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Brain oscillations in perception, timing and action
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ScienceDirect Brain oscillations in perception, timing and action Daya S Gupta1 and Lihan Chen2,3 Catching a thrown ball requires a tight coupling between perception and motor control. In this review, we examine multidimensional information processing across various perceptual and motor tasks. We summarize how perception, timing and action can be understood in terms of the coupling of gamma band oscillations, which represent the local activities of brain circuits, to a specific phase of long-range low-frequency oscillations. We propose a temporal window of integration that emerges from cross-frequency coupling that serves to produce optimized action. Addresses 1 Department of Biology, Camden County College, Blackwood, NJ 08012-0200, USA 2 Department of Psychology and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing 100871, China 3 Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing 100871, China Corresponding authors: Gupta, Daya S ([email protected]) and Chen, Lihan ([email protected])
Current Opinion in Behavioral Sciences 2016, 8:161–166 This review comes from a themed issue on Time in perception and action Edited by Warren H Meck and Richard B Ivry For a complete overview see the Issue and the Editorial Available online 17th February 2016 http://dx.doi.org/10.1016/j.cobeha.2016.02.021 2352-1546/# 2016 Elsevier Ltd. All rights reserved.
Introduction To hit a pitched baseball requires that the batter generates accurate estimates of the position, speed, and timing of the moving ball and use this information to produce a coordinated movement. Humans are adept in compensating for their own sensorimotor uncertainty in the execution of skilled actions [1,2,3]; however, the neural mechanisms underlying the temporal coordination between perception and action have not been fully established. Different lines of evidence have facilitated our understanding of the computations and physiological underpinnings that are involved in the coupling of perception and action. For example, a recent computational modeling study revealed that the internal representations of observers’ spatial visuomotor errors (in visuomotor decision tasks) are best described by a mixture of distributions that differ in location and scale [2]. This indicates that www.sciencedirect.com
the internal probability density functions of their own visuomotor error distributions are discrete [2]. These results suggest that discrete neurophysiological processes may be responsible for various sensorimotor tasks. In this review, we propose that the processing of multidimensional information by neuronal populations occurs in separate epochs and that these epochs represent different facets of the interaction between perception and action (Figure 1).
Multidimensional processing of sensorimotor information Over the last decade, there has been considerable interest in the roles of cross-frequency phase-amplitude coupling in various cognitive functions [4]. Cross-frequency phase-amplitude coupling is a phenomenon in which the phase of a lower frequency brain oscillation modulates the amplitudes, i.e., the differences in the maxima and minima of oscillation waves, of a higher frequency oscillation to create ‘packets’ of those higher frequency waves [4]. Coupling of the phases of low-frequency theta (4– 8 Hz) and alpha (8–12 Hz) oscillations to the amplitudes of high-frequency gamma (30–150 Hz) oscillations [5,6,7,8,9,10,11,12,13,14,15] has been observed in various sensory and motor tasks. In one study, visual processing is found to reflect local activity, whereas working memory retention and mental imagery, which likely involve multiple, distributed cortical areas, are related to theta (4–8 Hz) and alpha (8–12 Hz) frequency oscillations [11]. Furthermore, gamma band neuronal oscillations that relate to the spiking activities of neurons [9,15,16] in response to stimulus characteristics, including intensity and duration, have been observed in many cortical and subcortical structures [17,18,19,20,21]. Additionally, a recent study demonstrated zero and non-zero lags between the phases of gamma oscillations in spatially separate sites in the primary visual cortex [22]. These non-zero lags are modulated by the stimulus, suggesting that gamma oscillations can also dynamically influence the exchange of information among local populations of neurons [22]. In contrast to gamma oscillations, low-frequency oscillations generally synchronize larger areas of the cortex and play a role in long-range interactions [4,15]. Thus, as a result of phase-amplitude coupling, gamma-band oscillations reflect local activities, whereas low-frequency oscillations help to engage their long-range interactions [4,15]. In the framework of multidimensional processing, the amplitudes of high-frequency oscillations that synchronize the processing of information in multiple local circuits are modulated by the phases of synchronous long-range low-frequency brain oscillations. This phase-amplitude Current Opinion in Behavioral Sciences 2016, 8:161–166
162 Time in perception and action
Figure 1
Local circuits (Fast Oscillations) Buzsaki G, Watson BO: Dialogues Clin Neurosci (2012)
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Multidimensional Processing of Information (Low-frequency Oscillations) Local spiking activity is coupled to a phase of fast oscillation ISI = 30 ms Belitski A, et al: J Neurosci (2008) 28(22):5696-5709
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Schematic depiction of the multidimensional processing of information in the brain. (a) Activities in local circuits are modulated by the phase of long-range low-frequency oscillations (indicated by curved lines spanning a large area of the cortex). Sensory, motor, multimodal and association areas in the prefrontal cortex (oval boundaries) are shown as examples of local circuits. (b) Depicts cross-frequency phase-amplitude coupling. Note that multiunit spike bursts (shown inside a circle) have been observed in experimental studies to exhibit a dependency on the phase of fast (gamma band high-frequency) oscillations. t1, t2 and t3 are time points, present at intervals that equal the periodicity of low-frequency oscillation, when fast oscillations synchronize activities (spike bursts) of local circuits. Multidimensional information processing occurs as a result of the modulation of fast oscillations (synchronizing local circuits) by long-range low-frequency oscillations. The phase of low-frequency oscillation when spike bursts in local circuits are synchronized by fast oscillations will provide the temporal window of integration (TWI) of stimuli. Spike bursts contain information (see panel c) that is processed in the multidimensional domain during a sensorimotor task. (c) Interstimulus intervals (ISIs) between two frames of a Ternus display, which is a special case of apparent motion [52], are processed differently depending on the length of the ISI to produce different content levels of Shannon information. Notice that there is less uncertainty regarding the presence of spikes in time-bins of the same length that contain shorter ISIs, i.e., the information content is relatively reduced. The shorter ISIs, with less information content, are associated with the percept of element motion, which contrasts with the group motion observed with the longer ISIs.
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Oscillatory structures in the brain Gupta and Chen 163
coupling causes information to be coherently processed in distributed circuits, supporting the tight temporal coupling between various aspects of a sensorimotor task. The degree of this coupling depends on the demands of the task. For example, catching a faster-moving object requires a tighter coupling between various sensorimotor functions that occurs over shorter sub-second durations. The information resulting from the activities of local circuits can be described by Shannon entropy, i.e., the amount of uncertainty about whether a neuron that represents an information source, such as the primary visual cortex, will or will not fire within a particular temporal window [23,24]. Information or entropy in the proposed multidimensional domain is a consequence of the stochastic nature of the neuronal responses to a stimulus [25,26,27]. Other sources of information for multidimensional processing may include higher functional areas, such as the somatosensory areas, multimodal areas, motor areas and the anterior association area in the prefrontal cortex. Note that the information resulting from the coherent neuronal activities that are responsible for sensorimotor tasks can be quantified by different analyses, including wavelet information analysis and the study of spike structures in terms of complexity and randomness [26,27,28]. In one study in which primates were presented with naturalistic scenes, a positive correlation was observed between high-gamma local field potential (LFP) (60–100 Hz) and spikes, along with a strong positive correlation within highgamma LFPs in the primary visual cortex [16]. These findings suggest that high-gamma LFPs and spikes are generated within the same network [16]. Another study revealed significant phase correlations in approximately 60% of the multiunit activities of neurons and LFP recordings from visual cortices during a specific visual stimulation in addition to phase correlations between oscillatory events at different frequencies [15]. As such, the information in spike structure [27] during multidimensional processing is indicative of sensorimotor responses. It was observed that stimulus-driven excitability variations in local cortical ensembles are related to the phases of brain oscillations within the setting of a hierarchical cross-frequency coupling — amplitude of a higher frequency oscillation is coupled with the phase of a lower frequency oscillation [29,30]. Accordingly, we propose that synchronized long-range low-frequency oscillations provide a temporal window of integration (TWI) in the framework multidimensional processing. The TWI may manifest as a peak or trough of a low-frequency brain oscillation because coupling has been observed between the high gamma power and the peak phases of alpha (8–12 Hz) oscillations in a working memory task [31] and the troughs of theta (4–8 Hz) oscillations in motor, sensory and cognitive tasks [5]. Moreover, the TWI is likely to be related to the periodicity of the low-frequency www.sciencedirect.com
oscillations. Typical integrations of auditory and visual events occur within a time window of 100 ms, for example, those observed during the sound-induced doubleflash illusion [32]. The sound-induced double-flash illusion may be explained by the coupling of the activities of the auditory and visual circuits to the same phase of a slow wave oscillation in the framework of multidimensional processing. An important supporting piece of evidence comes from spatial ventriloquism in which a visual object can attract the perceived location of a spatially discordant but temporally synchronous sound [33,34]. This attraction can be understood as a phase-amplitude coupling of the circuits processing the sound to the synchronized lowfrequency oscillations that are devoted to processing different features of the visual object. Although spatial ventriloquism is an example of the integration of spatially separate events, phase-amplitude coupling is observed in the integration of information associated with inputs arising from a single source. For example, during simple visual tasks, alpha/high gamma coupling preferentially increases in the visual cortical regions [8]. A recent study provides the causal evidence that the temporal window of integration associated with sound-induced doubleflash illusion is constrained by the frequency of occipital oscillations in the alpha band [34]. Faster alpha frequencies also predict vision with a finer temporal resolution, which is consistent with the hypothesis that faster alpha oscillations provide more cycles for the separate rather than combined integration, which results in better resolution [35]. In another study of the integration of two stimuli (i.e., audio and tactile), the alpha power recorded over the superior and inferior parietal lobules was relatively increased in a pretactile stimulus time-window of approximately 330 ms prior to the subsequent perception of an integrated rather than segregated stimulus [36]. Due to the lack of a poststimulus effect in this study, the integrated/segregated perception was likely an effect of a network comprising ‘multisensory integration’ areas with unisensory areas rather than a specific multisensory area [36]. This interpretation is consistent with the multidimensional processing of auditory and tactile stimuli within a network. The increase in alpha power during the integration of two stimuli can be understood in terms of multidimensional processing. For multisensory integration to occur, the alpha oscillations modulating both unisensory circuits, i.e., tactile and auditory, must synchronize, producing an increase in alpha power. Therefore, one may argue that the processing of multiple sensory stimuli (as depicted in Figure 1) within successive but discrete TWIs results in the integration of sensory information from multiple sources in a dynamic network of circuits over a time period. This integration forms the basis of successful sensorimotor interactions with the external physical environment. Current Opinion in Behavioral Sciences 2016, 8:161–166
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Multidimensional processing in apparent visual motion In this section, we argue that pre-stimulus alpha power and spontaneous brain oscillations influence apparent motion perception [37]. Apparent motion is a visual illusion in which motion is perceived when two spatially distinct static objects alternate in sequence. Apparent motion illusions are robust at presentation frequencies of approximately 3 Hz. Apparent motion has been deemed to be dependent on the ability to use predictive feedback signals in the processing of ‘fragmented’ sensory information [38,39]. The perception of good apparent motion follows an optimal range of presentation frequencies. Lower alpha power predicts percepts between apparent motion and flickering at high presentation frequencies. In a study, higher alpha power predicts apparent motion percepts of visual objects with low presentation frequencies [37]. Specifically, apparent motion perception depends on both local neural synchronization (i.e., the power within the frontal and occipital regions of interests) and long-distance neural synchronization (i.e., frontal-occipital connectivity) in the pre-stimulus alpha oscillations [37].
Multidimensional processing in timing and action Phase-amplitude coupling provides a plausible mechanism for the calibration of modular clocks. This calibration involves endogenous oscillators within various networks, modules that calibrate the oscillators, and downstream circuits that process task-specific time intervals. These components are connected via flexible connections as proposed by Gupta (2014) [40]. The modular clock mechanism is calibrated by circuits, such as those in the cerebellum and posterior parietal cortex, which are important for feedback control during various sensorimotor tasks [40]. During complex sensorimotor tasks, such as catching a flying ball or lifting a cup, irregular spike bursts are produced during feedback processes and reflect unequal temporal epochs that separate the activities of individual muscles during these movements. Furthermore, the irregular changes in the neuronal activities during a sensorimotor task mirror the changes in the physical time-related parameters, such as speed and duration. The input from feedback circuits to endogenous oscillatory mechanisms aid in the transfer of the physical time information. Note that phase-amplitude coupling preserves the temporal relationship between the parallel inputs of irregular activities that arrive at the oscillator/local clock mechanism from a calibration module. Additionally, motor and sensory taskmodulated changes in the activities of neurons are observed in parts of the brain, such as the inferior temporal lobe and prefrontal cortex, which are not directly responsible for the feedback control of external sensorimotor tasks [41,42]. These changes may influence the effects of feedback processes and could also be responsible for the calibration of endogenous oscillators. Current Opinion in Behavioral Sciences 2016, 8:161–166
Several recent studies have also suggested the role of beta band oscillations in the representation of various time durations in the brain [43,44,45]. Interestingly, in a recent study by Bartolo and Merchant (2015), putaminal LFPs were recorded in monkeys that were performing a synchronization-continuation task. The LFPs exhibited an initial burst of beta band oscillation that was followed by another increase during the continuation phase of the task [45]. This latter increase depends on internally generated cues [45]. The dependence on internally generated cues suggests the involvement of other calibration modules, such as those arising from the basal ganglia circuit as previously proposed by Gupta (2014) [40], which may be responsible for the observed increase in the beta power via an increase in the number of longrange beta-oscillation interactions during the continuation phase of the task. Consistent with the role of low-frequency oscillations in the modulation of the amplitudes of high-frequency oscillations by long-range oscillations, previous studies utilizing motor tasks have demonstrated phase-amplitude coupling between movement-selective high gamma and alpha oscillations in humans [14] and between gamma and theta oscillations in the motor cortical areas of rats during distinct movement states [12]. Another study suggests a saccade-related phase-amplitude coupling between theta and low gamma activities [13]. Thus, the amplitudes of gamma oscillations in motor areas, which represent motor information including the time of the onset, performance and execution [46,47], nested within the low-frequency oscillations are likely to play an important role in the processing of information for the execution of sensorimotor tasks. A recent study simultaneously recorded neuronal activities in multiple cortical regions in monkeys that were trained to report the color or motion of the stimuli. This study revealed complex dynamics of information flow [48]. When information reaches one part of the brain from another, the phase of the synchronized low-frequency oscillations connecting the two regions is advanced [49]. This delay in phase affects the processing of the arriving information by altering the available size of the TWI. The effect of longrange oscillations on information flow has been indicated by an analysis of intracranial EEG data that revealed that power changes in different frequency bands significantly contribute to the late components of the event-related potentials [50]. The micro-stimulation of V1 (i.e., the primary visual area) generates gamma oscillations in the feedforward direction, whereas such stimulation of V4 (i.e., a downstream visual area) generates alpha oscillations in the feedback direction [49]. These findings suggest that during the multidimensional processing of visual information, the coupling of the amplitude of gamma oscillations (produced by feedforward activity) to the phase of lowfrequency oscillations (produced by feedback activity) www.sciencedirect.com
Oscillatory structures in the brain Gupta and Chen 165
could also modulate the flow of information between lower and higher visual areas. Thus, phase-amplitude coupling could form one of the important bases of sensorimotor choices in flexible visuomotor tasks; such choices are believed to result from the integration of opposite flows of sensory and task information [48].
Summary VanRullen and Koch (2003) proposed that cross-frequency interactions between gamma and alpha oscillations constrain perception [51]. We extend this proposal to include motor functions to understand the role of the multidimensional processing of information during sensorimotor tasks. The multidimensional processing provides an important basis for understanding how different circuits of the brain can be temporally coupled during various sensorimotor tasks.
Conflict of interest statement Nothing declared.
Acknowledgements This study was supported by grants from the Natural Science Foundation of China (31200760), the National High Technology Research and Development Program of China (863 Program, 2012AA011602) and the Fund for Fostering Talents in Basic Science (J1103602) to LC. DSG would like to acknowledge his gratitude to the students, faculty and staff of Camden County College, New Jersey, USA and the ResearchGateTM community.
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ScienceDirect Brain oscillations in perception, timing and action Daya S Gupta1 and Lihan Chen2,3 Catching a thrown ball requires a tight coupling between perception and motor control. In this review, we examine multidimensional information processing across various perceptual and motor tasks. We summarize how perception, timing and action can be understood in terms of the coupling of gamma band oscillations, which represent the local activities of brain circuits, to a specific phase of long-range low-frequency oscillations. We propose a temporal window of integration that emerges from cross-frequency coupling that serves to produce optimized action. Addresses 1 Department of Biology, Camden County College, Blackwood, NJ 08012-0200, USA 2 Department of Psychology and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing 100871, China 3 Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing 100871, China Corresponding authors: Gupta, Daya S ([email protected]) and Chen, Lihan ([email protected])
Current Opinion in Behavioral Sciences 2016, 8:161–166 This review comes from a themed issue on Time in perception and action Edited by Warren H Meck and Richard B Ivry For a complete overview see the Issue and the Editorial Available online 17th February 2016 http://dx.doi.org/10.1016/j.cobeha.2016.02.021 2352-1546/# 2016 Elsevier Ltd. All rights reserved.
Introduction To hit a pitched baseball requires that the batter generates accurate estimates of the position, speed, and timing of the moving ball and use this information to produce a coordinated movement. Humans are adept in compensating for their own sensorimotor uncertainty in the execution of skilled actions [1,2,3]; however, the neural mechanisms underlying the temporal coordination between perception and action have not been fully established. Different lines of evidence have facilitated our understanding of the computations and physiological underpinnings that are involved in the coupling of perception and action. For example, a recent computational modeling study revealed that the internal representations of observers’ spatial visuomotor errors (in visuomotor decision tasks) are best described by a mixture of distributions that differ in location and scale [2]. This indicates that www.sciencedirect.com
the internal probability density functions of their own visuomotor error distributions are discrete [2]. These results suggest that discrete neurophysiological processes may be responsible for various sensorimotor tasks. In this review, we propose that the processing of multidimensional information by neuronal populations occurs in separate epochs and that these epochs represent different facets of the interaction between perception and action (Figure 1).
Multidimensional processing of sensorimotor information Over the last decade, there has been considerable interest in the roles of cross-frequency phase-amplitude coupling in various cognitive functions [4]. Cross-frequency phase-amplitude coupling is a phenomenon in which the phase of a lower frequency brain oscillation modulates the amplitudes, i.e., the differences in the maxima and minima of oscillation waves, of a higher frequency oscillation to create ‘packets’ of those higher frequency waves [4]. Coupling of the phases of low-frequency theta (4– 8 Hz) and alpha (8–12 Hz) oscillations to the amplitudes of high-frequency gamma (30–150 Hz) oscillations [5,6,7,8,9,10,11,12,13,14,15] has been observed in various sensory and motor tasks. In one study, visual processing is found to reflect local activity, whereas working memory retention and mental imagery, which likely involve multiple, distributed cortical areas, are related to theta (4–8 Hz) and alpha (8–12 Hz) frequency oscillations [11]. Furthermore, gamma band neuronal oscillations that relate to the spiking activities of neurons [9,15,16] in response to stimulus characteristics, including intensity and duration, have been observed in many cortical and subcortical structures [17,18,19,20,21]. Additionally, a recent study demonstrated zero and non-zero lags between the phases of gamma oscillations in spatially separate sites in the primary visual cortex [22]. These non-zero lags are modulated by the stimulus, suggesting that gamma oscillations can also dynamically influence the exchange of information among local populations of neurons [22]. In contrast to gamma oscillations, low-frequency oscillations generally synchronize larger areas of the cortex and play a role in long-range interactions [4,15]. Thus, as a result of phase-amplitude coupling, gamma-band oscillations reflect local activities, whereas low-frequency oscillations help to engage their long-range interactions [4,15]. In the framework of multidimensional processing, the amplitudes of high-frequency oscillations that synchronize the processing of information in multiple local circuits are modulated by the phases of synchronous long-range low-frequency brain oscillations. This phase-amplitude Current Opinion in Behavioral Sciences 2016, 8:161–166
162 Time in perception and action
Figure 1
Local circuits (Fast Oscillations) Buzsaki G, Watson BO: Dialogues Clin Neurosci (2012)
Working Memory Multimodal Motor
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Multidimensional Processing of Information (Low-frequency Oscillations) Local spiking activity is coupled to a phase of fast oscillation ISI = 30 ms Belitski A, et al: J Neurosci (2008) 28(22):5696-5709
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Schematic depiction of the multidimensional processing of information in the brain. (a) Activities in local circuits are modulated by the phase of long-range low-frequency oscillations (indicated by curved lines spanning a large area of the cortex). Sensory, motor, multimodal and association areas in the prefrontal cortex (oval boundaries) are shown as examples of local circuits. (b) Depicts cross-frequency phase-amplitude coupling. Note that multiunit spike bursts (shown inside a circle) have been observed in experimental studies to exhibit a dependency on the phase of fast (gamma band high-frequency) oscillations. t1, t2 and t3 are time points, present at intervals that equal the periodicity of low-frequency oscillation, when fast oscillations synchronize activities (spike bursts) of local circuits. Multidimensional information processing occurs as a result of the modulation of fast oscillations (synchronizing local circuits) by long-range low-frequency oscillations. The phase of low-frequency oscillation when spike bursts in local circuits are synchronized by fast oscillations will provide the temporal window of integration (TWI) of stimuli. Spike bursts contain information (see panel c) that is processed in the multidimensional domain during a sensorimotor task. (c) Interstimulus intervals (ISIs) between two frames of a Ternus display, which is a special case of apparent motion [52], are processed differently depending on the length of the ISI to produce different content levels of Shannon information. Notice that there is less uncertainty regarding the presence of spikes in time-bins of the same length that contain shorter ISIs, i.e., the information content is relatively reduced. The shorter ISIs, with less information content, are associated with the percept of element motion, which contrasts with the group motion observed with the longer ISIs.
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Oscillatory structures in the brain Gupta and Chen 163
coupling causes information to be coherently processed in distributed circuits, supporting the tight temporal coupling between various aspects of a sensorimotor task. The degree of this coupling depends on the demands of the task. For example, catching a faster-moving object requires a tighter coupling between various sensorimotor functions that occurs over shorter sub-second durations. The information resulting from the activities of local circuits can be described by Shannon entropy, i.e., the amount of uncertainty about whether a neuron that represents an information source, such as the primary visual cortex, will or will not fire within a particular temporal window [23,24]. Information or entropy in the proposed multidimensional domain is a consequence of the stochastic nature of the neuronal responses to a stimulus [25,26,27]. Other sources of information for multidimensional processing may include higher functional areas, such as the somatosensory areas, multimodal areas, motor areas and the anterior association area in the prefrontal cortex. Note that the information resulting from the coherent neuronal activities that are responsible for sensorimotor tasks can be quantified by different analyses, including wavelet information analysis and the study of spike structures in terms of complexity and randomness [26,27,28]. In one study in which primates were presented with naturalistic scenes, a positive correlation was observed between high-gamma local field potential (LFP) (60–100 Hz) and spikes, along with a strong positive correlation within highgamma LFPs in the primary visual cortex [16]. These findings suggest that high-gamma LFPs and spikes are generated within the same network [16]. Another study revealed significant phase correlations in approximately 60% of the multiunit activities of neurons and LFP recordings from visual cortices during a specific visual stimulation in addition to phase correlations between oscillatory events at different frequencies [15]. As such, the information in spike structure [27] during multidimensional processing is indicative of sensorimotor responses. It was observed that stimulus-driven excitability variations in local cortical ensembles are related to the phases of brain oscillations within the setting of a hierarchical cross-frequency coupling — amplitude of a higher frequency oscillation is coupled with the phase of a lower frequency oscillation [29,30]. Accordingly, we propose that synchronized long-range low-frequency oscillations provide a temporal window of integration (TWI) in the framework multidimensional processing. The TWI may manifest as a peak or trough of a low-frequency brain oscillation because coupling has been observed between the high gamma power and the peak phases of alpha (8–12 Hz) oscillations in a working memory task [31] and the troughs of theta (4–8 Hz) oscillations in motor, sensory and cognitive tasks [5]. Moreover, the TWI is likely to be related to the periodicity of the low-frequency www.sciencedirect.com
oscillations. Typical integrations of auditory and visual events occur within a time window of 100 ms, for example, those observed during the sound-induced doubleflash illusion [32]. The sound-induced double-flash illusion may be explained by the coupling of the activities of the auditory and visual circuits to the same phase of a slow wave oscillation in the framework of multidimensional processing. An important supporting piece of evidence comes from spatial ventriloquism in which a visual object can attract the perceived location of a spatially discordant but temporally synchronous sound [33,34]. This attraction can be understood as a phase-amplitude coupling of the circuits processing the sound to the synchronized lowfrequency oscillations that are devoted to processing different features of the visual object. Although spatial ventriloquism is an example of the integration of spatially separate events, phase-amplitude coupling is observed in the integration of information associated with inputs arising from a single source. For example, during simple visual tasks, alpha/high gamma coupling preferentially increases in the visual cortical regions [8]. A recent study provides the causal evidence that the temporal window of integration associated with sound-induced doubleflash illusion is constrained by the frequency of occipital oscillations in the alpha band [34]. Faster alpha frequencies also predict vision with a finer temporal resolution, which is consistent with the hypothesis that faster alpha oscillations provide more cycles for the separate rather than combined integration, which results in better resolution [35]. In another study of the integration of two stimuli (i.e., audio and tactile), the alpha power recorded over the superior and inferior parietal lobules was relatively increased in a pretactile stimulus time-window of approximately 330 ms prior to the subsequent perception of an integrated rather than segregated stimulus [36]. Due to the lack of a poststimulus effect in this study, the integrated/segregated perception was likely an effect of a network comprising ‘multisensory integration’ areas with unisensory areas rather than a specific multisensory area [36]. This interpretation is consistent with the multidimensional processing of auditory and tactile stimuli within a network. The increase in alpha power during the integration of two stimuli can be understood in terms of multidimensional processing. For multisensory integration to occur, the alpha oscillations modulating both unisensory circuits, i.e., tactile and auditory, must synchronize, producing an increase in alpha power. Therefore, one may argue that the processing of multiple sensory stimuli (as depicted in Figure 1) within successive but discrete TWIs results in the integration of sensory information from multiple sources in a dynamic network of circuits over a time period. This integration forms the basis of successful sensorimotor interactions with the external physical environment. Current Opinion in Behavioral Sciences 2016, 8:161–166
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Multidimensional processing in apparent visual motion In this section, we argue that pre-stimulus alpha power and spontaneous brain oscillations influence apparent motion perception [37]. Apparent motion is a visual illusion in which motion is perceived when two spatially distinct static objects alternate in sequence. Apparent motion illusions are robust at presentation frequencies of approximately 3 Hz. Apparent motion has been deemed to be dependent on the ability to use predictive feedback signals in the processing of ‘fragmented’ sensory information [38,39]. The perception of good apparent motion follows an optimal range of presentation frequencies. Lower alpha power predicts percepts between apparent motion and flickering at high presentation frequencies. In a study, higher alpha power predicts apparent motion percepts of visual objects with low presentation frequencies [37]. Specifically, apparent motion perception depends on both local neural synchronization (i.e., the power within the frontal and occipital regions of interests) and long-distance neural synchronization (i.e., frontal-occipital connectivity) in the pre-stimulus alpha oscillations [37].
Multidimensional processing in timing and action Phase-amplitude coupling provides a plausible mechanism for the calibration of modular clocks. This calibration involves endogenous oscillators within various networks, modules that calibrate the oscillators, and downstream circuits that process task-specific time intervals. These components are connected via flexible connections as proposed by Gupta (2014) [40]. The modular clock mechanism is calibrated by circuits, such as those in the cerebellum and posterior parietal cortex, which are important for feedback control during various sensorimotor tasks [40]. During complex sensorimotor tasks, such as catching a flying ball or lifting a cup, irregular spike bursts are produced during feedback processes and reflect unequal temporal epochs that separate the activities of individual muscles during these movements. Furthermore, the irregular changes in the neuronal activities during a sensorimotor task mirror the changes in the physical time-related parameters, such as speed and duration. The input from feedback circuits to endogenous oscillatory mechanisms aid in the transfer of the physical time information. Note that phase-amplitude coupling preserves the temporal relationship between the parallel inputs of irregular activities that arrive at the oscillator/local clock mechanism from a calibration module. Additionally, motor and sensory taskmodulated changes in the activities of neurons are observed in parts of the brain, such as the inferior temporal lobe and prefrontal cortex, which are not directly responsible for the feedback control of external sensorimotor tasks [41,42]. These changes may influence the effects of feedback processes and could also be responsible for the calibration of endogenous oscillators. Current Opinion in Behavioral Sciences 2016, 8:161–166
Several recent studies have also suggested the role of beta band oscillations in the representation of various time durations in the brain [43,44,45]. Interestingly, in a recent study by Bartolo and Merchant (2015), putaminal LFPs were recorded in monkeys that were performing a synchronization-continuation task. The LFPs exhibited an initial burst of beta band oscillation that was followed by another increase during the continuation phase of the task [45]. This latter increase depends on internally generated cues [45]. The dependence on internally generated cues suggests the involvement of other calibration modules, such as those arising from the basal ganglia circuit as previously proposed by Gupta (2014) [40], which may be responsible for the observed increase in the beta power via an increase in the number of longrange beta-oscillation interactions during the continuation phase of the task. Consistent with the role of low-frequency oscillations in the modulation of the amplitudes of high-frequency oscillations by long-range oscillations, previous studies utilizing motor tasks have demonstrated phase-amplitude coupling between movement-selective high gamma and alpha oscillations in humans [14] and between gamma and theta oscillations in the motor cortical areas of rats during distinct movement states [12]. Another study suggests a saccade-related phase-amplitude coupling between theta and low gamma activities [13]. Thus, the amplitudes of gamma oscillations in motor areas, which represent motor information including the time of the onset, performance and execution [46,47], nested within the low-frequency oscillations are likely to play an important role in the processing of information for the execution of sensorimotor tasks. A recent study simultaneously recorded neuronal activities in multiple cortical regions in monkeys that were trained to report the color or motion of the stimuli. This study revealed complex dynamics of information flow [48]. When information reaches one part of the brain from another, the phase of the synchronized low-frequency oscillations connecting the two regions is advanced [49]. This delay in phase affects the processing of the arriving information by altering the available size of the TWI. The effect of longrange oscillations on information flow has been indicated by an analysis of intracranial EEG data that revealed that power changes in different frequency bands significantly contribute to the late components of the event-related potentials [50]. The micro-stimulation of V1 (i.e., the primary visual area) generates gamma oscillations in the feedforward direction, whereas such stimulation of V4 (i.e., a downstream visual area) generates alpha oscillations in the feedback direction [49]. These findings suggest that during the multidimensional processing of visual information, the coupling of the amplitude of gamma oscillations (produced by feedforward activity) to the phase of lowfrequency oscillations (produced by feedback activity) www.sciencedirect.com
Oscillatory structures in the brain Gupta and Chen 165
could also modulate the flow of information between lower and higher visual areas. Thus, phase-amplitude coupling could form one of the important bases of sensorimotor choices in flexible visuomotor tasks; such choices are believed to result from the integration of opposite flows of sensory and task information [48].
Summary VanRullen and Koch (2003) proposed that cross-frequency interactions between gamma and alpha oscillations constrain perception [51]. We extend this proposal to include motor functions to understand the role of the multidimensional processing of information during sensorimotor tasks. The multidimensional processing provides an important basis for understanding how different circuits of the brain can be temporally coupled during various sensorimotor tasks.
Conflict of interest statement Nothing declared.
Acknowledgements This study was supported by grants from the Natural Science Foundation of China (31200760), the National High Technology Research and Development Program of China (863 Program, 2012AA011602) and the Fund for Fostering Talents in Basic Science (J1103602) to LC. DSG would like to acknowledge his gratitude to the students, faculty and staff of Camden County College, New Jersey, USA and the ResearchGateTM community.
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