Activity in ventral and dorsal premotor cortex in response to predictable force-pulse perturbations in a precision grip task

Neural coding of intended and executed grasp force in macaque areas AIP, F5, and M1

Considerable progress has been made over the last decades in characterizing the neural coding of hand shape, but grasp force has been largely ignored. We trained two macaque monkeys (Macaca mulatta) on a delayed grasping task where grip type and grip force were instructed. Neural population activity was recorded from areas relevant for grasp planning and execution: the anterior intraparietal area (AIP), F5 of the ventral premotor cortex, and the hand area of the primary motor cortex (M1). Grasp force was strongly encoded by neural populations of all three areas, thereby demonstrating for the first time the coding of grasp force in single- and multi-units of AIP. Neural coding of intended grasp force was most strongly represented in area F5. In addition to tuning analysis, a dimensionality reduction method revealed low-dimensional responses to grip type and grip force. Additionally, this method revealed a high correlation between latent variables of the neural population representing grasp force and the corresponding latent variables of electromyographic forearm muscle activity. Our results therefore suggest an important role of the cortical areas AIP, F5, and M1 in coding grasp force during movement execution as well as of F5 for coding intended grasp force.

Perspectives on classical controversies about the motor cortex

Primary motor cortex has been studied for more than a century, yet a consensus on its functional contribution to movement control is still out of reach. In particular, there remains controversy as to the level of control produced by motor cortex ("low-level" movement dynamics vs. "high-level" movement kinematics) and the role of sensory feedback. In this review, we present different perspectives on the two following questions: What does activity in motor cortex reflect? and How do planned motor commands interact with incoming sensory feedback during movement? The four authors each present their independent views on how they think the primary motor cortex (M1) controls movement. At the end, we present a dialogue in which the authors synthesize their views and suggest possibilities for moving the field forward. While there is not yet a consensus on the role of M1 or sensory feedback in the control of upper limb movements, such dialogues are essential to take us closer to one.

Distributed task-specific processing of somatosensory feedback for voluntary motor control

Corrective responses to limb disturbances are surprisingly complex, but the neural basis of these goal-directed responses is poorly understood. Here we show that somatosensory feedback is transmitted to many sensory and motor cortical regions within 25 ms of a mechanical disturbance applied to the monkey’s arm. When limb feedback was salient to an ongoing motor action (task engagement), neurons in parietal area 5 immediately (~25 ms) increased their response to limb disturbances, whereas neurons in other regions did not alter their response until 15 to 40 ms later. In contrast, initiation of a motor action elicited by a limb disturbance (target selection) altered neural responses in primary motor cortex ~65 ms after the limb disturbance, and then in dorsal premotor cortex, with no effect in parietal regions until 150 ms post-perturbation. Our findings highlight broad parietofrontal circuits that provide the neural substrate for goal-directed corrections, an essential aspect of highly skilled motor behaviors.

DOI:http://dx.doi.org/10.7554/eLife.13141.001

Humans and other animals can change a movement they are making in a split second, such as when a basketball player has to move around an unexpected opponent to shoot a ball through the hoop. These on-the-fly corrections rely on information about the movement that comes in from the senses. However, it was unclear how the brain and spinal cord process this sensory information to guide movement.

Omrani et al. have now recorded electrical activity from the brains of monkeys while the animals tried to keep their hand at a target. Each monkey wore a robotic exoskeleton that would occasionally move the monkey’s arm. Even if the monkey was not engaged in a motor task, a small nudge of the limb by the robot caused neural activity to spread rapidly throughout the sensory and motor regions of the cerebral cortex (the outer layer of the brain).

In some trials, when the monkey was actively trying to keep its hand at a target, the robot would again nudge the monkey’s arm slightly. Omrani et al. observed that within 25 milliseconds of this nudge, the activity in an area of the cortex called parietal area 5 responded even more, suggesting that this area was using information from the senses to actively deal with the change in arm position. This change in activity then spread to other parts of the brain.

In another set of trials, the monkey was trained to move to a second target if the robot nudged its arm. In this case, the activity in an area called the primary motor cortex increased even more, likely supporting the monkey’s ability to rapidly move to this second target. Overall, the study by Omrani et al. highlights the complex way that sensory feedback is processed in the cerebral cortex, supporting our ability to perform highly skilled motor actions.

DOI:http://dx.doi.org/10.7554/eLife.13141.002

PMv Neuronal Firing May Be Driven by a Movement Command Trajectory within Multidimensional Gaussian Fields

The premotor cortex (PM) is known to be a site of visuo-somatosensory integration for the production of movement. We sought to better understand the ventral PM (PMv) by modeling its signal encoding in greater detail. Neuronal firing data was obtained from 110 PMv neurons in two male rhesus macaques executing four reach-grasp-manipulate tasks. We found that in the large majority of neurons (∼90%) the firing patterns across the four tasks could be explained by assuming that a high-dimensional position/configuration trajectory-like signal evolving ∼250 ms before movement was encoded within a multidimensional Gaussian field (MGF). Our findings are consistent with the possibility that PMv neurons process a visually specified reference command for the intended arm/hand position trajectory with respect to a proprioceptively or visually sensed initial configuration. The estimated MGF were (hyper) disc-like, such that each neuron's firing modulated strongly only with commands that evolved along a single direction within position/configuration space. Thus, many neurons appeared to be tuned to slices of this input signal space that as a collection appeared to well cover the space. The MGF encoding models appear to be consistent with the arm-referent, bell-shaped, visual target tuning curves and target selectivity patterns observed in PMV visual-motor neurons. These findings suggest that PMv may implement a lookup table-like mechanism that helps translate intended movement trajectory into time-varying patterns of activation in motor cortex and spinal cord. MGFs provide an improved nonlinear framework for potentially decoding visually specified, intended multijoint arm/hand trajectories well in advance of movement.

Primary motor cortex and fast feedback responses to mechanical perturbations: a primer on what we know now and some suggestions on what we should find out next

Many researchers have drawn a clear distinction between fast feedback responses to mechanical perturbations (e.g., stretch responses) and voluntary control processes. But this simple distinction is difficult to reconcile with growing evidence that long-latency stretch responses share most of the defining capabilities of voluntary control. My general view—and I believe a growing consensus—is that the functional similarities between long-latency stretch responses and voluntary control processes can be readily understood based on their shared neural circuitry, especially a transcortical pathway through primary motor cortex. Here I provide a very brief and selective account of the human and monkey studies linking a transcortical pathway through primary motor cortex to the generation and functional sophistication of the long-latency stretch response. I then lay out some of the notable issues that are ready to be answered.

Extracting kinetic information from human motor cortical signals

Brain machine interfaces (BMIs) have the potential to provide intuitive control of neuroprostheses to restore grasp to patients with paralyzed or amputated upper limbs. For these neuroprostheses to function, the ability to accurately control grasp force is critical. Grasp force can be decoded from neuronal spikes in monkeys, and hand kinematics can be decoded using electrocorticogram (ECoG) signals recorded from the surface of the human motor cortex. We hypothesized that kinetic information about grasping could also be extracted from ECoG, and sought to decode continuously-graded grasp force. In this study, we decoded isometric pinch force with high accuracy from ECoG in 10 human subjects. The predicted signals explained from 22% to 88% (60 ± 6%, mean ± SE) of the variance in the actual force generated. We also decoded muscle activity in the finger flexors, with similar accuracy to force decoding. We found that high gamma band and time domain features of the ECoG signal were most informative about kinetics, similar to our previous findings with intracortical LFPs. In addition, we found that peak cortical representations of force applied by the index and little fingers were separated by only about 4mm. Thus, ECoG can be used to decode not only kinematics, but also kinetics of movement. This is an important step toward restoring intuitively-controlled grasp to impaired patients.

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.

Maintaining Grip: Anticipatory and Reactive EEG Responses to Load Perturbations

Previous behavioral work has shown the existence of both anticipatory and reactive grip force responses to predictable load perturbations, but how the brain implements anticipatory control remains unclear. Here we recorded electroencephalographs while participants were subjected to predictable and unpredictable external load perturbations. Participants used precision grip to maintain the position of an object perturbed by load force pulses. The load perturbations were either distributed randomly over an interval 700- to 4,300-ms (unpredictable condition) or they were periodic with interval 2,000 ms (predictable condition). Preparation for the predictable load perturbation was manifested in slow preparatory brain potentials and in electromyographic and force signals recorded concurrently. Preparation modulated the long-latency reflex elicited by load perturbations with a higher amplitude reflex response for unpredictable compared with predictable perturbations. Importantly, this modulation was also reflected in the amplitude of sensorimotor cortex potentials just preceding the long-latency reflex. Together, these results support a transcortical pathway for the long-latency reflex and a central modulation of the reflex grip force response.

Failure to disrupt the ‘sensorimotor’ memory for lifting objects with a precision grip

When repetitively lifting an object with mechanical properties that vary from lift-to-lift, the fingertip forces for gripping and lifting are influenced strongly by the previous lift, revealing a 'sensorimotor' memory. Two recent reports indicate that the sensorimotor memory for grip force is easily disrupted by an unrelated task like a strong pinch or vibration, even when the lift was performed with the hand contralateral to the vibration or preceding pinch. These findings indicate that this memory may reflect sensory input or muscle contraction levels, rather than object properties or the specific task of gripping and lifting. Here we report that the predictive scaling of lift force was not disrupted by conditioning tasks that featured exerting a vertical isometric force with the upper extremity. When subjects lifted a 2 N object repetitively the peak lift force rate was 26.4 N/s. The lift force rate increased to 36.1 N/s when the 2 N object was lifted (regardless of hand) after lifting the 8 N object with the right hand, which reveals the expected 'sensorimotor' memory. The lift force rate did not increase (24.8 vs. 26.4 N/s for the control condition) when a bout of isometric exertion (9.8 N) in the vertical direction with the distal right forearm preceded lifts of the 2 N object. This finding was confirmed with another isometric task designed to more closely mimic lifting an object with a precision grip. This difference in the sensitivity of grip versus lift force to a preceding isometric contraction indicates that separate sensorimotor memories contribute to the predictive scaling of the commands for gripping and lifting an object.

Dopamine replacement therapy does not restore the ability of Parkinsonian patients to make rapid adjustments in motor strategies according to changing sensorimotor contexts

The ability of dopamine replacement to restore rapid motor adjustments in Parkinson's disease (PD) was investigated. Medicated and non-medicated patients performed finger-to-nose movements while simultaneously bending the trunk forward, without vision. Trunk motion was blocked unexpectedly, necessitating rapid adjustments in arm trajectories. Patients exhibited irregular hand paths, plateaus in hand velocity, and prolonged movement times, which were significantly greater in perturbed trials. Medication improved kinematics but perturbation-induced disturbances persisted and did not approximate the levels of non-perturbed trials nor those of controls. Dopaminergic replenishment in PD may therefore have limited restorative benefits for rapid context-specific motor control.

Role of the primary motor and dorsal premotor cortices in the anticipation of forces during object lifting

When lifting small objects, people apply forces that match the expected weight of the object. This expectation relies in part on information acquired during a previous lift and on associating a certain weight with a particular object. Our study examined the role of the primary motor and dorsal premotor cortices in predicting weight based either on information acquired during a previous lift (no-cue experiment) or on arbitrary color cues associated with a particular weight (cue experiment). In the two experiments, subjects used precision grip to lift two different weights in a series of trials both before and after we applied low-frequency repetitive transcranial magnetic stimulation over the primary motor and dorsal premotor cortices. In the no-cue experiment, subjects did not receive any previous information about which of two weights they would have to lift. In the cue experiment, a color cue provided information about which of the two weights subjects would have to lift. Our results demonstrate a double dissociation in the effects induced by repetitive stimulation. When applied over the primary motor cortex, repetitive stimulation disrupted the scaling of forces based on information acquired during a previous lift. In contrast, when applied over the dorsal premotor cortex, repetitive stimulation disrupted the scaling of forces based on arbitrary color cues. We conclude that the primary motor and dorsal premotor cortices have unique roles during the anticipatory scaling of forces associated with the lifting of different weights.

Neuronal correlates of movement dynamics in the dorsal and ventral premotor area in the monkey

We investigated how neurons in the different motor areas of the frontal lobe reflect the movement dynamics, and how their neuronal activity undergoes plastic changes when monkeys adapt to perturbing forces (they learn new dynamics). Here we describe the results obtained in the dorsal premotor area (PMd) and ventral premotor area (PMv). Monkeys performed visually instructed, delayed reaching movements before, during and after exposure and adaptation to a viscous, curl force field. During movement planning (i.e., during an instructed delay that followed the cue and preceded the go signal), we found dynamics-related activity in PMd but not in PMv. A closer analysis revealed that the population of PMd reflected the dynamics of the upcoming movement increasingly over the course of the delay, starting from a kinematics-related signal. During movement execution, dynamics-related activity was present in both PMd and PMv. In this respect, the results for PMd were similar to that previously found for the supplementary motor area (SMA) whereas the results for PMv were more similar to that previously found for the primary motor cortex (M1). Plastic changes associated with the acquisition of new dynamics found in PMd and PMv were qualitatively similar to those previously observed in M1 and SMA. The ensemble of our experiments suggest a broader picture of the cortical control of movements, whereby multiple areas all contribute to the various sensorimotor processes, including "low" computations such as the movement dynamics, but also express a degree of specialization.

The cutaneous contribution to adaptive precision grip

Only after injury, or perhaps prolonged exposure to cold that is sufficient to numb the fingers, do we suddenly appreciate the complex neural mechanisms that underlie our effortless dexterity in manipulating objects. The nervous system is capable of adapting grip forces to a wide range of object shapes, weights and frictional properties, to provide optimal and secure handling in a variety of potentially perturbing environments. The dynamic interplay between sensory information and motor commands provides the basis for this flexibility, and recent studies supply somewhat unexpected evidence of the essential role played by cutaneous feedback in maintaining and acquiring predictive grip force control. These examples also offer new insights into the adaptive control of other voluntary movements.

Responses of Cerebellar Interpositus Neurons to Predictable Perturbations Applied to an Object Held in a Precision Grip

Two monkeys were trained to lift and hold an instrumented object at a fixed height for 2.5 s using a precision grip. The device was equipped with load cells to measure both the grip and lifting or load forces. On selected blocks of 20-30 trials, a downward force-pulse perturbation was applied to the object after 1.5 s of stationary holding. The animals were required to resist the perturbation to obtain a fruit juice reward. The perturbations invariably elicited a reflex-like, time-locked increase in grip force at latencies between 50 and 100 ms. In this study, we searched for single cells in the interpositus and dentate nuclei with activity related to grasping and lifting, and we tested 127/150 task-related cells for their responses to the perturbation. Of the 127 cells, reflex-like increases or decreases in discharge frequency occurred in 75 cells (59%) at a mean latency of 36 ms. Preparatory increases in grip force preceding the perturbation appeared gradually and increased in strength with repetition in 39/127 (31%) cells. These preparatory increases did not immediately disappear when the perturbations were withdrawn but decreased progressively over repeated trials. Although a few cells showed anticipatory activity without a reflex-like response (15/127 or 12%), the majority of these cells (24/39) displayed both anticipatory and reflex-like responses. From an examination of the histological sections, cells with both anticipatory and reflex-like responses appeared to be confined to the dorsal anterior interpositus, adjacent to, but not within, the dentate nucleus. These results confirm and extend the suggestion by Dugas and Smith that the cerebellum plays a major role in organizing anticipatory responses to predictable perturbations in a manner that medial and lateral premotor areas of the cerebral cortex do not.

Selective use of visual information signaling objects' center of mass for anticipatory control of manipulative fingertip forces

The present study examines whether visual information indicating the center of mass (CM) of an object can be used for the appropriate scaling of fingertip forces at each digit during precision grip. In separate experiments subjects lifted an object with various types of visual cues concerning the CM location several times and then rotated and lifted it again to determine whether the visual cues signaling the new location of the CM could be used to appropriately scale the fingertip forces. Specifically, subjects had either no visual cues, visual instructional cues (i.e., an indicator) or visual geometric cues where the longer axis of the object indicated the CM. When no visual cues were provided, subjects were unable to appropriately scale the load forces at each digit following rotation despite their knowledge of the new weight distribution. When visual cues regarding the CM location were provided, the nature of the visual cues determined their effectiveness in retrieval of internal representations underlying the anticipatory scaling of fingertip forces. Specifically, when subjects were provided with visual instructional information, they were unable to appropriately scale the forces. More appropriate scaling of the load forces occurred when the visual cues were ecologically meaningful, i.e., when the shape of the object indicated the CM location. We suggest that visual instructional cues do not have access to the implicit processes underlying dynamic force control, whereas visual geometric cues can be used for the retrieval of the internal representation related to CM for appropriate partitioning of the forces in each digit.

Activity in rostral motor cortex in response to predictable force-pulse perturbations in a precision grip task

The purpose of this investigation was to characterize the discharge of neurons in the rostral area 4 motor cortex (MI) during performance of a precision grip task. Three monkeys were trained to grasp an object between the thumb and index finger and to lift and hold it stationary for 2-2.5 s within a narrow position window. The grip and load forces and the vertical displacement of the object were recorded on each trial. On some trials a downward force-pulse perturbation generating a shear force and slip on the skin was applied to the object after 1.5 s of static holding. In total, 72 neurons were recorded near the rostral limit of the hand area of the motor cortex, located close to the premotor areas. Of these, 30 neurons were examined for receptive fields, and all 30 were found to receive proprioceptive inputs from finger muscles. Intracortical microstimulation applied to 38 recording sites evoked brief hand movements, most frequently involving the thumb and index finger with an average threshold of 12 microA. Slightly more than one-half of the neurons (38/72) demonstrated significant increases in firing rate that on average began 284 +/- 186 ms before grip onset. Of 54 neurons tested with predictable force-pulse perturbations, 29 (53.7%) responded with a reflexlike reaction at a mean latency of 54.2 +/- 16.8 ms. This latency was 16 ms longer than the mean latency of reflexlike activity evoked in neurons with proprioceptive receptive fields in the more caudal motor cortex. No neurons exhibited anticipatory activity that preceded the perturbation even when the perturbations were delivered randomly and signaled by a warning stimulus. The results indicate the presence of a strong proprioceptive input to the rostral motor cortex, but raise the possibility that the afferent pathway or intracortical processing may be different because of the slightly longer latency.