Reaching movements can be redirected during their progress to handle unexpected visual changes, such as a change in target location. It is important to know when these redirections start, i.e. the online reaction time (oRT), but this information is not readily evident since redirections are embedded within a time-varying baseline movement which differs from trial to trial. The one previous study that evaluated the performance of different oRT identification methods utilized simulated redirections with the exact same onset, rather than a range of onsets as would be typically encountered. We addressed this gap by utilizing batches of \"hybrid\" trials with temporal spread in their oRTs. Each hybrid trial combined a sampled baseline movement with an idealized corrective response. Two new methods had the most accurate identification of online reaction times: i) a threshold-aligned grand mean regression and ii) a template-based approach we term the canonical correction search. The threshold-aligned grand mean regression is simple to implement and effective. The canonical correction search is a more complex procedure but arguably better linked to the underlying response. Applying the two methods to a published dataset revealed more delayed oRTs than was previously reported along with new information such as the width of oRT distributions. Taken together, our results demonstrate the utility of two new methods for dissecting corrective action from ongoing movement.

Bilateral coordination is commonly impaired in neurodevelopmental conditions including cerebral palsy, developmental coordination disorder, and autism spectrum disorder. However, we lack objective clinical assessments that can quantify bilateral coordination in a clinically feasible manner and determine age-based norms to identify impairments. The objective of this study was to use augmented reality and computer vision to characterize bilateral reaching abilities in typically developing children. Typically developing children (n = 133) ages 6-17 years completed symmetric and asymmetric bilateral reaching tasks in an augmented reality game environment. We analyzed the number of target pairs they could reach in 50 s as well as the time lag between their hands reaching the targets. We found that performance on both tasks developed in parallel, with development slowing but not plateauing after age 12. Children performed better on the symmetric task than asymmetric, both in targets reached and with shorter hand lags. Variability between children in hand lag decreased with age. We also found gender differences with females outperforming males, which were most pronounced in the 10-11 year olds. Overall, this study demonstrates parallel development through childhood and adolescence of symmetric and asymmetric reaching abilities. Furthermore, it demonstrates the ability to quantify bilateral coordination using computer vision and augmented reality, which can be applied to assess clinical populations.

Interhemispheric inhibition of the homotopic motor cortex is believed to be effective for accurate unilateral motor function. However, the cellular mechanisms underlying interhemispheric inhibition during unilateral motor behavior remain unclear. Furthermore, the impact of the neuromodulator acetylcholine on interhemispheric inhibition and the associated cellular mechanisms are not well understood. To address this knowledge gap, we conducted recordings of neuronal activity from the bilateral motor cortex of mice during the paw-reaching task. Subsequently, we analyzed interhemispheric spike correlation at the cell-pair level, classifying putative cell types to explore the underlying cellular circuitry mechanisms of interhemispheric inhibition. We found a cell-type pair-specific enhancement of the interhemispheric spike correlation when the mice were engaged in the reaching task. We also found that the interhemispheric spike correlation was modulated by pharmacological acetylcholine manipulation. The local field responses to contralateral excitation differed along the cortical depths, and muscarinic receptor antagonism enhanced the inhibitory component of the field response in deep layers. The muscarinic subtype M2 receptor is predominantly expressed in deep cortical neurons, including GABAergic interneurons. These results suggest that GABAergic interneurons expressing muscarinic receptors in deep layers mediate the neuromodulation of interhemispheric inhibition in the homotopic motor cortex.

Following events such as fatigue or stroke, individuals often move their trunks forward during reaching, leveraging a broader muscle group even when only arm movement would suffice. In previous work, we showed the existence of a \"force reserve\": a phenomenon where individuals, when challenged with a heavy weight, adjusted their motor coordination to preserve approximately 40% of their shoulder\'s force. Here, we investigated if such reserve can predict hip, shoulder, and elbow movements and torques resulting from an induced shoulder strength deficit. We engaged 20 healthy participants in a reaching task with incrementally heavier dumbbells, analyzing arm and trunk movements via motion capture and joint torques through inverse dynamics. We simulated these movements using an optimal control model of a 3-degree-of-freedom upper body, contrasting three cost functions: traditional sum of squared torques, a force reserve function incorporating a nonlinear penalty, and a normalized torque function. Our results demonstrate a clear increase in trunk movement correlated with heavier dumbbell weights, with participants employing compensatory movements to maintain a shoulder force reserve of approximately 40% of maximum torque. Simulations showed that while traditional and reserve functions accurately predicted trunk compensation, only the reserve function effectively predicted joint torques under heavier weights. These findings suggest that compensatory movements are strategically employed to minimize shoulder effort and distribute load across multiple joints in response to weakness. We discuss the implications of the force reserve cost function in the context of optimal control of human movements and its relevance for understanding compensatory movements poststroke.NEW & NOTEWORTHY Our study reveals key findings on compensatory movements during upper limb reaching tasks under shoulder strength deficits, as observed poststroke. Using heavy dumbbells with healthy volunteers, we demonstrate how forward trunk displacement conserves around 40% of shoulder strength reserve during reaching. We show that an optimal controller employing a cost function combining squared motor torque and a nonlinear penalty for excessive muscle activation outperforms traditional controllers in predicting torques and compensatory movements in these scenarios.

Precision reaching often requires corrective submovements to obtain the desired goal. Most studies of reaching have focused on single initial movements, and implied the cortical encoding model was the same for all submovements. However, corrective submovements may show different encoding patterns from the initial submovement with distinct patterns of activation across the population. Two rhesus macaques performed a precision center-out-task with small targets. Neural activity from single units in the primary motor cortex and associated behavioral data were recorded to evaluate movement characteristics. Neural population data and individual neuronal firing rates identified with a peak finding algorithm to identify peaks in hand speed were examined for encoding differences between initial and corrective submovements. Individual neurons were fitted with a regression model that included the reach vector, position, and speed to predict firing rate. For both initial and corrective submovements, the largest effect remained movement direction. We observed a large subset changed their preferred direction greater than 45° between initial and corrective submovements. Neuronal depth of modulation also showed considerable variation when adjusted for movement speed. By using principal component analysis, neural trajectories of initial and corrective submovements progressed through different neural subspaces. These findings all suggest that different neural encoding patterns exist for initial and corrective submovements within the cortex. We hypothesize that this variation in how neurons change to encode small, corrective submovements might allow for a larger portion of the neural space being used to encode a greater range of movements with varying amplitudes and levels of precision.NEW & NOTEWORTHY Neuronal recordings matched with kinematic behavior were collected in a precision center-out task that often required corrective movements. We reveal large differences in preferred direction and depth of modulation between initial and corrective submovements across the neural population. We then present a model of the neural population describing how these shifts in tuning create different subspaces for signaling initial and corrective movements likely to improve motor precision.

This study examined the co-development of infant reaching and postural control across the transition to arms-free sitting at home. We observed infants with typical likelihood (TL; n = 24) and elevated likelihood (EL; n = 20) for autism at four biweekly sessions spanning the transition to arms-free sitting (infant age = 4.5-8 months at first session). At each session, infants sat on a pressure-sensitive mat with external support or independently, wore magneto-inertial sensors on both wrists, and reached for toys presented at midline. Analyses focused on characterizing and comparing control of sitting during reaching actions and standard kinematic metrics of reaching during Supported versus Independent Sitting. Although EL infants achieved arms-free sitting later than TL peers, there were no group differences on any measures. Across sessions, infants\' control of the sitting posture during concurrent reaching movements improved in both contexts, though they were less stable as they reached when sitting independently compared to when sitting with support. A similar effect was apparent in the kinematics of reaches, with overall improvement over time, but evidence of poorer control in Independent relative to Supported Sitting. Taken together, these findings underscore the mutually influential and dynamic relations between emerging skills and well-established behaviors.

Movement sonification can improve motor control in both healthy subjects (e.g., learning or refining a sport skill) and those with sensorimotor deficits (e.g., stroke patients and deafferented individuals). It is not known whether improved motor control and learning from movement sonification are driven by feedback-based real-time (\"online\") trajectory adjustments, adjustments to internal models over multiple trials, or both. We searched for evidence of online trajectory adjustments (muscle twitches) in response to movement sonification feedback by comparing the kinematics and error of reaches made with online (i.e., real-time) and terminal sonification feedback. We found that reaches made with online feedback were significantly more jerky than reaches made with terminal feedback, indicating increased muscle twitching (i.e., online trajectory adjustment). Using a between-subject design, we found that online feedback was associated with improved motor learning of a reach path and target over terminal feedback; however, using a within-subjects design, we found that switching participants who had learned with online sonification feedback to terminal feedback was associated with a decrease in error. Thus, our results suggest that, with our task and sonification, movement sonification leads to online trajectory adjustments which improve internal models over multiple trials, but which themselves are not helpful online corrections.

Adaptive motor behavior depends on the coordinated activity of multiple neural systems distributed across the brain. While the role of sensorimotor cortex in motor learning has been well established, how higher-order brain systems interact with sensorimotor cortex to guide learning is less well understood. Using functional MRI, we examined human brain activity during a reward-based motor task where subjects learned to shape their hand trajectories through reinforcement feedback. We projected patterns of cortical and striatal functional connectivity onto a low-dimensional manifold space and examined how regions expanded and contracted along the manifold during learning. During early learning, we found that several sensorimotor areas in the dorsal attention network exhibited increased covariance with areas of the salience/ventral attention network and reduced covariance with areas of the default mode network (DMN). During late learning, these effects reversed, with sensorimotor areas now exhibiting increased covariance with DMN areas. However, areas in posteromedial cortex showed the opposite pattern across learning phases, with its connectivity suggesting a role in coordinating activity across different networks over time. Our results establish the neural changes that support reward-based motor learning and identify distinct transitions in the functional coupling of sensorimotor to transmodal cortex when adapting behavior.

BACKGROUND: In daily life tasks of the upper limb, we must make quick corrections with our hands in unstable postural situations. Postural and reaching control mechanisms are involved in the accurate execution of upper-limb tasks.
OBJECTIVE: This research aimed to determine the effect of different postural stability conditions on the motor performance of the upper limb in a reaching task with non-static targets.
METHODS: 19 young participants performed a reaching task toward targets that exhibited a change in position (at 200 or 600 ms) in different postural conditions (bipedal-firm, bipedal-foam, and unipedal-foam surface). Performance on the screen (motion time and spatial error), balance (center of pressure displacements, CoP), and index finger movements were recorded during the reaching task.
RESULTS: The instability affects the finger kinematic (displacements) and CoP kinematic (displacements, speed, and smoothness) without affecting the performance on the screen (precision and duration). The timing of target change affects the performance on the screen, finger kinematic (speed and smoothness), and CoP kinematic (displacements, speed, and smoothness).
CONCLUSIONS: Postural and reaching control systems enable accurate hand motions in less stable situations, even in reaching tasks with non-static targets. The postural and reaching control systems can protect the end-effector performance during unstable conditions but not during trials with less time to correct the motion.

Alzheimer\'s disease (AD) is characterized by an initial decline in declarative memory, while nondeclarative memory processing remains relatively intact. Error-based motor adaptation is traditionally seen as a form of nondeclarative memory, but recent findings suggest that it involves both fast, declarative, and slow, nondeclarative adaptive processes. If the declarative memory system shares resources with the fast process in motor adaptation, it can be hypothesized that the fast, but not the slow, process is disturbed in AD patients. To test this, we studied 20 early-stage AD patients and 21 age-matched controls of both sexes using a reach adaptation paradigm that relies on spontaneous recovery after sequential exposure to opposing force fields. Adaptation was measured using error clamps and expressed as an adaptation index (AI). Although patients with AD showed slightly lower adaptation to the force field than the controls, both groups demonstrated effects of spontaneous recovery. The time course of the AI was fitted by a hierarchical Bayesian two-state model in which each dynamic state is characterized by a retention and learning rate. Compared to controls, the retention rate of the fast process was the only parameter that was significantly different (lower) in the AD patients, confirming that the memory of the declarative, fast process is disturbed by AD. The slow adaptive process was virtually unaffected. Since the slow process learns only weakly from an error, our results provide neurocomputational evidence for the clinical practice of errorless learning of everyday tasks in people with dementia.