A Pneumatically Actuated Manipulandum for Neuromotor Control Research

Functional magnetic resonance imaging (fMRI) techniques have great potential for identifying which neural structures are involved in the control of goal-directed reaching movements. However, fMRI techniques alone are not capable of probing the neural mechanisms involved in acquisition of novel motor behaviors because such studies require that the moving limb be perturbed in a controlled fashion. We outline a plan to design and develop a non-metallic, pneumatically actuated tool that, along with systems identification techniques and functional magnetic resonance imaging (fMRI), will characterize and quantify how the human central nervous system uses sensory information during practice-based motor learning

Exploiting Multiple Sensory Modalities in Brain-Machine Interfaces

Recent improvements in cortically-controlled brain-machine interfaces (BMIs) have raised hopes that such technologies may improve the quality of life of severely motor-disabled patients. However, current generation BMIs do not perform up to their potential due to the neglect of the full range of sensory feedback in their strategies for training and control. Here we confirm that neurons in the primary motor cortex (MI) encode sensory information and demonstrate a significant heterogeneity in their responses with respect to the type of sensory modality available to the subject about a reaching task. We further show using mutual information and directional tuning analyses that the presence of multi-sensory feedback (i.e. vision and proprioception) during replay of movements evokes neural responses in MI that are almost indistinguishable from those responses measured during overt movement. Finally, we suggest how these playback-evoked responses may be used to improve BMI performance

Design and Validation of a MR-compatible Pneumatic Manipulandum

The combination of functional MR imaging and novel robotic tools may provide unique opportunities to probe the neural systems underlying motor control and learning. Here, we describe the design and validation of a MR-compatible, 1 degree-of-freedom pneumatic manipulandum along with experiments demonstrating its safety and efficacy. We first validated the robot\u27s ability to apply computer-controlled loads about the wrist, demonstrating that it possesses sufficient bandwidth to simulate torsional spring-like loads during point-to-point flexion movements. Next, we verified the MR-compatibility of the device by imaging a head phantom during robot operation. We observed no systematic differences in two measures of MRI signal quality (signal/noise and field homogeneity) when the robot was introduced into the scanner environment. Likewise, measurements of joint angle and actuator pressure were not adversely affected by scanning. Finally, we verified device efficacy by scanning 20 healthy human subjects performing rapid wrist flexions against a wide range of spring-like loads. We observed a linear relationship between joint torque at peak movement extent and perturbation magnitude, thus demonstrating the robot\u27s ability to simulate spring-like loads in situ. fMRI revealed task-related activation in regions known to contribute to the control of movement including the left primary sensorimotor cortex and right cerebellum

Temporal Evolution of Both Premotor and Motor Cortical Tuning Properties Reflect Changes in Limb Biomechanics

A prevailing theory in the cortical control of limb movement posits that premotor cortex initiates a high-level motor plan that is transformed by the primary motor cortex (MI) into a low-level motor command to be executed. This theory implies that the premotor cortex is shielded from the motor periphery and therefore its activity should not represent the low-level features of movement. Contrary to this theory, we show that both dorsal (PMd) and ventral premotor (PMv) cortices exhibit population-level tuning properties that reflect the biomechanical properties of the periphery similar to those observed in M1. We recorded single-unit activity from M1, PMd, and PMv and characterized their tuning properties while six rhesus macaques performed a reaching task in the horizontal plane. Each area exhibited a bimodal distribution of preferred directions during execution consistent with the known biomechanical anisotropies of the muscles and limb segments. Moreover, these distributions varied in orientation or shape from planning to execution. A network model shows that such population dynamics are linked to a change in biomechanics of the limb as the monkey begins to move, specifically to the state-dependent properties of muscles. We suggest that, like M1, neural populations in PMd and PMv are more directly linked with the motor periphery than previously thought

Incorporating Feedback from Multiple Sensory Modalities Enhances Brain–Machine Interface Control

The brain typically uses a rich supply of feedback from multiple sensory modalities to control movement in healthy individuals. In many individuals, these afferent pathways, as well as their efferent counterparts, are compromised by disease or injury resulting in significant impairments and reduced quality of life. Brain–machine interfaces (BMIs) offer the promise of recovered functionality to these individuals by allowing them to control a device using their thoughts. Most current BMI implementations use visual feedback for closed-loop control; however, it has been suggested that the inclusion of additional feedback modalities may lead to improvements in control. We demonstrate for the first time that kinesthetic feedback can be used together with vision to significantly improve control of a cursor driven by neural activity of the primary motor cortex (MI). Using an exoskeletal robot, the monkey\u27s arm was moved to passively follow a cortically controlled visual cursor, thereby providing the monkey with kinesthetic information about the motion of the cursor. When visual and proprioceptive feedback were congruent, both the time to successfully reach a target decreased and the cursor paths became straighter, compared with incongruent feedback conditions. This enhanced performance was accompanied by a significant increase in the amount of movement-related information contained in the spiking activity of neurons in MI. These findings suggest that BMI control can be significantly improved in paralyzed patients with residual kinesthetic sense and provide the groundwork for augmenting cortically controlled BMIs with multiple forms of natural or surrogate sensory feedback

Improving Brain–Machine Interface Performance by Decoding Intended Future Movements

Objective. A brain–machine interface (BMI) records neural signals in real time from a subject\u27s brain, interprets them as motor commands, and reroutes them to a device such as a robotic arm, so as to restore lost motor function. Our objective here is to improve BMI performance by minimizing the deleterious effects of delay in the BMI control loop. We mitigate the effects of delay by decoding the subject\u27s intended movements a short time lead in the future. Approach. We use the decoded, intended future movements of the subject as the control signal that drives the movement of our BMI. This should allow the user\u27s intended trajectory to be implemented more quickly by the BMI, reducing the amount of delay in the system. In our experiment, a monkey (Macaca mulatta) uses a future prediction BMI to control a simulated arm to hit targets on a screen. Main Results. Results from experiments with BMIs possessing different system delays (100, 200 and 300 ms) show that the monkey can make significantly straighter, faster and smoother movements when the decoder predicts the user\u27s future intent. We also characterize how BMI performance changes as a function of delay, and explore offline how the accuracy of future prediction decoders varies at different time leads. Significance. This study is the first to characterize the effects of control delays in a BMI and to show that decoding the user\u27s future intent can compensate for the negative effect of control delay on BMI performance

Limb Stabilization in Older Adults and Chronic Stroke Survivors: A Pilot Study

Visual fidelity influences many aspects of daily living, including stabilizing wrist movements against environmental perturbations. Here, we present a pilot investigation to determine how visual feedback impacts wrist stabilization for populations with age related declines in motor function and chronic stroke. To quantify these interactions, behavioral performance and local brain activation were observed during a task requiring stabilization of the wrist against constant and stochastic extensor torque perturbations. All subjects were better able to stabilize their wrist when veridical visual feedback of their limb was provided. Examination of the neural activation maps in the Control and Aging populations revealed patterns of neural activity commonly associated with feedback control of limb position. The Stroke survivor, however, exhibited a different pattern of neural activity, possibly due to the lack of sensory feedback employment

Neural Correlates of Multisensory Integration for Feedback Stabilization of the Wrist

Robust control of action relies on the ability to perceive, integrate, and act on information from multiple sensory modalities including vision and proprioception. How does the brain combine sensory information to regulate ongoing mechanical interactions between the body and its physical environment? Some behavioral studies suggest that the rules governing multisensory integration for action may differ from the maximum likelihood estimation rules that appear to govern multisensory integration for many perceptual tasks. We used functional magnetic resonance (MR) imaging techniques, a MR-compatible robot, and a multisensory feedback control task to test that hypothesis by investigating how neural mechanisms involved in regulating hand position against mechanical perturbation respond to the presence and fidelity of visual and proprioceptive information. Healthy human subjects rested supine in a MR scanner and stabilized their wrist against constant or pseudo-random torque perturbations imposed by the robot. These two stabilization tasks were performed under three visual feedback conditions: “No-vision”: Subjects had to rely solely on proprioceptive feedback; “true-vision”: visual cursor and hand motions were congruent; and “random-vision”: cursor and hand motions were uncorrelated in time. Behaviorally, performance errors accumulated more quickly during trials wherein visual feedback was absent or incongruous. We analyzed blood-oxygenation level-dependent (BOLD) signal fluctuations to compare task-related activations in a cerebello-thalamo-cortical neural circuit previously linked with feedback stabilization of the hand. Activation in this network varied systematically depending on the presence and fidelity of visual feedback of task performance. Addition of task related visual information caused activations in the cerebello-thalamo-cortical network to expand into neighboring brain regions. Specific loci and intensity of expanded activity depended on the fidelity of visual feedback. Remarkably, BOLD signal fluctuations within these regions correlated strongly with the time series of proprioceptive errors—but not visual errors—when the fidelity of visual feedback was poor, even though visual and hand motions had similar variability characteristics. These results provide insight into the neural control of the body’s physical interactions with its environment, rejecting the standard Gaussian cue combination model of multisensory integration in favor of models that account for causal structure in the sensory feedback

Spatio-Temporal Patterning in Primary Motor Cortex at Movement Onset

Voluntary movement initiation involves the engagement of large populations of motor cortical neurons around movement onset. Despite knowledge of the temporal dynamics that lead to movement, the spatial structure of these dynamics across the cortical surface remains unknown. In data from 4 rhesus macaques, we show that the timing of attenuation of beta frequency local field potential oscillations, a correlate of locally activated cortex, forms a spatial gradient across primary motor cortex (MI). We show that these spatio-temporal dynamics are recapitulated in the engagement order of ensembles of MI neurons. We demonstrate that these patterns are unique to movement onset and suggest that movement initiation requires a precise spatio-temporal sequential activation of neurons in MI