Neural population dynamics in motor cortex are different for reach and grasp.

Low-dimensional linear dynamics are observed in neuronal population activity in primary motor cortex (M1) when monkeys make reaching movements. This population-level behavior is consistent with a role for M1 as an autonomous pattern generator that drives muscles to give rise to movement. In the present study, we examine whether similar dynamics are also observed during grasping movements, which involve fundamentally different patterns of kinematics and muscle activations. Using a variety of analytical approaches, we show that M1 does not exhibit such dynamics during grasping movements. Rather, the grasp-related neuronal dynamics in M1 are similar to their counterparts in somatosensory cortex, whose activity is driven primarily by afferent inputs rather than by intrinsic dynamics. The basic structure of the neuronal activity underlying hand control is thus fundamentally different from that underlying arm control.

Unexpected complexity of everyday manual behaviors.

How does the brain control an effector as complex and versatile as the hand? One possibility is that neural control is simplified by limiting the space of hand movements. Indeed, hand kinematics can be largely described within 8 to 10 dimensions. This oft replicated finding has been construed as evidence that hand postures are confined to this subspace. A prediction from this hypothesis is that dimensions outside of this subspace reflect noise. To address this question, we track the hand of human participants as they perform two tasks-grasping and signing in American Sign Language. We apply multiple dimension reduction techniques and replicate the finding that most postural variance falls within a reduced subspace. However, we show that dimensions outside of this subspace are highly structured and task dependent, suggesting they too are under volitional control. We propose that hand control occupies a higher dimensional space than previously considered.

Decoding hand kinematics from population responses in sensorimotor cortex during grasping.

OBJECTIVE: The hand-a complex effector comprising dozens of degrees of freedom of movement-endows us with the ability to flexibly, precisely, and effortlessly interact with objects. The neural signals associated with dexterous hand movements in primary motor cortex (M1) and somatosensory cortex (SC) have received comparatively less attention than have those associated with proximal upper limb control. APPROACH: To fill this gap, we trained two monkeys to grasp objects varying in size and shape while tracking their hand postures and recording single-unit activity from M1 and SC. We then decoded their hand kinematics across tens of joints from population activity in these areas. MAIN RESULTS: We found that we could accurately decode kinematics with a small number of neural signals and that different cortical fields carry different amounts of information about hand kinematics. In particular, neural signals in rostral M1 led to better performance than did signals in caudal M1, whereas Brodmann's area 3a outperformed areas 1 and 2 in SC. Moreover, decoding performance was higher for joint angles than joint angular velocities, in contrast to what has been found with proximal limb decoders. SIGNIFICANCE: We conclude that cortical signals can be used for dexterous hand control in brain machine interface applications and that postural representations in SC may be exploited via intracortical stimulation to close the sensorimotor loop.

Postural Representations of the Hand in the Primate Sensorimotor Cortex.

Manual dexterity requires proprioceptive feedback about the state of the hand. To date, study of the neural basis of proprioception in the cortex has focused primarily on reaching movements to the exclusion of hand-specific behaviors such as grasping. To fill this gap, we record both time-varying hand kinematics and neural activity evoked in somatosensory and motor cortices as monkeys grasp a variety of objects. We find that neurons in the somatosensory cortex, as well as in the motor cortex, preferentially track time-varying postures of multi-joint combinations spanning the entire hand. This contrasts with neural responses during reaching movements, which preferentially track time-varying movement kinematics of the arm, such as velocity and speed of the limb, rather than its time-varying postural configuration. These results suggest different representations of arm and hand movements suited to the different functional roles of these two effectors.

Finger Posture and Finger Load are Perceived Independently.

The ability to track the time-varying postures of our hands and the forces they exert plays a key role in our ability to dexterously interact with objects. However, how precisely and accurately we sense hand kinematics and kinetics has not been completely characterized. Furthermore, the dominant source of information about hand postures stems from muscle spindles, whose responses can also signal isometric force and are modulated by fusimotor input. As such, one might expect that changing the state of the muscles - for example, by applying a load - would influence perceived finger posture. To address these questions, we measure the acuity of human hand proprioception, investigate the interplay between kinematic and kinetic signals, and determine the extent to which actively and passively achieved postures are perceived differently. We find that angle and torque perception are highly precise; that loads imposed on the finger do not affect perceived joint angle; that joint angle does not affect perceived load; and that hand postures are perceived similarly whether they are achieved actively or passively. The independence of finger posture and load perception contrasts with their interdependence in the upper arm, likely reflecting the special functional importance of the hand.

A Variation Code Accounts for the Perceived Roughness of Coarsely Textured Surfaces.

For decades, the dominant theory of roughness coding in the somatosensory nerves posited that perceived roughness was determined by the spatial pattern of activation in one population of tactile nerve fibers, namely slowly adapting type 1 (SA1) afferents. Indeed, the perceived roughness of coarsely textured surfaces tracks the spatial variation in SA1 responses - the degree to which response strength varies across SA1 afferents. However, in a later study, the roughness of a different set of dot patterns was found to be a monotonic function of dot spacing, a result interpreted as evidence that roughness was determined by the strength of SA1 responses - the population firing rate - rather than their spatial layout. Then again, the spatial variation hypothesis was not tested directly as afferent responses to the conflicting patterns were not measured. To fill this gap, we simulated afferent responses to the dot patterns used in these roughness coding experiments using a model of skin mechanics. We then implemented the spatial variation and firing rate models of roughness based on these simulated responses to generate predictions of perceived roughness. We found that the spatial variation model accounts for perceived roughness under all tested conditions whereas the firing rate model does not.

Controlled unilateral isometric force generated by epidural spinal cord stimulation in the rat hindlimb.

Epidural electrical stimulation (EES) has often been used to restore stereotypic locomotor movements after spinal cord injury (SCI). However, restoring freeform movement requires specific force generation and independently controlled limbs for changing environments. Therefore, a second stimulus location would be advantageous, controlling force separately from locomotor movements. In normal and transected rats treated with mineral oil or saline, EES was performed at L1-L6 vertebral levels, caudal to spinal segments typical for locomotion, identifying secondary sites capable of activating hindlimb musculature, producing unilateral force at the paw. Threshold for generating force was identified and stimulation amplitude and duration varied to assess effects on evoked forces. Stimulation at L2 and L3 vertebral levels elicited negative vertical forces from extensor musculature while stimulation at L4 and L5 elicited positive vertical forces from flexion musculature. Thresholds were unchanged with transection or hydration method. Peak force magnitude was significantly correlated to stimulus amplitude, and response duration significantly correlated to stimulus duration in all animals. No differences were found in correlation coefficients or slopes of the regression for force or duration analyses with spinal condition or hydration method. This model demonstrates the ability to induce controlled forces with EES after SCI.