • Google Scholar
47. 1. Pezzetta R
    2. Nicolardi V
    3. Tidoni E
    4. Aglioti SM

    (2018) Error, rather than its probability, elicits specific electrocortical signatures: a combined EEG-immersive virtual reality study of action observation

    Journal of Neurophysiology 120:1107–1118.

    https://doi.org/10.1152/jn.00130.2018

    • PubMed
    • Google Scholar
48. 1. Pfurtscheller G
    2. Berghold A

    (1989) Patterns of cortical activation during planning of voluntary movement

    Electroencephalography and Clinical Neurophysiology 72:250–258.

    https://doi.org/10.1016/0013-4694(89)90250-2

    • PubMed
    • Google Scholar
49. 1. Pfurtscheller G
    2. Neuper C

    (1997) Motor imagery activates primary sensorimotor area in humans

    Neuroscience Letters 239:65–68.

    https://doi.org/10.1016/S0304-3940(97)00889-6

    • PubMed
    • Google Scholar
50. 1. Ramos-Murguialday A
    2. Birbaumer N

    (2015) Brain oscillatory signatures of motor tasks

    Journal of Neurophysiology 113:3663–3682.

    https://doi.org/10.1152/jn.00467.2013

    • PubMed
    • Google Scholar
51. 1. Ruddy KL

    (2017) Directionality of interhemispheric communication

    Brain Structure and Function 222:4293–4296.

    https://doi.org/10.1007/s00429-017-1557-1

    • PubMed
    • Google Scholar
52. 1. Ruddy KL
    2. Leemans A
    3. Carson RG

    (2017a) Transcallosal connectivity of the human cortical motor network

    Brain Structure and Function 222:1243–1252.

    https://doi.org/10.1007/s00429-016-1274-1

    • PubMed
    • Google Scholar
53. 1. Ruddy KL
    2. Leemans A
    3. Woolley DG
    4. Wenderoth N
    5. Carson RG

    (2017b) Structural and Functional Cortical Connectivity Mediating Cross Education of Motor Function

    The Journal of Neuroscience 37:2555–2564.

    https://doi.org/10.1523/JNEUROSCI.2536-16.2017

    • PubMed
    • Google Scholar
54. Software

    1. Ruddy KL

    (2018) TMS Neurofeedback, version 1

    Github.

    https://github.com/ncmlabeth/TMS-neurofeedback
55. 1. Ruddy KL
    2. Woolley DG
    3. Mantini D
    4. Balsters JH
    5. Enz N
    6. Wenderoth N

    (2018) Improving the quality of combined EEG-TMS neural recordings: Introducing the coil spacer

    Journal of Neuroscience Methods 294:34–39.

    https://doi.org/10.1016/j.jneumeth.2017.11.001

    • PubMed
    • Google Scholar
56. 1. Sanger TD
    2. Garg RR
    3. Chen R

    (2001) Interactions between two different inhibitory systems in the human motor cortex

    The Journal of Physiology 530:307–317.

    https://doi.org/10.1111/j.1469-7793.2001.0307l.x

    • PubMed
    • Google Scholar
57. 1. Sauseng P
    2. Klimesch W
    3. Gerloff C
    4. Hummel FC

    (2009) Spontaneous locally restricted EEG alpha activity determines cortical excitability in the motor cortex

    Neuropsychologia 47:284–288.

    https://doi.org/10.1016/j.neuropsychologia.2008.07.021

    • PubMed
    • Google Scholar
58. 1. Schaworonkow N
    2. Triesch J
    3. Ziemann U
    4. Zrenner C

    (2018) EEG-triggered TMS reveals stronger brain state-dependent modulation of motor evoked potentials at weaker stimulation intensities

    bioRxiv 251363:e30311.

    https://doi.org/10.1016/j.brs.2018.09.009

    • Google Scholar
59. 1. Schubert R
    2. Blankenburg F
    3. Lemm S
    4. Villringer A
    5. Curio G

    (2006) Now you feel it--now you don't: ERP correlates of somatosensory awareness

    Psychophysiology 43:31–40.

    https://doi.org/10.1111/j.1469-8986.2006.00379.x

    • PubMed
    • Google Scholar
60. 1. Sitaram R
    2. Ros T
    3. Stoeckel L
    4. Haller S
    5. Scharnowski F
    6. Lewis-Peacock J
    7. Weiskopf N
    8. Blefari ML
    9. Rana M
    10. Oblak E
    11. Birbaumer N
    12. Sulzer J

    (2017) Closed-loop brain training: the science of neurofeedback

    Nature Reviews Neuroscience 18:86–100.

    https://doi.org/10.1038/nrn.2016.164

    • PubMed
    • Google Scholar
61. 1. Soekadar SR
    2. Birbaumer N
    3. Slutzky MW
    4. Cohen LG

    (2015a) Brain-machine interfaces in neurorehabilitation of stroke

    Neurobiology of Disease 83:172–179.

    https://doi.org/10.1016/j.nbd.2014.11.025

    • PubMed
    • Google Scholar
62. 1. Soekadar SR
    2. Witkowski M
    3. Birbaumer N
    4. Cohen LG

    (2015b) Enhancing Hebbian Learning to Control Brain Oscillatory Activity

    Cerebral Cortex 25:2409–2415.

    https://doi.org/10.1093/cercor/bhu043

    • Google Scholar
63. 1. Stinear CM
    2. Byblow WD
    3. Steyvers M
    4. Levin O
    5. Swinnen SP

    (2006) Kinesthetic, but not visual, motor imagery modulates corticomotor excitability

    Experimental Brain Research 168:157–164.

    https://doi.org/10.1007/s00221-005-0078-y

    • PubMed
    • Google Scholar
64. 1. Thompson AK
    2. Chen XY
    3. Wolpaw JR

    (2009) Acquisition of a simple motor skill: task-dependent adaptation plus long-term change in the human soleus H-reflex

    Journal of Neuroscience 29:5784–5792.

    https://doi.org/10.1523/JNEUROSCI.4326-08.2009

    • PubMed
    • Google Scholar
65. 1. Whitham EM
    2. Pope KJ
    3. Fitzgibbon SP
    4. Lewis T
    5. Clark CR
    6. Loveless S
    7. Broberg M
    8. Wallace A
    9. DeLosAngeles D
    10. Lillie P
    11. Hardy A
    12. Fronsko R
    13. Pulbrook A
    14. Willoughby JO

    (2007) Scalp electrical recording during paralysis: quantitative evidence that EEG frequencies above 20 Hz are contaminated by EMG

    Clinical Neurophysiology 118:1877–1888.

    https://doi.org/10.1016/j.clinph.2007.04.027

    • PubMed
    • Google Scholar
66. 1. World Medical Association

    (2013) World Medical Association Declaration of Helsinki

    Jama 310:2191–2194.

    https://doi.org/10.1001/jama.2013.281053

    • Google Scholar
67. 1. Yuval-Greenberg S
    2. Tomer O
    3. Keren AS
    4. Nelken I
    5. Deouell LY

    (2008) Transient induced gamma-band response in EEG as a manifestation of miniature saccades

    Neuron 58:429–441.

    https://doi.org/10.1016/j.neuron.2008.03.027

    • PubMed
    • Google Scholar
68. 1. Zaehle T
    2. Rach S
    3. Herrmann CS

    (2010) Transcranial alternating current stimulation enhances individual alpha activity in human EEG

    PLOS ONE 5:e13766.

    https://doi.org/10.1371/journal.pone.0013766

    • PubMed
    • Google Scholar
69. 1. Zarkowski P
    2. Shin CJ
    3. Dang T
    4. Russo J
    5. Avery D

    (2006) EEG and the variance of motor evoked potential amplitude

    Clinical EEG and Neuroscience 37:247–251.

    https://doi.org/10.1177/155005940603700316

    • PubMed
    • Google Scholar
70. 1. Zrenner C
    2. Desideri D
    3. Belardinelli P
    4. Ziemann U

    (2018) Real-time EEG-defined excitability states determine efficacy of TMS-induced plasticity in human motor cortex

    Brain Stimulation 11:374–389.

    https://doi.org/10.1016/j.brs.2017.11.016

    • PubMed
    • Google Scholar

Author details

1. Kathy Ruddy

   1. Neural Control of Movement Lab, ETH Zürich, Zürich, Switzerland
   2. Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Writing—original draft, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5501-0423

2. Joshua Balsters

   1. Neural Control of Movement Lab, ETH Zürich, Zürich, Switzerland
   2. Department of Psychology, Royal Holloway University of London, London, United Kingdom

   Contribution

   Formal analysis, Supervision, Methodology, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9856-6990

3. Dante Mantini

   1. Neural Control of Movement Lab, ETH Zürich, Zürich, Switzerland
   2. Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium

   Contribution

   Formal analysis, Validation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6485-5559

4. Quanying Liu

   1. Neural Control of Movement Lab, ETH Zürich, Zürich, Switzerland
   2. Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium

   Contribution

   Formal analysis, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2501-7656

5. Pegah Kassraian-Fard

   Neural Control of Movement Lab, ETH Zürich, Zürich, Switzerland

   Contribution

   Formal analysis, Validation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6562-7918

6. Nadja Enz

   Neural Control of Movement Lab, ETH Zürich, Zürich, Switzerland

   Contribution

   Data curation, Methodology, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2476-4710

7. Ernest Mihelj

   Neural Control of Movement Lab, ETH Zürich, Zürich, Switzerland

   Contribution

   Validation, Visualization, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6080-2553

8. Bankim Subhash Chander

   Applied Neurotechnology Laboratory, University of Tübingen, Tübingen, Germany

   Contribution

   Formal analysis, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3206-8822

9. Surjo R Soekadar

   1. Applied Neurotechnology Laboratory, University of Tübingen, Tübingen, Germany
   2. Clinical Neurotechnology Laboratory, Neuroscience Research Center (NWFZ), Department of Psychiatry and Psychotherapy, Charité – University Medicine Berlin, Berlin, Germany

   Contribution

   Resources, Validation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1280-5538

10. Nicole Wenderoth

    Neural Control of Movement Lab, ETH Zürich, Zürich, Switzerland

    Contribution

    Conceptualization, Funding acquisition, Methodology, Writing—original draft, Writing—review and editing

    For correspondence

    nicole.wenderoth@hest.ethz.ch

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-3246-9386

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