Spinal projecting neurons in rostral ventromedial medulla co-regulate motor and sympathetic tone.

Many behaviors require the coordinated actions of somatic and autonomic functions. However, the underlying mechanisms remain elusive. By opto-stimulating different populations of descending spinal projecting neurons (SPNs) in anesthetized mice, we show that stimulation of excitatory SPNs in the rostral ventromedial medulla (rVMM) resulted in a simultaneous increase in somatomotor and sympathetic activities. Conversely, opto-stimulation of rVMM inhibitory SPNs decreased both activities. Anatomically, these SPNs innervate both sympathetic preganglionic neurons and motor-related regions in the spinal cord. Fiber-photometry recording indicated that the activities of rVMM SPNs correlate with different levels of muscle and sympathetic tone during distinct arousal states. Inhibiting rVMM excitatory SPNs reduced basal muscle and sympathetic tone, impairing locomotion initiation and high-speed performance. In contrast, silencing the inhibitory population abolished muscle atonia and sympathetic hypoactivity during rapid eye movement (REM) sleep. Together, these results identify rVMM SPNs as descending spinal projecting pathways controlling the tone of both the somatomotor and sympathetic systems.

Structural and functional map for forelimb movement phases between cortex and medulla.

The cortex influences movement by widespread top-down projections to many nervous system regions. Skilled forelimb movements require brainstem circuitry in the medulla; however, the logic of cortical interactions with these neurons remains unexplored. Here, we reveal a fine-grained anatomical and functional map between anterior cortex (AC) and medulla in mice. Distinct cortical regions generate three-dimensional synaptic columns tiling the lateral medulla, topographically matching the dorso-ventral positions of postsynaptic neurons tuned to distinct forelimb action phases. Although medial AC (MAC) terminates ventrally and connects to forelimb-reaching-tuned neurons and its silencing impairs reaching, lateral AC (LAC) influences dorsally positioned neurons tuned to food handling, and its silencing impairs handling. Cortico-medullary neurons also extend collaterals to other subcortical structures through a segregated channel interaction logic. Our findings reveal a precise alignment between cortical location, its function, and specific forelimb-action-tuned medulla neurons, thereby clarifying interaction principles between these two key structures and beyond.

Hindbrain modules differentially transform activity of single collicular neurons to coordinate movements.

Seemingly simple behaviors such as swatting a mosquito or glancing at a signpost involve the precise coordination of multiple body parts. Neural control of coordinated movements is widely thought to entail transforming a desired overall displacement into displacements for each body part. Here we reveal a different logic implemented in the mouse gaze system. Stimulating superior colliculus (SC) elicits head movements with stereotyped displacements but eye movements with stereotyped endpoints. This is achieved by individual SC neurons whose branched axons innervate modules in medulla and pons that drive head movements with stereotyped displacements and eye movements with stereotyped endpoints, respectively. Thus, single neurons specify a mixture of endpoints and displacements for different body parts, not overall displacement, with displacements for different body parts computed at distinct anatomical stages. Our study establishes an approach for unraveling motor hierarchies and identifies a logic for coordinating movements and the resulting pose.

A brainstem integrator for self-location memory and positional homeostasis in zebrafish.

To track and control self-location, animals integrate their movements through space. Representations of self-location are observed in the mammalian hippocampal formation, but it is unknown if positional representations exist in more ancient brain regions, how they arise from integrated self-motion, and by what pathways they control locomotion. Here, in a head-fixed, fictive-swimming, virtual-reality preparation, we exposed larval zebrafish to a variety of involuntary displacements. They tracked these displacements and, many seconds later, moved toward their earlier location through corrective swimming ("positional homeostasis"). Whole-brain functional imaging revealed a network in the medulla that stores a memory of location and induces an error signal in the inferior olive to drive future corrective swimming. Optogenetically manipulating medullary integrator cells evoked displacement-memory behavior. Ablating them, or downstream olivary neurons, abolished displacement corrections. These results reveal a multiregional hindbrain circuit in vertebrates that integrates self-motion and stores self-location to control locomotor behavior.

A midbrain-thalamus-cortex circuit reorganizes cortical dynamics to initiate movement.

Motor behaviors are often planned long before execution but only released after specific sensory events. Planning and execution are each associated with distinct patterns of motor cortex activity. Key questions are how these dynamic activity patterns are generated and how they relate to behavior. Here, we investigate the multi-regional neural circuits that link an auditory "Go cue" and the transition from planning to execution of directional licking. Ascending glutamatergic neurons in the midbrain reticular and pedunculopontine nuclei show short latency and phasic changes in spike rate that are selective for the Go cue. This signal is transmitted via the thalamus to the motor cortex, where it triggers a rapid reorganization of motor cortex state from planning-related activity to a motor command, which in turn drives appropriate movement. Our studies show how midbrain can control cortical dynamics via the thalamus for rapid and precise motor behavior.

Local Axonal Conduction Shapes the Spatiotemporal Properties of Neural Sequences.

Sequential activation of neurons has been observed during various behavioral and cognitive processes, but the underlying circuit mechanisms remain poorly understood. Here, we investigate premotor sequences in HVC (proper name) of the adult zebra finch forebrain that are central to the performance of the temporally precise courtship song. We use high-density silicon probes to measure song-related population activity, and we compare these observations with predictions from a range of network models. Our results support a circuit architecture in which heterogeneous delays between sequentially active neurons shape the spatiotemporal patterns of HVC premotor neuron activity. We gauge the impact of several delay sources, and we find the primary contributor to be slow conduction through axonal collaterals within HVC, which typically adds between 1 and 7.5 ms for each link within the sequence. Thus, local axonal "delay lines" can play an important role in determining the dynamical repertoire of neural circuits.

Integrated Control of Predatory Hunting by the Central Nucleus of the Amygdala.

Superior predatory skills led to the evolutionary triumph of jawed vertebrates. However, the mechanisms by which the vertebrate brain controls predation remain largely unknown. Here, we reveal a critical role for the central nucleus of the amygdala in predatory hunting. Both optogenetic and chemogenetic stimulation of central amygdala of mice elicited predatory-like attacks upon both insect and artificial prey. Coordinated control of cervical and mandibular musculatures, which is necessary for accurately positioning lethal bites on prey, was mediated by a central amygdala projection to the reticular formation in the brainstem. In contrast, prey pursuit was mediated by projections to the midbrain periaqueductal gray matter. Targeted lesions to these two pathways separately disrupted biting attacks upon prey versus the initiation of prey pursuit. Our findings delineate a neural network that integrates distinct behavioral modules and suggest that central amygdala neurons instruct predatory hunting across jawed vertebrates.

Spinal Interneurons: Diversity and Connectivity in Motor Control.

The spinal cord is home to the intrinsic networks for locomotion. An animal in which the spinal cord has been fully severed from the brain can still produce rhythmic, patterned locomotor movements as long as some excitatory drive is provided, such as physical, pharmacological, or electrical stimuli. Yet it remains a challenge to define the underlying circuitry that produces these movements because the spinal cord contains a wide variety of neuron classes whose patterns of interconnectivity are still poorly understood. Computational models of locomotion accordingly rely on untested assumptions about spinal neuron network element identity and connectivity. In this review, we consider the classes of spinal neurons, their interconnectivity, and the significance of their circuit connections along the long axis of the spinal cord. We suggest several lines of analysis to move toward a definitive understanding of the spinal network.

Brainstem Circuits for Locomotion.

Locomotion is a universal motor behavior that is expressed as the output of many integrated brain functions. Locomotion is organized at several levels of the nervous system, with brainstem circuits acting as the gate between brain areas regulating innate, emotional, or motivational locomotion and executive spinal circuits. Here we review recent advances on brainstem circuits involved in controlling locomotion. We describe how delineated command circuits govern the start, speed, stop, and steering of locomotion. We also discuss how these pathways interface between executive circuits in the spinal cord and diverse brain areas important for context-specific selection of locomotion. A recurrent theme is the need to establish a functional connectome to and from brainstem command circuits. Finally, we point to unresolved issues concerning the integrated function of locomotor control.

Neural Algorithms and Circuits for Motor Planning.

The brain plans and executes volitional movements. The underlying patterns of neural population activity have been explored in the context of movements of the eyes, limbs, tongue, and head in nonhuman primates and rodents. How do networks of neurons produce the slow neural dynamics that prepare specific movements and the fast dynamics that ultimately initiate these movements? Recent work exploits rapid and calibrated perturbations of neural activity to test specific dynamical systems models that are capable of producing the observed neural activity. These joint experimental and computational studies show that cortical dynamics during motor planning reflect fixed points of neural activity (attractors). Subcortical control signals reshape and move attractors over multiple timescales, causing commitment to specific actions and rapid transitions to movement execution. Experiments in rodents are beginning to reveal how these algorithms are implemented at the level of brain-wide neural circuits.

The physiological control of eating: signals, neurons, and networks.

During the past 30 yr, investigating the physiology of eating behaviors has generated a truly vast literature. This is fueled in part by a dramatic increase in obesity and its comorbidities that has coincided with an ever increasing sophistication of genetically based manipulations. These techniques have produced results with a remarkable degree of cell specificity, particularly at the cell signaling level, and have played a lead role in advancing the field. However, putting these findings into a brain-wide context that connects physiological signals and neurons to behavior and somatic physiology requires a thorough consideration of neuronal connections: a field that has also seen an extraordinary technological revolution. Our goal is to present a comprehensive and balanced assessment of how physiological signals associated with energy homeostasis interact at many brain levels to control eating behaviors. A major theme is that these signals engage sets of interacting neural networks throughout the brain that are defined by specific neural connections. We begin by discussing some fundamental concepts, including ones that still engender vigorous debate, that provide the necessary frameworks for understanding how the brain controls meal initiation and termination. These include key word definitions, ATP availability as the pivotal regulated variable in energy homeostasis, neuropeptide signaling, homeostatic and hedonic eating, and meal structure. Within this context, we discuss network models of how key regions in the endbrain (or telencephalon), hypothalamus, hindbrain, medulla, vagus nerve, and spinal cord work together with the gastrointestinal tract to enable the complex motor events that permit animals to eat in diverse situations.

The science and engineering behind sensitized brain-controlled bionic hands.

Advances in our understanding of brain function, along with the development of neural interfaces that allow for the monitoring and activation of neurons, have paved the way for brain-machine interfaces (BMIs), which harness neural signals to reanimate the limbs via electrical activation of the muscles or to control extracorporeal devices, thereby bypassing the muscles and senses altogether. BMIs consist of reading out motor intent from the neuronal responses monitored in motor regions of the brain and executing intended movements with bionic limbs, reanimated limbs, or exoskeletons. BMIs also allow for the restoration of the sense of touch by electrically activating neurons in somatosensory regions of the brain, thereby evoking vivid tactile sensations and conveying feedback about object interactions. In this review, we discuss the neural mechanisms of motor control and somatosensation in able-bodied individuals and describe approaches to use neuronal responses as control signals for movement restoration and to activate residual sensory pathways to restore touch. Although the focus of the review is on intracortical approaches, we also describe alternative signal sources for control and noninvasive strategies for sensory restoration.

Basal Ganglia Circuits for Action Specification.

Behavior is readily classified into patterns of movements with inferred common goals-actions. Goals may be discrete; movements are continuous. Through the careful study of isolated movements in laboratory settings, or via introspection, it has become clear that animals can exhibit exquisite graded specification to their movements. Moreover, graded control can be as fundamental to success as the selection of which action to perform under many naturalistic scenarios: a predator adjusting its speed to intercept moving prey, or a tool-user exerting the perfect amount of force to complete a delicate task. The basal ganglia are a collection of nuclei in vertebrates that extend from the forebrain (telencephalon) to the midbrain (mesencephalon), constituting a major descending extrapyramidal pathway for control over midbrain and brainstem premotor structures. Here we discuss how this pathway contributes to the continuous specification of movements that endows our voluntary actions with vigor and grace.

Making sense of proprioception.

Proprioception - the sense of body position in space - is intimately linked to motor control. Here, we briefly review the current knowledge of the proprioceptive system and how advances in the genetic characterisation of proprioceptive sensory neurons in mice promise to dissect its role in health and disease.

Brainstem Circuits Controlling Action Diversification.

Neuronal circuits that regulate movement are distributed throughout the nervous system. The brainstem is an important interface between upper motor centers involved in action planning and circuits in the spinal cord ultimately leading to execution of body movements. Here we focus on recent work using genetic and viral entry points to reveal the identity of functionally dedicated and frequently spatially intermingled brainstem populations essential for action diversification, a general principle conserved throughout evolution. Brainstem circuits with distinct organization and function control skilled forelimb behavior, orofacial movements, and locomotion. They convey regulatory parameters to motor output structures and collaborate in the construction of complex natural motor behaviors. Functionally tuned brainstem neurons for different actions serve as important integrators of synaptic inputs from upstream centers, including the basal ganglia and cortex, to regulate and modulate behavioral function in different contexts.

Optimal reaching subject to computational and physical constraints reveals structure of the sensorimotor control system.

Optimal feedback control provides an abstract framework describing the architecture of the sensorimotor system without prescribing implementation details such as what coordinate system to use, how feedback is incorporated, or how to accommodate changing task complexity. We investigate how such details are determined by computational and physical constraints by creating a model of the upper limb sensorimotor system in which all connection weights between neurons, feedback, and muscles are unknown. By optimizing these parameters with respect to an objective function, we find that the model exhibits a preference for an intrinsic (joint angle) coordinate representation of inputs and feedback and learns to calculate a weighted feedforward and feedback error. We further show that complex reaches around obstacles can be achieved by augmenting our model with a path-planner based on via points. The path-planner revealed "avoidance" neurons that encode directions to reach around obstacles and "placement" neurons that make fine-tuned adjustments to via point placement. Our results demonstrate the surprising capability of computationally constrained systems and highlight interesting characteristics of the sensorimotor system.

The cerebellum acts as the analog to the medial temporal lobe for sensorimotor memory.

The cerebellum is critical for sensorimotor learning. The specific contribution that it makes, however, remains unclear. Inspired by the classic finding that for declarative memories, medial temporal lobe (MTL) structures provide a gateway to the formation of long-term memory but are not required for short-term memory, we hypothesized that for sensorimotor memories, the cerebellum may play an analogous role. Here, we studied the sensorimotor learning of individuals with severe ataxia from cerebellar degeneration. We dissected the memories they formed during sensorimotor learning into a short-term temporally-volatile component, that decays rapidly with a time constant of just 15 to 20 s and thus cannot lead to long-term retention, and a longer-term temporally-persistent component that is stable for 60 s or more and leads to long-term retention. Remarkably, we find that these individuals display dramatically reduced levels of temporally-persistent sensorimotor memory, despite spared and even elevated levels of temporally-volatile sensorimotor memory. In particular, we find both impairment that systematically worsens with memory window duration over shorter memory windows (<12 s) and near-complete impairment of memory maintenance over longer memory windows (>25 s). This dissociation uncovers a unique role for the cerebellum as a gateway for the formation of long-term but not short-term sensorimotor memories, mirroring the role of the MTL for declarative memories. It thus reveals the existence of distinct neural substrates for short-term and long-term sensorimotor memory, and it explains both the trial-to-trial differences identified in this study and long-standing study-to-study differences in the effects of cerebellar damage on sensorimotor learning ability.

Closing the Loop: From Motor Neuroscience to Neurorehabilitation.

The fields of human motor control, motor learning, and neurorehabilitation have long been linked by the intuition that understanding how we move (and learn to move) leads to better rehabilitation. In reality, these fields have remained largely separate. Our knowledge of the neural control of movement has expanded, but principles that can directly impact rehabilitation efficacy remain somewhat sparse. This raises two important questions: What can basic studies of motor learning really tell us about rehabilitation, and are we asking the right questions to improve the lives of patients? This review aims to contextualize recent advances in computational and behavioral studies of human motor learning within the framework of neurorehabilitation. We also discuss our views of the current challenges facing rehabilitation and outline potential clinical applications from recent theoretical and basic studies of motor learning and control.

Deep kinematic inference affords efficient and scalable control of bodily movements.

Performing goal-directed movements requires mapping goals from extrinsic (workspace-relative) to intrinsic (body-relative) coordinates and then to motor signals. Mainstream approaches based on optimal control realize the mappings by minimizing cost functions, which is computationally demanding. Instead, active inference uses generative models to produce sensory predictions, which allows a cheaper inversion to the motor signals. However, devising generative models to control complex kinematic chains like the human body is challenging. We introduce an active inference architecture that affords a simple but effective mapping from extrinsic to intrinsic coordinates via inference and easily scales up to drive complex kinematic chains. Rich goals can be specified in both intrinsic and extrinsic coordinates using attractive or repulsive forces. The proposed model reproduces sophisticated bodily movements and paves the way for computationally efficient and biologically plausible control of actuated systems.

Distributed feedforward and feedback cortical processing supports human speech production.

Speech production is a complex human function requiring continuous feedforward commands together with reafferent feedback processing. These processes are carried out by distinct frontal and temporal cortical networks, but the degree and timing of their recruitment and dynamics remain poorly understood. We present a deep learning architecture that translates neural signals recorded directly from the cortex to an interpretable representational space that can reconstruct speech. We leverage learned decoding networks to disentangle feedforward vs. feedback processing. Unlike prevailing models, we find a mixed cortical architecture in which frontal and temporal networks each process both feedforward and feedback information in tandem. We elucidate the timing of feedforward and feedback-related processing by quantifying the derived receptive fields. Our approach provides evidence for a surprisingly mixed cortical architecture of speech circuitry together with decoding advances that have important implications for neural prosthetics.