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Opinion ARTICLE

Front. Psychol., 27 August 2019 | https://doi.org/10.3389/fpsyg.2019.01930

Understanding Neural Oscillations in the Human Brain: From Movement to Consciousness and Vice Versa

  • 1Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles, Brussels, Belgium
  • 2Laboratory of Electrophysiology, Université de Mons-Hainaut, Mons, Belgium

Introduction

Considering perinatal human maturation of the cerebral cortex, movement occurs before consciousness. Considering human motor control, consciousness endorses voluntary action. On the arduous road to understanding consciousness and its mechanisms, new and refined experimental paradigms may determine the next avenue. From this perspective, involuntary and voluntary movements can be considered the starting point of consciousness, the consequence of consciousness, or just a tool for approaching it. From the recent consciousness models including neural group selection and the integrated information theories, we highlight some of the experimental considerations that could indicate that movement frames consciousness. On the one hand, it may be a trigger for searching the key features in the environment (e.g., eye-scan path), but on the other hand it may conclude the final reporting of consciousness (e.g., goal vocal or manual-oriented action). The aim of this opinion paper is to encourage a discussion in the framework of the research topic, “Understanding Neural Oscillation in the Human Brain: Consciousness of Movement Execution,” and to promote new scientific interest about the hypothesis that movement is inescapable in understanding consciousness. In the following subsections we underline that oscillatory brain mechanisms integrate movement into the dynamics of the default mode network, the bottom up and top down modulations, the intentional actions in social contexts, the individual selfness and body identity, suggesting that movement may be essential to consciousness and that the oscillations-movement-consciousness triad should be inextricable.

Before the emergence of long-range cortical connections that allow consciousness (Varela et al., 2001), the early emergence of spontaneous electrical activity in the brain is based on well-shaped intermittent spontaneous oscillations that produce fetal movements (Khazipov et al., 2004; Khazipov and Milh, 2018). In rat pups, these stochastic motor actions, which are described as “popcorn” movements, generate reafferentation activities via the thalamocortical system, allowing self-organized dynamics in the brain (Buzsaki, 2011). These immature movements allow exploration of the physic world, and finally conduct humans toward self-consciousness, for which body perception and action play an important role in elaborating a sense of self and differentiating between self and others (Keromnes et al., 2018).

Recent theories about consciousness (Edelman, 2003; Seth et al., 2006; Edelman et al., 2011; Park and Blanke, 2019) have paved the way for new experimental paradigms. Thirteen features have been proposed (Seth et al., 2006) in order to better characterize the theoretical frame of reference for consciousness. Among these items, the first three established that: (1) fast, irregular, and low-amplitude oscillations (~12–70 Hz) convey consciousness; (2) these oscillatory neuronal activities are organized by the thalamocortical system acting as a “dynamic core” modulated by subcortical influences; and (3) consciousness is dispatched in different cortical areas depending on the conscious content. The other 10 items highlight that the conscious events are unitary, and that only one conscious experience emerges at a time. Accordingly, the theory of neuronal group selection (TNGS) (Edelman, 1987) is advanced as a biological foundation of consciousness. Following the TNGS, Darwinistic selection has ontogenetically shaped neuronal circuits based on positive or negative outcomes on the environment and related feedback. In this context, the reentry process linking numerous brainstem nuclei with the thalamocortical system (Edelman and Gally, 2013) and the recurrent circuit in the cortex that assumes the function of working memory (McCormick, 2001) are crucial for consciousness (Edelman et al., 2011). This implies that consciousness is a dynamic embodied process (Seth et al., 2006) that is closely related not only to voluntary movement production, but also to internal body signals from visceral organs (Park et al., 2018; Park and Blanke, 2019). Electroencephalography (EEG) (Haegens et al., 2010; Braboszcz and Delorme, 2011; Baird et al., 2014; Horschig et al., 2014; Shafto and Pitts, 2015; Koivisto et al., 2016; Ye et al., 2019) and functional magnetic resonance imaging (fMRI) (Kucyi, 2018; Kucyi et al., 2018; Demertzi et al., 2019; Golkowski et al., 2019; Liégeois et al., 2019; Yin et al., 2019) are commonly used to study general attention and consciousness in humans. Although the high temporal resolution of the EEG (timing in milliseconds range) and high spatial resolution of the fMRI (location in millimeters range) are viewed as complementary for understanding neural processes (Bréchet et al., 2019; Shen et al., 2019), recent evidence (Itthipuripat et al., 2019) demonstrated that hemodynamic attentional modulations measured in the early sensory cortex are differentially related to evoked EEG potentials, as they are linked more to later than early evoked potentials.

The Basic Control of the Default Mode

A first trivial observation is that experiences related to consciousness involve a basic awareness state, during which the participant can verbally report self-consciousness. This implies that any kind of experimental tentative to detect the emergence of consciousness related to a sensory item or the production of a free will action depends on the quality of the reference state, commonly considered as the resting or default mode network (DMN) (Raichle et al., 2001; Raichle, 2015), which presumes a non-relevant task state. A significant correlation between the global field alpha power and respiration was demonstrated in the eyes-closed resting state (Yuan et al., 2013). Other internal (visceral) movements, such as heartbeats, contribute not only to the DMN state, but also to encoding the self (Babo-Rebelo et al., 2016, 2019; Azzalini et al., 2019). The relative permanency of the self-referential and time to time modulation of mind wandering (Smallwood and Schooler, 2015; Kucyi, 2018) should be taken into account as a basic experimental control for the measurement of conscious events (Northoff et al., 2010). Until now, such control remains difficult to generalize. Recently, Davey et al. (2016) demonstrated that body-self-referential processes are assumed by the posterior cingulate cortex and regulated by the medial prefrontal cortex, which are two areas of the DMN. In addition, slow fluctuations in the position of the eyes during visual fixation influence the intrinsic DMN activity (Fransson et al., 2014). This indicates that both gaze position and body posture likely influence the DMM activity, as illustrated by “zazen” meditation practice (Brandmeyer et al., 2019), during which postural control is required. As wellness behavioral methods emphasize attention on the breathing movements to enhance body consciousness, the related EEG oscillations linked to respiration and cardiac activity should be integrated into protocols.

The Oscillatory Dialogue Between Bottom-Up and Top-Down

The bottom-up process is recognized as stimulus-driven processing capable of producing movement without volition and being outside the scope of consciousness. In contrast, top-down is considered expectation-driven processing (Engel et al., 2001), which implies voluntary action realized in full consciousness. Three stages of voluntary movement have been differentiated: the first is preconceptual and involves an inner impulse, constituting the bottom-up component of the action; the second is where the intention is conceptual, specific, and more conscious; and the third is where the decision is made whether to perform the action, constituting the top-down component (Schmidt et al., 2016). Neuronal oscillations underlie bottom-up and top-down processes (Engel et al., 2001; Varela et al., 2001) by linking separate and distant brain areas involved at different levels of the network and ensuring complex and integrative functions. The functional integration role of oscillations make them an attractive candidate mechanism for approaching a high level of complexity such as consciousness (Crick, 1984; Tononi and Edelman, 1998). Thus, the dynamics of oscillations could underlie the mechanisms of unity of consciousness (Cleeremans, 2003). In the same way that the 40 Hz oscillation in the visual cortex (Eckhorn et al., 1988; Gray et al., 1989) has been indicate to be the neural correlate of visual perceptive consciousness, the thalamocortical 40 Hz oscillation may play a major role synchronizing the firing of separate and differentiated cortical neural populations underlying motion consciousness (Llinás, 2001). Along these lines, self-consciousness was recently shown to involve gamma oscillations (~40 Hz) carried out by dopamine-dependent recurrent GABAergic neurons located in a cortical network connecting the medial frontal pre-area, anterior cingulate area, medial parietal area, and posterior cingulate area (Lou et al., 2017).

Despite the early scientific and clinical interest of Charcot (1882) and Ramóny Cajal (1889) in hypnosis, its related underlying mechanisms remain unresolved (Sala et al., 2008). Recent attempts have been made to dissociate bottom-up and top-down processing during hypnosis, which would modulate consciousness and allow the discrimination of reports of actual movement from the intention to move (Terhune et al., 2017). Hypnosis has been used as a maneuver to enhance bottom-up processing in responders by reducing the top-down control exerted by the prefrontal cortex (Gruzelier and Warren, 1993). Notably, the motor paralysis induced by hypnosis would not be due to direct motor inhibition, but to complex self-monitoring processes generated by the suggestion guiding feigned behavior (Cojan et al., 2009, 2013). Interestingly, individual differences in hypnotic susceptibility have been supported by different levels of EEG phase synchronization in the frontal lobe (Egner et al., 2005; Baghdadi and Nasrabadi, 2012), suggesting that hypnotic susceptibility is linked to the efficiency of the frontal attention system.

Intentional Actions in Social Context

The way we process the intentional actions of others in a social context can offer new experimental perspectives (Decety and Cacioppo, 2012). According to the Social Relevance Hypothesis (SRH) (Neufeld et al., 2016), various capacities in social cognition crucially depend on social stimuli, to which a high degree of attentional relevance has been assigned automatically. Numerous social stimuli generate powerful bottom-up processes that produce automatic gestures. It is difficult to disregard, escape, or suppress such inputs in the social environment. In this context, the mu rhythm has been considered an oscillatory index of intentional action processing (Perry et al., 2011). Concretely, the mu rhythm has been suggested to play a crucial function in the sensorimotor transformation (Pineda, 2005) and the consciousness of motor action. For example, Simon and Mukamel (2016) studied the consciousness perception of hand movements displayed on videos with different degrees of visibility. Conscious perception was characterized by event-related desynchronization (ERD) of beta (15–25 Hz) oscillation approximately 500 ms after video onset, followed by mu (8–10 Hz) ERD oscillation at 800 ms. These ERDs were stronger in the contralateral sensorimotor cortex. During unconscious perception, only beta ERD occurred. These results are in favor of progressive recruitment of the neuronal activities of the mirror neuron system (MNS) from unconscious to conscious perception. The timing of the reported ERD (~500 ms) is compatible to the reentrant dynamic core concept (Edelman, 2003), which implies times for the activation of numerous loops integrating signals coming from the world, the body, and the self. The ability to be conscious of the actions and intentions of others has been suggested and supported by clinical studies, and is linked to the NMS (Avanzini et al., 2012; Neufeld et al., 2016). Patients with anosognosia induced by right frontal and parietal cortex lesions are not only unconscious of their own paralyzed limb, but also unconscious of the same side limb of another person (Ramachandran and Rogers-Ramachandran, 1996). Mu ERD has been linked to the MNS, as it is reduced in the affected sensorimotor hemisphere in stroke patients observing a grasping hand movement (Frenkel-Toledo et al., 2014). However, its role as an index of the MNS seems to be compromised, and it would be related more to the sensory processing (Coll et al., 2017).

The Individual Self and Body Identity

From the integrated information theory (Tononi et al., 2016), the complexity of the interconnected brain tissue quantifying the level of consciousness and movement consciousness originates from movement experienced together with the related and concomitant multi-sensory entries. The cause and effect relationships encoded during the movement experienced by the highly interconnected brain complex mechanisms will generate movement consciousness, which will be intrinsic and unified (Koch, 2018). Preservation of the individual self and body identity can be considered a premise for experiencing the consciousness of movement execution. Phase locking in beta oscillation has been shown in the superior temporal gyrus (BA39) in an experiment in which participants observed another person's hand movement, which triggered the electrical somatosensory stimulus they received (Cebolla et al., 2014). Self-aspects of experienced spatial unity would explain such involvement of the right angular gyrus (BA39) (Blanke et al., 2005). In the same experiment, alpha, beta, and gamma power spectrum increases were located in BA40 as part of the parietal operculum, which was explained as additional somatosensory information from the observer's body schema through reafferent signals associated with the observed action, which could be related to the preservation of his/ her individual “self” and “body identity” with respect to the person seated next to him performing the movement (Iacoboni et al., 1999).

The Oscillations-Movement-Consciousness Triad

For Kleinschmidt et al. (2012) the perceptive consciousness results from a “handshake” between the representation of the properties of physical stimulus and endogenous perceptual inference. This inference is supported by both the sensory signals (bottom-up processing) and the semantic properties of the stimulus (top-down processing). Reporting of the conscious percept always implies the production of a movement and whatever the actuators used (finger, eye, or vocal cords).

Understanding the complex interrelationships in the oscillations-movement-consciousness triad may also be approached from the perspective of sleep research by using lucid dreaming as a paradigm (LaBerge et al., 2018). Lucid dreaming happens only during paradoxical sleep, which is characterized by suppression of body electromyography and H-reflex amplitude, reduced EEG alpha oscillation, and rapid eye movements (Jouvet, 1994). Intriguingly, experienced lucid dreamers can exercise volitional control over their dreamed action while dreaming. Concretely, the tracking of visually imaged traced signs by the dreamers' eyes, pursuing the dreamed images of the dreamers' thumbs, resulted in the corresponding shapes in the electrooculogram recordings (LaBerge et al., 2018).

The visual perceptive consciousness is strongly dependent on eye movements (Costela et al., 2017), which are unconsciously or consciously directed toward specific points of interest. This eye scanpath concept was initially examined by Noton and Stark (1971), and later extended to visual imagery (Brandt and Stark, 1997). Following these authors, being conscious of a picture in our environment is accompanied by a specific sequence of saccadic eye movements (scanpath) representing a “playing out of an internal control from sensory-motor representation of a picture in the brain.” The gaze control expressed by the head-eye scanpaths is central to visual saliency models (Henderson, 2017; Henderson and Hayes, 2018; Tanner and Itti, 2019), in which bottom-up, learned top-down, goal relevance, and knowledge-driven prediction features are integrated. An interesting and easily feasible experiment is the Rubin's picture, which demonstrates the oscillating nature of perceptive consciousness (Strüber and Stadler, 1999). The perception of the image can be either a vase or two opposed human face profiles (Parkkonen et al., 2008). The two perceptions continually and rhythmically alternate up to the point that one becomes dominant. As suggested by Leopold and Logothetis (1999), visual multi-stability is linked more to the expression of a behavior than to passive sensory responses. The mechanism may be assimilated to that of competitive and recurrent oscillations around a dynamic attractor, and basic models include excitatory and inhibitory neurons reciprocally interconnected, forming competitive structures and acting as dynamic attractors. This latter notion defended by Kelso (1995) and others (Başar-Eroglu et al., 1996; Kruse et al., 1996; Strüber and Stadler, 1999; Tognoli and Kelso, 2014) is in agreement with the theory of neuronal groups (Changeux, 1983; Edelman, 1987), who stated that the synchronization of the oscillating activity determines the establishment of coherently acting neuronal sets, leading to conscious perception of the world. However, both the oscillatory nature of brain function and the fundamental role of the ocular movements (Peterson and Eckstein, 2013) should be considered to gain further understanding of such perceptive consciousness. Fixating a central point in the field of view does not preclude the absence of movement. Recent evidence (Shelchkova et al., 2019) using high precision methods to record eye movements have extended the scanpath concept of active vision into a small high-acuity region of the visual field (<1°) during fixation periods. During this apparent immobility, jittery movements in the form of small saccades, microsaccades, and drifts keep the visual point of interest within the foveola. The gaze placement at certain points over the Rubin's picture determines the emergence of perceptive alternation (Engel et al., 2001). An unresolved question is whether such movement results from a voluntary conscious command. Similarly, body movement is driven ontogenetically by reaching a reward linked to an object or to a living being situated somewhere in space. For this, gaze movement is oriented precisely to the target by means of head-eye saccade commanded by the superior colliculus, which receives specific basal ganglia inputs depending on whether they are selected voluntary consciously or automatic subconsciously (Kim and Hikosaka, 2015). Interestingly, Parkkonen et al. (2008) showed that the perception of vase or face in the Rubin's picture is accompanied by a respective modulation of 12 and 15 Hz for magnetoencephalographic oscillations. Shen et al. (2019) demonstrated by means of EEG and intracranial recordings that variation in the intrinsic frequency peak of the alpha oscillation predicts the perceptive consciousness of the bistable Ternus display (He and Ooi, 1999).

All in all, we emphasized the hypothesis that movement is inescapable in understanding consciousness and that oscillatory brain activity is their essential mechanism. Before verifying this hypothesis, it will be necessary to fully understand the influence exerted by the default mode network during the resting state, the dialogue and the distinction of the bottom-up and top-down processes producing, respectively, unconscious and conscious movement, the influence of the social stimuli on such bottom-up and top-down processes, and the influence of the movement experience on the individual self. A fundamental role of the ocular movements should be reconsidered to gain further understanding of the perceptive consciousness and finally of the complex interrelationships in the oscillations-movement-consciousness triad.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Funding

This work was funded by research funds from the Université Libre de Bruxelles (ULB), Belgium, the Sports Ministry of the Federation Wallonia-Brussels, and the Fonds G. Leibu.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

Avanzini, P., Fabbri-Destro, M., Dalla Volta, R., Daprati, E., Rizzolatti, G., and Cantalupo, G. (2012). The dynamics of sensorimotor cortical oscillations during the observation of hand movements: an EEG study. PloS ONE 7:e37534. doi: 10.1371/journal.pone.0037534

PubMed Abstract | CrossRef Full Text | Google Scholar

Azzalini, D., Rebollo, I., and Tallon-Baudry, C. (2019). Visceral signals shape brain dynamics and cognition. Trends Cogn. Sci. 23, 488–509. doi: 10.1016/j.tics.2019.03.007

PubMed Abstract | CrossRef Full Text | Google Scholar

Babo-Rebelo, M., Buot, A., and Tallon-Baudry, C. (2019). Neural responses to heartbeats distinguish self from other during imagination. NeuroImage 191, 10–20. doi: 10.1016/j.neuroimage.2019.02.012

PubMed Abstract | CrossRef Full Text | Google Scholar

Babo-Rebelo, M., Richter, C. G., and Tallon-Baudry, C. (2016). Neural responses to heartbeats in the default network encode the self in spontaneous thoughts. J. Neurosci. 36, 7829–7840. doi: 10.1523/JNEUROSCI.0262-16.2016

PubMed Abstract | CrossRef Full Text | Google Scholar

Baghdadi, G., and Nasrabadi, A. M. (2012). EEG phase synchronization during hypnosis induction. J. Med. Eng. Technol. 36, 222–229. doi: 10.3109/03091902.2012.668262

PubMed Abstract | CrossRef Full Text | Google Scholar

Baird, B., Smallwood, J., Lutz, A., and Schooler, J. W. (2014). The decoupled mind: mind-wandering disrupts cortical phase-locking to perceptual events. J. Cogn. Neurosci. 26, 2596–2607. doi: 10.1162/jocn_a_00656

PubMed Abstract | CrossRef Full Text | Google Scholar

Başar-Eroglu, C., Strüber, D., Kruse, P., Başar, E., and Stadler, M. (1996). Frontal gamma-band enhancement during multistable visual perception. Int. J. Psychophysiol. 24, 113–125. doi: 10.1016/S0167-8760(96)00055-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Blanke, O., Mohr, C., Michel, C. M., Pascual-Leone, A., Brugger, P., Seeck, M., et al. (2005). Linking out-of-body experience and self processing to mental own-body imagery at the temporoparietal junction. J. Neurosci. 25, 550–557. doi: 10.1523/JNEUROSCI.2612-04.2005

PubMed Abstract | CrossRef Full Text | Google Scholar

Braboszcz, C., and Delorme, A. (2011). Lost in thoughts: neural markers of low alertness during mind wandering. NeuroImage 54, 3040–3047. doi: 10.1016/j.neuroimage.2010.10.008

PubMed Abstract | CrossRef Full Text | Google Scholar

Brandmeyer, T., Delorme, A., and Wahbeh, H. (2019). The neuroscience of meditation: classification, phenomenology, correlates, and mechanisms. Prog. Brain Res. 244, 1–29. doi: 10.1016/bs.pbr.2018.10.020

PubMed Abstract | CrossRef Full Text | Google Scholar

Brandt, S. A., and Stark, L. W. (1997). Spontaneous eye movements during visual imagery reflect the content of the visual scene. J. Cogn. Neurosci. 9, 27–38. doi: 10.1162/jocn.1997.9.1.27

PubMed Abstract | CrossRef Full Text | Google Scholar

Bréchet, L., Brunet, D., Birot, G., Gruetter, R., Michel, C. M., and Jorge, J. (2019). Capturing the spatiotemporal dynamics of self-generated, task-initiated thoughts with EEG and fMRI. NeuroImage 194, 82–92. doi: 10.1016/j.neuroimage.2019.03.029

PubMed Abstract | CrossRef Full Text | Google Scholar

Buzsaki, G. (2011). Rhythms of the Brain. Oxford, NY: Oxford University Press.

Google Scholar

Cebolla, A. M., Palmero-Soler, E., Dan, B., and Cheron, G. (2014). Modulation of the N30 generators of the somatosensory evoked potentials by the mirror neuron system. NeuroImage 95, 48–60. doi: 10.1016/j.neuroimage.2014.03.039

PubMed Abstract | CrossRef Full Text | Google Scholar

Changeux, J. P. (1983). L'Homme Neural. Ed. Paris: Fayard.

Charcot, J.-M. (1882). Sur les Divers états Nerveux Déterminés par l'Hypnotisation chez les Hystériques. Comptes rendus hebdomadaires des séances de l'Académie des Sciences, XCIV, 403-405.

Cleeremans, A. (2003). The Unity of Consciousness: Binding, Integration, and Dissociation. Oxford: Oxford University Press.

Cojan, Y., Archimi, A., Cheseaux, N., Waber, L., and Vuilleumier, P. (2013). Time-course of motor inhibition during hypnotic paralysis: EEG topographical and source analysis. Cortex J. Devoted Study Nerv. Syst. Behav. 49, 423–436. doi: 10.1016/j.cortex.2012.09.013

PubMed Abstract | CrossRef Full Text | Google Scholar

Cojan, Y., Waber, L., Schwartz, S., Rossier, L., Forster, A., and Vuilleumier, P. (2009). The brain under self-control: modulation of inhibitory and monitoring cortical networks during hypnotic paralysis. Neuron 62, 862–875. doi: 10.1016/j.neuron.2009.05.021

PubMed Abstract | CrossRef Full Text | Google Scholar

Coll, M.-P., Press, C., Hobson, H., Catmur, C., and Bird, G. (2017). Crossmodal classification of Mu rhythm activity during action observation and execution suggests specificity to somatosensory features of actions. J. Neurosci. 37, 5936–5947. doi: 10.1523/JNEUROSCI.3393-16.2017

PubMed Abstract | CrossRef Full Text | Google Scholar

Costela, F. M., McCamy, M. B., Coffelt, M., Otero-Millan, J., Macknik, S. L., and Martinez-Conde, S. (2017). Changes in visibility as a function of spatial frequency and microsaccade occurrence. Eur. J. Neurosci. 45, 433–439. doi: 10.1111/ejn.13487

PubMed Abstract | CrossRef Full Text | Google Scholar

Crick, F. (1984). Function of the thalamic reticular complex: the searchlight hypothesis. Proc. Natl. Acad. Sci. U. S. A. 81, 4586–4590. doi: 10.1073/pnas.81.14.4586

PubMed Abstract | CrossRef Full Text | Google Scholar

Davey, C. G., Pujol, J., and Harrison, B. J. (2016). Mapping the self in the brain's default mode network. NeuroImage 132, 390–397. doi: 10.1016/j.neuroimage.2016.02.022

PubMed Abstract | CrossRef Full Text | Google Scholar

Decety, J., and Cacioppo, S. (2012). The speed of morality: a high-density electrical neuroimaging study. J. Neurophysiol. 108, 3068–3072. doi: 10.1152/jn.00473.2012

PubMed Abstract | CrossRef Full Text | Google Scholar

Demertzi, A., Tagliazucchi, E., Dehaene, S., Deco, G., Barttfeld, P., Raimondo, F., et al. (2019). Human consciousness is supported by dynamic complex patterns of brain signal coordination. Sci. Adv. 5:eaat7603. doi: 10.1126/sciadv.aat7603

PubMed Abstract | CrossRef Full Text | Google Scholar

Eckhorn, R., Bauer, R., Jordan, W., Brosch, M., Kruse, W., Munk, M., et al. (1988). Coherent oscillations: a mechanism of feature linking in the visual cortex? Multiple electrode and correlation analyses in the cat. Biol. Cybern. 60, 121–130. doi: 10.1007/BF00202899

PubMed Abstract | CrossRef Full Text | Google Scholar

Edelman, G. M. (1987). Neural Darwinism: The Theory of Neuronal Group Selection. New York: Basic Books.

Google Scholar

Edelman, G. M. (2003). Naturalizing consciousness: a theoretical framework. Proc. Natl. Acad. Sci. U. S. A. 100, 5520–5524. doi: 10.1073/pnas.0931349100

PubMed Abstract | CrossRef Full Text | Google Scholar

Edelman, G. M., and Gally, J. A. (2013). Reentry: a key mechanism for integration of brain function. Front. Integr. Neurosci. 7:63. doi: 10.3389/fnint.2013.00063

PubMed Abstract | CrossRef Full Text | Google Scholar

Edelman, G. M., Gally, J. A., and Baars, B. J. (2011). Biology of consciousness. Front. Psychol. 2:4. doi: 10.3389/fpsyg.2011.00004

PubMed Abstract | CrossRef Full Text | Google Scholar

Egner, T., Jamieson, G., and Gruzelier, J. (2005). Hypnosis decouples cognitive control from conflict monitoring processes of the frontal lobe. NeuroImage 27, 969–978. doi: 10.1016/j.neuroimage.2005.05.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Engel, A. K., Fries, P., and Singer, W. (2001). Dynamic predictions: oscillations and synchrony in top-down processing. Nat. Rev. Neurosci. 2, 704–716. doi: 10.1038/35094565

PubMed Abstract | CrossRef Full Text | Google Scholar

Fransson, P., Flodin, P., Seimyr, G. Ö., and Pansell, T. (2014). Slow fluctuations in eye position and resting-state functional magnetic resonance imaging brain activity during visual fixation. Eur. J. Neurosci. 40, 3828–3835. doi: 10.1111/ejn.12745

PubMed Abstract | CrossRef Full Text | Google Scholar

Frenkel-Toledo, S., Bentin, S., Perry, A., Liebermann, D. G., and Soroker, N. (2014). Mirror-neuron system recruitment by action observation: effects of focal brain damage on mu suppression. NeuroImage 87, 127–137. doi: 10.1016/j.neuroimage.2013.10.019

PubMed Abstract | CrossRef Full Text | Google Scholar

Golkowski, D., Larroque, S. K., Vanhaudenhuyse, A., Plenevaux, A., Boly, M., Di Perri, C., et al. (2019). Changes in whole brain dynamics and connectivity patterns during sevoflurane- and propofol-induced unconsciousness identified by functional magnetic resonance imaging. Anesthesiology 130, 898–911. doi: 10.1097/ALN.0000000000002704

PubMed Abstract | CrossRef Full Text | Google Scholar

Gray, C. M., König, P., Engel, A. K., and Singer, W. (1989). Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature 338, 334–337. doi: 10.1038/338334a0

PubMed Abstract | CrossRef Full Text | Google Scholar

Gruzelier, J., and Warren, K. (1993). Neuropsychological evidence of reductions on left frontal tests with hypnosis. Psychol. Med. 23, 93–101. doi: 10.1017/S0033291700038885

PubMed Abstract | CrossRef Full Text | Google Scholar

Haegens, S., Osipova, D., Oostenveld, R., and Jensen, O. (2010). Somatosensory working memory performance in humans depends on both engagement and disengagement of regions in a distributed network. Hum. Brain Mapp. 31, 26–35. doi: 10.1002/hbm.20842

PubMed Abstract | CrossRef Full Text | Google Scholar

He, Z. J., and Ooi, T. L. (1999). Perceptual organization of apparent motion in the Ternus display. Perception 28, 877–892. doi: 10.1068/p2941

PubMed Abstract | CrossRef Full Text | Google Scholar

Henderson, J. M. (2017). Gaze control as prediction. Trends Cogn. Sci. 21, 15–23. doi: 10.1016/j.tics.2016.11.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Henderson, J. M., and Hayes, T. R. (2018). Meaning guides attention in real-world scene images: evidence from eye movements and meaning maps. J. Vis. 18:10. doi: 10.1167/18.6.10

PubMed Abstract | CrossRef Full Text | Google Scholar

Horschig, J. M., Jensen, O., van Schouwenburg, M. R., Cools, R., and Bonnefond, M. (2014). Alpha activity reflects individual abilities to adapt to the environment. NeuroImage 89, 235–243. doi: 10.1016/j.neuroimage.2013.12.018

PubMed Abstract | CrossRef Full Text | Google Scholar

Iacoboni, M., Woods, R. P., Brass, M., Bekkering, H., Mazziotta, J. C., and Rizzolatti, G. (1999). Cortical mechanisms of human imitation. Science 286, 2526–2528. doi: 10.1126/science.286.5449.2526

PubMed Abstract | CrossRef Full Text | Google Scholar

Itthipuripat, S., Sprague, T. C., and Serences, J. T. (2019). Functional MRI and EEG index complementary attentional modulations. J. Neurosci. 39, 6162–6179. doi: 10.1523/JNEUROSCI.2519-18.2019

PubMed Abstract | CrossRef Full Text | Google Scholar

Jouvet, M. (1994). Paradoxical sleep mechanisms. Sleep 17, S77–S83. doi: 10.1093/sleep/17.suppl_8.S77

PubMed Abstract | CrossRef Full Text | Google Scholar

Kelso, J. A. S. (1995). Dynamic Patterns: The Self-Organization of Brain and Behavior. Cambridge, MA: The MIT Press.

Google Scholar

Keromnes, G., Motillon, T., Coulon, N., Berthoz, A., Du Boisgueheneuc, F., Wehrmann, M., et al. (2018). Self-other recognition impairments in individuals with schizophrenia: a new experimental paradigm using a double mirror. NPJ Schizophr. 4:24. doi: 10.1038/s41537-018-0065-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Khazipov, R., and Milh, M. (2018). Early patterns of activity in the developing cortex: focus on the sensorimotor system. Semin. Cell Dev. Biol. 76, 120–129. doi: 10.1016/j.semcdb.2017.09.014

PubMed Abstract | CrossRef Full Text | Google Scholar

Khazipov, R., Sirota, A., Leinekugel, X., Holmes, G. L., Ben-Ari, Y., and Buzsáki, G. (2004). Early motor activity drives spindle bursts in the developing somatosensory cortex. Nature 432, 758–761. doi: 10.1038/nature03132

PubMed Abstract | CrossRef Full Text | Google Scholar

Kim, H. F., and Hikosaka, O. (2015). Parallel basal ganglia circuits for voluntary and automatic behaviour to reach rewards. Brain J. Neurol. 138, 1776–1800. doi: 10.1093/brain/awv134

PubMed Abstract | CrossRef Full Text | Google Scholar

Kleinschmidt, A., Sterzer, P., and Rees, G. (2012). Variability of perceptual multistability: from brain state to individual trait. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 367, 988–1000. doi: 10.1098/rstb.2011.0367

PubMed Abstract | CrossRef Full Text | Google Scholar

Koch, C. (2018). What is consciousness? Sci. Am. 318, 60–64. doi: 10.1038/scientificamerican0618-60

PubMed Abstract | CrossRef Full Text

Koivisto, M., Salminen-Vaparanta, N., Grassini, S., and Revonsuo, A. (2016). Subjective visual awareness emerges prior to P3. Eur. J. Neurosci. 43, 1601–1611. doi: 10.1111/ejn.13264

PubMed Abstract | CrossRef Full Text | Google Scholar

Kruse, P., Carmesin, H. O., Pahlke, L., Strüber, D., and Stadler, M. (1996). Continuous phase transitions in the perception of multistable visual patterns. Biol. Cybern. 75, 321–330. doi: 10.1007/s004220050298

PubMed Abstract | CrossRef Full Text | Google Scholar

Kucyi, A. (2018). Just a thought: how mind-wandering is represented in dynamic brain connectivity. NeuroImage 180, 505–514. doi: 10.1016/j.neuroimage.2017.07.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Kucyi, A., Tambini, A., Sadaghiani, S., Keilholz, S., and Cohen, J. R. (2018). Spontaneous cognitive processes and the behavioral validation of time-varying brain connectivity. Netw. Neurosci. 2, 397–417. doi: 10.1162/netn_a_00037

PubMed Abstract | CrossRef Full Text | Google Scholar

LaBerge, S., Baird, B., and Zimbardo, P. G. (2018). Smooth tracking of visual targets distinguishes lucid REM sleep dreaming and waking perception from imagination. Nat. Commun. 9:3298. doi: 10.1038/s41467-018-05547-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Leopold, D. A., and Logothetis, N. K. (1999). Multistable phenomena: changing views in perception. Trends Cogn. Sci. 3, 254–264. doi: 10.1016/S1364-6613(99)01332-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Liégeois, R., Li, J., Kong, R., Orban, C., Van De Ville, D., Ge, T., et al. (2019). Resting brain dynamics at different timescales capture distinct aspects of human behavior. Nat. Commun. 10:2317. doi: 10.1038/s41467-019-10317-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Llinás, R. R. (2001). I of the Vortex: From Neurons to Self, 1st Edn. Cambridge, MA: The MIT Press

Google Scholar

Lou, H. C., Changeux, J. P., and Rosenstand, A. (2017). Towards a cognitive neuroscience of self-awareness. Neurosci. Biobehav. Rev. 83, 765–773. doi: 10.1016/j.neubiorev.2016.04.004

PubMed Abstract | CrossRef Full Text | Google Scholar

McCormick, D. A. (2001). Brain calculus: neural integration and persistent activity. Nat. Neurosci. 4, 113–114. doi: 10.1038/83917

PubMed Abstract | CrossRef Full Text | Google Scholar

Neufeld, E., Brown, E. C., Lee-Grimm, S.-I., Newen, A., and Brüne, M. (2016). Intentional action processing results from automatic bottom-up attention: an EEG-investigation into the Social Relevance Hypothesis using hypnosis. Conscious. Cogn. 42, 101–112. doi: 10.1016/j.concog.2016.03.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Northoff, G., Duncan, N. W., and Hayes, D. J. (2010). The brain and its resting state activity–experimental and methodological implications. Prog. Neurobiol. 92, 593–600. doi: 10.1016/j.pneurobio.2010.09.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Noton, D., and Stark, L. (1971). Scanpaths in eye movements during pattern perception. Science 171, 308–311. doi: 10.1126/science.171.3968.308

PubMed Abstract | CrossRef Full Text | Google Scholar

Park, H.-D., Bernasconi, F., Salomon, R., Tallon-Baudry, C., Spinelli, L., Seeck, M., et al. (2018). Neural sources and underlying mechanisms of neural responses to heartbeats, and their role in bodily self-consciousness: an intracranial EEG study. Cereb. Cortex 28, 2351–2364. doi: 10.1093/cercor/bhx136

PubMed Abstract | CrossRef Full Text | Google Scholar

Park, H.-D., and Blanke, O. (2019). Coupling inner and outer body for self-consciousness. Trends Cogn. Sci. 23, 377–388. doi: 10.1016/j.tics.2019.02.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Parkkonen, L., Andersson, J., Hämäläinen, M., and Hari, R. (2008). Early visual brain areas reflect the percept of an ambiguous scene. Proc. Natl. Acad. Sci. U. S. A. 105, 20500–20504. doi: 10.1073/pnas.0810966105

PubMed Abstract | CrossRef Full Text | Google Scholar

Perry, A., Stein, L., and Bentin, S. (2011). Motor and attentional mechanisms involved in social interaction–evidence from mu and alpha EEG suppression. NeuroImage 58, 895–904. doi: 10.1016/j.neuroimage.2011.06.060

PubMed Abstract | CrossRef Full Text | Google Scholar

Peterson, M. F., and Eckstein, M. P. (2013). Individual differences in eye movements during face identification reflect observer-specific optimal points of fixation. Psychol. Sci. 24, 1216–1225. doi: 10.1177/0956797612471684

PubMed Abstract | CrossRef Full Text | Google Scholar

Pineda, J. A. (2005). The functional significance of mu rhythms: translating “seeing” and “hearing” into “doing.” Brain Res. Brain Res. Rev. 50, 57–68. doi: 10.1016/j.brainresrev.2005.04.005

CrossRef Full Text | Google Scholar

Raichle, M. E. (2015). The brain's default mode network. Annu. Rev. Neurosci. 38, 433–447. doi: 10.1146/annurev-neuro-071013-014030

PubMed Abstract | CrossRef Full Text | Google Scholar

Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., and Shulman, G. L. (2001). A default mode of brain function. Proc. Natl. Acad. Sci. U. S. A. 98, 676–682. doi: 10.1073/pnas.98.2.676

CrossRef Full Text | Google Scholar

Ramachandran, V. S., and Rogers-Ramachandran, D. (1996). Denial of disabilities in anosognosia. Nature 382:501. doi: 10.1038/382501a0

PubMed Abstract | CrossRef Full Text | Google Scholar

Ramóny Cajal, S. (1889). Dolores del parto considerablemente atenuados por la sugestión hipnótica [Labor pains considerably attenuated through hypnotic suggestion]. 485–486.

Sala, J., Cardeña, E., Holgado, M. C., Añez, C., Pérez, P., Periñan, R., et al. (2008). The contributions of Ramon y Cajal and other Spanish authors to hypnosis. Int. J. Clin. Exp. Hypn. 56, 361–372. doi: 10.1080/00207140802255344

PubMed Abstract | CrossRef Full Text | Google Scholar

Schmidt, S., Jo, H. G., Wittmann, M., and Hinterberger, T. (2016). 'Catching the waves' - slow cortical potentials as moderator of voluntary action. Neurosci. Biobehav. Rev. 68, 639–650. doi: 10.1016/j.neubiorev.2016.06.023

PubMed Abstract | CrossRef Full Text | Google Scholar

Seth, A. K., Izhikevich, E., Reeke, G. N., and Edelman, G. M. (2006). Theories and measures of consciousness: an extended framework. Proc. Natl. Acad. Sci. U. S. A. 103, 10799–10804. doi: 10.1073/pnas.0604347103

PubMed Abstract | CrossRef Full Text | Google Scholar

Shafto, J. P., and Pitts, M. A. (2015). Neural signatures of conscious face perception in an inattentional blindness paradigm. J. Neurosci. 35, 10940–10948. doi: 10.1523/JNEUROSCI.0145-15.2015

PubMed Abstract | CrossRef Full Text | Google Scholar

Shelchkova, N., Tang, C., and Poletti, M. (2019). Task-driven visual exploration at the foveal scale. Proc. Natl. Acad. Sci. U. S. A. 116, 5811–5818. doi: 10.1073/pnas.1812222116

PubMed Abstract | CrossRef Full Text | Google Scholar

Shen, L., Han, B., Chen, L., and Chen, Q. (2019). Perceptual inference employs intrinsic alpha frequency to resolve perceptual ambiguity. PLoS Biol. 17:e3000025. doi: 10.1371/journal.pbio.3000025

PubMed Abstract | CrossRef Full Text | Google Scholar

Simon, S., and Mukamel, R. (2016). Power modulation of electroencephalogram mu and beta frequency depends on perceived level of observed actions. Brain Behav. 6:e00494. doi: 10.1002/brb3.494

PubMed Abstract | CrossRef Full Text | Google Scholar

Smallwood, J., and Schooler, J. W. (2015). The science of mind wandering: empirically navigating the stream of consciousness. Annu. Rev. Psychol. 66, 487–518. doi: 10.1146/annurev-psych-010814-015331

PubMed Abstract | CrossRef Full Text | Google Scholar

Strüber, D., and Stadler, M. (1999). Differences in top-down influences on the reversal rate of different categories of reversible figures. Perception 28, 1185–1196. doi: 10.1068/p2973

PubMed Abstract | CrossRef Full Text | Google Scholar

Tanner, J., and Itti, L. (2019). A top-down saliency model with goal relevance. J. Vis. 19:11. doi: 10.1167/19.1.11

PubMed Abstract | CrossRef Full Text | Google Scholar

Terhune, D. B., Cleeremans, A., Raz, A., and Lynn, S. J. (2017). Hypnosis and top-down regulation of consciousness. Neurosci. Biobehav. Rev. 81, 59–74. doi: 10.1016/j.neubiorev.2017.02.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Tognoli, E., and Kelso, J. A. (2014). The metastable brain. Neuron 81, 35–48. doi: 10.1016/j.neuron.2013.12.022

PubMed Abstract | CrossRef Full Text | Google Scholar

Tononi, G., Boly, M., Massimini, M., and Koch, C. (2016). Integrated information theory: from consciousness to its physical substrate. Nat. Rev. Neurosci. 17, 450–461. doi: 10.1038/nrn.2016.44

PubMed Abstract | CrossRef Full Text | Google Scholar

Tononi, G., and Edelman, G. M. (1998). Consciousness and complexity. Science 282, 1846–1851. doi: 10.1126/science.282.5395.1846

PubMed Abstract | CrossRef Full Text | Google Scholar

Varela, F., Lachaux, J. P., Rodriguez, E., and Martinerie, J. (2001). The brainweb: phase synchronization and large-scale integration. Nat. Rev. Neurosci. 2, 229–239. doi: 10.1038/35067550

PubMed Abstract | CrossRef Full Text | Google Scholar

Ye, M., Lyu, Y., Sclodnick, B., and Sun, H.-J. (2019). The P3 reflects awareness and can be modulated by confidence. Front. Neurosci. 13:510. doi: 10.3389/fnins.2019.00510

PubMed Abstract | CrossRef Full Text | Google Scholar

Yin, D., Zhang, Z., Wang, Z., Zeljic, K., Lv, Q., Cai, D., et al. (2019). Brain map of intrinsic functional flexibility in anesthetized monkeys and awake humans. Front. Neurosci. 13:174. doi: 10.3389/fnins.2019.00174

PubMed Abstract | CrossRef Full Text | Google Scholar

Yuan, H., Zotev, V., Phillips, R., and Bodurka, J. (2013). Correlated slow fluctuations in respiration, EEG, and BOLD fMRI. NeuroImage 79, 81–93. doi: 10.1016/j.neuroimage.2013.04.068

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: oscillation, movement, consciousness, brain, intention

Citation: Cebolla AM and Cheron G (2019) Understanding Neural Oscillations in the Human Brain: From Movement to Consciousness and Vice Versa. Front. Psychol. 10:1930. doi: 10.3389/fpsyg.2019.01930

Received: 08 April 2019; Accepted: 06 August 2019;
Published: 27 August 2019.

Edited by:

Maurizio Bertollo, Università degli Studi G. d'Annunzio Chieti e Pescara, Italy

Reviewed by:

Tsung-Min Hung, National Taiwan Normal University, Taiwan
Penny C. Werthner, University of Calgary, Canada
Stephane Perrey, Université de Montpellier, France

Copyright © 2019 Cebolla and Cheron. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Guy Cheron, gcheron@ulb.ac.be

These authors have contributed equally to this work