Human motor cortical activity is selectively phase-entrained on underlying rhythms

From intentions to actions: Neural oscillations encode motor processes through phase, amplitude and phase-amplitude coupling

Goal-directed motor behavior is associated with changes in patterns of rhythmic neuronal activity across widely distributed brain areas. In particular, movement initiation and execution are mediated by patterns of synchronization and desynchronization that occur concurrently across distinct frequency bands and across multiple motor cortical areas. To date, motor-related local oscillatory modulations have been predominantly examined by quantifying increases or suppressions in spectral power. However, beyond signal power, spectral properties such as phase and phase-amplitude coupling (PAC) have also been shown to carry information with regards to the oscillatory dynamics underlying motor processes. Yet, the distinct functional roles of phase, amplitude and PAC across the planning and execution of goal-directed motor behavior remain largely elusive. Here, we address this question with unprecedented resolution thanks to multi-site intracerebral EEG recordings in human subjects while they performed a delayed motor task. To compare the roles of phase, amplitude and PAC, we monitored intracranial brain signals from 748 sites across six medically intractable epilepsy patients at movement execution, and during the delay period where motor intention is present but execution is withheld. In particular, we used a machine-learning framework to identify the key contributions of various neuronal responses. We found a high degree of overlap between brain network patterns observed during planning and those present during execution. Prominent amplitude increases in the delta (2-4Hz) and high gamma (60-200Hz) bands were observed during both planning and execution. In contrast, motor alpha (8-13Hz) and beta (13-30Hz) power were suppressed during execution, but enhanced during the delay period. Interestingly, single-trial classification revealed that low-frequency phase information, rather than spectral power change, was the most discriminant feature in dissociating action from intention. Additionally, despite providing weaker decoding, PAC features led to statistically significant classification of motor states, particularly in anterior cingulate cortex and premotor brain areas. These results advance our understanding of the distinct and partly overlapping involvement of phase, amplitude and the coupling between them, in the neuronal mechanisms underlying motor intentions and executions.

Phase-amplitude coupling and the BOLD signal: A simultaneous intracranial EEG (icEEG) - fMRI study in humans performing a finger-tapping task

Although it has been consistently found that local blood-oxygen-level-dependent (BOLD) changes are better modelled by a combination of the power of multiple EEG frequency bands rather than by the power of a unique band alone, the local electro-haemodynamic coupling function is not yet fully characterised. Electrophysiological studies have revealed that the strength of the coupling between the phase of low- and the amplitude of high- frequency EEG activities (phase–amplitude coupling - PAC) has an important role in brain function in general, and in preparation and execution of movement in particular. Using electrocorticographic (ECoG) and functional magnetic resonance imaging (fMRI) data recorded simultaneously in humans performing a finger-tapping task, we investigated the single-trial relationship between the amplitude of the BOLD signal and the strength of PAC and the power of α, β, and γ bands, at a local level. In line with previous studies, we found a positive correlation for the γ band, and negative correlations for the PAC_βγ strength, and the α and β bands. More importantly, we found that the PAC_βγ strength explained variance of the amplitude of the BOLD signal that was not explained by a combination of the α, β, and γ band powers. Our main finding sheds further light on the distinct nature of PAC as a functionally relevant mechanism and suggests that the sensitivity of EEG-informed fMRI studies may increase by including the PAC strength in the BOLD signal model, in addition to the power of the low- and high- frequency EEG bands.

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First study of single-trial correlations between the phase amplitude coupling strength and BOLD.

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Intracranial EEG and fMRI data simultaneously recorded in humans during a motor task.

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PAC_βγ strength explains variance of BOLD in addition a combination of α, β, and γ band powers.

Cortico-Cortical Interactions during Acquisition and Use of a Neuroprosthetic Skill

A motor cortex-based brain-computer interface (BCI) creates a novel real world output directly from cortical activity. Use of a BCI has been demonstrated to be a learned skill that involves recruitment of neural populations that are directly linked to BCI control as well as those that are not. The nature of interactions between these populations, however, remains largely unknown. Here, we employed a data-driven approach to assess the interaction between both local and remote cortical areas during the use of an electrocorticographic BCI, a method which allows direct sampling of cortical surface potentials. Comparing the area controlling the BCI with remote areas, we evaluated relationships between the amplitude envelopes of band limited powers as well as non-linear phase-phase interactions. We found amplitude-amplitude interactions in the high gamma (HG, 70–150 Hz) range that were primarily located in the posterior portion of the frontal lobe, near the controlling site, and non-linear phase-phase interactions involving multiple frequencies (cross-frequency coupling between 8–11 Hz and 70–90 Hz) taking place over larger cortical distances. Further, strength of the amplitude-amplitude interactions decreased with time, whereas the phase-phase interactions did not. These findings suggest multiple modes of cortical communication taking place during BCI use that are specialized for function and depend on interaction distance.

The neurons in the human brain are densely interlaced, sharing upwards of 100 trillion physical connections. It is widely theorized that this tremendous connectivity is one of the facets of our nervous system that enables human intelligence. In this study, over the course of a week, human subjects learned to use electrical activity recorded directly from the surface of their brain to control a computer cursor. This provided us an opportunity to investigate patterns of interactivity that occur in the brain during the development of a new skill. We demonstrated two fundamentally different forms of interactions, one spanning only neighboring populations of neurons and the other covering much longer distances across the brain. The short-distance interaction type was notably stronger during early phases of learning, lessening with time, whereas the other was not. These findings point to evidence of multiple different forms of task-relevant communication taking place between regions in the human brain, and serve as a building block in our efforts to better understand human intelligence.

Neural mechanisms of transient neocortical beta rhythms: Converging evidence from humans, computational modeling, monkeys, and mice

Human neocortical 15-29-Hz beta oscillations are strong predictors of perceptual and motor performance. However, the mechanistic origin of beta in vivo is unknown, hindering understanding of its functional role. Combining human magnetoencephalography (MEG), computational modeling, and laminar recordings in animals, we present a new theory that accounts for the origin of spontaneous neocortical beta. In our MEG data, spontaneous beta activity from somatosensory and frontal cortex emerged as noncontinuous beta events typically lasting <150 ms with a stereotypical waveform. Computational modeling uniquely designed to infer the electrical currents underlying these signals showed that beta events could emerge from the integration of nearly synchronous bursts of excitatory synaptic drive targeting proximal and distal dendrites of pyramidal neurons, where the defining feature of a beta event was a strong distal drive that lasted one beta period (∼50 ms). This beta mechanism rigorously accounted for the beta event profiles; several other mechanisms did not. The spatial location of synaptic drive in the model to supragranular and infragranular layers was critical to the emergence of beta events and led to the prediction that beta events should be associated with a specific laminar current profile. Laminar recordings in somatosensory neocortex from anesthetized mice and awake monkeys supported these predictions, suggesting this beta mechanism is conserved across species and recording modalities. These findings make several predictions about optimal states for perceptual and motor performance and guide causal interventions to modulate beta for optimal function.

A method to establish the spatiotemporal evolution of task-related cortical activity from electrocorticographic signals in single trials

BACKGROUND

Progress in neuroscience depends substantially on the ability to establish the detailed spatial and temporal sequence of neuronal population-level activity across large areas of the brain. Because there is substantial inter-trial variability in neuronal activity, traditional techniques that rely on signal averaging obscure where and when neuronal activity occurs. Thus, up to the present, it has been difficult to examine the detailed progression of neuronal activity across large areas of the brain.

NEW METHOD

Here we describe a method for establishing the spatiotemporal evolution of neuronal population-level activity across large brain regions by determining exactly where and when neural activity occurs during a behavioral task in individual trials. We validate the efficacy of the method, examine the effects of its parameterization, and demonstrate its utility by highlighting two sets of results that could not readily be achieved with traditional methods.

RESULTS

Our results reveal the precise spatiotemporal evolution of neuronal population activity that unfolds during a sensorimotor task in individual trials, and establishes the relationship between neuronal oscillations and the onset of this activity.

CONCLUSIONS

The ability to identify the spatiotemporal evolution of neuronal population activity onsets in single trials gives investigators a powerful new tool with which to study large-scale cortical processes.

Reduced alpha-gamma phase amplitude coupling over right parietal cortex is associated with implicit visuomotor sequence learning

Implicit visuomotor sequence learning is important for our daily life, e.g., when writing or playing an instrument. Previous research identified a network of cortical regions that is relevant for motor sequence learning, namely primary motor cortex, premotor cortex, superior parietal cortex, and subcortical regions, including basal ganglia and cerebellum. Here, we investigated learning-related changes in oscillatory activity (theta, alpha and gamma power) and cross-frequency interactions (theta- and alpha-gamma phase-amplitude coupling) within cortical regions during sensorimotor memory formation. EEG was recorded from a large group of participants (n=73) performing the serial reaction time task (SRTT). Posterior parietal alpha power was larger early-on during sequence learning and smaller in later sessions. Alpha/low-gamma (8-13Hz and 30-48Hz) phase-amplitude coupling (PAC) was significantly smaller during sequence learning over right superior parietal cortex and frontal cortex. During the transition from sequential stimuli to random stimuli, participants made more errors, indicating that they still implicitly attempted to implement the learned motor sequence. At the same time, alpha/low-gamma phase-amplitude coupling was found to be smaller during the transition relative to later random trials. Our results show that learning and implementing a learned motor sequence reduces alpha/low-gamma PAC over parietal and frontal cortex. Fronto-parietal alpha/low-gamma PAC might be relevant for visuomotor mapping which becomes less relevant once the motor sequence has been encoded.

Movement-related dynamics of cortical oscillations in Parkinson's disease and essential tremor

Recent electrocorticography data have demonstrated excessive coupling of beta-phase to gamma-amplitude in primary motor cortex and that deep brain stimulation facilitates motor improvement by decreasing baseline phase-amplitude coupling. However, both the dynamic modulation of phase-amplitude coupling during movement and the general cortical neurophysiology of other movement disorders, such as essential tremor, are relatively unexplored. To clarify the relationship of these interactions in cortical oscillatory activity to movement and disease state, we recorded local field potentials from hand sensorimotor cortex using subdural electrocorticography during a visually cued, incentivized handgrip task in subjects with Parkinson's disease (n = 11), with essential tremor (n = 9) and without a movement disorder (n = 6). We demonstrate that abnormal coupling of the phase of low frequency oscillations to the amplitude of gamma oscillations is not specific to Parkinson's disease, but also occurs in essential tremor, most prominently for the coupling of alpha to gamma oscillations. Movement kinematics were not significantly different between these groups, allowing us to show for the first time that robust alpha and beta desynchronization is a shared feature of sensorimotor cortical activity in Parkinson's disease and essential tremor, with the greatest high-beta desynchronization occurring in Parkinson's disease and the greatest alpha desynchronization occurring in essential tremor. We also show that the spatial extent of cortical phase-amplitude decoupling during movement is much greater in subjects with Parkinson's disease and essential tremor than in subjects without a movement disorder. These findings suggest that subjects with Parkinson's disease and essential tremor can produce movements that are kinematically similar to those of subjects without a movement disorder by reducing excess sensorimotor cortical phase-amplitude coupling that is characteristic of these diseases.

Combining TMS and tACS for Closed-Loop Phase-Dependent Modulation of Corticospinal Excitability: A Feasibility Study

BACKGROUND

The corticospinal excitability indexed by motor evoked potentials (MEPs) following transcranial magnetic stimulation (TMS) of the sensorimotor cortex is characterized by large variability. The instantaneous phase of cortical oscillations at the time of the stimulation has been suggested as a possible source of this variability. To explore this hypothesis, a specific phase needs to be targeted by TMS pulses with high temporal precision.

OBJECTIVE

The aim of this feasibility study was to introduce a methodology capable of exploring the effects of phase-dependent stimulation by the concurrent application of alternating current stimulation (tACS) and TMS.

METHOD

We applied online calibration and closed-loop TMS to target four specific phases (0°, 90°, 180° and 270°) of simultaneous 20 Hz tACS over the primary motor cortex (M1) of seven healthy subjects.

RESULT

The integrated stimulation system was capable of hitting the target phase with high precision (SD ± 2.05 ms, i.e., ± 14.45°) inducing phase-dependent MEP modulation with a phase lag (CI95% = -40.37° to -99.61°) which was stable across subjects (p = 0.001).

CONCLUSION

The combination of different neuromodulation techniques facilitates highly specific brain state-dependent stimulation, and may constitute a valuable tool for exploring the physiological and therapeutic effect of phase-dependent stimulation, e.g., in the context of neurorehabilitation.

Regional Patterns of Cortical Phase Synchrony in the Resting State

Synchronized phase estimates between oscillating neuronal signals at the macroscale level reflect coordinated activities between neuronal assemblies. Recent electrophysiological evidence suggests the presence of significant spontaneous phase synchrony within the resting state. The purpose of this study was to investigate phase synchrony, including directional interactions, in resting state subdural electrocorticographic recordings to better characterize patterns of regional phase interactions across the lateral cortical surface during the resting state. We estimated spontaneous phase locking value (PLV) as a measure of functional connectivity, and phase slope index (PSI) as a measure of pseudo-causal phase interactions, across a broad range of canonical frequency bands and the modulation of the amplitude envelope of high gamma (amHG), a band that is believed to best reflect the physiological processes giving rise to the functional magnetic resonance imaging BOLD signal. Long-distance interactions had higher PLVs in slower frequencies (≤theta) than in higher ones (≥beta) with amHG behaving more like slow frequencies, and a general trend of increasing frequency band of significant PLVs when moving across the lateral surface along an anterior-posterior axis. Moreover, there was a strong trend of frontal-to-parietal directional phase synchronization, measured by PSI across multiple frequencies. These findings, which are likely indicative of coordinated and structured spontaneous cortical interactions, are important in the study of time scales and directional nature of resting state functional connectivity, and may ultimately contribute to a better understanding of how spontaneous synchrony is linked to variation in regional architecture across the lateral cortical surface.

Frequency-Dependent Representation of Reinforcement-Related Information in the Human Medial and Lateral Prefrontal Cortex

The feedback-related negativity (FRN) is a commonly observed potential in scalp electroencephalography (EEG) studies related to the valence of feedback about a subject's performance. This potential classically manifests as a negative deflection in medial frontocentral EEG contacts following negative feedback. Recent work has shown prominence of theta power in the spectral composition of the FRN, placing it within the larger class of "frontal midline theta" cognitive control signals. Although the dorsal anterior cingulate cortex (dACC) is thought to be the cortical generator of the FRN, conclusive data regarding its origin and propagation are lacking. Here we examine intracranial electrophysiology from the human medial and lateral prefrontal cortex (PFC) to better understand the anatomical localization and communication patterns of the FRN. We show that the FRN is evident in both low- and high-frequency local field potentials (LFPs) recorded on electrocorticography. The FRN is larger in medial compared with lateral PFC, and coupling between theta band phase and high-frequency LFP power is also greater in medial PFC. Using Granger causality and conditional mutual information analyses, we provide evidence that feedback-related information propagates from medial to lateral PFC, and that this information transfer oscillates with theta-range periodicity. These results provide evidence for the dACC as the cortical source of the FRN, provide insight into the local computation of frontal midline theta, and have implications for reinforcement learning models of cognitive control.

Predominance of Movement Speed Over Direction in Neuronal Population Signals of Motor Cortex: Intracranial EEG Data and A Simple Explanatory Model

How neuronal activity of motor cortex is related to movement is a central topic in motor neuroscience. Motor-cortical single neurons are more closely related to hand movement velocity than speed, that is, the magnitude of the (directional) velocity vector. Recently, there is also increasing interest in the representation of movement parameters in neuronal population activity, such as reflected in the intracranial EEG (iEEG). We show that in iEEG, contrasting to what has been previously found on the single neuron level, speed predominates over velocity. The predominant speed representation was present in nearly all iEEG signal features, up to the 600–1000 Hz range. Using a model of motor-cortical signals arising from neuronal populations with realistic single neuron tuning properties, we show how this reversal can be understood as a consequence of increasing population size. Our findings demonstrate that the information profile in large population signals may systematically differ from the single neuron level, a principle that may be helpful in the interpretation of neuronal population signals in general, including, for example, EEG and functional magnetic resonance imaging. Taking advantage of the robust speed population signal may help in developing brain–machine interfaces exploiting population signals.

Oscillatory phase modulates the timing of neuronal activations and resulting behavior

Human behavioral response timing is highly variable from trial to trial. While it is generally understood that behavioral variability must be due to trial-by-trial variations in brain function, it is still largely unknown which physiological mechanisms govern the timing of neural activity as it travels through networks of neuronal populations, and how variations in the timing of neural activity relate to variations in the timing of behavior. In our study, we submitted recordings from the cortical surface to novel analytic techniques to chart the trajectory of neuronal population activity across the human cortex in single trials, and found joint modulation of the timing of this activity and of consequent behavior by neuronal oscillations in the alpha band (8-12Hz). Specifically, we established that the onset of population activity tends to occur during the trough of oscillatory activity, and that deviations from this preferred relationship are related to changes in the timing of population activity and the speed of the resulting behavioral response. These results indicate that neuronal activity incurs variable delays as it propagates across neuronal populations, and that the duration of each delay is a function of the instantaneous phase of oscillatory activity. We conclude that the results presented in this paper are supportive of a general model for variability in the effective speed of information transmission in the human brain and for variability in the timing of human behavior.

Patterns of Cortical Synchronization in Isolated Dystonia Compared With Parkinson Disease

IMPORTANCE

Isolated dystonia and Parkinson disease (PD) are disorders of the basal gangliothalamocortical network. They have largely distinct clinical profiles, but both disorders respond to deep brain stimulation (DBS) in the same subcortical targets using similar stimulation paradigms, suggesting pathophysiologic overlap. We hypothesized that, similar to PD, isolated dystonia is associated with elevated cortical neuronal synchronization.

OBJECTIVE

To investigate the electrophysiologic characteristics of the sensorimotor cortex arm-related area using a temporary subdural electrode strip in patients with isolated dystonia and PD undergoing DBS implantation in the awake state.

DESIGN, SETTING, AND PARTICIPANTS

An observational study recruited patients scheduled for DBS at the University of California, San Francisco and the San Francisco Veterans Affairs Medical Center. Data were collected from May 1, 2008, through April 1, 2015. Findings are reported for 22 patients with isolated cervical or segmental dystonia (8 with [DYST-ARM] and 14 without [DYST] arm symptoms) and 14 patients with akinetic rigid PD. Data were analyzed from November 1, 2014, through May 1, 2015.

MAIN OUTCOMES AND MEASURES

Cortical local field potentials, power spectral density, and phase-amplitude coupling (PAC).

RESULTS

Among our 3 groups that together included 36 patients, cortical PAC was present in primary motor and premotor arm-related areas for all groups, but the DYST group was less likely to exhibit increased PAC (P = .008). Similar to what has been shown for patients with PD, subthalamic DBS reversibly decreased PAC in a subset of patients with dystonia who were studied before and during intraoperative test stimulation (n = 4). At rest, broadband gamma (50-200 Hz) power in the primary motor cortex was greater in the DYST-ARM and PD groups compared with the DYST group, whereas alpha (8-13 Hz) and beta (13-30 Hz) power was comparable in all 3 groups. During movement, the DYST-ARM group had impaired beta and low gamma desynchronization in the primary motor cortex.

CONCLUSIONS AND RELEVANCE

Isolated dystonia and PD have physiologic overlap with respect to high levels of motor cortex synchronization and reduction of cortical synchronization by subthalamic DBS, providing an explanation for their similar therapeutic response to basal ganglia stimulation.

Frontal and motor cortex contributions to response inhibition: evidence from electrocorticography

Changes in the environment require rapid modification or inhibition of ongoing behavior. We used the stop-signal paradigm and intracranial recordings to investigate response preparation, inhibition, and monitoring of task-relevant information. Electrocorticographic data were recorded in eight patients with electrodes covering frontal, temporal, and parietal cortex, and time-frequency analysis was used to examine power differences in the beta (13-30 Hz) and high-gamma bands (60-180 Hz). Over motor cortex, beta power decreased, and high-gamma power increased during motor preparation for both go trials (Go) and unsuccessful stops (US). For successful stops (SS), beta increased, and high-gamma was reduced, indexing the cancellation of the prepared response. In the middle frontal gyrus (MFG), stop signals elicited a transient high-gamma increase. The MFG response occurred before the estimated stop-signal reaction time but did not distinguish between SS and US trials, likely signaling attention to the salient stop stimulus. A postresponse high-gamma increase in MFG was stronger for US compared with SS and absent in Go, supporting a role in behavior monitoring. These results provide evidence for differential contributions of frontal subregions to response inhibition, including motor preparation and inhibitory control in motor cortex and cognitive control and action evaluation in lateral prefrontal cortex.

Spontaneous Decoding of the Timing and Content of Human Object Perception from Cortical Surface Recordings Reveals Complementary Information in the Event-Related Potential and Broadband Spectral Change

The link between object perception and neural activity in visual cortical areas is a problem of fundamental importance in neuroscience. Here we show that electrical potentials from the ventral temporal cortical surface in humans contain sufficient information for spontaneous and near-instantaneous identification of a subject's perceptual state. Electrocorticographic (ECoG) arrays were placed on the subtemporal cortical surface of seven epilepsy patients. Grayscale images of faces and houses were displayed rapidly in random sequence. We developed a template projection approach to decode the continuous ECoG data stream spontaneously, predicting the occurrence, timing and type of visual stimulus. In this setting, we evaluated the independent and joint use of two well-studied features of brain signals, broadband changes in the frequency power spectrum of the potential and deflections in the raw potential trace (event-related potential; ERP). Our ability to predict both the timing of stimulus onset and the type of image was best when we used a combination of both the broadband response and ERP, suggesting that they capture different and complementary aspects of the subject's perceptual state. Specifically, we were able to predict the timing and type of 96% of all stimuli, with less than 5% false positive rate and a ~20ms error in timing.

Mechanisms of deep brain stimulation

Deep brain stimulation (DBS) is widely used for the treatment of movement disorders including Parkinson's disease, essential tremor, and dystonia and, to a lesser extent, certain treatment-resistant neuropsychiatric disorders including obsessive-compulsive disorder. Rather than a single unifying mechanism, DBS likely acts via several, nonexclusive mechanisms including local and network-wide electrical and neurochemical effects of stimulation, modulation of oscillatory activity, synaptic plasticity, and, potentially, neuroprotection and neurogenesis. These different mechanisms vary in importance depending on the condition being treated and the target being stimulated. Here we review each of these in turn and illustrate how an understanding of these mechanisms is inspiring next-generation approaches to DBS.

The effects of spatial filtering and artifacts on electrocorticographic signals

OBJECTIVE

Electrocorticographic (ECoG) signals contain noise that is common to all channels and noise that is specific to individual channels. Most published ECoG studies use common average reference (CAR) spatial filters to remove common noise, but CAR filters may introduce channel-specific noise into other channels. To address this concern, scientists often remove artifactual channels prior to data analysis. However, removing these channels depends on expert-based labeling and may also discard useful data. Thus, the effects of spatial filtering and artifacts on ECoG signals have been largely unknown. This study aims to quantify these effects and thereby address this gap in knowledge.

APPROACH

In this study, we address these issues by exploring the effects of application of two types of unsupervised spatial filters and three methods of detecting signal artifacts using a large ECoG data set (20 subjects, four task conditions in each subject).

MAIN RESULTS

Our results confirm that spatial filtering improves performance, i.e., it reduces ECoG signal variance that is not related to the task. They also show that removing artifactual channels automatically (using quantitatively defined rejection criteria) or manually (using expert opinion) does not increase the total amount of task-related information, but does avoid potential contamination from one or more noisy channels. Finally, applying a novel 'median average reference' filter does not require the elimination of artifactual channels prior to spatial filtering and still mitigates the influence of channels with channel-specific noise. Thus, it allows the investigator to retain more potentially useful task-related data.

SIGNIFICANCE

In summary, our results show that appropriately designed spatial filters that account for both common noise and channel-specific noise greatly improve the quality of ECoG signal analyses, and that artifacts in only a single channel can result in profound and undesired effects on all other channels.

Motor System Interactions in the Beta Band Decrease during Loss of Consciousness

Communication between brain areas and how they are influenced by changes in consciousness are not fully understood. One hypothesis is that brain areas communicate via oscillatory processes, utilizing network-specific frequency bands, that can be measured with metrics that reflect between-region interactions, such as coherence and phase amplitude coupling (PAC). To evaluate this hypothesis and understand how these interactions are modulated by state changes, we analyzed electrophysiological recordings in humans at different nodes of one well-studied brain network: the basal ganglia-thalamocortical loops of the motor system during loss of consciousness induced by anesthesia. We recorded simultaneous electrocorticography over primary motor cortex (M1) with local field potentials from subcortical motor regions (either basal ganglia or thalamus) in 15 movement disorder patients during anesthesia (propofol) induction as a part of their surgery for deep brain stimulation. We observed reduced coherence and PAC between M1 and the subcortical nuclei, which was specific to the beta band (∼18-24 Hz). The fact that this pattern occurs selectively in beta underscores the importance of this frequency band in the motor system and supports the idea that oscillatory interactions at specific frequencies are related to the capacity for normal brain function and behavior.

Task-related activity in sensorimotor cortex in Parkinson's disease and essential tremor: changes in beta and gamma bands

In Parkinson's disease patients in the OFF medication state, basal ganglia local field potentials exhibit changes in beta and gamma oscillations that correlate with reduced voluntary movement, manifested as rigidity and akinesia. However, magnetoencephalography and low-resolution electrocorticography (ECoG) studies in Parkinson's patients suggest that changes in sensorimotor cortical oscillations differ from those of the basal ganglia. To more clearly define the role of sensorimotor cortex oscillatory activity in Parkinson's, we performed intraoperative, high-resolution (4 mm spacing) ECoG recordings in 10 Parkinson's patients (2 females, ages 47–72) undergoing deep brain stimulation (DBS) lead placement in the awake, OFF medication state. We analyzed ECoG potentials during a computer-controlled reaching task designed to separate movement preparation from movement execution and compared findings to similar invasive recordings in eight patients with essential tremor (3 females, ages 59–78), a condition not associated with rigidity or akinesia. We show that (1) cortical beta spectral power at rest does not differ between Parkinson's and essential tremor patients (p = 0.85), (2) early motor preparation in Parkinson's patients in the OFF medication state is associated with a larger beta desynchronization compared to patients with essential tremor (p = 0.0061), and (3) cortical broadband gamma power is elevated in Parkinson's patients compared to essential tremor patients during both rest and task recordings (p = 0.004). Our findings suggest an oscillatory profile in sensorimotor cortex of Parkinson's patients that, in contrast to the basal ganglia, may act to promote movement to oppose the anti-kinetic bias of the dopamine-depleted state.

Neuro-oscillatory phase alignment drives speeded multisensory response times: an electro-corticographic investigation

Even simple tasks rely on information exchange between functionally distinct and often relatively distant neuronal ensembles. Considerable work indicates oscillatory synchronization through phase alignment is a major agent of inter-regional communication. In the brain, different oscillatory phases correspond to low- and high-excitability states. Optimally aligned phases (or high-excitability states) promote inter-regional communication. Studies have also shown that sensory stimulation can modulate or reset the phase of ongoing cortical oscillations. For example, auditory stimuli can reset the phase of oscillations in visual cortex, influencing processing of a simultaneous visual stimulus. Such cross-regional phase reset represents a candidate mechanism for aligning oscillatory phase for inter-regional communication. Here, we explored the role of local and inter-regional phase alignment in driving a well established behavioral correlate of multisensory integration: the redundant target effect (RTE), which refers to the fact that responses to multisensory inputs are substantially faster than to unisensory stimuli. In a speeded detection task, human epileptic patients (N = 3) responded to unisensory (auditory or visual) and multisensory (audiovisual) stimuli with a button press, while electrocorticography was recorded over auditory and motor regions. Visual stimulation significantly modulated auditory activity via phase reset in the delta and theta bands. During the period between stimulation and subsequent motor response, transient synchronization between auditory and motor regions was observed. Phase synchrony to multisensory inputs was faster than to unisensory stimulation. This sensorimotor phase alignment correlated with behavior such that stronger synchrony was associated with faster responses, linking the commonly observed RTE with phase alignment across a sensorimotor network.

The effects of spatial filtering and artifacts on electrocorticographic signals

OBJECTIVE

Electrocorticographic (ECoG) signals contain noise that is common to all channels and noise that is specific to individual channels. Most published ECoG studies use common average reference (CAR) spatial filters to remove common noise, but CAR filters may introduce channel-specific noise into other channels. To address this concern, scientists often remove artifactual channels prior to data analysis. However, removing these channels depends on expert-based labeling and may also discard useful data. Thus, the effects of spatial filtering and artifacts on ECoG signals have been largely unknown. This study aims to quantify these effects and thereby address this gap in knowledge.

APPROACH

In this study, we address these issues by exploring the effects of application of two types of unsupervised spatial filters and three methods of detecting signal artifacts using a large ECoG data set (20 subjects, four task conditions in each subject).

MAIN RESULTS

Our results confirm that spatial filtering improves performance, i.e., it reduces ECoG signal variance that is not related to the task. They also show that removing artifactual channels automatically (using quantitatively defined rejection criteria) or manually (using expert opinion) does not increase the total amount of task-related information, but does avoid potential contamination from one or more noisy channels. Finally, applying a novel 'median average reference' filter does not require the elimination of artifactual channels prior to spatial filtering and still mitigates the influence of channels with channel-specific noise. Thus, it allows the investigator to retain more potentially useful task-related data.

SIGNIFICANCE

In summary, our results show that appropriately designed spatial filters that account for both common noise and channel-specific noise greatly improve the quality of ECoG signal analyses, and that artifacts in only a single channel can result in profound and undesired effects on all other channels.

Dynamic network communication as a unifying neural basis for cognition, development, aging, and disease

Perception, cognition, and social interaction depend upon coordinated neural activity. This coordination operates within noisy, overlapping, and distributed neural networks operating at multiple timescales. These networks are built upon a structural scaffolding with intrinsic neuroplasticity that changes with development, aging, disease, and personal experience. In this article, we begin from the perspective that successful interregional communication relies upon the transient synchronization between distinct low-frequency (<80 Hz) oscillations, allowing for brief windows of communication via phase-coordinated local neuronal spiking. From this, we construct a theoretical framework for dynamic network communication, arguing that these networks reflect a balance between oscillatory coupling and local population spiking activity and that these two levels of activity interact. We theorize that when oscillatory coupling is too strong, spike timing within the local neuronal population becomes too synchronous; when oscillatory coupling is too weak, spike timing is too disorganized. Each results in specific disruptions to neural communication. These alterations in communication dynamics may underlie cognitive changes associated with healthy development and aging, in addition to neurological and psychiatric disorders. A number of neurological and psychiatric disorders-including Parkinson's disease, autism, depression, schizophrenia, and anxiety-are associated with abnormalities in oscillatory activity. Although aging, psychiatric and neurological disease, and experience differ in the biological changes to structural gray or white matter, neurotransmission, and gene expression, our framework suggests that any resultant cognitive and behavioral changes in normal or disordered states or their treatment are a product of how these physical processes affect dynamic network communication.

Proceedings of the Second Annual Deep Brain Stimulation Think Tank: What's in the Pipeline

The proceedings of the 2nd Annual Deep Brain Stimulation Think Tank summarize the most contemporary clinical, electrophysiological, and computational work on DBS for the treatment of neurological and neuropsychiatric disease and represent the insights of a unique multidisciplinary ensemble of expert neurologists, neurosurgeons, neuropsychologists, psychiatrists, scientists, engineers and members of industry. Presentations and discussions covered a broad range of topics, including advocacy for DBS, improving clinical outcomes, innovations in computational models of DBS, understanding of the neurophysiology of Parkinson's disease (PD) and Tourette syndrome (TS) and evolving sensor and device technologies.

Global network influences on local functional connectivity

A central neuroscientific pursuit is understanding neuronal interactions that support computations underlying cognition and behavior. Although neurons interact across disparate scales, from cortical columns to whole-brain networks, research has been restricted to one scale at a time. We measured local interactions through multi-neuronal recordings while accessing global networks using scalp electroencephalography (EEG) in rhesus macaques. We measured spike count correlation, an index of functional connectivity with computational relevance, and EEG oscillations, which have been linked to various cognitive functions. We found a non-monotonic relationship between EEG oscillation amplitude and spike count correlation, contrary to the intuitive expectation of a direct relationship. With a widely used network model, we replicated these findings by incorporating a private signal targeting inhibitory neurons, a common mechanism proposed for gain modulation. Finally, we found that spike count correlation explained nonlinearities in the relationship between EEG oscillations and response time in a spatial selective attention task.

Oscillatory entrainment of the motor cortical network during motor imagery is modulated by the feedback modality

Neurofeedback of self-regulated brain activity in circumscribed cortical regions is used as a novel strategy to facilitate functional restoration following stroke. Basic knowledge about its impact on motor system oscillations and functional connectivity is however scarce. Specifically, a direct comparison between different feedback modalities and their neural signatures is missing. We assessed a neurofeedback training intervention of modulating β-activity in circumscribed sensorimotor regions by kinesthetic motor imagery (MI). Right-handed healthy participants received two different feedback modalities contingent to their MI-associated brain activity in a cross-over design: (I) visual feedback with a brain-computer interface (BCI) and (II) proprioceptive feedback with a brain-robot interface (BRI) orthosis attached to the right hand. High-density electroencephalography was used to examine the reactivity of the cortical motor system during the training session of each task by studying both local oscillatory power entrainment and distributed functional connectivity. Both feedback modalities activated a distributed functional connectivity network of coherent oscillations. A significantly higher skill and lower variability of self-controlled sensorimotor β-band modulation could, however, be achieved in the BRI condition. This gain in controlling regional motor oscillations was accompanied by functional coupling of remote β-band and θ-band activity in bilateral fronto-central regions and left parieto-occipital regions, respectively. The functional coupling of coherent θ-band oscillations correlated moreover with the skill of regional β-modulation thus revealing a motor learning related network. Our findings indicate that proprioceptive feedback is more suitable than visual feedback to entrain the motor network architecture during the interplay between motor imagery and feedback processing thus resulting in better volitional control of regional brain activity.

Attention drives synchronization of alpha and beta rhythms between right inferior frontal and primary sensory neocortex

The right inferior frontal cortex (rIFC) is specifically associated with attentional control via the inhibition of behaviorally irrelevant stimuli and motor responses. Similarly, recent evidence has shown that alpha (7-14 Hz) and beta (15-29 Hz) oscillations in primary sensory neocortical areas are enhanced in the representation of non-attended stimuli, leading to the hypothesis that allocation of these rhythms plays an active role in optimal inattention. Here, we tested the hypothesis that selective synchronization between rIFC and primary sensory neocortex occurs in these frequency bands during inattention. We used magnetoencephalography to investigate phase synchrony between primary somatosensory (SI) and rIFC regions during a cued-attention tactile detection task that required suppression of response to uncertain distractor stimuli. Attentional modulation of synchrony between SI and rIFC was found in both the alpha and beta frequency bands. This synchrony manifested as an increase in the alpha-band early after cue between non-attended SI representations and rIFC, and as a subsequent increase in beta-band synchrony closer to stimulus processing. Differences in phase synchrony were not found in several proximal control regions. These results are the first to reveal distinct interactions between primary sensory cortex and rIFC in humans and suggest that synchrony between rIFC and primary sensory representations plays a role in the inhibition of irrelevant sensory stimuli and motor responses.

Therapeutic deep brain stimulation reduces cortical phase-amplitude coupling in Parkinson's disease

Deep brain stimulation (DBS) is increasingly applied for the treatment of brain disorders, but its mechanism of action remains unknown. Here we evaluate the effect of basal ganglia DBS on cortical function using invasive cortical recordings in Parkinson's disease (PD) patients undergoing DBS implantation surgery. In the primary motor cortex of PD patients, neuronal population spiking is excessively synchronized to the phase of network oscillations. This manifests in brain surface recordings as exaggerated coupling between the phase of the beta rhythm and the amplitude of broadband activity. We show that acute therapeutic DBS reversibly reduces phase-amplitude interactions over a similar time course as that of the reduction in parkinsonian motor signs. We propose that DBS of the basal ganglia improves cortical function by alleviating excessive beta phase locking of motor cortex neurons.

Electrocorticographic activity over sensorimotor cortex and motor function in awake behaving rats

Sensorimotor cortex exerts both short-term and long-term control over the spinal reflex pathways that serve motor behaviors. Better understanding of this control could offer new possibilities for restoring function after central nervous system trauma or disease. We examined the impact of ongoing sensorimotor cortex (SMC) activity on the largely monosynaptic pathway of the H-reflex, the electrical analog of the spinal stretch reflex. In 41 awake adult rats, we measured soleus electromyographic (EMG) activity, the soleus H-reflex, and electrocorticographic activity over the contralateral SMC while rats were producing steady-state soleus EMG activity. Principal component analysis of electrocorticographic frequency spectra before H-reflex elicitation consistently revealed three frequency bands: μβ (5-30 Hz), low γ (γ1; 40-85 Hz), and high γ (γ2; 100-200 Hz). Ongoing (i.e., background) soleus EMG amplitude correlated negatively with μβ power and positively with γ1 power. In contrast, H-reflex size correlated positively with μβ power and negatively with γ1 power, but only when background soleus EMG amplitude was included in the linear model. These results support the hypothesis that increased SMC activation (indicated by decrease in μβ power and/or increase in γ1 power) simultaneously potentiates the H-reflex by exciting spinal motoneurons and suppresses it by decreasing the efficacy of the afferent input. They may help guide the development of new rehabilitation methods and of brain-computer interfaces that use SMC activity as a substitute for lost or impaired motor outputs.

Estimating cognitive load during self-regulation of brain activity and neurofeedback with therapeutic brain-computer interfaces

Neurofeedback (NFB) training with brain-computer interfaces (BCIs) is currently being studied in a variety of neurological and neuropsychiatric conditions in an aim to reduce disorder-specific symptoms. For this purpose, a range of classification algorithms has been explored to identify different brain states. These neural states, e.g., self-regulated brain activity vs. rest, are separated by setting a threshold parameter. Measures such as the maximum classification accuracy (CA) have been introduced to evaluate the performance of these algorithms. Interestingly enough, precisely these measures are often used to estimate the subject's ability to perform brain self-regulation. This is surprising, given that the goal of improving the tool that differentiates between brain states is different from the aim of optimizing NFB for the subject performing brain self-regulation. For the latter, knowledge about mental resources and work load is essential in order to adapt the difficulty of the intervention accordingly. In this context, we apply an analytical method and provide empirical data to determine the zone of proximal development (ZPD) as a measure of a subject's cognitive resources and the instructional efficacy of NFB. This approach is based on a reconsideration of item-response theory (IRT) and cognitive load theory for instructional design, and combines them with the CA curve to provide a measure of BCI performance.

Toward sophisticated basal ganglia neuromodulation: Review on basal ganglia deep brain stimulation

This review presents state-of-the-art knowledge about the roles of the basal ganglia (BG) in action-selection, cognition, and motivation, and how this knowledge has been used to improve deep brain stimulation (DBS) treatment of neurological and psychiatric disorders. Such pathological conditions include Parkinson's disease, Huntington's disease, Tourette syndrome, depression, and obsessive-compulsive disorder. The first section presents evidence supporting current hypotheses of how the cortico-BG circuitry works to select motor and emotional actions, and how defects in this circuitry can cause symptoms of the BG diseases. Emphasis is given to the role of striatal dopamine on motor performance, motivated behaviors and learning of procedural memories. Next, the use of cutting-edge electrochemical techniques in animal and human studies of BG functioning under normal and disease conditions is discussed. Finally, functional neuroimaging studies are reviewed; these works have shown the relationship between cortico-BG structures activated during DBS and improvement of disease symptoms.

Parametric estimation of cross-frequency coupling

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We revisit the general linear model (GLM) approach to cross-frequency coupling.

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Continuous time series were split into epochs for parametric statistical tests.

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The GLM and permutation tests produced similar results in experimental data.

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The GLM offers a good trade-off between computation time and statistical power.

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Other predictors such as amplitude-amplitude coupling can be easily included.

Background

Growing experimental evidence suggests an important role for cross-frequency coupling in neural processing, in particular for phase-amplitude coupling (PAC). Although the details of methods to detect PAC may vary, a common procedure to estimate the significance level is the comparison of observed values to those of at least 100 surrogate time series. When scanning large parts of the frequency spectrum and multiple recording sites, this could amount to very large computation times.

New method

We demonstrate that the general linear model (GLM) allows for a parametric estimation of significant PAC. Continuous recordings are split into epochs, of a few seconds duration, on which an F-test can be performed. We compared its performance against traditional non-parametric permutation tests in both simulated and experimental data.

Results

Our method was able to reproduce findings of phase-amplitude coupling in local field potential recordings obtained from the subthalamic nucleus in patients with Parkinson's disease. We also show that PAC may be detected between the subthalamic nucleus and cortical motor areas.

Comparison with existing method(s)

Although the GLM slightly underestimated significance compared to permutation tests in the simulations, for experimental data the two methods produced highly similar results. Computation times were drastically lower for the GLM. Furthermore, we demonstrate that the GLM can be easily extended by including additional predictors such as low-frequency amplitude to test for amplitude-amplitude coupling.

Conclusions

The GLM forms an adequate and computationally efficient approach for detecting cross-frequency coupling with the flexibility to add other explanatory variables of interest.

Instantaneous interactions between brain sites can distinguish movement from rest but are relatively poor at resolving different movement types

Classification of finger movements from brain signals is an important problem in emerging neural prosthetics research. Current techniques have focused primarily on aggregating data from different brain sites independently, without regard to the interaction between different brain sites. We performed a very simple experiment classifying finger movements during electrocorticographic recording from motor cortex. Unlike previous experiments of this type, we examined whether the most-simple type of instantaneous interactions between brain regions could improve classification. We focused on two simple, but salient markers: phase coherence of the 12-20 Hz beta range, and simple correlation in broadband spectral change. We compared behavioral classification using these interactions with simple amplitude changes in beta and broadband magnitude. We found that inter-electrode phase coherence and cross-correlation, were relatively poor at distinguishing between finger movements, and only mediocre at identifying null/rest periods.

Proceedings of the Fifth International Workshop on Advances in Electrocorticography

The Fifth International Workshop on Advances in Electrocorticography convened in San Diego, CA, on November 7-8, 2013. Advancements in methodology, implementation, and commercialization across both research and in the interval year since the last workshop were the focus of the gathering. Electrocorticography (ECoG) is now firmly established as a preferred signal source for advanced research in functional, cognitive, and neuroprosthetic domains. Published output in ECoG fields has increased tenfold in the past decade. These proceedings attempt to summarize the state of the art.

Localizing movement-related primary sensorimotor cortices with multi-band EEG frequency changes and functional MRI

Electroencephalographic (EEG) oscillations in multiple frequency bands can be observed during functional activity of the cerebral cortex. An important question is whether activity of focal areas of cortex, such as during finger movements, is tracked by focal oscillatory EEG changes. Although a number of studies have compared EEG changes to functional MRI hemodynamic responses, we can find no previous research that relates the fMRI hemodynamic activity to localization of the multiple EEG frequency changes observed in motor tasks. In the present study, five participants performed similar thumb and finger movement tasks in parallel EEG and functional MRI studies. We examined changes in five frequency bands (from 5–120 Hz) and localized them using 256 dense-array EEG (dEEG) recordings and high-resolution individual head models. These localizations were compared with fMRI localizations in the same participants. Results showed that beta-band (14–30 Hz) desynchronizations (power decreases) were the most robust effects, appearing in all individuals, consistently localized to the hand region of the primary motor cortex, and consistently aligned with fMRI localizations.

Toward more versatile and intuitive cortical brain-machine interfaces

Brain-machine interfaces have great potential for the development of neuroprosthetic applications to assist patients suffering from brain injury or neurodegenerative disease. One type of brain-machine interface is a cortical motor prosthetic, which is used to assist paralyzed subjects. Motor prosthetics to date have typically used the motor cortex as a source of neural signals for controlling external devices. The review will focus on several new topics in the arena of cortical prosthetics. These include using: recordings from cortical areas outside motor cortex; local field potentials as a source of recorded signals; somatosensory feedback for more dexterous control of robotics; and new decoding methods that work in concert to form an ecology of decode algorithms. These new advances promise to greatly accelerate the applicability and ease of operation of motor prosthetics.

Joint spatial-spectral feature space clustering for speech activity detection from ECoG signals

Brain-machine interfaces for speech restoration have been extensively studied for more than two decades. The success of such a system will depend in part on selecting the best brain recording sites and signal features corresponding to speech production. The purpose of this study was to detect speech activity automatically from electrocorticographic signals based on joint spatial-frequency clustering of the ECoG feature space. For this study, the ECoG signals were recorded while a subject performed two different syllable repetition tasks. We found that the optimal frequency resolution to detect speech activity from ECoG signals was 8 Hz, achieving 98.8% accuracy by employing support vector machines as a classifier. We also defined the cortical areas that held the most information about the discrimination of speech and nonspeech time intervals. Additionally, the results shed light on the distinct cortical areas associated with the two syllables repetition tasks and may contribute to the development of portable ECoG-based communication.

Dynamic changes in phase-amplitude coupling facilitate spatial attention control in fronto-parietal cortex

Electrocorticography reveals how coupling between two frequencies of neuronal oscillation allows the frontal and parietal areas of the cortex to control visual attention from moment to moment in the human brain.

Attention is a core cognitive mechanism that allows the brain to allocate limited resources depending on current task demands. A number of frontal and posterior parietal cortical areas, referred to collectively as the fronto-parietal attentional control network, are engaged during attentional allocation in both humans and non-human primates. Numerous studies have examined this network in the human brain using various neuroimaging and scalp electrophysiological techniques. However, little is known about how these frontal and parietal areas interact dynamically to produce behavior on a fine temporal (sub-second) and spatial (sub-centimeter) scale. We addressed how human fronto-parietal regions control visuospatial attention on a fine spatiotemporal scale by recording electrocorticography (ECoG) signals measured directly from subdural electrode arrays that were implanted in patients undergoing intracranial monitoring for localization of epileptic foci. Subjects (n = 8) performed a spatial-cuing task, in which they allocated visuospatial attention to either the right or left visual field and detected the appearance of a target. We found increases in high gamma (HG) power (70–250 Hz) time-locked to trial onset that remained elevated throughout the attentional allocation period over frontal, parietal, and visual areas. These HG power increases were modulated by the phase of the ongoing delta/theta (2–5 Hz) oscillation during attentional allocation. Critically, we found that the strength of this delta/theta phase-HG amplitude coupling predicted reaction times to detected targets on a trial-by-trial basis. These results highlight the role of delta/theta phase-HG amplitude coupling as a mechanism for sub-second facilitation and coordination within human fronto-parietal cortex that is guided by momentary attentional demands.

The frontal and parietal areas of the cortex control the ability to focus visuospatial attention, and damage to these areas results in profound attentional disturbances. Although much research has concentrated on where these areas are located, little is known about how these areas may function in humans. Previous studies have demonstrated that neuronal spiking is more likely to occur in specific time windows based upon the phase of lower frequency neural oscillations – rhythmic or repetitive neuronal activity. These low-frequency rhythms are hypothesized to coordinate the timing of neuronal firing within local and across network regions. Here, we investigated how human frontal and parietal cortices use neural oscillations to control visuospatial attention. We identified a high-frequency component of electrical brain activity, broadband high gamma (70–250 Hz) amplitude, that became phase-locked to a slower rhythm, delta/theta (2–5 Hz), over frontal, parietal, and visual areas while the study subjects paid attention to the peripheral visual field. Changes in the strength of the coupling between delta/theta phase and high gamma amplitude predicted the attentional behavior of the subjects across single trials. From these results, we conclude that coupling between delta/theta phase and high gamma amplitude serves to coordinate information within – and perhaps between – frontal and parietal areas during allocation of visuospatial attention.

Neural basis of postural focus effect on concurrent postural and motor tasks: phase-locked electroencephalogram responses

Dual-task performance is strongly affected by the direction of attentional focus. This study investigated neural control of a postural-suprapostural procedure when postural focus strategy varied. Twelve adults concurrently conducted force-matching and maintained stabilometer stance with visual feedback on ankle movement (visual internal focus, VIF) and on stabilometer movement (visual external focus, VEF). Force-matching error, dynamics of ankle and stabilometer movements, and event-related potentials (ERPs) were registered. Postural control with VEF caused superior force-matching performance, more complex ankle movement, and stronger kinematic coupling between the ankle and stabilometer movements than postural control with VIF. The postural focus strategy also altered ERP temporal-spatial patterns. Postural control with VEF resulted in later N1 with less negativity around the bilateral fronto-central and contralateral sensorimotor areas, earlier P2 deflection with more positivity around the bilateral fronto-central and ipsilateral temporal areas, and late movement-related potential commencing in the left frontal-central area, as compared with postural control with VIF. The time-frequency distribution of the ERP principal component revealed phase-locked neural oscillations in the delta (1-4Hz), theta (4-7Hz), and beta (13-35Hz) rhythms. The delta and theta rhythms were more pronounced prior to the timing of P2 positive deflection, and beta rebound was greater after the completion of force-matching in VEF condition than VIF condition. This study is the first to reveal the neural correlation of postural focusing effect on a postural-suprapostural task. Postural control with VEF takes advantage of efficient task-switching to facilitate autonomous postural response, in agreement with the "constrained-action" hypothesis.

Single trial prediction of self-paced reaching directions from EEG signals

Early detection of movement intention could possibly minimize the delays in the activation of neuroprosthetic devices. As yet, single trial analysis using non-invasive approaches for understanding such movement preparation remains a challenging task. We studied the feasibility of predicting movement directions in self-paced upper limb center-out reaching tasks, i.e., spontaneous movements executed without an external cue that can better reflect natural motor behavior in humans. We reported results of non-invasive electroencephalography (EEG) recorded from mild stroke patients and able-bodied participants. Previous studies have shown that low frequency EEG oscillations are modulated by the intent to move and therefore, can be decoded prior to the movement execution. Motivated by these results, we investigated whether slow cortical potentials (SCPs) preceding movement onset can be used to classify reaching directions and evaluated the performance using 5-fold cross-validation. For able-bodied subjects, we obtained an average decoding accuracy of 76% (chance level of 25%) at 62.5 ms before onset using the amplitude of on-going SCPs with above chance level performances between 875 to 437.5 ms prior to onset. The decoding accuracy for the stroke patients was on average 47% with their paretic arms. Comparison of the decoding accuracy across different frequency ranges (i.e., SCPs, delta, theta, alpha, and gamma) yielded the best accuracy using SCPs filtered between 0.1 to 1 Hz. Across all the subjects, including stroke subjects, the best selected features were obtained mostly from the fronto-parietal regions, hence consistent with previous neurophysiological studies on arm reaching tasks. In summary, we concluded that SCPs allow the possibility of single trial decoding of reaching directions at least 312.5 ms before onset of reach.

Oscillatory dynamics track motor performance improvement in human cortex

Improving performance in motor skill acquisition is proposed to be supported by tuning of neural networks. To address this issue we investigated changes of phase-amplitude cross-frequency coupling (paCFC) in neuronal networks during motor performance improvement. We recorded intracranially from subdural electrodes (electrocorticogram; ECoG) from 6 patients who learned 3 distinct motor tasks requiring coordination of finger movements with an external cue (serial response task, auditory motor coordination task, go/no-go). Performance improved in all subjects and all tasks during the first block and plateaued in subsequent blocks. Performance improvement was paralled by increasing neural changes in the trial-to-trial paCFC between theta (; 4–8 Hz) phase and high gamma (HG; 80–180 Hz) amplitude. Electrodes showing this covariation pattern (Pearson's r ranging up to .45) were located contralateral to the limb performing the task and were observed predominantly in motor brain regions. We observed stable paCFC when task performance asymptoted. Our results indicate that motor performance improvement is accompanied by adjustments in the dynamics and topology of neuronal network interactions in the and HG range. The location of the involved electrodes suggests that oscillatory dynamics in motor cortices support performance improvement with practice.

A θ-γ oscillation code for neuronal coordination during motor behavior

Sequential motor behavior requires a progression of discrete preparation and execution states. However, the organization of state-dependent activity in neuronal ensembles of motor cortex is poorly understood. Here, we recorded neuronal spiking and local field potential activity from rat motor cortex during reward-motivated movement and observed robust behavioral state-dependent coordination between neuronal spiking, γ oscillations, and θ oscillations. Slow and fast γ oscillations appeared during distinct movement states and entrained neuronal firing. γ oscillations, in turn, were coupled to θ oscillations, and neurons encoding different behavioral states fired at distinct phases of θ in a highly layer-dependent manner. These findings indicate that θ and nested dual band γ oscillations serve as the temporal structure for the selection of a conserved set of functional channels in motor cortical layer activity during animal movement. Furthermore, these results also suggest that cross-frequency couplings between oscillatory neuronal ensemble activities are part of the general coding mechanism in cortex.

Broadband changes in the cortical surface potential track activation of functionally diverse neuronal populations

We illustrate a general principal of electrical potential measurements from the surface of the cerebral cortex, by revisiting and reanalyzing experimental work from the visual, language and motor systems. A naive decomposition technique of electrocorticographic power spectral measurements reveals that broadband spectral changes reliably track task engagement. These broadband changes are shown to be a generic correlate of local cortical function across a variety of brain areas and behavioral tasks. Furthermore, they fit a power-law form that is consistent with simple models of the dendritic integration of asynchronous local population firing. Because broadband spectral changes covary with diverse perceptual and behavioral states on the timescale of 20-50 ms, they provide a powerful and widely applicable experimental tool.

Spike-timing-dependent plasticity in primate corticospinal connections induced during free behavior

Motor learning and functional recovery from brain damage involve changes in the strength of synaptic connections between neurons. Relevant in vivo evidence on the underlying cellular mechanisms remains limited and indirect. We found that the strength of neural connections between motor cortex and spinal cord in monkeys can be modified with an autonomous recurrent neural interface that delivers electrical stimuli in the spinal cord triggered by action potentials of corticospinal cells during free behavior. The activity-dependent stimulation modified the strength of the terminal connections of single corticomotoneuronal cells, consistent with a bidirectional spike-timing-dependent plasticity rule previously derived from in vitro experiments. For some cells, the changes lasted for days after the end of conditioning, but most effects eventually reverted to preconditioning levels. These results provide direct evidence of corticospinal synaptic plasticity in vivo at the level of single neurons induced by normal firing patterns during free behavior.

Large-scale information flow in conscious and unconscious states: an ECoG study in monkeys

Consciousness is an emergent property of the complex brain network. In order to understand how consciousness is constructed, neural interactions within this network must be elucidated. Previous studies have shown that specific neural interactions between the thalamus and frontoparietal cortices; frontal and parietal cortices; and parietal and temporal cortices are correlated with levels of consciousness. However, due to technical limitations, the network underlying consciousness has not been investigated in terms of large-scale interactions with high temporal and spectral resolution. In this study, we recorded neural activity with dense electrocorticogram (ECoG) arrays and used the spectral Granger causality to generate a more comprehensive network that relates to consciousness in monkeys. We found that neural interactions were significantly different between conscious and unconscious states in all combinations of cortical region pairs. Furthermore, the difference in neural interactions between conscious and unconscious states could be represented in 4 frequency-specific large-scale networks with unique interaction patterns: 2 networks were related to consciousness and showed peaks in alpha and beta bands, while the other 2 networks were related to unconsciousness and showed peaks in theta and gamma bands. Moreover, networks in the unconscious state were shared amongst 3 different unconscious conditions, which were induced either by ketamine and medetomidine, propofol, or sleep. Our results provide a novel picture that the difference between conscious and unconscious states is characterized by a switch in frequency-specific modes of large-scale communications across the entire cortex, rather than the cessation of interactions between specific cortical regions.

Proceedings of the Fourth International Workshop on Advances in Electrocorticography

The Fourth International Workshop on Advances in Electrocorticography (ECoG) convened in New Orleans, LA, on October 11-12, 2012. The proceedings of the workshop serves as an accurate record of the most contemporary clinical and experimental work on brain surface recording and represents the insights of a unique multidisciplinary ensemble of expert clinicians and scientists. Presentations covered a broad range of topics, including innovations in passive functional mapping, increased understanding of pathologic high-frequency oscillations, evolving sensor technologies, a human trial of ECoG-driven brain-machine interface, as well as fresh insights into brain electrical stimulation.

The role of ECoG magnitude and phase in decoding position, velocity, and acceleration during continuous motor behavior

In neuronal population signals, including the electroencephalogram (EEG) and electrocorticogram (ECoG), the low-frequency component (LFC) is particularly informative about motor behavior and can be used for decoding movement parameters for brain-machine interface (BMI) applications. An idea previously expressed, but as of yet not quantitatively tested, is that it is the LFC phase that is the main source of decodable information. To test this issue, we analyzed human ECoG recorded during a game-like, one-dimensional, continuous motor task with a novel decoding method suitable for unfolding magnitude and phase explicitly into a complex-valued, time-frequency signal representation, enabling quantification of the decodable information within the temporal, spatial and frequency domains and allowing disambiguation of the phase contribution from that of the spectral magnitude. The decoding accuracy based only on phase information was substantially (at least 2 fold) and significantly higher than that based only on magnitudes for position, velocity and acceleration. The frequency profile of movement-related information in the ECoG data matched well with the frequency profile expected when assuming a close time-domain correlate of movement velocity in the ECoG, e.g., a (noisy) "copy" of hand velocity. No such match was observed with the frequency profiles expected when assuming a copy of either hand position or acceleration. There was also no indication of additional magnitude-based mechanisms encoding movement information in the LFC range. Thus, our study contributes to elucidating the nature of the informative LFC of motor cortical population activity and may hence contribute to improve decoding strategies and BMI performance.

Dominant frequencies of resting human brain activity as measured by the electrocorticogram

The brain's spontaneous, intrinsic activity is increasingly being shown to reveal brain function, delineate large scale brain networks, and diagnose brain disorders. One of the most studied and clinically utilized types of intrinsic brain activity are oscillations in the electrocorticogram (ECoG), a relatively localized measure of cortical synaptic activity. Here we objectively characterize the types of ECoG oscillations commonly observed over particular cortical areas when an individual is awake and immobile with eyes closed, using a surface-based cortical atlas and cluster analysis. Both methods show that [1] there is generally substantial variability in the dominant frequencies of cortical regions and substantial overlap in dominant frequencies across the areas sampled (primarily lateral central, temporal, and frontal areas), [2] theta (4-8 Hz) is the most dominant type of oscillation in the areas sampled with a mode around 7 Hz, [3] alpha (8-13 Hz) is largely limited to parietal and occipital regions, and [4] beta (13-30 Hz) is prominent peri-Rolandically, over the middle frontal gyrus, and the pars opercularis. In addition, the cluster analysis revealed seven types of ECoG spectral power densities (SPDs). Six of these have peaks at 3, 5, 7 (narrow), 7 (broad), 10, and 17 Hz, while the remaining cluster is broadly distributed with less pronounced peaks at 8, 19, and 42 Hz. These categories largely corroborate conventional sub-gamma frequency band distinctions (delta, theta, alpha, and beta) and suggest multiple sub-types of theta. Finally, we note that gamma/high gamma activity (30+ Hz) was at times prominently observed, but was too infrequent and variable across individuals to be reliably characterized. These results should help identify abnormal patterns of ECoG oscillations, inform the interpretation of EEG/MEG intrinsic activity, and provide insight into the functions of these different oscillations and the networks that produce them. Specifically, our results support theories of the importance of theta oscillations in general cortical function, suggest that alpha activity is primarily related to sensory processing/attention, and demonstrate that beta networks extend far beyond primary sensorimotor regions.

Cortical theta wanes for language

The role of low frequency oscillations in language areas is not yet understood. Using ECoG in six human subjects, we studied whether different language regions show prominent power changes in a specific rhythm, in similar manner as the alpha rhythm shows the most prominent power changes in visual areas. Broca's area and temporal language areas were localized in individual subjects using fMRI. In these areas, the theta rhythm showed the most pronounced power changes and theta power decreased significantly during verb generation. To better understand the role of this language-related theta decrease, we then studied the interaction between low frequencies and local neuronal activity reflected in high frequencies. Amplitude-amplitude correlations showed that theta power correlated negatively with high frequency activity, specifically across verb generation trials. Phase-amplitude coupling showed that during control trials, high frequency power was coupled to theta phase, but this coupling decreased significantly during verb generation trials. These results suggest a dynamic interaction between the neuronal mechanisms underlying the theta rhythm and local neuronal activity in language areas. As visual areas show a pronounced alpha rhythm that may reflect pulsed inhibition, language regions show a pronounced theta rhythm with highly similar features.

Repeated stimuli elicit diminished high-gamma electrocorticographic responses

In the phenomenon of repetition suppression (RS), when a person views a stimulus, the neural activity involved in processing that item is relatively diminished if that stimulus had been previously viewed. Previous noninvasive imaging studies mapped the prevalence of RS for different stimulus types to identify brain regions involved in representing a range of cognitive information. However, these noninvasive findings are challenging to interpret because they do not provide information on how RS relates to the brain's electrophysiological activity. We examined the electrophysiological basis of RS directly using brain recordings from implanted electrocorticographic (ECoG) electrodes in neurosurgical patients. Patients performed a memory task during ECoG recording and we identified high-gamma signals (65-128 Hz) that distinguished the neuronal representation of specific memory items. We then compared the neural representation of each item between novel and repeated viewings. This revealed the presence of RS, in which the neuronal representation of a repeated item had a significantly decreased amplitude and duration compared with novel stimuli. Furthermore, the magnitude of RS was greatest for the stimuli that initially elicited the largest activation at each site. These results have implications for understanding the neural basis of RS and human memory by showing that individual cortical sites exhibit the largest RS for the stimuli that they most actively represent.