The Motor Cortex Has Independent Representations for Ipsilateral and Contralateral Arm Movements But Correlated Representations for Grasping

Adaptation in the spinal cord after stroke: Implications for restoring cortical control over the final common pathway

Control of voluntary movement is predicated on integration between circuits in the brain and spinal cord. Although damage is often restricted to supraspinal or spinal circuits in cases of neurological injury, both spinal motor neurons and axons linking these cells to the cortical origins of descending motor commands begin showing changes soon after the brain is injured by stroke. The concept of 'transneuronal degeneration' is not new and has been documented in histological, imaging and electrophysiological studies dating back over a century. Taken together, evidence from these studies agrees more with a system attempting to survive rather than one passively surrendering to degeneration. There tends to be at least some preservation of fibres at the brainstem origin and along the spinal course of the descending white matter tracts, even in severe cases. Myelin-associated proteins are observed in the spinal cord years after stroke onset. Spinal motor neurons remain morphometrically unaltered. Skeletal muscle fibres once innervated by neurons that lose their source of trophic input receive collaterals from adjacent neurons, causing spinal motor units to consolidate and increase in size. Although some level of excitability within the distributed brain network mediating voluntary movement is needed to facilitate recovery, minimal structural connectivity between cortical and spinal motor neurons can support meaningful distal limb function. Restoring access to the final common pathway via the descending input that remains in the spinal cord therefore represents a viable target for directed plasticity, particularly in light of recent advances in rehabilitation medicine.

Brain control of bimanual movement enabled by recurrent neural networks

Brain-computer interfaces have so far focused largely on enabling the control of a single effector, for example a single computer cursor or robotic arm. Restoring multi-effector motion could unlock greater functionality for people with paralysis (e.g., bimanual movement). However, it may prove challenging to decode the simultaneous motion of multiple effectors, as we recently found that a compositional neural code links movements across all limbs and that neural tuning changes nonlinearly during dual-effector motion. Here, we demonstrate the feasibility of high-quality bimanual control of two cursors via neural network (NN) decoders. Through simulations, we show that NNs leverage a neural 'laterality' dimension to distinguish between left and right-hand movements as neural tuning to both hands become increasingly correlated. In training recurrent neural networks (RNNs) for two-cursor control, we developed a method that alters the temporal structure of the training data by dilating/compressing it in time and re-ordering it, which we show helps RNNs successfully generalize to the online setting. With this method, we demonstrate that a person with paralysis can control two computer cursors simultaneously. Our results suggest that neural network decoders may be advantageous for multi-effector decoding, provided they are designed to transfer to the online setting.

Differential contribution of sensorimotor cortex and subthalamic nucleus to unimanual and bimanual hand movements

Why does unilateral deep brain stimulation improve motor function bilaterally? To address this clinical observation, we collected parallel neural recordings from sensorimotor cortex (SMC) and the subthalamic nucleus (STN) during repetitive ipsilateral, contralateral, and bilateral hand movements in patients with Parkinson's disease. We used a cross-validated electrode-wise encoding model to map electromyography data to the neural signals. Electrodes in the STN encoded movement at a comparable level for both hands, whereas SMC electrodes displayed a strong contralateral bias. To examine representational overlap across the two hands, we trained the model with data from one condition (contralateral hand) and used the trained weights to predict neural activity for movements produced with the other hand (ipsilateral hand). Overall, between-hand generalization was poor, and this limitation was evident in both regions. A similar method was used to probe representational overlap across different task contexts (unimanual vs. bimanual). Task context was more important for the STN compared to the SMC indicating that neural activity in the STN showed greater divergence between the unimanual and bimanual conditions. These results indicate that SMC activity is strongly lateralized and relatively context-free, whereas the STN integrates contextual information with the ongoing behavior.

Pseudo-linear Summation explains Neural Geometry of Multi-finger Movements in Human Premotor Cortex

How does the motor cortex combine simple movements (such as single finger flexion/extension) into complex movements (such hand gestures or playing piano)? Motor cortical activity was recorded using intracortical multi-electrode arrays in two people with tetraplegia as they attempted single, pairwise and higher order finger movements. Neural activity for simultaneous movements was largely aligned with linear summation of corresponding single finger movement activities, with two violations. First, the neural activity was normalized, preventing a large magnitude with an increasing number of moving fingers. Second, the neural tuning direction of weakly represented fingers (e.g. middle) changed significantly as a result of the movement of other fingers. These deviations from linearity resulted in non-linear methods outperforming linear methods for neural decoding. Overall, simultaneous finger movements are thus represented by the combination of individual finger movements by pseudo-linear summation.

Representation and decoding of bilateral arm motor imagery using unilateral cerebral LFP signals

Introduction

In the field of upper limb brain computer interfaces (BCIs), the research focusing on bilateral decoding mostly based on the neural signals from two cerebral hemispheres. In addition, most studies used spikes for decoding. Here we examined the representation and decoding of different laterality and regions arm motor imagery in unilateral motor cortex based on local field potentials (LFPs).

Methods

The LFP signals were recorded from a 96-channel Utah microelectrode array implanted in the left primary motor cortex of a paralyzed participant. There were 7 kinds of tasks: rest, left, right and bilateral elbow and wrist flexion. We performed time-frequency analysis on the LFP signals and analyzed the representation and decoding of different tasks using the power and energy of different frequency bands.

Results

The frequency range of <8 Hz and >38 Hz showed power enhancement, whereas 8-38 Hz showed power suppression in spectrograms while performing motor imagery. There were significant differences in average energy between tasks. What's more, the movement region and laterality were represented in two dimensions by demixed principal component analysis. The 135-300 Hz band signal had the highest decoding accuracy among all frequency bands and the contralateral and bilateral signals had more similar single-channel power activation patterns and larger signal correlation than contralateral and ipsilateral signals, bilateral and ipsilateral signals.

Discussion

The results showed that unilateral LFP signals had different representations for bilateral motor imagery on the average energy of the full array and single-channel power levels, and different tasks could be decoded. These proved the feasibility of multilateral BCI based on the unilateral LFP signal to broaden the application of BCI technology.

Clinical trial registration

https://www.chictr.org.cn/showproj.aspx?proj=130829, identifier ChiCTR2100050705.

Decoding and geometry of ten finger movements in human posterior parietal cortex and motor cortex

Objective. Enable neural control of individual prosthetic fingers for participants with upper-limb paralysis.Approach. Two tetraplegic participants were each implanted with a 96-channel array in the left posterior parietal cortex (PPC). One of the participants was additionally implanted with a 96-channel array near the hand knob of the left motor cortex (MC). Across tens of sessions, we recorded neural activity while the participants attempted to move individual fingers of the right hand. Offline, we classified attempted finger movements from neural firing rates using linear discriminant analysis with cross-validation. The participants then used the neural classifier online to control individual fingers of a brain-machine interface (BMI). Finally, we characterized the neural representational geometry during individual finger movements of both hands.Main Results. The two participants achieved 86% and 92% online accuracy during BMI control of the contralateral fingers (chance = 17%). Offline, a linear decoder achieved ten-finger decoding accuracies of 70% and 66% using respective PPC recordings and 75% using MC recordings (chance = 10%). In MC and in one PPC array, a factorized code linked corresponding finger movements of the contralateral and ipsilateral hands.Significance. This is the first study to decode both contralateral and ipsilateral finger movements from PPC. Online BMI control of contralateral fingers exceeded that of previous finger BMIs. PPC and MC signals can be used to control individual prosthetic fingers, which may contribute to a hand restoration strategy for people with tetraplegia.

Translating deep learning to neuroprosthetic control

Advances in deep learning have given rise to neural network models of the relationship between movement and brain activity that appear to far outperform prior approaches. Brain-computer interfaces (BCIs) that enable people with paralysis to control external devices, such as robotic arms or computer cursors, might stand to benefit greatly from these advances. We tested recurrent neural networks (RNNs) on a challenging nonlinear BCI problem: decoding continuous bimanual movement of two computer cursors. Surprisingly, we found that although RNNs appeared to perform well in offline settings, they did so by overfitting to the temporal structure of the training data and failed to generalize to real-time neuroprosthetic control. In response, we developed a method that alters the temporal structure of the training data by dilating/compressing it in time and re-ordering it, which we show helps RNNs successfully generalize to the online setting. With this method, we demonstrate that a person with paralysis can control two computer cursors simultaneously, far outperforming standard linear methods. Our results provide evidence that preventing models from overfitting to temporal structure in training data may, in principle, aid in translating deep learning advances to the BCI setting, unlocking improved performance for challenging applications.

Shared Control of Bimanual Robotic Limbs With a Brain-Machine Interface for Self-Feeding

Advances in intelligent robotic systems and brain-machine interfaces (BMI) have helped restore functionality and independence to individuals living with sensorimotor deficits; however, tasks requiring bimanual coordination and fine manipulation continue to remain unsolved given the technical complexity of controlling multiple degrees of freedom (DOF) across multiple limbs in a coordinated way through a user input. To address this challenge, we implemented a collaborative shared control strategy to manipulate and coordinate two Modular Prosthetic Limbs (MPL) for performing a bimanual self-feeding task. A human participant with microelectrode arrays in sensorimotor brain regions provided commands to both MPLs to perform the self-feeding task, which included bimanual cutting. Motor commands were decoded from bilateral neural signals to control up to two DOFs on each MPL at a time. The shared control strategy enabled the participant to map his four-DOF control inputs, two per hand, to as many as 12 DOFs for specifying robot end effector position and orientation. Using neurally-driven shared control, the participant successfully and simultaneously controlled movements of both robotic limbs to cut and eat food in a complex bimanual self-feeding task. This demonstration of bimanual robotic system control via a BMI in collaboration with intelligent robot behavior has major implications for restoring complex movement behaviors for those living with sensorimotor deficits.

Beyond language: The unspoken sensory-motor representation of the tongue in non-primates, non-human and human primates

The English idiom "on the tip of my tongue" commonly acknowledges that something is known, but it cannot be immediately brought to mind. This phrase accurately describes sensorimotor functions of the tongue, which are fundamental for many tongue-related behaviors (e.g., speech), but often neglected by scientific research. Here, we review a wide range of studies conducted on non-primates, non-human and human primates with the aim of providing a comprehensive description of the cortical representation of the tongue's somatosensory inputs and motor outputs across different phylogenetic domains. First, we summarize how the properties of passive non-noxious mechanical stimuli are encoded in the putative somatosensory tongue area, which has a conserved location in the ventral portion of the somatosensory cortex across mammals. Second, we review how complex self-generated actions involving the tongue are represented in more anterior regions of the putative somato-motor tongue area. Finally, we describe multisensory response properties of the primate and non-primate tongue area by also defining how the cytoarchitecture of this area is affected by experience and deafferentation.

A TMS-induced cortical silent period delays the contralateral limb for bimanual symmetrical movements and the reaction time delay is reduced on startle trials

Bimanual actions are typically initiated and executed in a temporally synchronous manner, likely due to planning bilateral commands as a single motor "program." Applying high intensity transcranial magnetic stimulation (TMS) to the motor cortex can result in a contralateral cortical silent period that delays reaction time (RT), if timed to coincide with the final motor output stage. The current study examined the impact of a unilateral TMS silent period on the RT and inter-limb timing of bilateral wrist extension. In addition, because a loud, startling acoustic stimulus (SAS) can result in the involuntary release of pre-programmed actions via increased reticulospinal activation, it was of interest whether startle-induced speeding of response initiation would moderate the impact of the TMS-induced RT delay. Participants performed blocks of unilateral and bilateral wrist extension in response to an acoustic (82dB) go-signal. On selected trials, either TMS was applied to the left motor cortex 70 ms prior to the expected EMG response onset, a SAS (120dB) replaced the go-signal, or both TMS and SAS were delivered. Results showed that TMS led to a significant RT delay in the right limb during both unimanual and bimanual extension but had no impact on the left limb initiation. In addition, the magnitude of the right limb RT delay was smaller when the response was triggered by a SAS. These results imply that preplanned bimanually synchronous movements are susceptible to lateralized dissociation late into the cortical motor output stage and movements triggered by startle involve increased reticulospinal output.

Left hemisphere dominance for bilateral kinematic encoding in the human brain

Neurophysiological studies in humans and nonhuman primates have revealed movement representations in both the contralateral and ipsilateral hemispheres. Inspired by clinical observations, we ask if this bilateral representation differs for the left and right hemispheres. Electrocorticography was recorded in human participants during an instructed-delay reaching task, with movements produced with either the contralateral or ipsilateral arm. Using a cross-validated kinematic encoding model, we found stronger bilateral encoding in the left hemisphere, an effect that was present during preparation and was amplified during execution. Consistent with this asymmetry, we also observed better across-arm generalization in the left hemisphere, indicating similar neural representations for right and left arm movements. Notably, these left hemisphere electrodes were centered over premotor and parietal regions. The more extensive bilateral encoding in the left hemisphere adds a new perspective to the pervasive neuropsychological finding that the left hemisphere plays a dominant role in praxis.

Distinct representation of ipsilateral hand movements in sensorimotor areas

There is ample evidence that the contralateral sensorimotor areas play an important role in movement generation, with the primary motor cortex and the primary somatosensory cortex showing a detailed spatial organization of the representation of contralateral body parts. Interestingly, there are also indications for a role of the motor cortex in controlling the ipsilateral side of the body. However, the precise function of ipsilateral sensorimotor cortex in unilateral movement control is still unclear. Here, we show hand movement representation in the ipsilateral sensorimotor hand area, in which hand gestures can be distinguished from each other and from contralateral hand gestures. High‐field functional magnetic resonance imaging (fMRI) data acquired during the execution of six left‐ and six right‐hand gestures by healthy volunteers showed ipsilateral activation mainly in the anterior section of precentral gyrus and the posterior section of the postcentral gyrus. Despite the lower activation in ipsilateral areas closer to the central sulcus, activity patterns for the 12 hand gestures could be mutually distinguished in these areas. The existence of a unique representation of ipsilateral hand movements in the human sensorimotor cortex favours the notion of transcallosal integrative processes that support optimal coordination of hand movements.

Decoding attempted phantom hand movements from ipsilateral sensorimotor areas after amputation

Objective.The sensorimotor cortex is often selected as target in the development of a Brain-Computer Interface, as activation patterns from this region can be robustly decoded to discriminate between different movements the user executes. Up until recently, such BCIs were primarily based on activity in the contralateral hemisphere, where decoding movements still works even years after denervation. However, there is increasing evidence for a role of the sensorimotor cortex in controlling the ipsilateral body. The aim of this study is to investigate the effects of denervation on the movement representation on the ipsilateral sensorimotor cortex.Approach.Eight subjects with acquired above-elbow arm amputation and nine controls performed a task in which they made (or attempted to make with their phantom hand) six different gestures from the American Manual Alphabet. Brain activity was measured using 7T functional MRI, and a classifier was trained to discriminate between activation patterns on four different regions of interest (ROIs) on the ipsilateral sensorimotor cortex.Main results.Classification scores showed that decoding was possible and significantly better than chance level for both the phantom and intact hands from all ROIs. Decoding both the left (intact) and right (phantom) hand from the same hemisphere was also possible with above-chance level classification score.Significance.The possibility to decode both hands from the same hemisphere, even years after denervation, indicates that implantation of motor-electrodes for BCI control possibly need only cover a single hemisphere, making surgery less invasive, and increasing options for people with lateralized damage to motor cortex like after stroke.

Maintained Representations of the Ipsilateral and Contralateral Limbs during Bimanual Control in Primary Motor Cortex

Primary motor cortex (M1) almost exclusively controls the contralateral side of the body. However, M1 activity is also modulated during ipsilateral body movements. Previous work has shown that M1 activity related to the ipsilateral arm is independent of the M1 activity related to the contralateral arm. How do these patterns of activity interact when both arms move simultaneously? We explored this problem by training 2 monkeys (male, Macaca mulatta) in a postural perturbation task while recording from M1. Loads were applied to one arm at a time (unimanual) or both arms simultaneously (bimanual). We found 83% of neurons (n = 236) were responsive to both the unimanual and bimanual loads. We also observed a small reduction in activity magnitude during the bimanual loads for both limbs (25%). Across the unimanual and bimanual loads, neurons largely maintained their preferred load directions. However, there was a larger change in the preferred loads for the ipsilateral limb (∼25%) than the contralateral limb (∼9%). Lastly, we identified the contralateral and ipsilateral subspaces during the unimanual loads and found they captured a significant amount of the variance during the bimanual loads. However, the subspace captured more of the bimanual variance related to the contralateral limb (97%) than the ipsilateral limb (66%). Our results highlight that, even during bimanual motor actions, M1 largely retains its representations of the contralateral and ipsilateral limbs.SIGNIFICANCE STATEMENT Previous work has shown that primary motor cortex (M1) represents information related to the contralateral limb, its downstream target, but also reflects information related to the ipsilateral limb. Can M1 still represent both sources of information when performing simultaneous movements of the limbs? Here we record from M1 during a postural perturbation task. We show that activity related to the contralateral limb is maintained between unimanual and bimanual motor actions, whereas the activity related to the ipsilateral limb undergoes a small change between unimanual and bimanual motor actions. Our results indicate that two independent representations can be maintained and expressed simultaneously in M1.

Survivors of Chronic Stroke Experience Continued Impairment of Dexterity But Not Strength in the Nonparetic Upper Limb

OBJECTIVE

To investigate the performance of the less affected upper limb in people with stroke compared with normative values. To examine less affected upper limb function in those whose prestroke dominant limb became paretic and those whose prestroke nondominant limb became paretic.

DESIGN

Cohort study of survivors of chronic stroke (7.2±6.7y post incident).

SETTING

The study was performed at a freestanding academic rehabilitation hospital.

PARTICIPANTS

Survivors of chronic stroke (N=40) with severe hand impairment (Chedoke-McMaster Stroke Assessment rating of 2-3 on Stage of Hand) participated in the study. In 20 participants the prestroke dominant hand (DH) was tested (nondominant hand [NH] affected by stroke), and in 20 participants the prestroke NH was tested (DH affected by stroke).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURE

Jebsen-Taylor Hand Function Test. Data from survivors of stroke were compared with normative age- and sex-matched data from neurologically intact individuals.

RESULTS

When combined, DH and NH groups performed significantly worse on fine motor tasks with their nonparetic hand relative to normative data (P<.007 for all measures). Even the participants who continued to use their prestroke DH as their primary hand after the stroke demonstrated reduced fine motor skills compared with normative data. In contrast, grip strength was not significantly affected in either group of survivors of stroke (P>.140).

CONCLUSIONS

Survivors of stroke with severe impairment of the paretic limb continue to present significant upper extremity impairment in their nominally nonparetic limb even years after stroke. This phenomenon was observed regardless of whether the DH or NH hand was primarily affected. Because this group of survivors of stroke is especially dependent on the nonparetic limb for performing functional tasks, our results suggest that the nonparetic upper limb should be targeted for rehabilitation.