Formation of Anisotropic Conducting Interlayer for High-Resolution Epidermal Electromyography Using Mixed-Conducting Particulate Composite.

Epidermal electrophysiology is a non-invasive method used in research and clinical practices to study the electrical activity of the brain, heart, nerves, and muscles. However, electrode/tissue interlayer materials such as ionically conducting pastes can negatively affect recordings by introducing lateral electrode-to-electrode ionic crosstalk and reducing spatial resolution. To overcome this issue, biocompatible, anisotropic-conducting interlayer composites (ACI) that establish an electrically anisotropic interface with the skin are developed, enabling the application of dense cutaneous sensor arrays. High-density, conformable electrodes are also microfabricated that adhere to the ACI and follow the curvilinear surface of the skin. The results show that ACI significantly enhances the spatial resolution of epidermal electromyography (EMG) recording compared to conductive paste, permitting the acquisition of single muscle action potentials with distinct spatial profiles. The high-density EMG in developing mice, non-human primates, and humans is validated. Overall, high spatial-resolution epidermal electrophysiology enabled by ACI has the potential to advance clinical diagnostics of motor system disorders and enhance data quality for human-computer interface applications.

Preparatory activity and the expansive null-space.

The study of the cortical control of movement experienced a conceptual shift over recent decades, as the basic currency of understanding shifted from single-neuron tuning towards population-level factors and their dynamics. This transition was informed by a maturing understanding of recurrent networks, where mechanism is often characterized in terms of population-level factors. By estimating factors from data, experimenters could test network-inspired hypotheses. Central to such hypotheses are 'output-null' factors that do not directly drive motor outputs yet are essential to the overall computation. In this Review, we highlight how the hypothesis of output-null factors was motivated by the venerable observation that motor-cortex neurons are active during movement preparation, well before movement begins. We discuss how output-null factors then became similarly central to understanding neural activity during movement. We discuss how this conceptual framework provided key analysis tools, making it possible for experimenters to address long-standing questions regarding motor control. We highlight an intriguing trend: as experimental and theoretical discoveries accumulate, the range of computational roles hypothesized to be subserved by output-null factors continues to expand.

Identifying Interpretable Latent Factors with Sparse Component Analysis.

In many neural populations, the computationally relevant signals are posited to be a set of 'latent factors' - signals shared across many individual neurons. Understanding the relationship between neural activity and behavior requires the identification of factors that reflect distinct computational roles. Methods for identifying such factors typically require supervision, which can be suboptimal if one is unsure how (or whether) factors can be grouped into distinct, meaningful sets. Here, we introduce Sparse Component Analysis (SCA), an unsupervised method that identifies interpretable latent factors. SCA seeks factors that are sparse in time and occupy orthogonal dimensions. With these simple constraints, SCA facilitates surprisingly clear parcellations of neural activity across a range of behaviors. We applied SCA to motor cortex activity from reaching and cycling monkeys, single-trial imaging data from C. elegans, and activity from a multitask artificial network. SCA consistently identified sets of factors that were useful in describing network computations.

DREDge: robust motion correction for high-density extracellular recordings across species.

High-density microelectrode arrays (MEAs) have opened new possibilities for systems neuroscience in human and non-human animals, but brain tissue motion relative to the array poses a challenge for downstream analyses, particularly in human recordings. We introduce DREDge (Decentralized Registration of Electrophysiology Data), a robust algorithm which is well suited for the registration of noisy, nonstationary extracellular electrophysiology recordings. In addition to estimating motion from spikes in the action potential (AP) frequency band, DREDge enables automated tracking of motion at high temporal resolution in the local field potential (LFP) frequency band. In human intraoperative recordings, which often feature fast (period <1s) motion, DREDge correction in the LFP band enabled reliable recovery of evoked potentials, and significantly reduced single-unit spike shape variability and spike sorting error. Applying DREDge to recordings made during deep probe insertions in nonhuman primates demonstrated the possibility of tracking probe motion of centimeters across several brain regions while simultaneously mapping single unit electrophysiological features. DREDge reliably delivered improved motion correction in acute mouse recordings, especially in those made with an recent ultra-high density probe. We also implemented a procedure for applying DREDge to recordings made across tens of days in chronic implantations in mice, reliably yielding stable motion tracking despite changes in neural activity across experimental sessions. Together, these advances enable automated, scalable registration of electrophysiological data across multiple species, probe types, and drift cases, providing a stable foundation for downstream scientific analyses of these rich datasets.

The centrality of population-level factors to network computation is demonstrated by a versatile approach for training spiking networks.

Neural activity is often described in terms of population-level factors extracted from the responses of many neurons. Factors provide a lower-dimensional description with the aim of shedding light on network computations. Yet, mechanistically, computations are performed not by continuously valued factors but by interactions among neurons that spike discretely and variably. Models provide a means of bridging these levels of description. We developed a general method for training model networks of spiking neurons by leveraging factors extracted from either data or firing-rate-based networks. In addition to providing a useful model-building framework, this formalism illustrates how reliable and continuously valued factors can arise from seemingly stochastic spiking. Our framework establishes procedures for embedding this property in network models with different levels of realism. The relationship between spikes and factors in such networks provides a foundation for interpreting (and subtly redefining) commonly used quantities such as firing rates.

Flexible neural control of motor units.

Voluntary movement requires communication from cortex to the spinal cord, where a dedicated pool of motor units (MUs) activates each muscle. The canonical description of MU function rests upon two foundational tenets. First, cortex cannot control MUs independently but supplies each pool with a common drive. Second, MUs are recruited in a rigid fashion that largely accords with Henneman's size principle. Although this paradigm has considerable empirical support, a direct test requires simultaneous observations of many MUs across diverse force profiles. In this study, we developed an isometric task that allowed stable MU recordings, in a rhesus macaque, even during rapidly changing forces. Patterns of MU activity were surprisingly behavior-dependent and could be accurately described only by assuming multiple drives. Consistent with flexible descending control, microstimulation of neighboring cortical sites recruited different MUs. Furthermore, the cortical population response displayed sufficient degrees of freedom to potentially exert fine-grained control. Thus, MU activity is flexibly controlled to meet task demands, and cortex may contribute to this ability.

Motor cortex activity across movement speeds is predicted by network-level strategies for generating muscle activity.

Learned movements can be skillfully performed at different paces. What neural strategies produce this flexibility? Can they be predicted and understood by network modeling? We trained monkeys to perform a cycling task at different speeds, and trained artificial recurrent networks to generate the empirical muscle-activity patterns. Network solutions reflected the principle that smooth well-behaved dynamics require low trajectory tangling. Network solutions had a consistent form, which yielded quantitative and qualitative predictions. To evaluate predictions, we analyzed motor cortex activity recorded during the same task. Responses supported the hypothesis that the dominant neural signals reflect not muscle activity, but network-level strategies for generating muscle activity. Single-neuron responses were better accounted for by network activity than by muscle activity. Similarly, neural population trajectories shared their organization not with muscle trajectories, but with network solutions. Thus, cortical activity could be understood based on the need to generate muscle activity via dynamics that allow smooth, robust control over movement speed.

Cortical Control of Virtual Self-Motion Using Task-Specific Subspaces.

Brain-machine interfaces (BMIs) for reaching have enjoyed continued performance improvements, yet there remains significant need for BMIs that control other movement classes. Recent scientific findings suggest that the intrinsic covariance structure of neural activity depends strongly on movement class, potentially necessitating different decode algorithms across classes. To address this possibility, we developed a self-motion BMI based on cortical activity as monkeys cycled a hand-held pedal to progress along a virtual track. Unlike during reaching, we found no high-variance dimensions that directly correlated with to-be-decoded variables. This was due to no neurons having consistent correlations between their responses and kinematic variables. Yet we could decode a single variable-self-motion-by nonlinearly leveraging structure that spanned multiple high-variance neural dimensions. Resulting online BMI-control success rates approached those during manual control. These findings make two broad points regarding how to build decode algorithms that harmonize with the empirical structure of neural activity in motor cortex. First, even when decoding from the same cortical region (e.g., arm-related motor cortex), different movement classes may need to employ very different strategies. Although correlations between neural activity and hand velocity are prominent during reaching tasks, they are not a fundamental property of motor cortex and cannot be counted on to be present in general. Second, although one generally desires a low-dimensional readout, it can be beneficial to leverage a multidimensional high-variance subspace. Fully embracing this approach requires highly nonlinear approaches tailored to the task at hand, but can produce near-native levels of performance.SIGNIFICANCE STATEMENT Many brain-machine interface decoders have been constructed for controlling movements normally performed with the arm. Yet it is unclear how these will function beyond the reach-like scenarios where they were developed. Existing decoders implicitly assume that neural covariance structure, and correlations with to-be-decoded kinematic variables, will be largely preserved across tasks. We find that the correlation between neural activity and hand kinematics, a feature typically exploited when decoding reach-like movements, is essentially absent during another task performed with the arm: cycling through a virtual environment. Nevertheless, the use of a different strategy, one focused on leveraging the highest-variance neural signals, supported high performance real-time brain-machine interface control.

Independent generation of sequence elements by motor cortex.

Rapid execution of motor sequences is believed to depend on fusing movement elements into cohesive units that are executed holistically. We sought to determine the contribution of primary motor and dorsal premotor cortex to this ability. Monkeys performed highly practiced two-reach sequences, interleaved with matched reaches performed alone or separated by a delay. We partitioned neural population activity into components pertaining to preparation, initiation and execution. The hypothesis that movement elements fuse makes specific predictions regarding all three forms of activity. We observed none of these predicted effects. Rapid two-reach sequences involved the same set of neural events as individual reaches but with preparation for the second reach occurring as the first was in flight. Thus, at the level of dorsal premotor and primary motor cortex, skillfully executing a rapid sequence depends not on fusing elements, but on the ability to perform two key processes at the same time.

Neural Trajectories in the Supplementary Motor Area and Motor Cortex Exhibit Distinct Geometries, Compatible with Different Classes of Computation.

The supplementary motor area (SMA) is believed to contribute to higher order aspects of motor control. We considered a key higher order role: tracking progress throughout an action. We propose that doing so requires population activity to display low "trajectory divergence": situations with different future motor outputs should be distinct, even when present motor output is identical. We examined neural activity in SMA and primary motor cortex (M1) as monkeys cycled various distances through a virtual environment. SMA exhibited multiple response features that were absent in M1. At the single-neuron level, these included ramping firing rates and cycle-specific responses. At the population level, they included a helical population-trajectory geometry with shifts in the occupied subspace as movement unfolded. These diverse features all served to reduce trajectory divergence, which was much lower in SMA versus M1. Analogous population-trajectory geometry, also with low divergence, naturally arose in networks trained to internally guide multi-cycle movement.

Postural control of arm and fingers through integration of movement commands.

Every movement ends in a period of stillness. Current models assume that commands that hold the limb at a target location do not depend on the commands that moved the limb to that location. Here, we report a surprising relationship between movement and posture in primates: on a within-trial basis, the commands that hold the arm and finger at a target location depend on the mathematical integration of the commands that moved the limb to that location. Following damage to the corticospinal tract, both the move and hold period commands become more variable. However, the hold period commands retain their dependence on the integral of the move period commands. Thus, our data suggest that the postural controller possesses a feedforward module that uses move commands to calculate a component of hold commands. This computation may arise within an unknown subcortical system that integrates cortical commands to stabilize limb posture.

Moving an arm requires the brain to send electrical signals to the arm's muscles, causing them to contract. Neuroscientists call these types of brain signals "move signals". The brain also sends so-called hold signals, which hold the arm still in a desired position. Part of the brain known as the primary motor cortex helps to calculate the move signals for the arm, but it was unclear how the brain produces the corresponding hold signals. Fortunately, the fact that the brain moves other things besides arms may help answer this question. Previous research has shown, for example, that a brain area called the "neural integrator" calculates the hold signals needed to hold the eye in a specific position. The neural integrator does this by using basic principles of physics, and details of the speed and duration of the eye's movements. Now, Albert et al. show a similar mechanism appears to control hold signals for arm movements. In one set of experiments, muscle activity was measured as monkeys moved their arms or fingers to different target positions. In other experiments, human volunteers held a robot arm, and Albert et al. measured the forces they produced while reaching and holding still. Both the human and monkey experiments revealed a relationship between move signals and hold signals. Like for eye movements, hold signals for the arm could be calculated from the move signals. In further experiments with stroke patients where the brain had been damaged, the move signals were found to be deteriorated, but the way hold signals were calculated stayed the same. This suggests that there is an unknown structure within the brain that calculates hold signals based on move signals. Investigating how the brain holds the arm still may help scientists understand why some neurological conditions like stroke or dystonia cause unwanted movements or unusual postures. This might also lead scientists to develop new ways to treat these conditions.

Motor cortex signals for each arm are mixed across hemispheres and neurons yet partitioned within the population response.

Motor cortex (M1) has lateralized outputs, yet neurons can be active during movements of either arm. What is the nature and role of activity across the two hemispheres? We recorded muscles and neurons bilaterally while monkeys cycled with each arm. Most neurons were active during movement of either arm. Responses were strongly arm-dependent, raising two possibilities. First, population-level signals might differ depending on the arm used. Second, the same population-level signals might be present, but distributed differently across neurons. The data supported this second hypothesis. Muscle activity was accurately predicted by activity in either the ipsilateral or contralateral hemisphere. More generally, we failed to find signals unique to the contralateral hemisphere. Yet if signals are shared across hemispheres, how do they avoid impacting the wrong arm? We found that activity related to each arm occupies a distinct subspace, enabling muscle-activity decoders to naturally ignore signals related to the other arm.

Perturbation of Macaque Supplementary Motor Area Produces Context-Independent Changes in the Probability of Movement Initiation.

The contribution of the supplementary motor area (SMA) to movement initiation remains unclear. SMA exhibits premovement activity across a variety of contexts, including externally cued and self-initiated movements. Yet SMA lesions impair initiation primarily for self-initiated movements. Does SMA influence initiation across contexts or does it play a more specialized role, perhaps contributing only when initiation is less dependent on external cues? To address this question, we perturbed SMA activity via microstimulation at variable times before movement onset. Experiments used two adult male rhesus monkeys trained on a reaching task. We used three contexts that differed regarding how tightly movement initiation was linked to external cues. Movement kinematics were not altered by microstimulation. Instead, microstimulation induced a variety of changes in the timing of movement initiation, with different effects dominating for different contexts. Despite their diversity, these changes could be explained by a simple model where microstimulation has a stereotyped impact on the probability of initiation. Surprisingly, a unified model accounted for effects across all three contexts, regardless of whether initiation was determined more by external cues versus internal considerations. All effects were present for stimulation both contralateral and ipsilateral to the moving arm. Thus, the probability of initiating a pending movement is altered by perturbation of SMA activity. However, changes in initiation probability are independent of the balance of internal and external factors that establish the baseline initiation probability.SIGNIFICANCE STATEMENT The role of the supplementary motor area (SMA) in initiating movement remains unclear. Lesion experiments suggest that SMA makes a critical contribution only for self-initiated movements. Yet SMA is active before movements made under a range of contexts, suggesting a less-specialized role in movement initiation. Here, we use microstimulation to probe the role of SMA across a range of behavioral contexts that vary in the degree to which movement onset is influenced by external cues. We demonstrate that microstimulation produces a temporally stereotyped change in the probability of initiation that is independent of context. These results argue that SMA participates in the computations that lead to movement initiation and does so across a variety of contexts.

Conservation of preparatory neural events in monkey motor cortex regardless of how movement is initiated.

A time-consuming preparatory stage is hypothesized to precede voluntary movement. A putative neural substrate of motor preparation occurs when a delay separates instruction and execution cues. When readiness is sustained during the delay, sustained neural activity is observed in motor and premotor areas. Yet whether delay-period activity reflects an essential preparatory stage is controversial. In particular, it has remained ambiguous whether delay-period-like activity appears before non-delayed movements. To overcome that ambiguity, we leveraged a recently developed analysis method that parses population responses into putatively preparatory and movement-related components. We examined cortical responses when reaches were initiated after an imposed delay, at a self-chosen time, or reactively with low latency and no delay. Putatively preparatory events were conserved across all contexts. Our findings support the hypothesis that an appropriate preparatory state is consistently achieved before movement onset. However, our results reveal that this process can consume surprisingly little time.

Motor Cortex Embeds Muscle-like Commands in an Untangled Population Response.

Primate motor cortex projects to spinal interneurons and motoneurons, suggesting that motor cortex activity may be dominated by muscle-like commands. Observations during reaching lend support to this view, but evidence remains ambiguous and much debated. To provide a different perspective, we employed a novel behavioral paradigm that facilitates comparison between time-evolving neural and muscle activity. We found that single motor cortex neurons displayed many muscle-like properties, but the structure of population activity was not muscle-like. Unlike muscle activity, neural activity was structured to avoid "tangling": moments where similar activity patterns led to dissimilar future patterns. Avoidance of tangling was present across tasks and species. Network models revealed a potential reason for this consistent feature: low tangling confers noise robustness. Finally, we were able to predict motor cortex activity from muscle activity by leveraging the hypothesis that muscle-like commands are embedded in additional structure that yields low tangling.

Behaviorally Selective Engagement of Short-Latency Effector Pathways by Motor Cortex.

Blocking motor cortical output with lesions or pharmacological inactivation has identified movements that require motor cortex. Yet, when and how motor cortex influences muscle activity during movement execution remains unresolved. We addressed this ambiguity using measurement and perturbation of motor cortical activity together with electromyography in mice during two forelimb movements that differ in their requirement for cortical involvement. Rapid optogenetic silencing and electrical stimulation indicated that short-latency pathways linking motor cortex with spinal motor neurons are selectively activated during one behavior. Analysis of motor cortical activity revealed a dramatic change between behaviors in the coordination of firing patterns across neurons that could account for this differential influence. Thus, our results suggest that changes in motor cortical output patterns enable a behaviorally selective engagement of short-latency effector pathways. The model of motor cortical influence implied by our findings helps reconcile previous observations on the function of motor cortex.

Reorganization between preparatory and movement population responses in motor cortex.

Neural populations can change the computation they perform on very short timescales. Although such flexibility is common, the underlying computational strategies at the population level remain unknown. To address this gap, we examined population responses in motor cortex during reach preparation and movement. We found that there exist exclusive and orthogonal population-level subspaces dedicated to preparatory and movement computations. This orthogonality yielded a reorganization in response correlations: the set of neurons with shared response properties changed completely between preparation and movement. Thus, the same neural population acts, at different times, as two separate circuits with very different properties. This finding is not predicted by existing motor cortical models, which predict overlapping preparation-related and movement-related subspaces. Despite orthogonality, responses in the preparatory subspace were lawfully related to subsequent responses in the movement subspace. These results reveal a population-level strategy for performing separate but linked computations.

Tensor Analysis Reveals Distinct Population Structure that Parallels the Different Computational Roles of Areas M1 and V1.

Cortical firing rates frequently display elaborate and heterogeneous temporal structure. One often wishes to compute quantitative summaries of such structure-a basic example is the frequency spectrum-and compare with model-based predictions. The advent of large-scale population recordings affords the opportunity to do so in new ways, with the hope of distinguishing between potential explanations for why responses vary with time. We introduce a method that assesses a basic but previously unexplored form of population-level structure: when data contain responses across multiple neurons, conditions, and times, they are naturally expressed as a third-order tensor. We examined tensor structure for multiple datasets from primary visual cortex (V1) and primary motor cortex (M1). All V1 datasets were 'simplest' (there were relatively few degrees of freedom) along the neuron mode, while all M1 datasets were simplest along the condition mode. These differences could not be inferred from surface-level response features. Formal considerations suggest why tensor structure might differ across modes. For idealized linear models, structure is simplest across the neuron mode when responses reflect external variables, and simplest across the condition mode when responses reflect population dynamics. This same pattern was present for existing models that seek to explain motor cortex responses. Critically, only dynamical models displayed tensor structure that agreed with the empirical M1 data. These results illustrate that tensor structure is a basic feature of the data. For M1 the tensor structure was compatible with only a subset of existing models.