Changes in EEG alpha-band power during prehension indicates neural motor drive inhibition.

Changes in alpha band activity (8-12 Hz) indicate the downregulation of brain regions during cognitive tasks, reflecting real-time cognitive load. Despite this, its feasibility to be used in a more dynamic environment with ongoing motor corrections has not been studied. This research used electroencephalography (EEG) to explore how different brain regions are engaged during a simple grasp and lift task where unexpected changes to the object's weight or surface friction are introduced. The results suggest that alpha activity changes related to motor error correction occur only in motor-related areas (i.e. central areas) but not in error processing areas (i.e., frontoparietal network) during unexpected weight changes. This suggests that oscillations over motor areas reflect the reduction of motor drive related to motor error correction, thus, being a potential cortical electrophysiological biomarker for the process and not solely as a proxy for cognitive demands. This observation is particularly relevant in scenarios where these signals are used to evaluate high cognitive demands co-occurring with high levels of motor errors and corrections, such as prosthesis use. The establishment of electrophysiological biomarkers of mental resource allocation during movement and cognition can help identify indicators of mental workload and motor drive, which may be useful for improving brain-machine interfaces.NEW & NOTEWORTHY We demonstrated that alpha suppression, an EEG phenomenon with high temporal resolution, occurs over the primary sensorimotor area during error correction during lift movements. Interpretations of alpha activity are often attributed to high cognitive demands, thus recognizing that it is also influenced by motor processes is important in situations where cognitive demands are paired with movement errors. This could further have application as a biomarker for error correction in human-machine interfaces, such as neuroprostheses.

Sensory prediction error drives subconscious motor learning outside of the laboratory.

Sensorimotor adaptation is supported by at least two parallel learning systems: an intentionally controlled explicit strategy and an involuntary implicit learning system. Past work focused on constrained reaches or finger movements in laboratory environments has shown subconscious learning systems to be driven in part by sensory prediction error (SPE), i.e., the mismatch between the realized and expected outcome of an action. We designed a ball rolling task to explore whether SPEs can drive implicit motor adaptation during complex whole body movements that impart physical motion on external objects. After applying a visual shift, participants rapidly adapted their rolling angles to reduce the error between the ball and the target. We removed all visual feedback and told participants to aim their throw directly toward the primary target, revealing an unintentional 5.06° implicit adjustment to reach angles that decayed over time. To determine whether this implicit adaptation was driven by SPE, we gave participants a second aiming target that would "solve" the visual shift, as in the study by Mazzoni and Krakauer (Mazzoni P, Krakauer JW. J Neurosci 26: 3642-3645, 2006). Remarkably, after rapidly reducing ball-rolling error to zero (due to enhancements in strategic aiming), the additional aiming target caused rolling angles to deviate beyond the primary target by 3.15°. This involuntary overcompensation, which worsened task performance, is a hallmark of SPE-driven implicit learning. These results show that SPE-driven implicit processes, previously observed within simplified finger or planar reaching movements, actively contribute to motor adaptation in more complex naturalistic skill-based tasks.NEW & NOTEWORTHY Implicit and explicit learning systems have been detected using simple, constrained movements inside the laboratory. How these systems impact movements during complex whole body, skill-based tasks has not been established. Here, we demonstrate that sensory prediction errors significantly impact how a person updates their movements, replicating findings from the laboratory in an unconstrained ball-rolling task. This real-world validation is an important step toward explaining how subconscious learning helps humans execute common motor skills in dynamic environments.

An analytical method reduces noise bias in motor adaptation analysis.

When a person makes a movement, a motor error is typically observed that then drives motor planning corrections on subsequent movements. This error correction, quantified as a trial-by-trial adaptation rate, provides insight into how the nervous system is operating, particularly regarding how much confidence a person places in different sources of information such as sensory feedback or motor command reproducibility. Traditional analysis has required carefully controlled laboratory conditions such as the application of perturbations or error clamping, limiting the usefulness of motor analysis in clinical and everyday environments. Here we focus on error adaptation during unperturbed and naturalistic movements. With increasing motor noise, we show that the conventional estimation of trial-by-trial adaptation increases, a counterintuitive finding that is the consequence of systematic bias in the estimate due to noise masking the learner's intention. We present an analytic solution relying on stochastic signal processing to reduce this effect of noise, producing an estimate of motor adaptation with reduced bias. The result is an improved estimate of trial-by-trial adaptation in a human learner compared to conventional methods. We demonstrate the effectiveness of the new method in analyzing simulated and empirical movement data under different noise conditions.

Crossmodal congruency effect scores decrease with repeat test exposure.

The incorporation of feedback into a person's body schema is well established. The crossmodal congruency task (CCT) is used to objectively quantify incorporation without being susceptible to experimenter biases. This visual-tactile interference task is used to calculate the crossmodal congruency effect (CCE) score as a difference in response time between incongruent and congruent trials. Here we show that this metric is susceptible to a learning effect that causes attenuation of the CCE score due to repeated task exposure sessions. We demonstrate that this learning effect is persistent, even after a 6 month hiatus in testing. Two mitigation strategies are proposed: 1. Only use CCE scores that are taken after learning has stabilized, or 2. Use a modified CCT protocol that decreases the task exposure time. We show that the modified and shortened CCT protocol, which may be required to meet time or logistical constraints in laboratory or clinical settings, reduced the impact of the learning effect on CCT results. Importantly, the CCE scores from the modified protocol were not significantly more variable than results obtained with the original protocol. This study highlights the importance of considering exposure time to the CCT when designing experiments and suggests two mitigation strategies to improve the utility of this psychophysical assessment.

Conventional analysis of trial-by-trial adaptation is biased: Empirical and theoretical support using a Bayesian estimator.

Research on human motor adaptation has often focused on how people adapt to self-generated or externally-influenced errors. Trial-by-trial adaptation is a person's response to self-generated errors. Externally-influenced errors applied as catch-trial perturbations are used to calculate a person's perturbation adaptation rate. Although these adaptation rates are sometimes compared to one another, we show through simulation and empirical data that the two metrics are distinct. We demonstrate that the trial-by-trial adaptation rate, often calculated as a coefficient in a linear regression, is biased under typical conditions. We tested 12 able-bodied subjects moving a cursor on a screen using a computer mouse. Statistically different adaptation rates arise when sub-sets of trials from different phases of learning are analyzed from within a sequence of movement results. We propose a new approach to identify when a person's learning has stabilized in order to identify steady-state movement trials from which to calculate a more reliable trial-by-trial adaptation rate. Using a Bayesian model of human movement, we show that this analysis approach is more consistent and provides a more confident estimate than alternative approaches. Constraining analyses to steady-state conditions will allow researchers to better decouple the multiple concurrent learning processes that occur while a person makes goal-directed movements. Streamlining this analysis may help broaden the impact of motor adaptation studies, perhaps even enhancing their clinical usefulness.

Author Correction: Assessing the quality of supplementary sensory feedback using the crossmodal congruency task.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

Assessing the quality of supplementary sensory feedback using the crossmodal congruency task.

Advanced neural interfaces show promise in making prosthetic limbs more biomimetic and ultimately more intuitive and useful for patients. However, approaches to assess these emerging technologies are limited in scope and the insight they provide. When outfitting a prosthesis with a feedback system, such as a peripheral nerve interface, it would be helpful to quantify its physiological correspondence, i.e. how well the prosthesis feedback mimics the perceived feedback in an intact limb. Here we present an approach to quantify this aspect of feedback quality using the crossmodal congruency effect (CCE) task. We show that CCE scores are sensitive to feedback modality, an important characteristic for assessment purposes, but are confounded by the spatial separation between the expected and perceived location of a stimulus. Using data collected from 60 able-bodied participants trained to control a bypass prosthesis, we present a model that results in adjusted-CCE scores that are unaffected by percept misalignment which may result from imprecise neural stimulation. The adjusted-CCE score serves as a proxy for a feedback modality's physiological correspondence or 'naturalness'. This quantification approach gives researchers a tool to assess an aspect of emerging augmented feedback systems that is not measurable with current motor assessments.

Validation of a constrained-time movement task for use in rehabilitation outcome measures.

Current motor assessment tools can provide numerical indicators of performance but do not provide actionable information to target further improvement in rehabilitation interventions. Psychophysics-based outcome measures show promise to provide more useful information in the laboratory environment but have been limited in clinical implementation. Here we present a constrained-time task to assess paced and non-rhythmic movements. The task's output metrics include trial-by-trial adaptation rate and the just noticeable difference of a perturbation. We show that the task's metrics are reliable (i.e. high test-retest reliability) and are responsive to changes in feedback type and experience. We also discuss the task's versatility to be used for other types of movements including grasping. The consistent, sensitive and flexible time-constrained movement task we present provides a foundation from which to develop advanced outcome measures for prosthesis users and for other rehabilitation contexts.

A third arm - Design of a bypass prosthesis enabling incorporation.

A variety of factors affect the performance of a person using a myoelectric prosthesis, including increased control noise, reduced sensory feedback, and muscle fatigue. Many studies use able-bodied subjects to control a myoelectric prosthesis using a bypass socket in order to make comparisons to movements made with intact limbs. Depending on the goals of the study, this approach can also allow for greater subject numbers and more statistical power in the analysis of the results. As we develop assessment tools and techniques to evaluate how peripheral nerve interfaces impact prosthesis incorporation, involving normally limbed subjects in the studies becomes challenging. We have designed a novel bypass prosthesis to allow for the assessment of prosthesis incorporation in able-bodied subjects. Incorporation of a prosthetic hand worn by a normally limbed subject requires that the prosthesis is a convincing, functional extension of their own body. We present the design and development of the bypass prosthesis with special attention to mounting position and angle of the prosthetic hand, the quality of the control system and the responsiveness of the feedback. The bypass prosthesis has been fitted with a myoelectrically-controlled hand that has been instrumented to measure the forces applied to the thumb, index, and middle fingers. The prosthetic hand was mounted on the bypass socket such that it is the same length as the subject's intact limb but at a medial rotation angle of 20° to prevent visual occlusion of the prosthetic hand. Force feedback is provided in the form of electrical stimulation, vibration, or force applied to the intact limb with milliseconds of delay. Preliminary data results from a cross-modal congruency task are included showing evidence of prosthesis incorporation in able-bodied subjects. This bypass will allow able-bodied subjects to participate in research studies that require the use of a prosthetic limb while also allowing the subjects to sense that the prosthesis is an extension of the body.

Designing and implementing nervous system simulations on LEGO robots.

We present a method to use the commercially available LEGO Mindstorms NXT robotics platform to test systems level neuroscience hypotheses. The first step of the method is to develop a nervous system simulation of specific reflexive behaviors of an appropriate model organism; here we use the American Lobster. Exteroceptive reflexes mediated by decussating (crossing) neural connections can explain an animal's taxis towards or away from a stimulus as described by Braitenberg and are particularly well suited for investigation using the NXT platform.(1) The nervous system simulation is programmed using LabVIEW software on the LEGO Mindstorms platform. Once the nervous system is tuned properly, behavioral experiments are run on the robot and on the animal under identical environmental conditions. By controlling the sensory milieu experienced by the specimens, differences in behavioral outputs can be observed. These differences may point to specific deficiencies in the nervous system model and serve to inform the iteration of the model for the particular behavior under study. This method allows for the experimental manipulation of electronic nervous systems and serves as a way to explore neuroscience hypotheses specifically regarding the neurophysiological basis of simple innate reflexive behaviors. The LEGO Mindstorms NXT kit provides an affordable and efficient platform on which to test preliminary biomimetic robot control schemes. The approach is also well suited for the high school classroom to serve as the foundation for a hands-on inquiry-based biorobotics curriculum.