Abstract
Purpose of Review
An increase in oscillatory activity in the γ-frequency band (approximately 50–100 Hz) has long been noted during human movement. However, its functional role has been difficult to elucidate. The advent of novel techniques, particularly transcranial alternating current stimulation (tACS), has dramatically increased our ability to study γ oscillations. Here, we review our current understanding of the role of γ oscillations in the human motor cortex, with reference to γ activity outside the motor system, and evidence from animal models.
Recent Findings
Evidence for the neurophysiological basis of human γ oscillations is beginning to emerge. Multimodal studies, essential given the necessarily indirect measurements acquired in humans, are beginning to provide convergent evidence for the role of γ oscillations in movement, and their relationship to plasticity.
Summary
Human motor cortical γ oscillations appear to play a key role in movement, and relate to learning. However, there are still major questions to be answered about their physiological basis and precise role in human plasticity. It is to be hoped that future research will take advantage of recent technical advances and the physiological basis and functional significance of this intriguing and important brain rhythm will be fully elucidated.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Introduction
Motor cortical gamma-frequency oscillations (γ; 30–100 Hz) have been recorded from multiple brain regions, across many spatial scales, including extracellular recordings within the brain (single- or multi-unit, local field potentials [LFP]) [1, 2, 3•], surface electrocorticographic (ECoG) recordings [4, 5], and scalp-recorded EEG/MEG in humans [6,7,8]. A substantial body of research performed in hippocampal slice preparations, as well as in neocortical and other brain regions suggests that γ oscillations arise from interactions between reciprocally connected inhibitory interneurons and pyramidal cells [9]. While motor cortex γ activity is considered to be prokinetic in nature [7, 10•, 11], this view does not fully reflect its functional complexity [12, 13], and its precise role in motor processes remains to be elucidated. Recently developed interventional approaches, such as optogenetic tools in animals and transcranial alternating current stimulation (tACS) in humans, offer unique opportunity to study mechanisms underlying rhythmic activity and establish a causal relationship between motor cortical oscillations and various aspects of motor control.
This review aims to briefly summarise the mechanisms underlying γ oscillations, focusing on the primary motor cortex (M1). In addition, the functional significance of neocortical motor γ activity and the current state of the field are discussed. Finally, the contribution and limitations, of optogenetic- and tACS-based approaches to inform our understanding of motor cortical γ activity are reviewed.
Mechanisms of Neocortical γ Oscillations
Identifying the mechanisms underlying neocortical γ oscillations is vital for understanding their precise role in motor processes as well as for the development and assessment of therapeutic interventions aimed at modulating motor γ activity.
γ oscillations are considered to emerge through activation of reciprocally connected excitatory pyramidal neurons and inhibitory interneurons (with increasing involvement of interneuron-only networks in a higher frequency γ activity), whose period is regulated by the decay time constant of GABAA-mediated synaptic current [9, 14]. Among the various interneuron subtypes, fast-spiking (FS) parvalbumin positive (PV) cells are a key element of neocortical γ oscillations [15, 16], displaying unique electrophysiological properties, such as intrinsic resonance in the γ-frequency band [17, 18]. A recent pharmacological dissection of γ oscillations in rat M1 in vitro has shown a reduction of γ oscillatory power by the GABAA and AMPA receptor antagonists; an increase in the γ power as a result of an augmentation of GABAA receptor activity or the blockade of GABAB, NMDA and metabotropic glutamate receptor activity; and a relatively complex effect of the above pharmacological agents on the γ frequency [3•]. Another important ingredient of γ oscillations are electrical synapses, both between pyramidal cells and between interneurons [19]. The blockade of gap junctions has been shown to abolish γ activity in M1 in vitro [3•].
While the modelling and animal studies have played a vital role in elucidating the physiological mechanisms underpinning γ activity, it is not clear whether these findings can be directly translated into humans. To date, only a limited number of human studies has been performed, few of which have focused on the motor domain. The results of these have often been inconclusive, or contradictory, possibly due to the inherent difficulties in recording a low-signal oscillation with necessarily indirect, non-invasive, noisy methods.
Neither diazepam, a non-specific GABAA receptor modulator, nor tiagabine, a GABA transporter 1 (GAT-1) blocker, modulate movement-related γ synchronisation (MRGS) power or frequency in M1, as measured using MEG, suggesting that MRGS may not be a GABAA-mediated process, in contradiction to the animal literature [20, 21]. However, MRGS frequency, but not power, has been found to be related to M1 GABA concentration, as measured using magnetic resonance spectroscopy (MRS) [22]. Further, driving cortical γ oscillations has been demonstrated to reduce transcranial magnetic stimulation (TMS)-assessed synaptic GABAA inhibition in the human M1 [23•]. While the above findings may appear to be inconsistent, it is worth noting that the techniques employed to measure GABA activity may reflect different aspects of GABAergic signalling. For example, the TMS protocol employed in our recent study [23•] measures primarily synaptic GABAA activity [24], whereas MRS is likely to reflect extra-synaptic, tonic, inhibition [25, 26], although further investigation may be required [27]. Moreover, MEG-detected MRGS appears to be stronger in earlier trials in a sequence (which may influence MRGS over extended trials) [7], and cannot be reliably detected in all individuals [23•], thus adding additional challenge to studying this type of oscillatory activity in humans.
It is important to note, particularly when considering systems-level activity, that γ oscillations are routinely coupled to oscillatory activity at other frequencies, most commonly at the theta (θ) frequency [3•, 9, 28]. Outside the motor system, it has long been hypothesised that this cross-frequency coupling enables local γ-generating networks to be coupled by long-range connections [29]. In the motor domain, M1 γ activity has been shown to be coherent with that in the subcortical basal ganglia structures, with evidence that cortical γ may be driven by subcortical γ [12, 30]. In addition, computational modelling approaches have provided evidence that different mechanisms may be responsible for local pyramidal-interneuronal γ as opposed to γ driven by basal ganglia-thalamocortical sources [31], although it remains to be examined how these small- and large-scale γ networks interact.
Movement-Related Changes in γ Amplitude and Their Function
Movement-related changes in γ amplitude in the human M1 were first observed in ECoG recordings [5], and have subsequently been observed using a variety of invasive [4, 32,33,34] and non-invasive [35,36,37,38] recording techniques. Specifically, an increase in the amplitude of γ oscillations occurs during movement execution, a process referred to as movement-related γ synchronisation (MRGS) [5, 7, 35, 39]. The MRGS has been described as spatially and temporally more specific, i.e., it shows a greater somatotopic organisation and lateralisation as well as a greater temporal congruence with the movement than concurrently occurring movement-related changes in α- and β-frequency oscillations [7, 40]. However, in contrast to α and β oscillations, the majority of studies have reported no changes in γ oscillations prior to movement onset [7, 8]. For these reasons, it has been suggested that movement-related changes in γ amplitude could be the result of afferent proprioceptive feedback to the motor cortex during movement [7, 41]. Others have argued that the lack of MRGS during both passive movements and movement observation indicates that movement-related changes in γ oscillations relate to more active motor control processes rather than just to afferent proprioceptive feedback [7]. A parsimonious explanation for these findings might be that movement-related changes in γ oscillations stemming from sensorimotor areas are involved in the processing of afferent proprioceptive feedback in order to control movements. However, a recent study demonstrated movement-related changes in γ oscillations in the absence of proprioceptive feedback signalling using the mirror box paradigm [42], suggesting that the hypothesis of γ oscillations solely reflecting proprioception may be too simplistic.
Beyond cortical changes in movement-related γ oscillations, direct recordings from patients with Parkinson’s disease and animal models have demonstrated that the degree of local, subcortical MRGS is related to velocity, effort, or force levels underpinning movements [43,44,45]. The importance of subcortical γ is underlined by the converging evidence that supressed γ oscillatory activity (and enhanced β oscillatory activity) in the cortico-basal ganglia loop circuits is directly linked to symptoms in patients with Parkinson’s disease [46,47,48,49,50]. Moreover, it has been demonstrated that effective pharmacologic treatments (i.e., levodopa and dopamine receptor agonists) enhance subcortical γ and reduce subcortical β oscillatory activity [51, 52]. The opposing direction of changes in γ and β oscillatory activity in Parkinson’s disease suggests a reciprocal relationship between these two frequency ranges. Further, evidence supporting this hypothesis was provided by a recent study demonstrating reciprocity of γ and β oscillatory activity in the supplementary motor area (SMA) and pre-SMA of monkeys during performance of a bimanual motor sequence [53•].
Taken together, these studies suggest that movement execution is related to an increase in γ amplitude in cortical and subcortical regions and, further, that the degree of that increase is closely related to movement properties, both in health and disease.
The Role of γ Oscillations in Plasticity
Outside the motor system, γ oscillations have routinely been suggested to reflect local synaptic plasticity. γ oscillations are a prime candidate for modulating synaptic plasticity because they reflect co-ordinated activity on the time scale of excitatory postsynaptic responses, and are within the range of relative spike timing that is optimal for spike timing dependent plasticity (10–20 ms).
Outside the motor network, it has been demonstrated that the magnitude of γ oscillations present at baseline accurately predicts subsequent learning across a range of tasks [54,55,56,57]. For example, in a human episodic working memory task, the degree of increase in γ oscillatory activity during encoding predicted subsequent recall [55]. In a rodent model, the degree of increase of activity in the γ band in response to a tone at baseline accurately predicted both the acquisition of subsequent associative memory, when that tone was paired with a shock, and receptive field plasticity [54].
A recent study from our group took a similar approach in the human motor system. We applied transcranial alternating current stimulation (tACS) to the M1 of healthy controls. tACS applied at 75 Hz, thought to drive activity in local γ circuits, led to a significant decrease in GABAA activity, as assessed by TMS. Pertinently for this discussion, in a separate experimental session to the tACS application, we asked subjects to learn an explicit, visually cued serial reaction time task (SRTT), allowing us to quantify each individual’s learning ability. We demonstrated a strong relationship between 75 Hz tACS-induced change in GABAA activity at rest and learning ability on a subject-by-subject basis [23•]. Taken together with the studies outside the motor domain above, this result strongly supports the hypothesis that the magnitude of γ oscillations are predictive of learning, and further suggests that this may be due to GABAergic activity.
As discussed above, local γ oscillations within a network can be coupled via long-range θ frequency activity. As learning of complex motor skills requires not only changes in local activity but also changes in communication, or functional connectivity, between regions, this raises the clear hypothesis that perhaps this θ-γ coupling may underpin at least some network-level plastic changes. In support of this rather speculative hypothesis, in vitro studies have shown that local θ-γ coupling determines whether applied stimuli result in LTP or LTD [58], and θ-γ coupling has been demonstrated to be modulated during human non-motor learning [59, 60].
Novel Interventional Approaches to Study γ Oscillations
A large proportion of our understanding of neocortical γ oscillations has been acquired through correlational studies involving passive measurements of brain activity during a task or at rest. Recently, the development of interventional approaches, such as optogenetics in animals and tACS in humans has played a pivotal role in probing the mechanisms and neural circuits underlying neocortical γ oscillations and establishing for the first time a causal relationship between brain oscillations and behaviour.
Optogenetics is a powerful tool for rapid selective activation/inhibition of specific neurons and can be used to exogenously introduce oscillatory activity at a desired frequency into a specific brain region [61]. An elegant series of studies have used optogenetic manipulations in mice to show that stimulation of PV interneurons can generate LFP oscillations in vivo selectively at γ frequencies, whereas inhibition of PV interneurons suppresses the power of evoked oscillations at this frequency band [16, 17]. Together, these findings have provided strong evidence on a powerful role of PV interneurons in γ oscillations in vivo. While these studies were performed in non-motor cortical regions, the presence of inhibitory interneurons and their actions through GABAA synapses are considered to be common denominators of various areas of neocortex, hippocampus, striatum and other brain regions [9].
Behavioural effects of optogenetic manipulations of γ oscillations have been reported in somatosensory [62] and prefrontal regions [63, 64] but very limited evidence exists in the motor domain. Constant optogenetic stimulation has been shown to elicit intrinsic propagating waves of γ-band (40–80 Hz) oscillations in the LFPs of the non-human primate motor cortex [2]. Interestingly, γ oscillations induced during movement preparation were suppressed upon voluntary movement execution, and predominantly in cortical sites distant to the stimulation (although this data included only one subject). The interpretation of these findings is difficult, particularly in light of human studies showing an increase, rather than a decrease, in M1 γ activity during movement execution [7, 8]. In addition, disentangling the contribution of different cell types to the measured γ activity is difficult as there is evidence that the optogenetic construct used is expressed primarily in pyramidal neurons and to a lesser degree in inhibitory interneurons [65], Further, recruitment of other cell types via network interactions cannot be excluded.
In humans, tACS has emerged as a promising non-invasive approach involving the application of a low amplitude alternating current of up to 2 mA (peak-to-peak) via two or more scalp electrodes. It offers the unique opportunity to drive human cortical rhythms in a frequency-specific manner and study associated behavioural performance [66, 67]. The application of γ-frequency (70 Hz) tACS to the left M1 has led to a frequency-specific increase in movement velocity in a visually guided movement task, thus providing first-time evidence on the causal relationship between motor cortical γ activity and motor behaviour [10•]. Previous studies have suggested that γ oscillations provide a high temporal resolution “updating system” for online motor control [68, 69], which may be vital for sudden rearrangements of the motor plan [53•]. In agreement with this, γ tACS has been found to improve specific components of the visuo-motor task where a sudden online rearrangement of the motor plan is required [70•]. These findings may be of clinical relevance in movement disorders, such as Parkinson’s disease, where switching from one motor program to another contributes to bradykinesia, one of the cardinal motor manifestations of the disease [71].
More recently, a novel combined tACS-functional magnetic resonance imaging (fMRI) approach has been employed to identify the neural mechanisms underlying improvements in motor performance induced by γ tACS over the M1 [72•]. The results have demonstrated that the degree of γ tACS-induced enhancements in movement performance correlates with the magnitude of the γ tACS-induced change in neural activity in the stimulated M1, and results in specific compensatory changes of brain activity in the remote dorsomedial prefrontal cortex. Overall, these findings suggest a direct link between the behavioural effects of motor cortical γ entrainment and both the modulation of local activity in the targeted site as well as compensatory modulation within connected and functionally related brain networks.
From a mechanistic point of view, another tool of an increasing value is a combined tACS-transcranial magnetic stimulation (TMS) approach, which has been utilised to assess the effects of exogenously driven oscillations on various parameters of motor excitability. As discussed previously, our recent study has demonstrated that driving motor cortical γ oscillations modulates GABAA inhibition in the human M1, in agreement with the evidence from modelling and animal studies pointing to the key role of GABAA receptor mediated inhibition in the generation of γ activity [9, 73]. Interestingly, γ tACS-induced change in inhibition was closely related to an individual’s ability to learn a motor task, suggesting for the first time that local inhibitory interneuronal circuits in M1 involved in γ oscillations may also be important for motor learning. In support of these findings, MRGS peak frequency, as measured using MEG, has been found to be related to performance in a motor learning task (Nowak et al., unpublished observations). This relationship was absent when the individual level of GABAA-mediated inhibition was controlled for.
Undoubtedly, tACS has started emerging as a valuable tool to study the mechanisms underlying cortical γ oscillations and their functional significance. However, a major drawback is that it is not possible to ascertain how effective the employed tACS paradigms were in driving cortical oscillations and whether, and to what extent, the exogenous stimulation changed the physical connectivity through spike timing dependent plasticity [74, 75], which in turn could have affected behaviour. To provide unequivocal evidence on tACS-induced entrainment of endogenous oscillations, concurrent MEG/EEG monitoring would be necessary [76,77,78], although this line of research remains a non-trivial challenge due to concerns surrounding stimulation artefact removal methods [79]. Further, the development of intermittent tACS paradigms [75, 80], with longer windows of stimulation-free periods would reduce the chance of inducing plastic changes.
Conclusion
This review aimed to summarise the mechanisms underlying neocortical γ oscillations, with the primary focus on the studies performed in the motor cortex. In addition, the functional significance of motor cortical γ activity, including its role in plasticity was discussed. Finally, we reviewed the contribution of novel interventional approaches to our understanding of motor cortical γ gamma activity as well as their limitations. Addressing the limitations of these tools is likely to open a more fruitful avenue to further dissect the underlying mechanisms and the causal relationship between motor cortex γ activity and various aspects of motor behaviour.
References
Papers of particular interest, published recently, have been highlighted as: • Of importance
Engelhard B, Ozeri N, Israel Z, Bergman H, Vaadia E. Inducing gamma oscillations and precise spike synchrony by operant conditioning via brain-machine interface. Neuron. 2013;77(2):361–75.
Lu Y, Truccolo W, Wagner FB, Vargas-Irwin CE, Ozden I, Zimmermann JB, et al. Optogenetically induced spatiotemporal gamma oscillations and neuronal spiking activity in primate motor cortex. J Neurophysiol. 2015;113(10):3574–87.
• Johnson NW, Özkan M, Burgess AP, Prokic EJ, Wafford KA, O’Neill MJ, et al. Phase-amplitude coupled persistent theta and gamma oscillations in rat primary motor cortex in vitro. Neuropharmacology. 2017;119:141–56. The first demonstration of theta-gamma coupling in M1 in vitro and pharmacological dissection of mechanisms of both rhythms.
Pfurtscheller G, Graimann B, Huggins JE, Levine SP, Schuh LA. Spatiotemporal patterns of beta desynchronization and gamma synchronization in corticographic data during self-paced movement. Clin Neurophysiol. 2003;114(7):1226–36.
Crone NE, Miglioretti DL, Gordon B, Lesser RP. Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis. II. Event-related synchronization in the gamma band. Brain. 1998;121(12)
Darvas F, Scherer R, Ojemann JG, Rao RP, Miller KJ, Sorensen LB. High gamma mapping using EEG. Neuroimage. 2010;49(1):930–8.
Muthukumaraswamy SD. Functional properties of human primary motor cortex gamma oscillations. J Neurophysiol. 2010;104:2873–85.
Cheyne D, Ferrari P. MEG studies of motor cortex gamma oscillations: evidence for a gamma “fingerprint” in the brain? Front Hum Neurosci. 2013;7:575.
Buzsáki G, Wang X-J. Mechanisms of gamma oscillations. Annu Rev Neurosci. 2012;35:203–25.
• Joundi RA, Jenkinson N, Brittain J-S, Aziz TZ, Brown P. Driving oscillatory activity in the human cortex enhances motor performance. Curr Biol. 2012;22(5):403–7. A study showing the role of motor cortical γ as a prokinetic rhythm in the motor system (first evidence of causal relationship).
Swann NC, de Hemptinne C, Miocinovic S, Qasim S, Wang SS, Ziman N, et al. Gamma oscillations in the hyperkinetic state detected with chronic human brain recordings in Parkinson’s disease. J Neurosci. 2016;36(24):6445–58.
Jenkinson N, Kühn AA, Brown P. Gamma oscillations in the human basal ganglia. Exp Neurol. 2013;245:72–6.
Fischer P, Pogosyan A, Herz DM, Cheeran B, Green AL, Fitzgerald J, et al. Subthalamic nucleus gamma activity increases not only during movement but also during movement inhibition. Elife. 2017;6:e23947.
Gonzalez-Burgos G, Lewis DA. GABA neurons and the mechanisms of network oscillations: implications for understanding cortical dysfunction in schizophrenia. Schizophr Bull. 2008;34(5):944–61.
Bartos M, Vida I, Jonas P. Synaptic mechanisms of synchronized gamma oscillations in inhibitory interneuron networks. Nat Rev Neurosci. 2007;8(1):45–56.
Sohal VS, Zhang F, Yizhar O, Deisseroth K. Parvalbumin neurons and gamma rhythms enhance cortical circuit performance. Nature. 2009;459(7247):698–702.
Cardin JA, Carlén M, Meletis K, Knoblich U, Zhang F, Deisseroth K, et al. Driving fast-spiking cells induces gamma rhythm and controls sensory responses. Nature. 2009;459(7247):663–7.
Otte S, Hasenstaub A, Callaway EM. Cell type-specific control of neuronal responsiveness by gamma-band oscillatory inhibition. J Neurosci. 2010;30(6):2150–9.
Traub RD, Pais I, Bibbig A, LeBeau FEN, Buhl EH, Hormuzdi SG, et al. Contrasting roles of axonal (pyramidal cell) and dendritic (interneuron) electrical coupling in the generation of neuronal network oscillations. Proc Natl Acad Sci U S A. 2003;100(3):1370–4.
Hall SD, Stanford IM, Yamawaki N, McAllister CJ, Rönnqvist KC, Woodhall GL, et al. The role of GABAergic modulation in motor function related neuronal network activity. NeuroImage. 2011;56(3):1506–10.
Muthukumaraswamy SD, Myers JFM, Wilson SJ, Nutt DJ, Lingford-Hughes A, Singh KD, et al. The effects of elevated endogenous GABA levels on movement-related network oscillations. NeuroImage. 2013;66:36–41.
Gaetz W, Edgar JC, Wang DJ, Roberts TPL. Relating MEG measured motor cortical oscillations to resting γ-aminobutyric acid (GABA) concentration. NeuroImage. 2011;55(2):616–21.
• Nowak M, Hinson E, van Ede F, Pogosyan A, Guerra A, Quinn A, et al. Driving human motor cortical oscillations leads to behaviorally relevant changes in local GABAA inhibition: a tACS-TMS study. J Neurosci. 2017;37(17) The first study showing a link between motor cortical γ activity, GABA A inhibition and motor learning.
Ilic TV, Meintzschel F, Cleff U, Ruge D, Kessler KR, Ziemann U. Short-interval paired-pulse inhibition and facilitation of human motor cortex: the dimension of stimulus intensity. J Physiol. 2002;545(1):153–67.
Stagg CJ, Bestmann S, Constantinescu AO, Moreno LM, Allman C, Mekle R, et al. Relationship between physiological measures of excitability and levels of glutamate and GABA in the human motor cortex. J Physiol. 2011;589(Pt 23):5845–55.
Tremblay S, Beaulé V, Proulx S, de Beaumont L, Marjanska M, Doyon J, et al. Relationship between transcranial magnetic stimulation measures of intracortical inhibition and spectroscopy measures of GABA and glutamate+glutamine. J Neurophysiol. 2013;109(5):1343–9.
Dyke K, Pépés SE, Chen C, Kim S, Sigurdsson HP, Draper A, et al. Comparing GABA-dependent physiological measures of inhibition with proton magnetic resonance spectroscopy measurement of GABA using ultra-high-field MRI. NeuroImage. 2017;152:360–70.
Igarashi J, Isomura Y, Arai K, Harukuni R, Fukai TA. θ-γ oscillation code for neuronal coordination during motor behavior. J Neurosci. 2013;33(47):18515–30.
Traub RD, Whittington MA, Colling SB, Buzsáki G, Jefferys JG. Analysis of gamma rhythms in the rat hippocampus in vitro and in vivo. J Physiol. 1996;493(Pt 2):471–84.
Litvak V, Eusebio A, Jha A, Oostenveld R, Barnes G, Foltynie T, et al. Movement-related changes in local and long-range synchronization in Parkinson’s disease revealed by simultaneous magnetoencephalography and intracranial recordings. J Neurosci. 2012;32(31):10541–53.
Lee S, Jones SR. Distinguishing mechanisms of gamma frequency oscillations in human current source signals using a computational model of a laminar neocortical network. Front Hum Neurosci. 2013;7:869.
Szurhaj W, Bourriez J-L, Kahane P, Chauvel P, Mauguière F, Derambure P. Intracerebral study of gamma rhythm reactivity in the sensorimotor cortex. Eur J Neurosci. 2005;21(5):1223–35.
Szurhaj W, Labyt E, Bourriez J-L, Kahane P, Chauvel P, Mauguière F, et al. Relationship between intracerebral gamma oscillations and slow potentials in the human sensorimotor cortex. Eur J Neurosci. 2006;24(3):947–54.
Miller KJ, Leuthardt EC, Schalk G, Rao RPN, Anderson NR, Moran DW, et al. Spectral changes in cortical surface potentials during motor movement. J Neurosci. 2007;27(9):2424–32.
Cheyne D, Bells S, Ferrari P, Gaetz W, Bostan AC. Self-paced movements induce high-frequency gamma oscillations in primary motor cortex. NeuroImage. 2008;42(1):332–42.
Tecchio F, Zappasodi F, Porcaro C, Barbati G, Assenza G, Salustri C, et al. High-gamma band activity of primary hand cortical areas: a sensorimotor feedback efficiency index. NeuroImage. 2008;40(1):256–64.
Waldert S, Preissl H, Demandt E, Braun C, Birbaumer N, Aertsen A, et al. Hand movement direction decoded from MEG and EEG. J Neurosci. 2008;28(4):1000–8.
Gaetz W, MacDonald M, Cheyne D, Snead OC. Neuromagnetic imaging of movement-related cortical oscillations in children and adults: age predicts post-movement beta rebound. NeuroImage. 2010;51(2):792–807.
Pfurtscheller G, Lopes da Silva FH. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol. 1999;110(11):1842–57.
Miller KJ, Zanos S, Fetz EE, den Nijs M, Ojemann JG. Decoupling the cortical power spectrum reveals real-time representation of individual finger movements in humans. J Neurosci. 2009;29(10):3132–7.
Miller KJ, Schalk G, Fetz EE, den Nijs M, Ojemann JG, Rao RPN. Cortical activity during motor execution, motor imagery, and imagery-based online feedback. Proc Natl Acad Sci. 2010;107(9):4430–5.
Butorina A, Prokofyev A, Nazarova M, Litvak V, Stroganova T. The mirror illusion induces high gamma oscillations in the absence of movement. NeuroImage. 2014;103:181–91.
Brucke C, Huebl J, Schonecker T, Neumann W-J, Yarrow K, Kupsch A, et al. Scaling of movement is related to pallidal γ oscillations in patients with dystonia. J Neurosci. 2012;32(3):1008–19.
Joundi RA, Brittain J-S, Green AL, Aziz TZ, Brown P, Jenkinson N. Oscillatory activity in the subthalamic nucleus during arm reaching in Parkinson’s disease. Exp Neurol. 2012;236(2):319–26.
Anzak A, Tan H, Pogosyan A, Foltynie T, Limousin P, Zrinzo L, et al. Subthalamic nucleus activity optimizes maximal effort motor responses in Parkinson’s disease. Brain. 2012;135(9):2766–78.
Brown P, Oliviero A, Mazzone P, Insola A, Tonali P, Di Lazzaro V. Dopamine dependency of oscillations between subthalamic nucleus and pallidum in Parkinson’s disease. J Neurosci. 2001;21(3):1033–8.
Kühn AA, Williams D, Kupsch A, Limousin P, Hariz M, Schneider G, et al. Event-related beta desynchronization in human subthalamic nucleus correlates with motor performance. Brain. 2004;127(4):735–46.
Kühn AA, Trottenberg T, Kivi A, Kupsch A, Schneider G-H, Brown P. The relationship between local field potential and neuronal discharge in the subthalamic nucleus of patients with Parkinson’s disease. Exp Neurol. 2005;194(1):212–20.
Levy R, Ashby P, Hutchison WD, Lang AE, Lozano AM, Dostrovsky JO. Dependence of subthalamic nucleus oscillations on movement and dopamine in Parkinson’s disease. Brain. 2002;125(Pt 6):1196–209.
Shimamoto SA, Ryapolova-Webb ES, Ostrem JL, Galifianakis NB, Miller KJ, Starr PA. Subthalamic nucleus neurons are synchronized to primary motor cortex local field potentials in Parkinson’s disease. J Neurosci. 2013;33(17):7220–33.
Kühn AA, Tsui A, Aziz T, Ray N, Brücke C, Kupsch A, et al. Pathological synchronisation in the subthalamic nucleus of patients with Parkinson’s disease relates to both bradykinesia and rigidity. Exp Neurol. 2009;215(2):380–7.
Ray NJ, Jenkinson N, Wang S, Holland P, Brittain JS, Joint C, et al. Local field potential beta activity in the subthalamic nucleus of patients with Parkinson’s disease is associated with improvements in bradykinesia after dopamine and deep brain stimulation. Exp Neurol. 2008;213(1):108–13.
• Hosaka R, Nakajima T, Aihara K, Yamaguchi Y, Mushiake H. The suppression of beta oscillations in the primate supplementary motor complex reflects a volatile state during the updating of action sequences. Cereb Cortex. 2016;26(8):3442–52. γ oscillations support dynamic action planning via a distinct balance between β and γ oscillations.
Headley DB, Weinberger NM. Gamma-band activation predicts both associative memory and cortical plasticity. J Neurosci. 2011;31(36):12748–58.
Sederberg PB, Schulze-Bonhage A, Madsen JR, Bromfield EB, McCarthy DC, Brandt A, et al. Hippocampal and neocortical gamma oscillations predict memory formation in humans. Cereb Cortex. 2006;17(5):1190–6.
Sederberg PB, Kahana MJ, Howard MW, Donner EJ, Madsen JR. Theta and gamma oscillations during encoding predict subsequent recall. J Neurosci. 2003;23(34):10809–14.
Lutzenberger W, Ripper B, Busse L, Birbaumer N, Kaiser J. Dynamics of gamma-band activity during an audiospatial working memory task in humans. J Neurosci. 2002;22(13):5630–8.
Wespatat V, Tennigkeit F, Singer W. Phase sensitivity of synaptic modifications in oscillating cells of rat visual cortex. J Neurosci. 2004;24(41):9067–75.
Tort ABL, Komorowski RW, Manns JR, Kopell NJ, Eichenbaum H. Theta-gamma coupling increases during the learning of item-context associations. Proc Natl Acad Sci U S A. 2009;106(49):20942–7.
Fell J, Klaver P, Lehnertz K, Grunwald T, Schaller C, Elger CE, et al. Human memory formation is accompanied by rhinal–hippocampal coupling and decoupling. Nat Neurosci. 2001;4(12):1259–64.
Sohal VS. Insights into cortical oscillations arising from optogenetic studies. Biol Psychiatry. 2012;71(12):1039–45.
Siegle JH, Pritchett DL, Moore CI. Gamma-range synchronization of fast-spiking interneurons can enhance detection of tactile stimuli. Nat Neurosci. 2014;17(10):1371–9.
Cho KKA, Hoch R, Lee AT, Patel T, Rubenstein JLR, Sohal VS. Gamma rhythms link prefrontal interneuron dysfunction with cognitive inflexibility in Dlx5/6+/− mice. Neuron. 2015;85(6):1332–43.
Kim H, Ährlund-Richter S, Wang X, Deisseroth K, Carlén M. Prefrontal parvalbumin neurons in control of attention. Cell. 2016;164(1–2):208–18.
Watakabe A, Ohtsuka M, Kinoshita M, Takaji M, Isa K, Mizukami H, et al. Comparative analyses of adeno-associated viral vector serotypes 1, 2, 5, 8 and 9 in marmoset, mouse and macaque cerebral cortex. Neurosci Res. 2015;93:144–57.
Thut G, Schyns PG, Gross J. Entrainment of perceptually relevant brain oscillations by non-invasive rhythmic stimulation of the human brain. Front Psychol. 2011;2:170.
Herrmann CS, Strüber D, Helfrich RF, Engel AKEEG. Oscillations: from correlation to causality. Int J Psychophysiol. 2016;103:12–21.
Engel AK, Singer W. Temporal binding and the neural correlates of sensory awareness. Trends Cogn Sci. 2001;5(1):16–25.
Fries P, Nikolić D, Singer W. The gamma cycle. Trends Neurosci. 2007;30(7):309–16.
• Santarnecchi E, Biasella A, Tatti E, Rossi A, Prattichizzo D, Rossi S. High-gamma oscillations in the motor cortex during visuo-motor coordination: a tACS interferential study. Brain Res Bull. 2017;131:47–54. Motor cortex γ oscillations play a role in visuo-motor tasks involving the sudden rearrangement of motor plan/execution.
Berardelli A, Rothwell JC, Thompson PD, Hallett M. Pathophysiology of bradykinesia in Parkinson’s disease. Brain. 2001;124(11):2131–46.
• Moisa M, Polania R, Grueschow M, Ruff CC. Brain network mechanisms underlying motor enhancement by transcranial entrainment of gamma oscillations. J Neurosci. 2016;36(47):12053–65. tACS-fMRI is a useful approach to examine a causal link between γ oscillations, neural systems, and behaviour.
Traub RD, Cunningham MO, Gloveli T, LeBeau FEN, Bibbig A, Buhl EH, et al. GABA-enhanced collective behavior in neuronal axons underlies persistent gamma-frequency oscillations. Proc Natl Acad Sci U S A. 2003;100(19):11047–52.
Zaehle T, Rach S, Herrmann CS. Transcranial alternating current stimulation enhances individual alpha activity in human EEG. PLoS One. 2010;5(11):e13766.
Vossen A, Gross J, Thut G. Alpha power increase after Transcranial alternating current stimulation at alpha frequency (α-tACS) reflects plastic changes rather than entrainment. Brain Stimul. 2015;8(3):499–508.
Soekadar SR, Witkowski M, Cossio EG, Birbaumer N, Robinson SE, Cohen LG. In vivo assessment of human brain oscillations during application of transcranial electric currents. Nat Commun. 2013;4(May):2032.
Witkowski M, Cossio EG, Chander BS, Braun C, Birbaumer N, Robinson SE, et al. Mapping entrained brain oscillations during transcranial alternating current stimulation (tACS). NeuroImage. 2015;140:89–98.
Neuling T, Ruhnau P, Weisz N, Herrmann CS, Faith DG. Oscillations recovered: on analyzing EEG/MEG signals during tACS. NeuroImage. 2016;147:960–3.
Noury N, Hipp JF, Siegel M. Physiological processes non-linearly affect electrophysiological recordings during transcranial electric stimulation. NeuroImage. 2016;140:99–109.
Weinrich CA, Brittain J-S, Nowak M, Salimi-Khorshidi R, Brown P, Stagg CJ. Modulation of long-range connectivity patterns via frequency-specific stimulation of human cortex. Curr Biol. 2017;27(19):3061–3068.e3.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare that they have no conflicts of interest.
Human and Animal Rights and Informed Consent
All reported studies/experiments with human or animal subjects performed by the authors have been complied with all applicable ethical standards (including the Helsinki declaration and its amendments, institutional/national research committee standards, and international/national/institutional guidelines).
Additional information
This article is part of the Topical Collection on Neuromodulation
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
About this article
Cite this article
Nowak, M., Zich, C. & Stagg, C.J. Motor Cortical Gamma Oscillations: What Have We Learnt and Where Are We Headed?. Curr Behav Neurosci Rep 5, 136–142 (2018). https://doi.org/10.1007/s40473-018-0151-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40473-018-0151-z