Emerging ideas and tools to study the emergent properties of the cortical neural circuits for voluntary motor control in non-human primates

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.

Evaluation of abstract rule-based associations in the human premotor cortex during passive observation

Decision-making often manifests in behavior, typically yielding overt motor actions. This complex process requires the registration of sensory information with one's internal representation of the current context, before a categorical judgment of the most appropriate motor behavior can be issued. The construct concept of embodied decision-making encapsulates this sequence of complex processes, whereby behaviorally salient information from the environment is represented in an abstracted space of potential motor actions rather than only in an abstract cognitive "decision" space. Theoretical foundations and some empirical evidence account for support the involvement of premotor cortical circuits in embodied cognitive functions. Animal models show that premotor circuits participate in the registration and evaluation of actions performed by peers in social situations, that is, prior to controlling one's voluntary movements guided by arbitrary stimulus-response rules. However, such evidence from human data is currently limited. Here we used time-resolved magnetoencephalography imaging to characterize activations of the premotor cortex as human participants observed arbitrary, non-biological visual stimuli that either respected or violated a simple stimulus-response association rule. The participants had learned this rule previously, either actively, by performing a motor task (active learning), or passively, by observing a computer perform the same task (passive learning). We discovered that the human premotor cortex is activated during the passive observation of the correct execution of a sequence of events according to a rule learned previously. Premotor activation also differs when the subjects observe incorrect stimulus sequences. These premotor effects are present even when the observed events are of a non-motor, abstract nature, and even when the stimulus-response association rule was learned via passive observations of a computer agent performing the task, without requiring overt motor actions from the human participant. We found evidence of these phenomena by tracking cortical beta-band signaling in temporal alignment with the observation of task events and behavior. We conclude that premotor cortical circuits that are typically engaged during voluntary motor behavior are also involved in the interpretation of events of a non-ecological, unfamiliar nature but related to a learned abstract rule. As such, the present study provides the first evidence of neurophysiological processes of embodied decision-making in human premotor circuits when the observed events do not involve motor actions of a third party.

Coordination Rigidity in the Gait, Posture, and Speech of Persons with Parkinson's Disease

Parkinson's disease (PD) is associated with reduced coordination abilities. These can result either in random or rigid patterns of movement. The latter, described here as coordination rigidity (CR), have been studied less often. We explored whether CR was present in gait, quiet stance, and speech-tasks involving coordination among multiple joints and muscles. Kinematic and voice recordings were used to compute measures describing the dynamics of systems with multiple degrees of freedom and nonlinear interactions. After clinical evaluation, patients with moderate stage PD were compared against matched healthy participants. In the PD group, gait dynamics was associated with decreased dynamic divergence-lower instability-in the vertical axis. Postural fluctuations were associated with increased regularity in the anterior-posterior axis, and voice dynamics with increased predictability, all consistent with CR. The clinical relevance of CR was confirmed by showing that some of those features contribute to disease classification with supervised machine learning (82/81/85% accuracy/sensitivity/specificity).

New insights on single-neuron selectivity in the era of population-level approaches

In the past, neuroscience was focused on individual neurons seen as the functional units of the nervous system, but this approach fell short over time to account for new experimental evidence, especially for what concerns associative and motor cortices. For this reason and thanks to great technological advances, a part of modern research has shifted the focus from the responses of single neurons to the activity of neural ensembles, now considered the real functional units of the system. However, on a microscale, individual neurons remain the computational components of these networks, thus the study of population dynamics cannot prescind from studying also individual neurons which represent their natural substrate. In this new framework, ideas such as the capability of single cells to encode a specific stimulus (neural selectivity) may become obsolete and need to be profoundly revised. One step in this direction was made by introducing the concept of “mixed selectivity,” the capacity of single cells to integrate multiple variables in a flexible way, allowing individual neurons to participate in different networks. In this review, we outline the most important features of mixed selectivity and we also present recent works demonstrating its presence in the associative areas of the posterior parietal cortex. Finally, in discussing these findings, we present some open questions that could be addressed by future studies.

Rotational dynamics in motor cortex are consistent with a feedback controller

Recent studies have identified rotational dynamics in motor cortex (MC), which many assume arise from intrinsic connections in MC. However, behavioral and neurophysiological studies suggest that MC behaves like a feedback controller where continuous sensory feedback and interactions with other brain areas contribute substantially to MC processing. We investigated these apparently conflicting theories by building recurrent neural networks that controlled a model arm and received sensory feedback from the limb. Networks were trained to counteract perturbations to the limb and to reach toward spatial targets. Network activities and sensory feedback signals to the network exhibited rotational structure even when the recurrent connections were removed. Furthermore, neural recordings in monkeys performing similar tasks also exhibited rotational structure not only in MC but also in somatosensory cortex. Our results argue that rotational structure may also reflect dynamics throughout the voluntary motor system involved in online control of motor actions.

Reducing behavioral dimensions to study brain-environment interactions

Movement vigor provides a window on action valuation. But what is vigor, and how to measure it in the first place? Strikingly, many different co-varying vigor-related metrics can be found in the literature. I believe this is because vigor, just like the neural circuits that determine it, is an integrated, low-dimensional parameter. As such, it can only be roughly estimated.

A markerless platform for ambulatory systems neuroscience

Motor systems neuroscience seeks to understand how the brain controls movement. To minimize confounding variables, large-animal studies typically constrain body movement from areas not under observation, ensuring consistent, repeatable behaviors. Such studies have fueled decades of research, but they may be artificially limiting the richness of neural data observed, preventing generalization to more natural movements and settings. Neuroscience studies of unconstrained movement would capture a greater range of behavior and a more complete view of neuronal activity, but instrumenting an experimental rig suitable for large animals presents substantial engineering challenges. Here, we present a markerless, full-body motion tracking and synchronized wireless neural electrophysiology platform for large, ambulatory animals. Composed of four depth (RGB-D) cameras that provide a 360° view of a 4.5-square-meters enclosed area, this system is designed to record a diverse range of neuroethologically relevant behaviors. This platform also allows for the simultaneous acquisition of hundreds of wireless neural recording channels in multiple brain regions. As behavioral and neuronal data are generated at rates below 200 megabytes per second, a single desktop can facilitate hours of continuous recording. This setup is designed for systems neuroscience and neuroengineering research, where synchronized kinematic behavior and neural data are the foundation for investigation. By enabling the study of previously unexplored movement tasks, this system can generate insights into the functioning of the mammalian motor system and provide a platform to develop brain-machine interfaces for unconstrained applications.

Computation Through Neural Population Dynamics

Significant experimental, computational, and theoretical work has identified rich structure within the coordinated activity of interconnected neural populations. An emerging challenge now is to uncover the nature of the associated computations, how they are implemented, and what role they play in driving behavior. We term this computation through neural population dynamics. If successful, this framework will reveal general motifs of neural population activity and quantitatively describe how neural population dynamics implement computations necessary for driving goal-directed behavior. Here, we start with a mathematical primer on dynamical systems theory and analytical tools necessary to apply this perspective to experimental data. Next, we highlight some recent discoveries resulting from successful application of dynamical systems. We focus on studies spanning motor control, timing, decision-making, and working memory. Finally, we briefly discuss promising recent lines of investigation and future directions for the computation through neural population dynamics framework.

Dendritic calcium signals in rhesus macaque motor cortex drive an optical brain-computer interface

Calcium imaging is a powerful tool for recording from large populations of neurons in vivo. Imaging in rhesus macaque motor cortex can enable the discovery of fundamental principles of motor cortical function and can inform the design of next generation brain-computer interfaces (BCIs). Surface two-photon imaging, however, cannot presently access somatic calcium signals of neurons from all layers of macaque motor cortex due to photon scattering. Here, we demonstrate an implant and imaging system capable of chronic, motion-stabilized two-photon imaging of neuronal calcium signals from macaques engaged in a motor task. By imaging apical dendrites, we achieved optical access to large populations of deep and superficial cortical neurons across dorsal premotor (PMd) and gyral primary motor (M1) cortices. Dendritic signals from individual neurons displayed tuning for different directions of arm movement. Combining several technical advances, we developed an optical BCI (oBCI) driven by these dendritic signalswhich successfully decoded movement direction online. By fusing two-photon functional imaging with CLARITY volumetric imaging, we verified that many imaged dendrites which contributed to oBCI decoding originated from layer 5 output neurons, including a putative Betz cell. This approach establishes new opportunities for studying motor control and designing BCIs via two photon imaging.

From motor planning to execution: a sensorimotor loop perspective

How is an evanescent wish to move translated into a concrete action? This simple question and puzzling miracle remains a focal point of motor systems neuroscience. Where does the difficulty lie? A great deal has been known about biomechanics for quite some time. More recently, there have been significant advances in our understanding of how the spinal system is organized into modules corresponding to spinal synergies, which are fixed patterns of multimuscle recruitment. But much less is known about how the supraspinal system recruits these synergies in the correct spatiotemporal pattern to effectively control movement. We argue that what makes the problem of supraspinal control so difficult is that it emerges as a result of multiple convergent and redundant sensorimotor loops. Because these loops are convergent, multiple modes of information are mixed before being sent to the spinal system; because they are redundant, information is overlapping such that a mechanism must exist to eliminate the redundancy before the signal is sent to the spinal system. Given these complex interactions, simple correlation analyses between movement variables and neural activity are likely to render a confusing and inconsistent picture. Here, we suggest that the perspective of sensorimotor loops might help in achieving a better systems-level understanding. Furthermore, state-of-the-art techniques in neurotechnology, such as optogenetics, appear to be well suited for investigating the problem of motor control at the level of loops.

Controlling motor neurons of every muscle for fly proboscis reaching

We describe the anatomy of all the primary motor neurons in the fly proboscis and characterize their contributions to its diverse reaching movements. Pairing this behavior with the wealth of Drosophila's genetic tools offers the possibility to study motor control at single-neuron resolution, and soon throughout entire circuits. As an entry to these circuits, we provide detailed anatomy of proboscis motor neurons, muscles, and joints. We create a collection of fly strains to individually manipulate every proboscis muscle through control of its motor neurons, the first such collection for an appendage. We generate a model of the action of each proboscis joint, and find that only a small number of motor neurons are needed to produce proboscis reaching. Comprehensive control of each motor element in this numerically simple system paves the way for future study of both reflexive and flexible movements of this appendage.

Distributed processing of movement signaling

Basic neurophysiological research with monkeys has shown how neurons in the motor cortex have firing rates tuned to movement direction. This original finding would have been difficult to uncover without the use of a behaving primate paradigm in which subjects grasped a handle and moved purposefully to targets in different directions. Subsequent research, again using behaving primate models, extended these findings to continuous drawing and to arm and hand movements encompassing action across multiple joints. This research also led to robust extraction algorithms in which information from neuronal populations is used to decode movement intent. The ability to decode intended movement provided the foundation for neural prosthetics in which brain-controlled interfaces are used by paralyzed human subjects to control computer cursors or high-performance motorized prosthetic arms and hands. This translation of neurophysiological laboratory findings to therapy is a clear example of why using nonhuman primates for basic research is valuable for advancing treatment of neurological disorders. Recent research emphasizes the distribution of intention signaling through neuronal populations and shows how many movement parameters are encoded simultaneously. In addition to direction and velocity, the arm's impedance has now been found to be encoded as well. The ability to decode motion and force from neural populations will make it possible to extend neural prosthetic paradigms to precise interaction with objects, enabling paralyzed individuals to perform many tasks of daily living.