Cortical potentials at the frequency of absolute wrist velocity become phase-locked during slow sinusoidal tracking movements

Proprioception: A New Era Set in Motion by Emerging Genetic and Bionic Strategies?

The generation of an internal body model and its continuous update is essential in sensorimotor control. Although known to rely on proprioceptive sensory feedback, the underlying mechanism that transforms this sensory feedback into a dynamic body percept remains poorly understood. However, advances in the development of genetic tools for proprioceptive circuit elements, including the sensory receptors, are beginning to offer new and unprecedented leverage to dissect the central pathways responsible for proprioceptive encoding. Simultaneously, new data derived through emerging bionic neural machine-interface technologies reveal clues regarding the relative importance of kinesthetic sensory feedback and insights into the functional proprioceptive substrates that underlie natural motor behaviors.

Free will strikes back: Steady-state movement-related cortical potentials are modulated by cognitive control

In psychology and neuroscience, opposition to free will has asserted that any degree of perceived self-control or choice is a mere epiphenomenon which provides no meaningful influence on action. The present research tested the validity of this conclusion by designing a paradigm in which the potential effect of self-monitoring on motor output could be investigated. Using a repetitive finger tapping task that evokes automatic patterns in participants tapping responses, we have obtained evidence that (1) participants may voluntarily reduce the predictability of their tapping patterns (2) by exercising cognitive control that (3) modulates response-locked steady-state movement-related potentials over primary and supplementary motor areas. These findings challenge the most radical accounts of the nonexistence of free will and instead provide support for a more balanced model of human behaviour in which cognitive control may constrain automatic response tendencies in response preparation and action execution.

Disrupted cortico-peripheral interactions in motor disorders

Motor disorders may arise from neurological damage or diseases at different levels of the hierarchical motor control system and side-loops. Altered cortico-peripheral interactions might be essential characteristics indicating motor dysfunctions. By integrating cortical and peripheral responses, top-down and bottom-up cortico-peripheral coupling measures could provide new insights into the motor control and recovery process. This review first discusses the neural bases of cortico-peripheral interactions, and corticomuscular coupling and corticokinematic coupling measures are addressed. Subsequently, methodological efforts are summarized to enhance the modeling reliability of neural coupling measures, both linear and nonlinear approaches are introduced. The latest progress, limitations, and future directions are discussed. Finally, we emphasize clinical applications of cortico-peripheral interactions in different motor disorders, including stroke, neurodegenerative diseases, tremor, and other motor-related disorders. The modified interaction patterns and potential changes following rehabilitation interventions are illustrated. Altered coupling strength, modified coupling directionality, and reorganized cortico-peripheral activation patterns are pivotal attributes after motor dysfunction. More robust coupling estimation methodologies and combination with other neurophysiological modalities might more efficiently shed light on motor control and recovery mechanisms. Future studies with large sample sizes might be necessary to determine the reliabilities of cortico-peripheral interaction measures in clinical practice.

Sensorimotor Mapping With MEG: An Update on the Current State of Clinical Research and Practice With Considerations for Clinical Practice Guidelines

In this article, we present the clinical indications and advances in the use of magnetoencephalography to map the primary sensorimotor (SM1) cortex in neurosurgical patients noninvasively. We emphasize the advantages of magnetoencephalography over sensorimotor mapping using functional magnetic resonance imaging. Recommendations to the referring physicians and the clinical magnetoencephalographers to achieve appropriate sensorimotor cortex mapping using magnetoencephalography are proposed. We finally provide some practical advice for the use of corticomuscular coherence, cortico-kinematic coherence, and mu rhythm suppression in this indication. Magnetoencephalography should now be considered as a method of reference for presurgical functional mapping of the sensorimotor cortex.

MEG Insight into the Spectral Dynamics Underlying Steady Isometric Muscle Contraction

To gain fundamental knowledge on how the brain controls motor actions, we studied in detail the interplay between MEG signals from the primary sensorimotor (SM1) cortex and the contraction force of 17 healthy adult humans (7 females, 10 males). SM1 activity was coherent at ∼20 Hz with surface electromyogram (as already extensively reported) but also with contraction force. In both cases, the effective coupling was dominant in the efferent direction. Across subjects, the level of ∼20 Hz coherence between cortex and periphery positively correlated with the “burstiness” of ∼20 Hz SM1 (Pearson r ≈ 0.65) and peripheral fluctuations (r ≈ 0.9). Thus, ∼20 Hz coherence between cortex and periphery is tightly linked to the presence of ∼20 Hz bursts in SM1 and peripheral activity. However, the very high correlation with peripheral fluctuations suggests that the periphery is the limiting factor. At frequencies <3 Hz, both SM1 signals and ∼20 Hz SM1 envelope were coherent with both force and its absolute change rate. The effective coupling dominated in the efferent direction between (1) force and the ∼20 Hz SM1 envelope and (2) the absolute change rate of the force and SM1 signals. Together, our data favor the view that ∼20 Hz coherence between cortex and periphery during isometric contraction builds on the presence of ∼20 Hz SM1 oscillations and needs not rely on feedback from the periphery. They also suggest that effective cortical proprioceptive processing operates at <3 Hz frequencies, even during steady isometric contractions.

SIGNIFICANCE STATEMENT Accurate motor actions are made possible by continuous communication between the cortex and spinal motoneurons, but the neurophysiological basis of this communication is poorly understood. Using MEG recordings in humans maintaining steady isometric muscle contractions, we found evidence that the cortex sends population-level motor commands that tend to structure according to the ∼20 Hz sensorimotor rhythm, and that it dynamically adapts these commands based on the <3 Hz fluctuations of proprioceptive feedback. To our knowledge, this is the first report to give a comprehensive account of how the human brain dynamically handles the flow of proprioceptive information and converts it into appropriate motor command to keep the contraction force steady.

Relationships among low-frequency local field potentials, spiking activity, and three-dimensional reach and grasp kinematics in primary motor and ventral premotor cortices

A prominent feature of motor cortex field potentials during movement is a distinctive low-frequency local field potential (lf-LFP) (<4 Hz), referred to as the movement event-related potential (mEP). The lf-LFP appears to be a global signal related to regional synaptic input, but its relationship to nearby output signaled by single unit spiking activity (SUA) or to movement remains to be established. Previous studies comparing information in primary motor cortex (MI) lf-LFPs and SUA in the context of planar reaching tasks concluded that lf-LFPs have more information than spikes about movement. However, the relative performance of these signals was based on a small number of simultaneously recorded channels and units, or for data averaged across sessions, which could miss information of larger-scale spiking populations. Here, we simultaneously recorded LFPs and SUA from two 96-microelectrode arrays implanted in two major motor cortical areas, MI and ventral premotor (PMv), while monkeys freely reached for and grasped objects swinging in front of them. We compared arm end point and grip aperture kinematics' decoding accuracy for lf-LFP and SUA ensembles. The results show that lf-LFPs provide enough information to reconstruct kinematics in both areas with little difference in decoding performance between MI and PMv. Individual lf-LFP channels often provided more accurate decoding of single kinematic variables than any one single unit. However, the decoding performance of the best single unit among the large population usually exceeded that of the best single lf-LFP channel. Furthermore, ensembles of SUA outperformed the pool of lf-LFP channels, in disagreement with the previously reported superiority of lf-LFP decoding. Decoding results suggest that information in lf-LFPs recorded from intracortical arrays may allow the reconstruction of reach and grasp for real-time neuroprosthetic applications, thus potentially supplementing the ability to decode these same features from spiking populations.

Functional motor-cortex mapping using corticokinematic coherence

We present a novel method, corticokinematic coherence (CKC), for functional mapping of the motor cortex by computing coherence between cortical magnetoencephalographic (MEG) signals and the kinematics of voluntary movements. Ten subjects performed self-paced flexion-extensions of the right-hand fingers at about 3 Hz, with a three-axis accelerometer attached to the index finger. Cross-correlogram and coherence spectra were computed between 306 MEG channels and the accelerometer signals. In all subjects, accelerometer and coherence spectra showed peaks around 3-5 Hz and 6-10 Hz, corresponding to the movement frequencies. The coherence was statistically significant (P<0.05) in all subjects, with sources at the hand area of the primary motor cortex contralateral to the movement. CKC appears to be a promising and robust method for reliable and convenient functional mapping of the human motor cortex.

Decoding center-out hand velocity from MEG signals during visuomotor adaptation

During reaching or drawing, the primate cortex carries information about the current and upcoming position of the hand. Researchers have decoded hand position, velocity, and acceleration during center-out reaching or drawing tasks from neural recordings acquired invasively at the microscale and mesoscale levels. Here we report that we can continuously decode information about hand velocity at the macroscale level from magnetoencephalography (MEG) data acquired from the scalp during a center-out drawing task with an imposed hand-cursor rotation. The grand mean (n=5) correlation coefficients (CCs) between measured and decoded velocity profiles were 0.48, 0.40, 0.38, and 0.28 for the horizontal dimension of movement and 0.32, 0.49, 0.56, and 0.23 for the vertical dimension of movement where the order of the CCs indicates pre-exposure, early-exposure, late-exposure, and post-exposure to the hand-cursor rotation. By projecting the sensor contributions to decoding onto whole-head scalp maps, we found that a macroscale sensorimotor network carries information about detailed hand velocity and that contributions from sensors over central and parietal scalp areas change due to adaptation to the rotated environment. Moreover, a 3-D linear estimation of distributed current sources using standardized low-resolution brain electromagnetic tomography (sLORETA) permitted a more detailed investigation into the cortical network that encodes for hand velocity in each of the adaptation phases. Beneficial implications of these findings include a non-invasive methodology to examine the neural correlates of behavior on a macroscale with high temporal resolution and the potential to provide continuous, complex control of a non-invasive neuromotor prosthesis for movement-impaired individuals.

Coherent neural representation of hand speed in humans revealed by MEG imaging

The spiking activity of single neurons in the primate motor cortex is correlated with various limb movement parameters, including velocity. Recent findings obtained using local field potentials suggest that hand speed may also be encoded in the summed activity of neuronal populations. At this macroscopic level, the motor cortex has also been shown to display synchronized rhythmic activity modulated by motor behavior. Yet whether and how neural oscillations might be related to limb speed control is still poorly understood. Here, we applied magnetoencephalography (MEG) source imaging to the ongoing brain activity in subjects performing a continuous visuomotor (VM) task. We used coherence and phase synchronization to investigate the coupling between the estimated activity throughout the brain and the simultaneously recorded instantaneous hand speed. We found significant phase locking between slow (2- to 5-Hz) oscillatory activity in the contralateral primary motor cortex and time-varying hand speed. In addition, we report long-range task-related coupling between primary motor cortex and multiple brain regions in the same frequency band. The detected large-scale VM network spans several cortical and subcortical areas, including structures of the frontoparietal circuit and the cerebello-thalamo-cortical pathway. These findings suggest a role for slow coherent oscillations in mediating neural representations of hand kinematics in humans and provide further support for the putative role of long-range neural synchronization in large-scale VM integration. Our findings are discussed in the context of corticomotor communication, distributed motor encoding, and possible implications for brain-machine interfaces.