Limited functional grouping of neurons in the motor cortex hand area during individuated finger movements: A cluster analysis

Evolution, biomechanics, and neurobiology converge to explain selective finger motor control

Humans use their fingers to perform a variety of tasks, from simple grasping to manipulating objects, to typing and playing musical instruments, a variety wider than any other species. The more sophisticated the task, the more it involves individuated finger movements, those in which one or more selected fingers perform an intended action while the motion of other digits is constrained. Here we review the neurobiology of such individuated finger movements. We consider their evolutionary origins, the extent to which finger movements are in fact individuated, and the evolved features of neuromuscular control that both enable and limit individuation. We go on to discuss other features of motor control that combine with individuation to create dexterity, the impairment of individuation by disease, and the broad extent of capabilities that individuation confers on humans. We comment on the challenges facing the development of a truly dexterous bionic hand. We conclude by identifying topics for future investigation that will advance our understanding of how neural networks interact across multiple regions of the central nervous system to create individuated movements for the skills humans use to express their cognitive activity.

Cortico-cortical drive in a coupled premotor-primary motor cortex dynamical system

In the conventional view of sensorimotor control, the premotor cortex (PM) plans actions that are executed by the primary motor cortex (M1). This notion arises in part from many experiments that have imposed a preparatory "planning" period, during which PM becomes active without M1. But during many natural movements, PM and M1 are co-activated, making it difficult to distinguish their functional roles. We leverage coupled dynamical systems models (cDSMs) to uncover interactions between PM and M1 during movements performed with no preparatory period. We build cDSMs using neural and behavioral data recorded from two non-human primates as they performed a reach-grasp-manipulate task. PM and M1 interact dynamically throughout these movements. Whereas PM drives the M1 in some situations, in other situations, M1 drives PM activity, contrary to the conventional assumption. Our DSM framework provides additional predictions differentiating the roles of PM and M1 in controlling movement.

Targeting Primary Motor Cortex (M1) Functional Components in M1 Gliomas Enhances Safe Resection and Reveals M1 Plasticity Potentials

Simple Summary

Primary-Motor-Cortex (M1) hosts two functional components, at its posterior and anterior borders, the first being faster and more excitable than the second. Our study reports a novel technique for the on-line identification of these functional components during M1 tumors resection. It reports for the first time the potential plastic reorganization of M1 and specifically how its functional organization is affected by a growing tumor and correlated to clinical, tumor-related factors and patient motor functional performance. It also shows for the first time that detecting the M1 functional architecture and targeting the two M1 functional components facilitates tumor resection, increasing the rate of complete tumor removal, while maintaining the patient’s functional motor capacity.

Abstract

Primary-Motor-Cortex (M1) hosts two functional components, at its posterior and anterior borders, being the first faster and more excitable. We developed a mapping-technique for M1 components identification and determined their functional cortical-subcortical architecture in M1 gliomas and the impact of their identification on tumor resection and motor performance. A novel advanced mapping technique was used in 102 tumors within M1 or CorticoSpinal-Tract to identify M1-two components. High-Frequency-stimulation (2–5 pulses) with an on-line qualitative and quantitative analysis of motor responses was used; the two components’ cortical/subcortical spatial distribution correlated to clinical, tumor-related factor and patients’ motor outcome; a cohort treated with standard-mapping was used for comparison. The two functional components were always identified on-line; in tumors not affecting M1, its functional segregation was preserved. In M1 tumors, two architectures, both preserving the two components, were disclosed: in 50%, a normal cortical/subcortical architecture emerged, while 50% revealed a distorted architecture with loss of anatomical reference and somatotopy, not associated with tumor histo-molecular features or volume, but with a previous treatment. Motor performance was maintained, suggesting functional compensation. By preserving the highest and resecting the lowest excitability component, the complete-resection increased with low morbidity. The real-time identification of two M1 functional components and the preservation of the highest excitability one increases safe resection, revealing M1 plasticity potentials.

Structure of Population Activity in Primary Motor Cortex for Single Finger Flexion and Extension

How is the primary motor cortex (M1) organized to control fine finger movements? We investigated the population activity in M1 for single finger flexion and extension, using 7T functional magnetic resonance imaging (fMRI) in female and male human participants and compared these results to the neural spiking patterns recorded in two male monkeys performing the identical task. fMRI activity patterns were distinct for movements of different fingers, but were quite similar for flexion and extension of the same finger. In contrast, spiking patterns in monkeys were quite distinct for both fingers and directions, which is similar to what was found for muscular activity patterns. The discrepancy between fMRI and electrophysiological measurements can be explained by two (non-mutually exclusive) characteristics of the organization of finger flexion and extension movements. Given that fMRI reflects predominantly input and recurrent activity, the results can be explained by an architecture in which neural populations that control flexion or extension of the same finger produce distinct outputs, but interact tightly with each other and receive similar inputs. Additionally, neurons tuned to different movement directions for the same finger (or combination of fingers) may cluster closely together, while neurons that control different finger combinations may be more spatially separated. When measuring this organization with fMRI at a coarse spatial scale, the activity patterns for flexion and extension of the same finger would appear very similar. Overall, we suggest that the discrepancy between fMRI and electrophysiological measurements provides new insights into the general organization of fine finger movements in M1.SIGNIFICANCE STATEMENT The primary motor cortex (M1) is important for producing individuated finger movements. Recent evidence shows that movements that commonly co-occur are associated with more similar activity patterns in M1. Flexion and extension of the same finger, which never co-occur, should therefore be associated with distinct representations. However, using carefully controlled experiments and multivariate analyses, we demonstrate that human fMRI activity patterns for flexion or extension of the same finger are highly similar. In contrast, spiking patterns measured in monkey M1 are clearly distinct. This suggests that populations controlling opposite movements of the same finger, while producing distinct outputs, may cluster together and share inputs and local processing. These results provide testable hypotheses about the organization of hand control in M1.

Precise finger somatotopy revealed by focal motor cortex injury

BACKGROUND

Somatotopy is considered the hallmark of the primary motor cortex. While this is fundamentally true for the major body parts (head, upper and lower extremities), evidence supporting the existence of within-limb somatotopy is scarce.

METHOD

We report a young man presenting recurrent ischemic strokes with selective finger weakness in whom serial motor cortex mapping procedures were performed.

RESULT

Following the first stroke, which largely spared the motor cortex, motor mapping displayed overlap of the motor representations of the hand muscles. The second focal stroke, affecting the lateral part of the hand knob, resulted in selective loss of the first dorsal interosseous muscle motor evoked potentials while sparing those of the adductor digiti minimi muscle. This observation is in apparent contradiction with the first mapping results that suggested complete overlap of motor representations.

DISCUSSION

Our mapping results provide evidence for the existence of very precise within-limb somatotopy and confirm the proposed homuncular order, whereby lateral fingers are represented laterally and medial fingers medially. The discrepancy between the initial and subsequent mapping results is discussed in light of functional organization of the primary motor cortex.

Wrist Posture Does Not Influence Finger Interdependence

A task involving an instructed finger movement causes involuntary movements in the noninstructed fingers of the hand, also known as finger interdependence. It is associated with both mechanical and neural mechanisms. The current experiment investigated the effect of finger interdependence due to systematic changes of the wrist posture, close to neutral. Eight right-handed healthy human participants performed submaximal cyclic flexion and extension at the metacarpophalangeal joint at 0° neutral, 30° extension, and 30° flexion wrist postures, respectively. The experiment comprised of an instruction to move one of the 4 fingers-index, middle, ring, and little. Movements of the instructed and noninstructed fingers were recorded. Finger interdependence was quantified using enslavement matrix, individuation index, and stationarity index, and it was compared across wrist postures. The authors found that the finger interdependence does not change with changes in wrist posture. Further analysis showed that individuation and stationarity indices were mostly equivalent across wrist postures, and their effects were much smaller than the average differences present among the fingers. The authors conclude that at wrist postures close to neutral, the finger interdependence is not affected by wrist posture.

Encoding of kinetic and kinematic movement parameters in the sensorimotor cortex: A Brain-Computer Interface perspective

For severely paralyzed people, Brain-Computer Interfaces (BCIs) can potentially replace lost motor output and provide a brain-based control signal for augmentative and alternative communication devices or neuroprosthetics. Many BCIs focus on neuronal signals acquired from the hand area of the sensorimotor cortex, employing changes in the patterns of neuronal firing or spectral power associated with one or more types of hand movement. Hand and finger movement can be described by two groups of movement features, namely kinematics (spatial and motion aspects) and kinetics (muscles and forces). Despite extensive primate and human research, it is not fully understood how these features are represented in the SMC and how they lead to the appropriate movement. Yet, the available information may provide insight into which features are most suitable for BCI control. To that purpose, the current paper provides an in-depth review on the movement features encoded in the SMC. Even though there is no consensus on how exactly the SMC generates movement, we conclude that some parameters are well represented in the SMC and can be accurately used for BCI control with discrete as well as continuous feedback. However, the vast evidence also suggests that movement should be interpreted as a combination of multiple parameters rather than isolated ones, pleading for further exploration of sensorimotor control models for accurate BCI control.

Corticocortical Systems Underlying High-Order Motor Control

Cortical networks are characterized by the origin, destination, and reciprocity of their connections, as well as by the diameter, conduction velocity, and synaptic efficacy of their axons. The network formed by parietal and frontal areas lies at the core of cognitive-motor control because the outflow of parietofrontal signaling is conveyed to the subcortical centers and spinal cord through different parallel pathways, whose orchestration determines, not only when and how movements will be generated, but also the nature of forthcoming actions. Despite intensive studies over the last 50 years, the role of corticocortical connections in motor control and the principles whereby selected cortical networks are recruited by different task demands remain elusive. Furthermore, the synaptic integration of different cortical signals, their modulation by transthalamic loops, and the effects of conduction delays remain challenging questions that must be tackled to understand the dynamical aspects of parietofrontal operations. In this article, we evaluate results from nonhuman primate and selected rodent experiments to offer a viewpoint on how corticocortical systems contribute to learning and producing skilled actions. Addressing this subject is not only of scientific interest but also essential for interpreting the devastating consequences for motor control of lesions at different nodes of this integrated circuit. In humans, the study of corticocortical motor networks is currently based on MRI-related methods, such as resting-state connectivity and diffusion tract-tracing, which both need to be contrasted with histological studies in nonhuman primates.

A hierarchy of corticospinal plasticity in human hand and forearm muscles

Key points

Pairing stimulation of a finger flexor or extensor muscle at the motor point with transcranial magnetic stimulation (TMS) of the motor cortex generated plastic changes in motor output.

Increases in output were greater in intrinsic hand muscles than in the finger flexor. No changes occurred in the finger extensor. This gradient was seen irrespective of which muscle was stimulated paired with transcranial magnetic stimulation.

Intermittent theta‐burst stimulation also produced increases in output, although these were similar across muscles.

We suggest that intrinsic hand and flexor muscles have a higher potential to show plasticity than extensors, although only when plasticity is induced by sensory input. This may relate to differences seen in recovery of function in these muscles after injury, such as post‐stroke.

Abstract

The ability of the motor system to show plastic change underlies skill learning and also permits recovery after injury. One puzzling observation is that, after stroke, upper limb flexor muscles show good recovery but extensors remain weak, with this being a major contributor to residual disability. We hypothesized that there might be differences in potential for plasticity across hand and forearm muscles. In the present study, we investigated this using two protocols based on transcranial magnetic brain stimulation (TMS) in healthy human subjects. Baseline TMS responses were recorded from two intrinsic hand muscles: flexor digitorum superficialis (FDS) and extensor digitorum communis (EDC). In the first study, paired associative stimulation (PAS) was delivered by pairing motor point stimulation of FDS or EDC with TMS. Responses were then remeasured. Increases were greatest in the hand muscles, smaller in FDS and non‐significant in EDC, irrespective of whether stimulation of FDS or EDC was used. In the second study, intermittent theta‐burst rapid rate TMS was applied instead of PAS. In this case, all muscles showed similar increases in TMS responses. We conclude that the potential to show plastic changes in motor cortical output has the gradient: hand muscles > flexors > extensors. However, this was only seen in a protocol that requires integration of sensory input (PAS) and not when plasticity was induced purely by cortical stimulation (rapid rate TMS). This observation may relate to why functional recovery tends to favour flexor and hand muscles over extensors.

Pairing stimulation of a finger flexor or extensor muscle at the motor point with transcranial magnetic stimulation (TMS) of the motor cortex generated plastic changes in motor output.

Increases in output were greater in intrinsic hand muscles than in the finger flexor. No changes occurred in the finger extensor. This gradient was seen irrespective of which muscle was stimulated paired with transcranial magnetic stimulation.

Intermittent theta‐burst stimulation also produced increases in output, although these were similar across muscles.

We suggest that intrinsic hand and flexor muscles have a higher potential to show plasticity than extensors, although only when plasticity is induced by sensory input. This may relate to differences seen in recovery of function in these muscles after injury, such as post‐stroke.

Indirect, referent control of motor actions underlies directional tuning of neurons

Many neurons of the primary motor cortex (M1) are maximally sensitive to "preferred" hand movement directions and generate progressively less activity with movements away from these directions. M1 activity also correlates with other biomechanical variables. These findings are predominantly interpreted in a framework in which the brain preprograms and directly specifies the desired motor outcome. This approach is inconsistent with the empirically derived equilibrium-point hypothesis, in which the brain can control motor actions only indirectly, by changing neurophysiological parameters that may influence, but remain independent of, biomechanical variables. The controversy is resolved on the basis of experimental findings and theoretical analysis of how sensory and central influences are integrated in the presence of the fundamental nonlinearity of neurons: electrical thresholds. In the presence of sensory inputs, electrical thresholds are converted into spatial thresholds that predetermine the position of the body segments at which muscles begin to be activated. Such thresholds may be considered as referent points of respective spatial frames of reference (FRs) in which neurons, including motoneurons, are centrally predetermined to work. By shifting the referent points of respective FRs, the brain elicits intentional actions. Pure involuntary reactions to perturbations are accomplished in motionless FRs. Neurons are primarily sensitive to shifts in referent directions, i.e., shifts in spatial FRs, whereas emergent neural activity may or may not correlate with different biomechanical variables depending on the motor task and external conditions. Indirect, referent control of posture and movement symbolizes a departure from conventional views based on direct preprogramming of the motor outcome.

Single-Finger Neural Basis Information-Based Neural Decoder for Multi-Finger Movements

In this paper, we investigate the relationship between single and multi-finger movements. By exploiting the neural correlation between the temporal firing patterns between movements, we show that the Pearson's correlation coefficient for the physically related movement pairs are greater than those of others; the firing rates of the neurons that are tuned to a single-finger movements also increases when the corresponding multi-finger movements are instructed. We also use a hierarchical cluster analysis to verify not only the relationship between the single and multi-finger movements, but also the relationship between the flexion and extension movements. Furthermore, we propose a novel decoding method of modeling neural firing patterns while omitting the training process of the multi-finger movements. For the decoding, the Skellam and Gaussian probability distributions are used as mathematical models. The probabilistic distribution model of the multi-finger movements was estimated using the neural activity that was acquired during single-finger movements. As a result, the proposed neural decoding accuracy comparable with that of the supervised neural decoding accuracy when all of the neurons were used for the multi-finger movements. These results suggest that only the neural activities of single-finger movements can be exploited for the control of dexterous multi-finger neuroprosthetics.

Estimation of voluntary elicited motor neuron discharge using a peripheral nerve collision technique at different contraction strengths

OBJECTIVE

To estimate non-invasively the amount, recruitment pattern and discharge frequency of spinal motor neurons (MN) at contraction strength >20% of maximal voluntary contraction (MVC) of small hand muscles.

METHODS

A peripheral collision technique was used and consisted of supramaximal electrical stimuli at Erb's point and at the wrist, synchronizing descending volleys of action potential during voluntary isometric contractions of the abductor digiti minimi muscle at 20-80% of MVC strength and 1-8 s of contraction duration. Responses of 13 healthy volunteers were quantified and analysed using a recently described model of MN behaviour.

RESULTS

A linear relationship between MN discharge and force generation was noticed with R2 = 0.996, and was confirmed using the simulation results (R2 = 0.997) for contraction durations up to 8 s. For each investigated force level, discharge frequency and recruitment pattern were calculated for individual MN.

CONCLUSIONS

Using this method, MN discharge properties during voluntary activity can be estimated non-invasively.

SIGNIFICANCE

This method provides new opportunities for the non-invasive study of MN behaviour, and could be expanded to patients with conduction failure and during fatigue.

Representation of Muscle Synergies in the Primate Brain

Evidence suggests that the CNS uses motor primitives to simplify movement control, but whether it actually stores primitives instead of computing solutions on the fly to satisfy task demands is a controversial and still-unanswered possibility. Also in contention is whether these primitives take the form of time-invariant muscle coactivations ("spatial" synergies) or time-varying muscle commands ("spatiotemporal" synergies). Here, we examined forelimb muscle patterns and motor cortical spiking data in rhesus macaques (Macaca mulatta) handling objects of variable shape and size. From these data, we extracted both spatiotemporal and spatial synergies using non-negative decomposition. Each spatiotemporal synergy represents a sequence of muscular or neural activations that appeared to recur frequently during the animals' behavior. Key features of the spatiotemporal synergies (including their dimensionality, timing, and amplitude modulation) were independently observed in the muscular and neural data. In addition, both at the muscular and neural levels, these spatiotemporal synergies could be readily reconstructed as sequential activations of spatial synergies (a subset of those extracted independently from the task data), suggestive of a hierarchical relationship between the two levels of synergies. The possibility that motor cortex may execute even complex skill using spatiotemporal synergies has novel implications for the design of neuroprosthetic devices, which could gain computational efficiency by adopting the discrete and low-dimensional control that these primitives imply.

SIGNIFICANCE STATEMENT

We studied the motor cortical and forearm muscular activity of rhesus macaques (Macaca mulatta) as they reached, grasped, and carried objects of varied shape and size. We applied non-negative matrix factorization separately to the cortical and muscular data to reduce their dimensionality to a smaller set of time-varying "spatiotemporal" synergies. Each synergy represents a sequence of cortical or muscular activity that recurred frequently during the animals' behavior. Salient features of the synergies (including their dimensionality, timing, and amplitude modulation) were observed at both the cortical and muscular levels. The possibility that the brain may execute even complex behaviors using spatiotemporal synergies has implications for neuroprosthetic algorithm design, which could become more computationally efficient by adopting the discrete and low-dimensional control that they afford.

Representation of continuous hand and arm movements in macaque areas M1, F5, and AIP: a comparative decoding study

OBJECTIVE

In the last decade, multiple brain areas have been investigated with respect to their decoding capability of continuous arm or hand movements. So far, these studies have mainly focused on motor or premotor areas like M1 and F5. However, there is accumulating evidence that anterior intraparietal area (AIP) in the parietal cortex also contains information about continuous movement.

APPROACH

In this study, we decoded 27 degrees of freedom representing complete hand and arm kinematics during a delayed grasping task from simultaneously recorded activity in areas M1, F5, and AIP of two macaque monkeys (Macaca mulatta).

MAIN RESULTS

We found that all three areas provided decoding performances that lay significantly above chance. In particular, M1 yielded highest decoding accuracy followed by F5 and AIP. Furthermore, we provide support for the notion that AIP does not only code categorical visual features of objects to be grasped, but also contains a substantial amount of temporal kinematic information.

SIGNIFICANCE

This fact could be utilized in future developments of neural interfaces restoring hand and arm movements.

Decoding of human hand actions to handle missing limbs in neuroprosthetics

The only way we can interact with the world is through movements, and our primary interactions are via the hands, thus any loss of hand function has immediate impact on our quality of life. However, to date it has not been systematically assessed how coordination in the hand's joints affects every day actions. This is important for two fundamental reasons. Firstly, to understand the representations and computations underlying motor control "in-the-wild" situations, and secondly to develop smarter controllers for prosthetic hands that have the same functionality as natural limbs. In this work we exploit the correlation structure of our hand and finger movements in daily-life. The novelty of our idea is that instead of averaging variability out, we take the view that the structure of variability may contain valuable information about the task being performed. We asked seven subjects to interact in 17 daily-life situations, and quantified behavior in a principled manner using CyberGlove body sensor networks that, after accurate calibration, track all major joints of the hand. Our key findings are: (1) We confirmed that hand control in daily-life tasks is very low-dimensional, with four to five dimensions being sufficient to explain 80-90% of the variability in the natural movement data. (2) We established a universally applicable measure of manipulative complexity that allowed us to measure and compare limb movements across tasks. We used Bayesian latent variable models to model the low-dimensional structure of finger joint angles in natural actions. (3) This allowed us to build a naïve classifier that within the first 1000 ms of action initiation (from a flat hand start configuration) predicted which of the 17 actions was going to be executed-enabling us to reliably predict the action intention from very short-time-scale initial data, further revealing the foreseeable nature of hand movements for control of neuroprosthetics and tele operation purposes. (4) Using the Expectation-Maximization algorithm on our latent variable model permitted us to reconstruct with high accuracy (<5-6° MAE) the movement trajectory of missing fingers by simply tracking the remaining fingers. Overall, our results suggest the hypothesis that specific hand actions are orchestrated by the brain in such a way that in the natural tasks of daily-life there is sufficient redundancy and predictability to be directly exploitable for neuroprosthetics.

Coding of movements in the motor cortex

The issue of coding of movement in the motor cortex has recently acquired special significance due to its fundamental importance in neuroprosthetic applications. The challenge of controlling a prosthetic arm by processed motor cortical activity has opened a new era of research in applied medicine but has also provided an 'acid test' for hypotheses regarding coding of movement in the motor cortex. The successful decoding of movement information from the activity of motor cortical cells using their directional tuning and population coding has propelled successful neuroprosthetic applications and, at the same time, asserted the utility of those early discoveries, dating back to the early 1980s.

Neuron selection based on deflection coefficient maximization for the neural decoding of dexterous finger movements

Future generations of brain-machine interface (BMI) will require more dexterous motion control such as hand and finger movements. Since a population of neurons in the primary motor cortex (M1) area is correlated with finger movements, neural activities recorded in M1 area are used to reconstruct an intended finger movement. In a BMI system, decoding discrete finger movements from a large number of input neurons does not guarantee a higher decoding accuracy in spite of the increase in computational burden. Hence, we hypothesize that selecting neurons important for coding dexterous flexion/extension of finger movements would improve the BMI performance. In this paper, two metrics are presented to quantitatively measure the importance of each neuron based on Bayes risk minimization and deflection coefficient maximization in a statistical decision problem. Since motor cortical neurons are active with movements of several different fingers, the proposed method is more suitable for a discrete decoding of flexion-extension finger movements than the previous methods for decoding reaching movements. In particular, the proposed metrics yielded high decoding accuracies across all subjects and also in the case of including six combined two-finger movements. While our data acquisition and analysis was done off-line and post processing, our results point to the significance of highly coding neurons in improving BMI performance.

Decoding repetitive finger movements with brain activity acquired via non-invasive electroencephalography

We investigated how well repetitive finger tapping movements can be decoded from scalp electroencephalography (EEG) signals. A linear decoder with memory was used to infer continuous index finger angular velocities from the low-pass filtered fluctuations of the amplitude of a plurality of EEG signals distributed across the scalp. To evaluate the accuracy of the decoder, the Pearson's correlation coefficient (r) between the observed and predicted trajectories was calculated in a 10-fold cross-validation scheme. We also assessed attempts to decode finger kinematics from EEG data that was cleaned with independent component analysis (ICA), EEG data from peripheral sensors, and EEG data from rest periods. A genetic algorithm (GA) was used to select combinations of EEG channels that maximized decoding accuracies. Our results (lower quartile r = 0.18, median r = 0.36, upper quartile r = 0.50) show that delta-band EEG signals contain useful information that can be used to infer finger kinematics. Further, the highest decoding accuracies were characterized by highly correlated delta band EEG activity mostly localized to the contralateral central areas of the scalp. Spectral analysis of EEG also showed bilateral alpha band (8–13 Hz) event related desynchronizations (ERDs) and contralateral beta band (20–30 Hz) event related synchronizations (ERSs) localized over central scalp areas. Overall, this study demonstrates the feasibility of decoding finger kinematics from scalp EEG signals.

Motor cortical regulation of sparse synergies provides a framework for the flexible control of precision walking

We have previously described a modular organization of the locomotor step cycle in the cat in which a number of sparse synergies are activated sequentially during the swing phase of the step cycle (Krouchev et al., 2006). Here, we address how these synergies are modified during voluntary gait modifications. Data were analysed from 27 bursts of muscle activity (recorded from 18 muscles) recorded in the forelimb of the cat during locomotion. These were grouped into 10 clusters, or synergies, during unobstructed locomotion. Each synergy was comprised of only a small number of muscles bursts (sparse synergies), some of which included both proximal and distal muscles. Eight (8/10) of these synergies were active during the swing phase of locomotion. Synergies observed during the gait modifications were very similar to those observed during unobstructed locomotion. Constraining these synergies to be identical in both the lead (first forelimb to pass over the obstacle) and the trail (second limb) conditions allowed us to compare the changes in phase and magnitude of the synergies required to modify gait. In the lead condition, changes were observed particularly in those synergies responsible for transport of the limb and preparation for landing. During the trail condition, changes were particularly evident in those synergies responsible for lifting the limb from the ground at the onset of the swing phase. These changes in phase and magnitude were adapted to the size and shape of the obstacle over which the cat stepped. These results demonstrate that by modifying the phase and magnitude of a finite number of muscle synergies, each comprised of a small number of simultaneously active muscles, descending control signals could produce very specific modifications in limb trajectory during locomotion. We discuss the possibility that these changes in phase and magnitude could be produced by changes in the activity of neurones in the motor cortex.

Neural bases of hand synergies

The human hand has so many degrees of freedom that it may seem impossible to control. A potential solution to this problem is “synergy control” which combines dimensionality reduction with great flexibility. With applicability to a wide range of tasks, this has become a very popular concept. In this review, we describe the evolution of the modern concept using studies of kinematic and force synergies in human hand control, neurophysiology of cortical and spinal neurons, and electromyographic (EMG) activity of hand muscles. We go beyond the often purely descriptive usage of synergy by reviewing the organization of the underlying neuronal circuitry in order to propose mechanistic explanations for various observed synergy phenomena. Finally, we propose a theoretical framework to reconcile important and still debated concepts such as the definitions of “fixed” vs. “flexible” synergies and mechanisms underlying the combination of synergies for hand control.

Neuron Selection by Relative Importance for Neural Decoding of Dexterous Finger Prosthesis Control Application

Future generations of upper limb prosthesis will have dexterous hand with individual fingers and will be controlled directly by neural signals. Neurons from the primary motor (M1) cortex code for finger movements and provide the source for neural control of dexterous prosthesis. Each neuron's activation can be quantified by the change in firing rate before and after finger movement, and the quantified value is then represented by the neural activity over each trial for the intended movement. Since this neural activity varies with the intended movement, we define the relative importance of each neuron independent of specific intended movements. The relative importance of each neuron is determined by the inter-movement variance of the neural activities for respective intended movements. Neurons are ranked by the relative importance and then a subpopulation of rank-ordered neurons is selected for the neural decoding. The use of the proposed neuron selection method in individual finger movements improved decoding accuracy by 21.5% in the case of decoding with only 5 neurons and by 9.2% in the case of decoding with only 10 neurons. With only 15 highly-ranked neurons, a decoding accuracy of 99.5% was achieved. The performance improvement is still maintained when combined movements of two fingers were included though the decoding accuracy fell to 95.7%. Since the proposed neuron selection method can achieve the targeting accuracy of decoding algorithms with less number of input neurons, it can be significant for developing brain-machine interfaces for direct neural control of hand prostheses.

A method to monitor upper limb movement direction encoding in the corticomotor pathway

Abnormal shoulder and elbow muscle coactivation patterns, or muscle synergies, are commonly present following stroke and may arise through dysfunctional descending neural control from the cortex. The authors evaluated a novel technique for examining corticomotor movement encoding of the upper limb in three dimensions. A 6-degree-of-freedom loadcell recorded arm twitch responses in healthy adults following stimulation over the cortex or over Erb's point in the periphery. Stimuli were delivered while the arm generated a 5 N preload in each of the 6 axial directions. The initial force twitch response to stimulation was used to construct twitch direction vectors for each preload direction. General linear mixed model analyses were used to determine the influence of stimulation location, preload direction, posture, and stimulation intensity on twitch direction. Cortical stimulation gave rise to arm twitch responses that were predictably modified by preload direction. Peripheral stimulation elicited stereotypical twitches that were not influenced by preload. Our stimulation, recording, and analysis techniques were able to capture movement encoding of the upper limb in three dimensions. Such techniques could be utilized in the stroke population to determine and monitor the presence of upper limb synergies during muscle activation.

Spatiotemporal variation of multiple neurophysiological signals in the primary motor cortex during dexterous reach-to-grasp movements

To examine the spatiotemporal distribution of discriminable information about reach-to-grasp movements in the primary motor cortex upper extremity representation, we implanted four microelectrode arrays in the anterior bank and lip of the central sulcus in each of two monkeys. We used linear discriminant analysis to compare information, quantified as decoding accuracy, contained in various neurophysiological signals. For all signal types, decoding accuracy increased immediately after the movement cue, peaked around movement onset, and declined during the static hold. Spike recordings and local field potential (LFP) time domain amplitude provided more discriminable information than LFP frequency domain power. Discriminable information on movement type was distributed evenly across recording sites by LFP amplitude and 1-4 Hz power but unevenly by 100-170 Hz power and spike recordings. These latter two signal types provided higher decoding accuracies closer to the hemispheric surface than deep in the anterior bank and also provided accuracies that varied along the central sulcus. This variation in the distribution of movement-type information may be related to differences in the rostral versus caudal regions of the primary motor cortex and to its underlying somatotopic organization. The even distribution of information by LFP amplitude and 1-4 Hz power compared with the more localized distribution by 100-170 Hz power and spikes suggest that these different neurophysiological signals reflect different underlying processes that distribute information through the motor cortex during reach-to-grasp movements.

Encoding of coordinated grasp trajectories in primary motor cortex

Few studies have investigated how the cortex encodes the preshaping of the hand as an object is grasped, an ethological movement referred to as prehension. We developed an encoding model of hand kinematics to test whether primary motor cortex (MI) neurons encode temporally extensive combinations of joint motions that characterize a prehensile movement. Two female rhesus macaque monkeys were trained to grasp 4 different objects presented by a robot while their arm was held in place by a thermoplastic brace. We used multielectrode arrays to record MI neurons and an infrared camera motion tracking system to record the 3-D positions of 14 markers placed on the monkeys' wrist and digits. A generalized linear model framework was used to predict the firing rate of each neuron in a 4 ms time interval, based on its own spiking history and the spatiotemporal kinematics of the joint angles of the hand. Our results show that the variability of the firing rate of MI neurons is better described by temporally extensive combinations of finger and wrist joint angle kinematics rather than any individual joint motion or any combination of static kinematic parameters at their optimal lag. Moreover, a higher percentage of neurons encoded joint angular velocities than joint angular positions. These results suggest that neurons encode the covarying trajectories of the hand's joints during a prehensile movement.

Movement decoding from noninvasive neural signals

It is generally assumed that noninvasively-acquired neural signals contain an insufficient level of information for decoding or reconstructing detailed kinematics of natural, multi-joint limb movements and hand gestures. Here, we review recent findings from our laboratory at the University of Maryland showing that noninvasive scalp electroencephalography (EEG) or magnetoencephalography (MEG) can be used to continuously decode the kinematics of 2D 'center-out' drawing, unconstrained 3D 'center-out' reaching and 3D finger gesturing. These findings suggest that these 'far-field', extra-cranial neural signals contain rich information about the neural representation of movement at the macroscale, and thus these neural representations provide alternative methods for developing noninvasive brain-machine interfaces with wide-ranging clinical relevance and for understanding functional and pathological brain states at various stages of development and aging.

Finger interaction in a three-dimensional pressing task

Accurate control of forces produced by the fingers is essential for performing object manipulation. This study examines the indices of finger interaction when accurate time profiles of force are produced in different directions, while using one of the fingers or all four fingers of the hand. We hypothesized that patterns of unintended force production among shear force components may involve features not observed in the earlier studies of vertical force production. In particular, we expected to see unintended forces generated by non-task fingers not in the direction of the instructed force but in the opposite direction as well as substantial force production in directions orthogonal to the instructed direction. We also tested a hypothesis that multi-finger synergies, quantified using the framework of the uncontrolled manifold hypothesis, will help reduce across-trials variance of both total force magnitude and direction. Young, healthy subjects were required to produce accurate ramps of force in five different directions by pressing on force sensors with the fingers of the right (dominant) hand. The index finger induced the smallest unintended forces in non-task fingers. The little finger showed the smallest unintended forces when it was a non-task finger. Task fingers showed substantial force production in directions orthogonal to the intended force direction. During four-finger tasks, individual force vectors typically pointed off the task direction, with these deviations nearly perfectly matched to produce a resultant force in the task direction. Multi-finger synergy indices reflected strong co-variation in the space of finger modes (commands to fingers) that reduced variability of the total force magnitude and direction across trials. The synergy indices increased in magnitude over the first 30% of the trial time and then stayed at a nearly constant level. The synergy index for stabilization of total force magnitude was higher for shear force components when compared to the downward pressing force component. The results suggest complex interactions between enslaving and synergic force adjustments, possibly reflecting the experience with everyday prehensile tasks. For the first time, the data document multi-finger synergies stabilizing both shear force magnitude and force vector direction. These synergies may play a major role in stabilizing the hand action during object manipulation.

Decoding flexion of individual fingers using electrocorticographic signals in humans

Brain signals can provide the basis for a non-muscular communication and control system, a brain-computer interface (BCI), for people with motor disabilities. A common approach to creating BCI devices is to decode kinematic parameters of movements using signals recorded by intracortical microelectrodes. Recent studies have shown that kinematic parameters of hand movements can also be accurately decoded from signals recorded by electrodes placed on the surface of the brain (electrocorticography (ECoG)). In the present study, we extend these results by demonstrating that it is also possible to decode the time course of the flexion of individual fingers using ECoG signals in humans, and by showing that these flexion time courses are highly specific to the moving finger. These results provide additional support for the hypothesis that ECoG could be the basis for powerful clinically practical BCI systems, and also indicate that ECoG is useful for studying cortical dynamics related to motor function.

Stability of muscle synergies for voluntary actions after cortical stroke in humans

Production of voluntary movements relies critically on the functional integration of several motor cortical areas, such as the primary motor cortex, and the spinal circuitries. Surprisingly, after almost 40 years of research, how the motor cortices specify descending neural signals destined for the downstream interneurons and motoneurons has remained elusive. In light of the many recent experimental demonstrations that the motor system may coordinate muscle activations through a linear combination of muscle synergies, we hypothesize that the motor cortices may function to select and activate fixed muscle synergies specified by the spinal or brainstem networks. To test this hypothesis, we recorded electromyograms (EMGs) from 12-16 upper arm and shoulder muscles from both the unaffected and the stroke-affected arms of stroke patients having moderate-to-severe unilateral ischemic lesions in the frontal motor cortical areas. Analyses of EMGs using a nonnegative matrix factorization algorithm revealed that in seven of eight patients the muscular compositions of the synergies for both the unaffected and the affected arms were strikingly similar to each other despite differences in motor performance between the arms, and differences in cerebral lesion sizes and locations between patients. This robustness of muscle synergies that we observed supports the notion that descending cortical signals represent neuronal drives that select, activate, and flexibly combine muscle synergies specified by networks in the spinal cord and/or brainstem. Our conclusion also suggests an approach to stroke rehabilitation by focusing on those synergies with altered activations after stroke.

Characterization of EMG patterns from proximal arm muscles during object- and orientation-specific grasps

Reach-to-grasp tasks are composed of several actions that are more and more considered as simultaneously controlled by the central nervous system in a feedforward manner (at least for well-known activities). If this hypothesis is correct, during prehension tasks, the activity of proximal muscles (and not only of the distal ones used to control finger movements) is modulated according to the kind of object to be grasped and its position. This means that different objects could be identified by processing the electromyographic (EMG) signals recorded from proximal muscles. In this paper, specific experiments have been carried out to support this hypothesis in able-bodied subjects. The results achieved seem to confirm this possibility by showing that the activation of proximal muscles can be statistically different for different grip types. This finding supports the hypothesis that proximal and distal muscles are simultaneously controlled during reaching and grasping. Moreover, this kind of information could allow the development of an EMG-based control strategy based on the natural muscular activities selected by the central nervous system.

Signaling of grasp dimension and grasp force in dorsal premotor cortex and primary motor cortex neurons during reach to grasp in the monkey

A fundamental question is how the CNS controls the hand with its many degrees of freedom. Several motor cortical areas, including the dorsal premotor cortex (PMd) and primary motor cortex (M1), are involved in reach to grasp. Although neurons in PMd are known to modulate in relation to the type of grasp and neurons in M1 in relation to grasp force and finger movements, whether specific parameters of whole hand shaping are encoded in the discharge of these cells has not been studied. In this study, two monkeys were trained to reach and grasp 16 objects varying in shape, size, and orientation. Grasp force was explicitly controlled, requiring the monkeys to exert either three or five levels of grasp force on each object. The animals were unable to see the objects or their hands. Single PMd and M1 neurons were recorded during the task, and cell firing was examined for modulation with object properties and grasp force. The firing of the vast majority of PMd and M1 neurons varied significantly as a function of the object presented as well as the object grasp dimension. Grasp dimension of the object was an important determinant of the firing of cells in both PMd and M1. A smaller percentage of PMd and M1 neurons were modulated by grasp force. Linear encoding was prominent with grasp force but less so with grasp dimension. The correlations with grasp dimension and grasp force were stronger in the firing of M1 than PMd neurons and across both regions the modulation with these parameters increased as reach to grasp proceeded. All PMd and M1 neurons that signaled grasp force also signaled grasp dimension, yet the two signals showed limited interactions, providing a neural substrate for the independent control of these two parameters at the behavioral level.

Neural decoding of finger movements using Skellam-based maximum-likelihood decoding

We present an optimal method for decoding the activity of primary motor cortex (M1) neurons in a nonhuman primate during single finger movements. The method is based on the maximum-likelihood (ML) inference, which assuming the probability of finger movements is uniform, is equivalent to the maximum a posteriori (MAP) inference. Each neuron's activation is first quantified by the change in firing rate before and after finger movement. We then estimate the probability density function of this activation given finger movement, i.e., Pr(neuronal activation (x) | finger movements (m)). Based on the ML criterion, we choose finger movements to maximize Pr(x |m). Experimentally, data were collected from 115 task-related neurons in M1 as the monkey performed flexion and extension of each finger and the wrist (12 movements). With as few as 20--25 randomly selected neurons, the proposed method decoded single-finger movements with 99% accuracy. Since the training and decoding procedures in the proposed method are simple and computationally efficient, the method can be extended for real-time neuroprosthetic control of a dexterous hand.

Comparison of neural responses in primary motor cortex to transient and continuous loads during posture

The present study examined whether neurons in primary motor cortex (M1) exhibit similar responses to transient and continuous loads applied during posture. Rapid responses to whole-limb perturbations were examined by transiently applying (300 ms) flexor and extensor torques to the shoulder and/or elbow during postural maintenance. Over half of M1 neurons responded to these transient loads within 80 ms and many responded within 20-40 ms. These rapid responses exhibited a broad continuum of modulation patterns across load directions. At one extreme, neurons exhibited reciprocal increases and decreases in activity for opposing loads. At the other extreme, neurons (particularly those with onset times of 20-40 ms) displayed relatively uniform increases in activity for all loads. Activity of proximal arm muscles displayed a narrower distribution of modulation patterns characterized by broadly tuned excitation combined with little or no reciprocal inhibition. Both neurons and muscles showed a directional preference for whole-limb flexor and whole-limb extensor torques (flexor at one joint and extensor at the other). Most neurons with rapid responses also showed steady-state responses to continuous loads, although these responses generally displayed reciprocal increases and decreases in activity for opposing loads. Importantly, the preferred-torque directions were quantitatively similar across tasks. For example, a neuron with a maximal rapid response to a transient elbow flexor torque tended to exhibit a maximal steady-state response to a continuous elbow flexor torque. Activity of proximal arm muscles also showed this preservation of directional tuning. These results illustrate that M1 neurons respond rapidly to transient multijoint loads and their patterns of activity share some, but not all, features related to continuous multijoint loads applied during posture.

Asynchronous decoding of dexterous finger movements using M1 neurons

Previous efforts in brain-machine interfaces (BMI) have looked at decoding movement intent or hand and arm trajectory, but current cortical control strategies have not focused on the decoding of dexterous [corrected] actions such as finger movements. The present work demonstrates the asynchronous decoding (i.e., where cues indicating the onset of movement are not known) of individual and combined finger movements. Single-unit activities were recorded sequentially from a population of neurons in the M1 hand area of trained rhesus monkeys during flexion and extension movements of each finger and the wrist. Nonlinear filters were designed to detect the onset of movement and decode the movement type from randomly selected neuronal ensembles (assembled from individually recorded single-unit activities). Average asynchronous decoding accuracies as high as 99.8%, 96.2%, and 90.5%, were achieved for individuated finger and wrist movements with three monkeys. Average decoding accuracy was still 92.5% when combined movements of two fingers were included. These results demonstrate that it is possible to asynchronously decode dexterous finger movements from a neuronal ensemble with high accuracy. This work takes an important step towards the development of a BMI for direct neural control of a state-of-the-art, multifingered hand prosthesis.

Decoding individuated finger movements using volume-constrained neuronal ensembles in the M1 hand area

Individuated finger and wrist movements can be decoded using random subpopulations of neurons that are widely distributed in the primary motor (M1) hand area. This work investigates 1) whether it is possible to decode dexterous finger movements using spatially-constrained volumes of neurons as typically recorded from a microelectrode array; and 2) whether decoding accuracy differs due to the configuration or location of the array within the M1 hand area. Single-unit activities were sequentially recorded from task-related neurons in two rhesus monkeys as they performed individuated movements of the fingers and the wrist. Simultaneous neuronal ensembles were simulated by constraining these activities to the recording field dimensions of conventional microelectrode array architectures. Artificial neural network (ANN) based filters were able to decode individuated finger movements with greater than 90% accuracy for the majority of movement types, using as few as 20 neurons from these ensemble activities. Furthermore, for the large majority of cases there were no significant differences (p < 0.01) in decoding accuracy as a function of the location of the recording volume. The results suggest that a brain-machine interface (BMI) for dexterous control of individuated fingers and the wrist can be implemented using microelectrode arrays placed broadly in the M1 hand area.

The statistics of natural hand movements

Humans constantly use their hands to interact with the environment and they engage spontaneously in a wide variety of manual activities during everyday life. In contrast, laboratory-based studies of hand function have used a limited range of predefined tasks. The natural movements made by the hand during everyday life have thus received little attention. Here, we developed a portable recording device that can be worn by subjects to track movements of their right hand as they go about their daily routine outside of a laboratory setting. We analyse the kinematic data using various statistical methods. Principal component analysis of the joint angular velocities showed that the first two components were highly conserved across subjects, explained 60% of the variance and were qualitatively similar to those reported in previous studies of reach-to-grasp movements. To examine the independence of the digits, we developed a measure based on the degree to which the movements of each digit could be linearly predicted from the movements of the other four digits. Our independence measure was highly correlated with results from previous studies of the hand, including the estimated size of the digit representations in primary motor cortex and other laboratory measures of digit individuation. Specifically, the thumb was found to be the most independent of the digits and the index finger was the most independent of the fingers. These results support and extend laboratory-based studies of the human hand.

Hand sensory-motor cortical network assessed by functional source separation

The functional source separation procedure (FSS) was applied to identify the activities of the primary sensorimotor areas (SM1) devoted to hand control. FSS adds a functional constraint to the cost function of the basic independent component analysis, and obtains source activity all along different processing states. Magnetoencephalographic signals from the left SM1 were recorded in 14 healthy subjects during a simple sensorimotor paradigm--galvanic right median nerve stimuli intermingled with submaximal isometric thumb opposition. Two functional sources related to the sensory flow in the primary cortex were extracted requiring maximal responsiveness to the nerve stimulation at around 20 and 30 ms (S1a, S1b). Maximal cortico-muscular coherence was required for the extraction of the motor source (M1). Sources were multiplied by the Euclidean norm of their corresponding weight vectors, allowing amplitude comparisons among sources in a fixed position. In all subjects, S1a, S1b, M1 were successfully obtained, positioned consistently with the SM1 organization, and behaved as physiologically expected during the movement and processing of the sensory stimuli. The M1 source reacted to the nerve stimulation with higher intensity at latencies around 30 ms than around 20 ms. The FSS method was demonstrated to be able to obtain the dynamics of different primary cortical network activities, two devoted mainly to sensory inflow, and the other to the motor control of the contralateral hand. It was possible to observe each source both during pure sensory processing and during motor tasks. In all conditions, a direct comparison of source intensities can be achieved.

Decoding M1 neurons during multiple finger movements

We tested several techniques for decoding the activity of primary motor cortex (M1) neurons during movements of single fingers or pairs of fingers. We report that single finger movements can be decoded with >99% accuracy using as few as 30 neurons randomly selected from populations of task-related neurons recorded from the M1 hand representation. This number was reduced to 20 neurons or less when the neurons were not picked randomly but selected on the basis of their information content. We extended techniques for decoding single finger movements to the problem of decoding the simultaneous movement of two fingers. Movements of pairs of fingers were decoded with 90.9% accuracy from 100 neurons. The techniques we used to obtain these results can be applied, not only to movements of single fingers and pairs of fingers as reported here, but also to movements of arbitrary combinations of fingers. The remarkably small number of neurons needed to decode a relatively large repertoire of movements involving either one or two effectors is encouraging for the development of neural prosthetics that will control hand movements.

Characterization of torque-related activity in primary motor cortex during a multijoint postural task

The present study examined neural activity in the shoulder/elbow region of primary motor cortex (M1) during a whole-limb postural task. By selectively imposing torques at the shoulder, elbow, or both joints we addressed how neurons represent changes in torque at a single joint, multiple joints, and their interrelation. We observed that similar proportions of neurons reflected changes in torque at the shoulder, elbow, and both joints and these neurons were highly intermingled across the cortical surface. Most torque-related neurons were reciprocally excited and inhibited (relative to their unloaded baseline activity) by opposing flexor and extensor torques at a single joint. Although coexcitation/coinhibition was occasionally observed at a single joint, it was rarely observed at both joints. A second analysis assessed the relationship between single-joint and multijoint activity. In contrast to our previous observations, we found that neither linear nor vector summation of single-joint activities could capture the breadth of neural responses to multijoint torques. Finally, we studied the neurons' directional tuning across all the torque conditions, i.e., in joint-torque space. Our population of M1 neurons exhibited a strong bimodal distribution of preferred-torque directions (PTDs) that was biased toward shoulder-extensor/elbow-flexor (whole-limb flexor) and shoulder-flexor/elbow-extensor (whole-limb extensor) torques. Notably, we recently observed a similar bimodal distribution of PTDs in a sample of proximal arm muscles. This observation illustrates the intimate relationship between M1 and the motor periphery.

Modular Organization of Finger Movements by the Human Central Nervous System

The motor system may generate automated movements, such as walking, by combining modular spinal motor synergies. However, it remains unknown whether a modular neuronal architecture is sufficient to generate the unique flexibility of human finger movements, which rely on cortical structures. Here we show that finger movements evoked by transcranial magnetic stimulation (TMS) of the primary motor cortex reproduced distinctive features of the spatial representation of voluntary movements as identified in previous neuroimaging studies, consistent with naturalistic activation of neuronal elements. Principal component analysis revealed that the dimensionality of TMS-evoked movements was low. Principal components extracted from TMS-induced finger movements resembled those derived from end-postures of voluntary movements performed to grasp imagined objects, and a small subset of them was sufficient to reconstruct these movements with remarkable fidelity. The motor system may coordinate even the most dexterous movements by using a modular architecture involving cortical components.

Partial reconstruction of muscle activity from a pruned network of diverse motor cortex neurons

Primary motor cortex (M1) neurons traditionally have been viewed as "upper motor neurons" that directly drive spinal motoneuron pools, particularly during finger movements. We used spike-triggered averages (SpikeTAs) of electromyographic (EMG) activity to select M1 neurons whose spikes signaled the arrival of input in motoneuron pools, and examined the degree of similarity between the activity patterns of these M1 neurons and their target muscles during 12 individuated finger and wrist movements. Neuron-EMG similarity generally was low. Similarity was unrelated to the strength of the SpikeTA effect, to whether the effect was pure versus synchrony, or to the number of muscles influenced by the neuron. Nevertheless, the sum of M1 neuron activity patterns, each weighted by the sign and strength of its SpikeTA effect, could be more similar to the EMG than the average similarity of individual neurons. Significant correlations between the weighted sum of M1 neuron activity patterns and EMG were obtained in six of 17 muscles, but showed R(2) values ranging from only 0.26 to 0.42. These observations suggest that additional factors-including inputs from sources other than M1 and nonlinear summation of inputs to motoneuron pools-also contributed substantially to EMG activity patterns. Furthermore, although each of these M1 neurons produced SpikeTA effects with a significant peak or trough 6-16 ms after the triggering spike, shifting the weighted sum of neuron activity to lead the EMG by 40-60 ms increased their similarity, suggesting that the influence of M1 neurons that produce SpikeTA effects includes substantial synaptic integration that in part may reach the motoneuron pools over less-direct pathways.

Forelimb movements and muscle responses evoked by microstimulation of cervical spinal cord in sedated monkeys

Documenting the forelimb responses evoked by stimulating sites in primate cervical spinal cord is significant for understanding spinal circuitry and for potential neuroprosthetic applications involving hand and arm. We examined the forelimb movements and electromyographic (EMG) muscle responses evoked by intraspinal microstimulation in three M. nemestrina monkeys sedated with ketamine. Trains of three stimulus pulses (10-80 muA) at 300 Hz were delivered at sites in regularly spaced tracks from C6 to T1. Hand and/or arm movements were evoked at 76% of the 745 sites stimulated. Specifically, movements were evoked in digits (76% of effective sites), wrist (15% of sites), elbow (26%), and shoulder (17%). To document the muscle activity evoked by a stimulus current just capable of eliciting consistent joint rotation, stimulus-triggered averages of rectified EMG were calculated at each site where a movement was observed. Typically, many muscles were coactivated at threshold currents needed to evoke movements. Out of the 13-15 muscles recorded per animal, only one muscle was active at 14% of the effective sites and two to six muscles were coactivated at 47% of sites. Thus intraspinal stimulation at threshold currents adequate for evoking movement typically coactivated multiple muscles, including antagonists. Histologic reconstruction of stimulation sites indicated that responses were elicited from the dorsal and ventral horn and from fiber tracts in the white matter, with little somatotopic organization for movement or muscle activation. The absence of a clear somatotopic map of output sites is probably a result of the stimulation of complex mixtures of fibers and cells.

Finger movements during reach-to-grasp in the monkey: amplitude scaling of a temporal synergy

To reduce the complexity of controlling hand-shaping, recent evidence suggests that the central nervous system uses synergies. In this study, two Rhesus monkeys reached-to-grasp 15 objects, varying in geometric properties, at five grasp force levels. Hand kinematics were recorded using a video-based tracking system. Individual finger movements were described as vectors varying in length and angle. Inflection points (i.e., stereotypic minima/maxima in the temporal profile of each finger vector) exhibited a temporal synchrony for individual fingers and in the coupling across fingers. Inflection point amplitudes varied significantly across objects grasped, scaling linearly with the object grasp dimension. Thus, differences in the vectors as a function of the objects were in the relative scaling of the vector parameters over time rather than a change in the temporal structure. Mahalanobis distance analysis of the inflection points confirmed that changes in inflection point amplitude as a function of objects were greater than changes in timing. Inflection points were independent of the grasp force, consistent with the observation that reach-to-grasp kinematics and grasp force are controlled independently. In summary, the shaping of the hand during reach-to-grasp involves scaling the amplitude of highly stereotypic temporal movements of the fingers.

Neuronal activity in primary motor cortex differs when monkeys perform somatosensory and visually guided wrist movements

This study was designed to investigate how activity patterns of primary motor cortical (MI) neurons change when monkeys perform the same movements guided by somatosensory and/or visual cues. Two adult male rhesus monkeys were trained to make wrist extensions and flexions after holding a steady position during an instructed delay period lasting 0.5-2.0 s. Monkeys held against a 0.07 Nm load that opposed flexion movements. Wrist movements were guided by vibratory cues (VIB-trials), visual cues (VIS-trials), or both in combination (COM-trials). Extracellular recordings of 188 MI neurons were made during all three paradigms. Individual neurons were counted twice, once for each movement direction, yielding 376 cases. All neurons had significant task-related activity (TRA) changes relative to delay period activity during at least one of the three paradigms. TRA was analyzed to determine if it was different as a function of the sensory cue(s) that initiated movement and that specified movement endpoints. Cases were grouped by whether the TRA changes were greater in VIB- or VIS-trials; this defined their "bias". One hundred and eighteen cases (31.4%) had greater TRA changes in VIB-trials (Vb-neurons), whereas 185 (49.2%) showed greater TRA changes in VIS-trials (Vs-neurons). The remaining 73 cases (19.4%) had similar TRA changes in VIB- and VIS-trials (Nb-neurons). For Vb- and Vs-neurons, earlier TRA onsets and greater TRA changes were observed in the trials for which these neurons were biased. During the COM-trials, the TRA was intermediate. During the trials for which the activity was not biased, the TRA was the least. For Nb-neurons, no significant TRA differences were observed across paradigms. TRA changes of MI neurons may represent movement planning-related inputs from other central, presumably cortical, sources as well as contribute to motor outflow from the cortex. These data suggest that Vb- and Vs-neurons are affected differently by somatosensory- and visually related central inputs, resulting in different TRAs, even for essentially identical movements. Such differences may depend not only on the type of sensory information that initiates movement but also whether that information specifies movement endpoints or might interfere with movement monitoring.

A spectrum from pure post-spike effects to synchrony effects in spike-triggered averages of electromyographic activity during skilled finger movements

During individuated finger movements, a high proportion of synchrony effects was found in spike-triggered averages (SpikeTAs) of rectified electromyographic activity aligned on the spikes discharged by primary motor cortex (M1) neurons. Because synchrony effects can be produced even if the trigger neuron itself provides no direct synaptic connections to motoneurons, such nonoscillatory synchrony effects often are discounted when considering control of motoneuron pools. We therefore examined the distinctions between pure postspike effects and synchrony effects. The criteria usually applied to distinguish pure and synchrony effects-onset latency and peak width-failed to separate the present SpikeTA effects objectively into distinct subpopulations. Synchrony effects generally were larger than pure effects. Many M1 neurons produced pure effects in some muscles while producing synchrony effects in others. M1 neurons producing no effects, only pure effects, only synchrony effects, or both pure and synchrony effects did not fall into different groups based on discharge characteristics during finger movements. Nor were neurons producing different types of SpikeTA effects segregated spatially in M1. These observations suggest that neurons producing pure and synchrony SpikeTA effects come from similar M1 populations. We discuss potential mechanisms that might have produced a continuous spectrum of variation from pure to synchrony effects in the present monkeys. Although synchrony effects cannot be taken as evidence of mono- or disynaptic connections from the recorded neuron to the motoneuron pool, the functional linkages indicated by synchrony effects represent a substantial fraction of M1 input to motoneuron pools during skilled, individuated finger movements.

Motor cortex neural correlates of output kinematics and kinetics during isometric-force and arm-reaching tasks

We recorded the activity of 132 proximal-arm-related neurons in caudal primary motor cortex (M1) of two monkeys while they generated either isometric forces against a rigid handle or arm movements with a heavy movable handle, in the same eight directions in a horizontal plane. The isometric forces increased in monotonic fashion in the direction of the force target. The forces exerted against the handle in the movement task were more complex, including an initial accelerating force in the direction of movement followed by a transient decelerating force opposite to the direction of movement as the hand approached the target. EMG activity of proximal-arm muscles reflected the difference in task dynamics, showing directional ramplike activity changes in the isometric task and reciprocally tuned "triphasic" patterns in the movement task. The apparent instantaneous directionality of muscle activity, when expressed in hand-centered spatial coordinates, remained relatively stable during the isometric ramps but often showed a large transient shift during deceleration of the arm movements. Single-neuron and population-level activity in M1 showed similar task-dependent changes in temporal pattern and instantaneous directionality. The momentary dissociation of the directionality of neuronal discharge and movement kinematics during deceleration indicated that the activity of many arm-related M1 neurons is not coupled only to the direction and speed of hand motion. These results also demonstrate that population-level signals reflecting the dynamics of motor tasks and of interactions with objects in the environment are available in caudal M1. This task-dynamics signal could greatly enhance the performance capabilities of neuroprosthetic controllers.

The integrated nature of motor cortical function

Recent studies on the functional organization and operational principles of motor cortical function, taken together, strongly support the notion that the motor cortex controls the muscle activities subserving movements in an integrated manner. For example, during pointing the shoulder, elbow and wrist muscles appear to be controlled as a coupled functional system, rather than individually and separately. The pattern of intrinsic connections between motor cortical points is likely part of the explanation of this operational principle. So too is the manner in which muscles and muscle synergies are represented in the motor cortex. However, selection of movement-related muscle synergies is likely a dynamic process involving the functional linking of a variety of motor cortical points, rather than the selection of fixed patterns embedded in the motor cortical circuitry. One of the mechanisms that may be involved in the functional linking of motor cortical points is disinhibition. Thus, motor cortical points are recruited into action by selected excitation as well as by selected release from inhibition. The incoordination of limb movements in patients after a stroke may be understood, at least in part, as a disruption of the connections between motor cortical points and of the neural mechanisms involved in their functional linking.

Hand function: peripheral and central constraints on performance

The hand is one of the most fascinating and sophisticated biological motor systems. The complex biomechanical and neural architecture of the hand poses challenging questions for understanding the control strategies that underlie the coordination of finger movements and forces required for a wide variety of behavioral tasks, ranging from multidigit grasping to the individuated movements of single digits. Hence, a number of experimental approaches, from studies of finger movement kinematics to the recording of electromyographic and cortical activities, have been used to extend our knowledge of neural control of the hand. Experimental evidence indicates that the simultaneous motion and force of the fingers are characterized by coordination patterns that reduce the number of independent degrees of freedom to be controlled. Peripheral and central constraints in the neuromuscular apparatus have been identified that may in part underlie these coordination patterns, simplifying the control of multi-digit grasping while placing certain limitations on individuation of finger movements. We review this evidence, with a particular emphasis on how these constraints extend through the neuromuscular system from the behavioral aspects of finger movements and forces to the control of the hand from the motor cortex.

Patterns of muscle activity underlying object-specific grasp by the macaque monkey

During object grasp, a coordinated activation of distal muscles is required to shape the hand in relation to the physical properties of the object. Despite the fundamental importance of the grasping action, little is known of the muscular activation patterns that allow objects of different sizes and shapes to be grasped. In a study of two adult macaque monkeys, we investigated whether we could distinguish between EMG activation patterns associated with grasp of 12 differently shaped objects, chosen to evoke a wide range of grasping postures. Each object was mounted on a horizontal shuttle held by a weak spring (load force 1-2 N). Objects were located in separate sectors of a "carousel," and inter-trial rotation of the carousel allowed sequential presentation of the objects in pseudorandom order. EMG activity from 10 to 12 digit, hand, and arm muscles was recorded using chronically implanted electrodes. We show that the grasp of different objects was characterized by complex but distinctive patterns of EMG activation. Cluster analysis shows that these object-related EMG patterns were specific and consistent enough to identify the object unequivocally from the EMG recordings alone. EMG-based object identification required a minimum of six EMGs from simultaneously recorded muscles. EMG patterns were consistent across recording sessions in a given monkey but showed some differences between animals. These results identify the specific patterns of activity required to achieve distinct hand postures for grasping, and they open the way to our understanding of how these patterns are generated by the central motor network.