This is a review of the proprioceptive senses generated as a result of our own actions. They include the senses of position and movement of our limbs and trunk, the sense of effort, the sense of force, and the sense of heaviness. Receptors involved in proprioception are located in skin, muscles, and joints. Information about limb position and movement is not generated by individual receptors, but by populations of afferents. Afferent signals generated during a movement are processed to code for endpoint position of a limb. The afferent input is referred to a central body map to determine the location of the limbs in space. Experimental phantom limbs, produced by blocking peripheral nerves, have shown that motor areas in the brain are able to generate conscious sensations of limb displacement and movement in the absence of any sensory input. In the normal limb tendon organs and possibly also muscle spindles contribute to the senses of force and heaviness. Exercise can disturb proprioception, and this has implications for musculoskeletal injuries. Proprioceptive senses, particularly of limb position and movement, deteriorate with age and are associated with an increased risk of falls in the elderly. The more recent information available on proprioception has given a better understanding of the mechanisms underlying these senses as well as providing new insight into a range of clinical conditions.

The Proprioceptive Senses: Their Roles in Signaling Body Shape, Body Position and Movement, and Muscle Force

Locomotion results from intricate dynamic interactions between a central program and feedback mechanisms. The central program relies fundamentally on a genetically determined spinal circuitry (central pattern generator) capable of generating the basic locomotor pattern and on various descending pathways that can trigger, stop, and steer locomotion. The feedback originates from muscles and skin afferents as well as from special senses (vision, audition, vestibular) and dynamically adapts the locomotor pattern to the requirements of the environment. The dynamic interactions are ensured by modulating transmission in locomotor pathways in a state- and phase-dependent manner. For instance, proprioceptive inputs from extensors can, during stance, adjust the timing and amplitude of muscle activities of the limbs to the speed of locomotion but be silenced during the opposite phase of the cycle. Similarly, skin afferents participate predominantly in the correction of limb and foot placement during stance on uneven terrain, but skin stimuli can evoke different types of responses depending on when they occur within the step cycle. Similarly, stimulation of descending pathways may affect the locomotor pattern in only certain phases of the step cycle. Section ii reviews dynamic sensorimotor interactions mainly through spinal pathways. Section iii describes how similar sensory inputs from the spinal or supraspinal levels can modify locomotion through descending pathways. The sensorimotor interactions occur obviously at several levels of the nervous system. Section iv summarizes presynaptic, interneuronal, and motoneuronal mechanisms that are common at these various levels. Together these mechanisms contribute to the continuous dynamic adjustment of sensorimotor interactions, ensuring that the central program and feedback mechanisms are congruous during locomotion.

https://www.physiology.org/doi/pdf/10.1152/physrev.00028.2005

Dynamic Sensorimotor Interactions in Locomotion

Skilled motor behaviour, from the graceful leap of a ballerina to a precise pitch by a baseball player, appears effortless but reflects an intimate interaction between the complex mechanical properties of the body and control by a highly distributed circuit in the CNS An important challenge for understanding motor function is to connect these three levels of the motor system — motor behaviour, limb mechanics and neural control Optimal feedback control theory might provide the important link across these levels of the motor system and help to unravel how the primary motor cortex and other regions of the brain plan and control movement

/pdf/optimal-feedback-control-and-the-neural-basis-of-volitional-2ypl1d3rcs.pdf

Optimal feedback control and the neural basis of volitional motor control

Biological Pattern Generation: The Cellular and Computational Logic of Networks in Motion

The cerebellum and neural control

How do we perceive the visual motion of objects that are accelerated by gravity? We propose that, because vision is poorly sensitive to accelerations, an internal model that calculates the effects of gravity is derived from graviceptive information, is stored in the vestibular cortex, and is activated by visual motion that appears to be coherent with natural gravity. The acceleration of visual targets was manipulated while brain activity was measured using functional magnetic resonance imaging. In agreement with the internal model hypothesis, we found that the vestibular network was selectively engaged when acceleration was consistent with natural gravity. These findings demonstrate that predictive mechanisms of physical laws of motion are represented in the human brain.

/pdf/representation-of-visual-gravitational-motion-in-the-human-532v59ktop.pdf

Representation of Visual Gravitational Motion in the Human Vestibular Cortex

This review explores how proprioceptive sensory information is organized at spinal cord levels as it relates to a sense of body position and movement. The topic is considered in an historical context and develops a different framework that may be more in tune with current views of sensorimotor processing in other central nervous system structures. The dorsal spinocerebellar tract (DSCT) system is considered in detail as a model system that may be considered as an end point for the processing of proprioceptive sensory information in the spinal cord. An analysis of this system examines sensory processing at the lowest levels of synaptic connectivity with central neurons in the nervous system. The analysis leads to a framework for proprioception that involves a highly flexible network organization based in some way on whole limb kinematics. The functional organization underlying this framework originates with the biomechanical linkages in the limb that establish functional relationships among the limb segments. Afferent information from limb receptors is processed further through a distributed neural network in the spinal cord. The result is a global representation of hindlimb parameters rather than a muscle-by-muscle or joint-by-joint representation.

https://www.physiology.org/doi/pdf/10.1152/physrev.2001.81.2.539

Proprioception from a spinocerebellar perspective.

Prevailing views on how we time the interception of a moving object assume that the visual inputs are informationally sufficient to estimate the time-to-contact from the object's kinematics. Here we present evidence in favor of a different view: the brain makes the best estimate about target motion based on measured kinematics and an a priori guess about the causes of motion. According to this theory, a predictive model is used to extrapolate time-to-contact from expected dynamics (kinetics). We projected a virtual target moving vertically downward on a wide screen with different randomized laws of motion. In the first series of experiments, subjects were asked to intercept this target by punching a real ball that fell hidden behind the screen and arrived in synchrony with the visual target. Subjects systematically timed their motor responses consistent with the assumption of gravity effects on an object's mass, even when the visual target did not accelerate. With training, the gravity model was not switched off but adapted to nonaccelerating targets by shifting the time of motor activation. In the second series of experiments, there was no real ball falling behind the screen. Instead the subjects were required to intercept the visual target by clicking a mousebutton. In this case, subjects timed their responses consistent with the assumption of uniform motion in the absence of forces, even when the target actually accelerated. Overall, the results are in accord with the theory that motor responses evoked by visual kinematics are modulated by a prior of the target dynamics. The prior appears surprisingly resistant to modifications based on performance errors.

https://www.physiology.org/doi/pdf/10.1152/jn.00862.2003

Internal models of target motion: expected dynamics overrides measured kinematics in timing manual interceptions.

http://www.psy.vanderbilt.edu/courses/psy216/Poppele_2003_Trends-in-Neurosciences.pdf

Sophisticated spinal contributions to motor control.

Many sensorimotor neurons in the CNS encode global parameters of limb movement and posture rather than specific muscle or joint parameters. Our investigations of spinocerebellar activity have demonstrated that these second-order spinal neurons also may encode proprioceptive information in a limb-based rather than joint-based reference frame. However, our finding that each foot position was determined by a unique combination of joint angles in the passive limb made it difficult to distinguish unequivocally between a limb-based and a joint-based representation. In this study, we decoupled foot position from limb geometry by applying mechanical constraints to individual hindlimb joints in anesthetized cats. We quantified the effect of the joint constraints on limb geometry by analyzing joint-angle covariance in the free and constrained conditions. One type of constraint, a rigid constraint of the knee angle, both changed the covariance pattern and significantly reduced the strength of joint-angle covariance. The other type, an elastic constraint of the ankle angle, changed only the covariance pattern and not its overall strength. We studied the effect of these constraints on the activity in 70 dorsal spinocerebellar tract (DSCT) neurons using a multivariate regression model, with limb axis length and orientation as predictors of neuronal activity. This model also included an experimental condition indicator variable that allowed significant intercept or slope changes in the relationships between foot position parameters and neuronal activity to be determined across conditions. The result of this analysis was that the spatial tuning of 37/70 neurons (53%) was unaffected by the constraints, suggesting that they were somehow able to signal foot position independently from the specific joint angles. We also investigated the extent to which cell activity represented individual joint angles by means of a regression model based on a linear combination of joint angles. A backward elimination of the insignificant predictors determined the set of independent joint angles that best described the neuronal activity for each experimental condition. Finally, by comparing the results of these two approaches, we could determine whether a DSCT neuron represented foot position, specific joint angles, or none of these variables consistently. We found that 10/70 neurons (14%) represented one or more specific joint-angles. The activity of another 27 neurons (39%) was significantly affected by limb geometry changes, but 33 neurons (47%) consistently elaborated a foot position representation in the coordinates of the limb axis.

https://www.physiology.org/doi/pdf/10.1152/jn.2000.83.5.2931

Gianfranco Bosco

Papers

Representation of Visual Gravitational Motion in the Human Vestibular Cortex

Proprioception from a spinocerebellar perspective.

Internal models of target motion: expected dynamics overrides measured kinematics in timing manual interceptions.

Sophisticated spinal contributions to motor control.

Reference Frames for Spinal Proprioception: Limb Endpoint Based or Joint-Level Based?