Simon A. Overduin

Microstimulation Activates a Handful of Muscle Synergies

Muscle synergies have been proposed as a mechanism to simplify movement control. Whether these coactivation patterns have any physiological reality within the nervous system remains unknown. Here we applied electrical microstimulation to motor cortical areas of rhesus macaques to evoke hand movements. Movements tended to converge toward particular postures, driven by synchronous bursts of muscle activity. Across stimulation sites, the muscle activations were reducible to linear sums of a few basic patterns-each corresponding to a muscle synergy evident in voluntary reach, grasp, and transport movements made by the animal. These synergies were represented nonuniformly over the cortical surface. We argue that the brain exploits these properties of synergies-postural equivalence, low dimensionality, and topographical representation-to simplify motor planning, even for complex hand movements.

Modulation of muscle synergy recruitment in primate grasping.

In grasping, the CNS controls a particularly large number of degrees of freedom. We tested the idea that this control is facilitated by the presence of muscle synergies. According to the strong version of this concept, these synergies are invariant, hard-wired patterns of activation across muscles. Synergies may serve as modules that linearly sum, each with specific amplitude and timing coefficients, to generate a large array of muscle patterns. We tested two predictions of the synergy model. A small number of synergies should (1) account for a large fraction of variation in muscle activity, and (2) be modulated in their recruitment by task variables, even in novel behavioral contexts. We also examined whether the synergies would (3) have broadly similar structures across animals. We recorded from 15 to 19 electrodes implanted in forelimb muscles of two rhesus macaques as they grasped and transported 25 objects of variable shape and size. We show that three synergies accounted for 81% of the electromyographic data variation in each monkey. Each synergy was modulated in its recruitment strength and/or timing by object shape and/or size. Even when synergies were extracted from a small subset of object shape and size conditions and then used to reconstruct the entire dataset, we observed highly similar synergies and patterns of modulation. The synergies were well conserved between monkeys, with two of the synergies exceeding chance structural similarity, and the third being recruited, in both animals, in proportion to the size of the object handled.

Disruption of Primary Motor Cortex before Learning Impairs Memory of Movement Dynamics

Although multiple lines of evidence implicate the primary motor cortex (M1) in motor learning, the precise role of M1 in the adaptation to novel movement dynamics and in the subsequent consolidation of a memory of those dynamics remains unclear. Here we used repetitive transcranial magnetic stimulation (rTMS) to dissociate the contribution of M1 to these distinct aspects of motor learning. Subjects performed reaching movements in velocity-dependent force fields over three epochs: a null-field baseline epoch, a clockwise-field learning epoch (15 min after the baseline epoch), and a clockwise-field retest epoch (24 h after the learning epoch). Half of the subjects received 15 min of 1 Hz rTMS to M1 between the baseline and learning epochs. Subjects given rTMS performed identically to control subjects during the learning epoch. However, control subjects performed with significantly less error than rTMS subjects in the retest epoch on the following day. These results suggest that M1 is not critical to the network supporting motor adaptation per se but that, within this network, M1 may be important for initiating the development of long-term motor memories.

Closed-Loop Decoder Adaptation Shapes Neural Plasticity for Skillful Neuroprosthetic Control

Neuroplasticity may play a critical role in developing robust, naturally controlled neuroprostheses. This learning, however, is sensitive to system changes such as the neural activity used for control. The ultimate utility of neuroplasticity in real-world neuroprostheses is thus unclear. Adaptive decoding methods hold promise for improving neuroprosthetic performance in nonstationary systems. Here, we explore the use of decoder adaptation to shape neuroplasticity in two scenarios relevant for real-world neuroprostheses: nonstationary recordings of neural activity and changes in control context. Nonhuman primates learned to control a cursor to perform a reaching task using semistationary neural activity in two contexts: with and without simultaneous arm movements. Decoder adaptation was used to improve initial performance and compensate for changes in neural recordings. We show that beneficial neuroplasticity can occur alongside decoder adaptation, yielding performance improvements, skill retention, and resistance to interference from native motor networks. These results highlight the utility of neuroplasticity for real-world neuroprostheses.

Distributed digit somatotopy in primary somatosensory cortex

We obtained high-resolution somatotopic maps of the human digits using 4.0 T functional magnetic resonance imaging (fMRI). In separate experiments, the volar surface of either the right thumb, index, or ring finger was stimulated in a sliding-window fashion in both distal-to-proximal and proximal-to-distal directions using a custom-built pneumatic apparatus. Analysis of the functional images was restricted to Brodmanns areas 3b and 1 and control areas 4 and 3a, as well as a randomized simulation of the functional data in each of these areas. Using in-house algorithms, we detected discrete regions of cortical activation showing phase reversal coinciding with alternation in stimulation direction. Most stimulation-related phase maps of the digits were obtained in areas 3b and 1, rather than areas 3a or 4, despite the somatic input to the latter two areas. The area 3b and 1 representations thus appear to be relatively discrete and somatotopic compared to other somatic processing regions. Our results within areas 3b and 1 confirm the nonlinear mapping of the body surface suggested by recordings in nonhuman primates in terms of phase band topography, scaling, and frequency relative to the actual digit surfaces. The scaling and frequency nonlinearities were more evident within area 3b than area 1, suggesting a functional differentiation of these regions as has previously been observed only in more invasive recordings. Specifically, the area 1 representations were larger overall than those observed in area 3b, and the frequencies of area 3b phase bands and voxels were related disproportionately to thumb and index finger stimulation and to particular areas on the digit surface, suggesting a weighting based in part on receptor distribution.

Intermittent practice facilitates stable motor memories

Humans adaptively control reaching movements to maintain good performance in the presence of novel forces acting on the arm. A recent study suggested that motor memories of different force conditions are not transformed from fragile to stable states, but rather are always vulnerable to interference from newly learned conditions (Caithness et al., 2004). This is contrary to the results of previous studies (Brashers-Krug et al., 1996; Shadmehr and Brashers-Krug, 1997), although all of these studies followed similar methods. Here, we show that a seemingly insignificant and inconsistently applied methodological detail may reconcile this discrepancy. Catch trials, in which the novel forces are removed, may be randomly interspersed among the more frequent force trials to assess how a subject is learning to predict the pattern of forces. In the absence of an interfering condition, subjects retained their learning until retest a day later regardless of whether they experienced catch trials. But in the presence of an interfering condition, only the subjects who had experienced forces intermittently retained their learning and thereby showed resistance to the interference. Thus, intermittent rather than constant practice conditions appear to be critical for dynamic motor memory stabilization.

Muscle synergies evoked by microstimulation are preferentially encoded during behavior.

Electrical microstimulation studies provide some of the most direct evidence for the neural representation of muscle synergies. These synergies, i.e., coordinated activations of groups of muscles, have been proposed as building blocks for the construction of motor behaviors by the nervous system. Intraspinal or intracortical microstimulation (ICMS) has been shown to evoke muscle patterns that can be resolved into a small set of synergies similar to those seen in natural behavior. However, questions remain about the validity of microstimulation as a probe of neural function, particularly given the relatively long trains of supratheshold stimuli used in these studies. Here, we examined whether muscle synergies evoked during ICMS in two rhesus macaques were similarly encoded by nearby motor cortical units during a purely voluntary behavior involving object reach, grasp, and carry movements. At each microstimulation site we identified the synergy most strongly evoked among those extracted from muscle patterns evoked over all microstimulation sites. For each cortical unit recorded at the same microstimulation site, we then identified the synergy most strongly encoded among those extracted from muscle patterns recorded during the voluntary behavior. We found that the synergy most strongly evoked at an ICMS site matched the synergy most strongly encoded by proximal units more often than expected by chance. These results suggest a common neural substrate for microstimulation-evoked motor responses and for the generation of muscle patterns during natural behaviors.

Symmetric sensorimotor somatotopy.

Background Functional imaging has recently been used to investigate detailed somatosensory organization in human cortex. Such studies frequently assume that human cortical areas are only identifiable insofar as they resemble those measured invasively in monkeys. This is true despite the electrophysiological basis of the latter recordings, which are typically extracellular recordings of action potentials from a restricted sample of cells. Methodology/Principal Findings Using high-resolution functional magnetic resonance imaging in human subjects, we found a widely distributed cortical response in both primary somatosensory and motor cortex upon pneumatic stimulation of the hairless surface of the thumb, index and ring fingers. Though not organized in a discrete somatotopic fashion, the population activity in response to thumb and index finger stimulation indicated a disproportionate response to fingertip stimulation, and one that was modulated by stimulation direction. Furthermore, the activation was structured with a line of symmetry through the central sulcus reflecting inputs both to primary somatosensory cortex and, precentrally, to primary motor cortex. Conclusions/Significance In considering functional activation that is not somatotopically or anatomically restricted as in monkey electrophysiology studies, our methodology reveals finger-related activation that is not organized in a simple somatotopic manner but is nevertheless as structured as it is widespread. Our findings suggest a striking functional mirroring in cortical areas conventionally ascribed either an input or an output somatotopic function.

Designing dynamical properties of brain-machine interfaces to optimize task-specific performance.

Brain-machine interfaces (BMIs) are dynamical systems whose properties ultimately influence performance. For instance, a 2-D BMI in which cursor position is controlled using a Kalman filter will, by default, create an attractor point that “pulls” the cursor to particular points in the workspace. If created unintentionally, such effects may not be beneficial for BMI performance. However, there have been few empirical studies exploring how various dynamical effects of closed-loop BMIs ultimately influence performance. In this work, we utilize experimental data from two macaque monkeys operating a closed-loop BMI to reach to 2-D targets and show that certain dynamical properties correlate with performance loss. We also show that other dynamical properties represent tradeoffs between naturally competing objectives, such as speed versus accuracy. These findings highlight the importance of fine-tuning the dynamical properties of closed-loop BMIs to optimize task-specific performance.

Cortical Processing during Dynamic Motor Adaptation

In this chapter we investigate the role of motor cortex in adapting movements to novel dynamic environments. We present results from two experiments in which monkey or human subjects learned to make two-dimensional reaching movements while holding a robotic manipulandum that applied a predictable pattern of forces (a curl field) to their hand. In the first study, we analyzed electrophysiological data recorded in motor cortex while monkeys adapted or readapted to the novel forces on each day of the experiment. In the second study, we perturbed the excitability of motor cortex using repetitive transcranial magnetic stimulation (rTMS) as human participants adapted to the forces. From the first experiment, we present qualitative evidence that a network of cortical areas including the supplementary motor area, premotor cortex, and primary motor cortex (M1) not only encodes kinematic and dynamic parameters of motor execution, but also registers changes in encoding that could provide a substrate for motor memory. Based on the second experiment, we qualify the role of M1 in motor memory, by showing that its disruption by rTMS does not interfere with the process of initial motor adaptation, but rather with offline improvement as measured at retest on the following day.