Nedialko I. Krouchev

Muscle synergies during locomotion in the cat: a model for motor cortex control

It is well established that the motor cortex makes an important contribution to the control of visually guided gait modifications, such as those required to step over an obstacle. However, it is less clear how the descending cortical signal interacts with the interneuronal networks in the spinal cord to ensure that precise changes in limb trajectory are appropriately incorporated into the base locomotor rhythm. Here we suggest that subpopulations of motor cortical neurones, active sequentially during the step cycle, may regulate the activity of small groups of synergistic muscles, likewise active sequentially throughout the step cycle. These synergies, identified by a novel associative cluster analysis, are defined by periods of muscle activity that are coextensive with respect to the onset and offset of the EMG activity. Moreover, the synergies are sparse and are frequently composed of muscles acting around more than one joint. During gait modifications, we suggest that subpopulations of motor cortical neurones may modify the magnitude and phase of the EMG activity of all muscles contained within a given synergy. Different limb trajectories would be produced by differentially modifying the activity in each synergy thus providing a flexible substrate for the control of intralimb coordination during locomotion.

Sequential Activation of Motor Cortical Neurons Contributes to Intralimb Coordination During Reaching in the Cat by Modulating Muscle Synergies

We examined the contribution of the motor cortex to the control of intralimb coordination during reaching in the standing cat. We recorded the activity of 151 pyramidal tract neurons (PTNs) in the forelimb representation of three cats during a task in which the cat reached forward from a standing position to press a lever. We simultaneously recorded the activity of muscles in the contralateral forelimb acting around each of the major joints. Cell activity was recorded with and without the presence of an obstacle requiring a modification of limb trajectory. The majority of the PTNs (134/151, 89%) modulated their discharge activity at some period of the reach while 84/151 (56%) exhibited a significant peak or trough of activity as the limb was transported from its initial position to the lever. These phasic changes of activity were distributed sequentially throughout the transport phase. A cluster analysis of muscle activity in two of the cats showed the presence of five muscle synergies during this transport period. One of the synergies was related to the lift of the paw from the support surface, two to flexion of the limb and dorsiflexion of the paw, one to preparation for contact with the lever, and one to the transport of the entire limb forward; a sixth synergy was activated during the lever press. An analysis of the phase of cell activity with respect to the phase of activity of muscles selected to represent each of these synergies showed that different populations of PTNs were activated sequentially and coincidentally with each synergy. We suggest that this sequential activation of populations of PTNs is compatible with a contribution to the initiation and modulation of functionally distinct groups of synergistic muscles and ultimately serves to ensure the appropriate multiarticular, intralimb coordination of the limb during reaching.

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.

Colored context cues can facilitate the ability to learn and to switch between multiple dynamical force fields

We tested the efficacy of color context cues during adaptation to dynamic force fields. Four groups of human subjects performed elbow flexion/extension movements to move a cursor between targets on a monitor while encountering a resistive (Vr) or assistive (Va) viscous force field. They performed two training sets of 256 trials daily, for 10 days. The monitor background color changed (red, green) every four successful trials but provided different degrees of force field context information to each group. For the irrelevant-cue groups, the color changed every four trials, but one group encountered only the Va field and the other only the Vr field. For the reliable-cue group, the force field alternated between Va and Vr each time the monitor changed color (Vr, red; Va, green). For the unreliable-cue group, the force field changed between Va and Vr pseudorandomly at each color change. All subjects made increasingly stereotyped movements over 10 training days. Reliable-cue subjects typically learned the association between color cues and fields and began to make predictive changes in motor output at each color change during the first day. Their performance continued to improve over the remaining days. Unreliable-cue subjects also improved their performance across training days but developed a strategy of probing the nature of the field at each color change by emitting a default motor response and then adjusting their motor output in subsequent trials. These findings show that subjects can extract explicit and implicit information from color context cues during force field adaptation.

Motor cortex single-neuron and population contributions to compensation for multiple dynamic force fields

To elucidate how primary motor cortex (M1) neurons contribute to the performance of a broad range of different and even incompatible motor skills, we trained two monkeys to perform single-degree-of-freedom elbow flexion/extension movements that could be perturbed by a variety of externally generated force fields. Fields were presented in a pseudorandom sequence of trial blocks. Different computer monitor background colors signaled the nature of the force field throughout each block. There were five different force fields: null field without perturbing torque, assistive and resistive viscous fields proportional to velocity, a resistive elastic force field proportional to position and a resistive viscoelastic field that was the linear combination of the resistive viscous and elastic force fields. After the monkeys were extensively trained in the five field conditions, neural recordings were subsequently made in M1 contralateral to the trained arm. Many caudal M1 neurons altered their activity systematically across most or all of the force fields in a manner that was appropriate to contribute to the compensation for each of the fields. The net activity of the entire sample population likewise provided a predictive signal about the differences in the time course of the external forces encountered during the movements across all force conditions. The neurons showed a broad range of sensitivities to the different fields, and there was little evidence of a modular structure by which subsets of M1 neurons were preferentially activated during movements in specific fields or combinations of fields.

Virtual worlds and games for rehabilitation and research

Expensive and bulky setups, and center-out whole-arm reaching paradigms have been used extensively in motor control research to explore the neural control of movement in humans and primates. They have led to a number of robust findings about the effect of modified task dynamics onto movement kinematics and motor skill acquisition. A number of controversial findings are related to the role of peripheral reflexes and the effect of practice on motor learning, adaptation, and consolidation A related issue is whether the brain actually uses strategies similar to engineering optimal control. Finally, aspects that typically do not get much attention are the roles of subject motivation and the functional relevance of the task to daily activity. Here we introduce a suite of virtual environments for motor skill acquisition, running on ubiquitous highly affordable and portable PC hardware. We explore case studies in both a research and a rehabilitation context, and suggest that our observations are compatible with theory by N.A.Bernstein according to which the brain may perform feasibility search. Unlike a robotic system, the brain tries to achieve solutions to the behavioral tasks that satisfy imposed constraints, even if not yet optimal. When more than one solution is identified, the brain retains the better one for future use. In comparison, a robotic system would search (depth- or width-first) throughout the whole parameter space. This difference is not unlike the one between a grand-master and a chess playing program. Based on a rich variety of acquired behavioral data, we demonstrate clearly that the virtual environments we introduced here have a very large potential to: 1) Reproduce individually targeted elements of motor behavior, which are functionally relevant in everyday life; 2) Drive incremental re-acquisition of increasingly complex and adapted motor programs; 3) Achieve the latter goal by selectively augmenting or suppressing particular strategies depending on their compatibility with residual and readapted patient capacity at a particular point of the rehabilitation process.

Principal component analysis of M1 neurophysiology data suggests a motor-control system-architecture template

Stereotyped reaching tasks are used to study how primate subjects learn and recall motor skills required to compensate for different external forces during arm movements. To unveil mechanisms accounting for skilled performance under a wide range of rapidly switching task dynamics conditions, we recorded neural data from the primary motor-cortex (M1). Here we present a systematic analysis of changes in the M1 activity of a monkey with extensive practice compensating for five different dynamic fields in an elbow flexion/extension task. We show how they reflect differences in task kinematics and dynamics. Making extensive use of principal component analysis (PCA) and in preparation for computational modeling (see the companion paper) we demonstrate how M1 activity can be related functionally to the dynamics of feed-forward (FF), fast- and slow- feedback (FB) loops of the adaptive controller implemented by the brain to guide skilled motor behavior.

A functional approach to modeling M1 single-unit activity recorded in three primate motor control studies

When monkeys make movements with or without external force perturbations, or generate isometric forces in different directions from different workspace positions, primary motor cortex (M1) cell activity shows systematic changes in directional tuning and in force-generation gains as a function of arm posture. However, it may be simplistic to assume most control intelligence is in the cortex while the brainstem and especially the spinal cord do little more than passively implement pontifical descending commands. More recent studies like [1–4] do suggest a different perspective. Furthermore, systematic changes in directionality of M1 cell and limb muscle EMG activity may stem partly from the feedback (aka reflex) loops, physical properties of limb biomechanics, muscle anisotropy and force production nonlinearities, and their interplay with task conditions, and not only due to predictive feedforward central commands.