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Visual and proprioceptive feedback both contribute to perceptual decisions, but it remains unknown how these feedback signals are integrated together or consider factors such as delays and variance during online control. We investigated this question by having participants reach to a target with randomly applied mechanical and/or visual disturbances. We observed that the presence of visual feedback during a mechanical disturbance did not increase the size of the muscle response significantly but did decrease variance, consistent with a dynamic Bayesian integration model. In a control experiment, we verified that vision had a potent influence when mechanical and visual disturbances were both present but opposite in sign. These results highlight a complex process for multisensory integration, where visual feedback has a relatively modest influence when the limb is mechanically disturbed, but a substantial influence when visual feedback becomes misaligned with the limb.

0.AbstractEssential tremor (ET) is a neurological disorder characterized by involuntary oscillations of the limbs. Previous studies have hypothesized that ET was a cerebellar disorder and reported impairments in motor adaptation. However, recent advances have highlighted that motor adaptation involved several components linked to anticipation and control, all dependent on cerebellum, and the specific alteration of adaptation of ET has not been identified. To address this question we investigated behavioural markers of adaptation in ET patients (n=20) and age-matched healthy volunteers (n=20) in saccadic and upper limb adaptation tasks, probing compensation for target jumps and for velocity-dependent force fields, respectively. We found that both groups adapted their movements to the novel contexts, however, ET patients adapted to a lesser extent compared to healthy volunteers. Importantly, we decomposed movements into components linked to anticipation, preserved here, and real-time execution, which were responsible for the adaptation deficit. Altogether, our results suggest that execution deficits may be a specific functional consequence of the alteration of neural pathways associated with ET.Significance StatementWe tested Essential Tremor patients’ adaptation abilities in classical tasks including saccadic adaptation to target jumps and reaching adaptation to force field disturbances. Patients’ adaptation was present but impaired in both tasks. Interestingly, the deficits were mainly present during movement execution, while the anticipatory components of movements were similar to healthy volunteers. These findings reinforce the hypothesis of a cerebellar origin for essential tremor and details the motor adaptation impairments previously found in this disorder.

Gait variability of healthy adults exhibits Long-Range Autocorrelations (LRA), meaning that the stride interval at any time statistically depends on previous gait cycles; and this dependency spans over several hundreds of strides. Previous works have shown that this property is altered in patients with Parkinson’s disease, such that their gait pattern corresponds to a more random process. Here, we adapted a model of gait control to interpret the reduction in LRA that characterized patients in a computational framework. Gait regulation was modeled as a Linear-Quadratic-Gaussian control problem where the objective was to maintain a fixed velocity through the coordinated regulation of stride duration and length. This objective offers a degree of redundancy in the way the controller can maintain a given velocity, resulting in the emergence of LRA. In this framework, the model suggested that patients exploited less the task redundancy, likely to compensate for an increased stride-to-stride variability. Furthermore, we used this model to predict the potential benefit of an active orthosis on the gait pattern of patients. The orthosis was embedded in the model as a low-pass filter on the series of stride parameters. We show in simulations that, with a suitable level of assistance, the orthosis could help patients recovering a gait pattern with LRA comparable to that of healthy controls. Assuming that the presence of LRA in a stride series is a marker of healthy gait control, our study provides a rationale for developing gait assistance technology to reduce the fall risk associated with Parkinson’s disease.

Humans consider the parameters linked to movement goal during reaching to adjust their control strategy online. Indeed, rapid changes in target structure or disturbances interfering with their initial plan elicit rapid changes in behavior. Here, we hypothesize that these changes could result from the continuous use of a decision variable combining motor and cognitive components. We combine an optimal feedback controller with a real-time monitoring of the expected cost-to-go, which considers target- and movement-related costs, in a common theoretical framework. This model reproduces human behaviors in presence of changes in the target structure occurring during movement and of online decisions to flexibly change target following external perturbations. It also predicts that the time taken to decide to select a novel goal after a perturbation depends on the amplitude of the disturbance and on the rewards of the different options, which is a direct result of the continuous monitoring of the cost-to-go. We show that this result was present in our previously collected dataset. Together our developments point towards a continuous monitoring of the cost-to-go during reaching to update control online and make efficient decisions about movement goal.Author summaryThe way humans perform reaching movements is compatible with models considering that they result from the minimization of a task-related cost function. However, these models typically assume a cost function that does not change within movement, which is incompatible with experimental findings highlighting humans’ ability to adjust reaching control online and change target flexibly. We hypothesized that this later ability relied on the cost-to-go, which integrates task- and body-related information, being evaluated continuously during movement. We show that this model can optimally select and adjust control during movement in a way that reproduces human behavior in a set of tasks involving change in cost function and change in goal target. Our model predicted that decision-time to change target must be postponed when limb displacements and alternative rewards are smaller, which was borne out in our previous experimental dataset. To conclude, our model explains dynamic updates in reach control and suggests the cost-to-go as decision variable linking decision-making and motor control.

Humans exhibit lateralization such that most individuals typically show a preference for using one arm over the other for a range of movement tasks. The computational aspects of movement control leading to these differences in skill are not yet understood. It has been hypothesized that the dominant and nondominant arms differ in terms of the use of predictive or impedance control mechanisms. However, previous studies present confounding factors that prevented clear conclusions: either the performances were compared across two different groups, or in a design in which asymmetrical transfer between limbs could take place. To address these concerns, we studied a reach adaptation task during which healthy volunteers performed movements with their right and left arms in random order. We performed two experiments. Experiment 1 (18 participants) focused on adaptation to the presence of a perturbing force field (FF) and experiment 2 (12 participants) focused on rapid adaptations in feedback responses. The randomization of the left and right arm led to simultaneous adaptation, allowing us to study lateralization in single individuals with symmetrical and minimal transfer between limbs. This design revealed that participants could adapt control of both arms, with both arms showing similar performance levels. The nondominant arm initially presented a slightly worst performance but reached similar levels of performance in late trials. We also observed that the nondominant arm showed a different control strategy compatible with robust control when adapting to the force field perturbation. EMG data showed that these differences in control were not caused by differences in co-contraction across the arms. Thus, instead of assuming differences in predictive or reactive control schemes, our data show that in the context of optimal control, both arms can adapt, and that the nondominant arm uses a more robust, model-free strategy likely to compensate for less accurate internal representations of movement dynamics.

Saccades are often directed toward a stimulus that provides useful information for observers to navigate the visual world. The quality of visual signals of a stimulus is influenced by global luminance, and the pupil constricts or dilates after a luminance increase or decrease, respectively, to optimize visual signals for further information processing. Although luminance level changes regularly in the real environment, saccades are mostly studied in the luminance-unchanged setup. Whether pupillary responses triggered by global luminance changes modulate saccadic behavior are yet to be explored. Through varying background luminance level in an interleaved pro- and anti-saccade paradigm, we investigated the modulation of pupillary luminance responses on the generation of reflexive and voluntary saccades. Subjects were instructed to either automatically look at the peripheral stimulus (pro-saccade) or to suppress the automatic response and voluntarily look in the opposite direction from the stimulus (anti-saccade). Level of background luminance was increased (light), decreased (dark), or unchanged (control) during the instructed fixation period. Saccade reaction time distributions of correct pro- and anti-saccades in the light and dark conditions were differed significantly from those in the control condition. Moreover, the luminance condition modulated saccade kinematics, showing reduced performances in the light condition than in the control condition, particularly in pro-saccades. Modeling results further suggested that both pupil diameter and pupil size derivative significantly modulated saccade behavior, though effect sizes were small and mainly mediated by intersubject differences. Together, our results demonstrated the influence of pupillary luminance responses on the generation of pro- and anti-saccades.

Target reward influences motor planning strategies through modulation of movement vigor. Considering current theories of sensorimotor control suggesting that movement planning consists in selecting a goal-directed control strategy, we sought to investigate the influence of reward on feedback control. Here, we explored this question in three human reaching experiments. First, we altered the explicit reward associated with the goal target and found an overall increase in feedback gains for higher target rewards, highlighted by larger velocities, feedback responses to external loads, and background muscle activity. Then, we investigated whether the differences in target rewards across multiple goals impacted rapid motor decisions during movement. We observed idiosyncratic switching strategies dependent on both target rewards and, surprisingly, the feedback gains at perturbation onset: the more vigorous movements were less likely to switch to a new goal following perturbations. To gain further insight into a causal influence of the feedback gains on rapid motor decisions, we demonstrated that biasing the baseline activity and reflex gains by means of a background load evoked a larger proportion of target switches in the direction opposite to the background load associated with lower muscle activity. Together, our results demonstrate an impact of target reward on feedback control and highlight the competition between movement vigor and flexibility.

Savings have been described as the ability of healthy humans to relearn a previously acquired motor skill faster than the first time, which in the context of motor adaptation suggests that the learning rate in the brain could be adjusted when a perturbation is recognized. Alternatively, it has been argued that apparent savings were the consequence of a distinct process that instead of reflecting a change in the learning rate, revealed an explicit re-aiming strategy. Based on recent evidence that feedback adaptation may be central to both planning and control, we hypothesized that this component could genuinely accelerate relearning in human adaptation to force fields during reaching. Consistent with our hypothesis, we observed that upon re-exposure to a previously learned force field, the very first movement performed by healthy volunteers in the relearning context was better adapted to the external disturbance, and this occurred without any anticipation or cognitive strategy because the relearning session was started unexpectedly. We conclude that feedback adaptation is a medium by which the nervous system can genuinely accelerate learning across movements.Significance StatementSavings describe the ability of healthy humans to faster relearn a previously acquired motor skill. It is debated whether savings result from implicit changes in the learning rate or an explicit strategy. Given recent evidence that feedback control plays a key role in movement adaptation, we hypothesized that this component also contributed to savings in the context of force field adaptation. We confirm that relearning was faster and demonstrate that the very first relearning movement was better adapted to the external disturbance, i.e., the online corrections in the first re-exposure trial carried imprints of feedback adaptation from the previous sessions in the absence of any explicit strategy. We conclude that this non-explicit feedback component can genuinely accelerate motor learning.

Current models of motor learning suggest that multiple timescales support adaptation to changes in visual or mechanical properties of the environment. These models capture patterns of learning and memory across a broad range of tasks, yet do not consider the possibility that rapid changes in behavior may occur without adaptation. Such changes in behavior may be desirable when facing transient disturbances, or when unpredictable changes in visual or mechanical properties of the task make it difficult to form an accurate model of the perturbation. Whether humans can modulate control strategies without an accurate model of the perturbation remains unknown. Here we frame this question in the context of robust control (H∞-control), a control strategy that specifically considers unpredictable disturbances by increasing initial movement speed and feedback gains. Correspondingly, we demonstrate in two human reaching experiments including males and females that the occurrence of a single unpredictable disturbance led to an increase in movement speed and in the gain of rapid feedback responses to mechanical disturbances on subsequent movements. This strategy reduced perturbation-related motion regardless of the direction of the perturbation. Furthermore, we found that changes in the control strategy were associated with co-contraction, which amplified the gain of muscle responses to both lengthening and shortening perturbations. These results have important implications for studies on motor adaptation because they highlight that trial-by-trial changes in limb motion also reflected changes in control strategies dissociable from error-based adaptation.SIGNIFICANCE STATEMENTHumans and animals use internal representations of movement dynamics to anticipate the impact of predictable disturbances. However, we are often confronted with transient or unpredictable disturbances, and it remains unknown whether and how the nervous system handles these disturbances over fast time scales. Here we hypothesized that humans can modulate their control strategy to make reaching movements less sensitive to perturbations. We tested this hypothesis in the framework of robust control, and found changes in movement speed and feedback gains consistent with the model predictions. These changes impacted participants' behavior on a trial-by-trial basis. We conclude that compensation for disturbances over fast time scales involves a robust control strategy, which potentially plays a key role in motor planning and execution.

Compensating for sensorimotor noise and for temporal delays has been identified as a major function of the nervous system. Although these aspects have often been described separately in the frameworks of optimal cue combination or motor prediction during movement planning, control-theoretic models suggest that these two operations are performed simultaneously, and mounting evidence supports that motor commands are based on sensory predictions rather than sensory states. In this letter, we study the benefit of state estimation for predictive sensorimotor control. More precisely, we combine explicit compensation for sensorimotor delays and optimal estimation derived in the context of Kalman filtering. We show, based on simulations of human-inspired eye and arm movements, that filtering sensory predictions improves the stability margin of the system against prediction errors due to low-dimensional predictions or to errors in the delay estimate. These simulations also highlight that prediction errors qualitatively account for a broad variety of movement disorders typically associated with cerebellar dysfunctions. We suggest that adaptive filtering in cerebellum, instead of often-assumed feedforward predictions, may achieve simple compensation for sensorimotor delays and support stable closed-loop control of movements.

Humans can quickly adjust their grip force to a change in friction at the object-skin interface during dexterous manipulation in a precision grip. To perform this adjustment, they rely on the feedback of the mechanoreceptive afferents innervating the fingertip skin. Because these tactile afferents encode information related to skin deformation, the nature of the feedback signaling a change in friction must somehow originate from a difference in the way the skin deforms when manipulating objects of different frictions. To better characterize the origin of the underlying sensory events, we asked human participants to perform a grip-lifting task with a manipulandum equipped with an optical imaging system. This system enabled to monitor fingertip skin strains through transparent plates of glass that had different levels of friction. We observed that, following an unexpected change in friction, participants adapted their grip force within 370ms after contact with the surface. By comparing the deformation patterns when unexpectedly switching from a high to a low friction condition, we found a significant increase in skin deformation inside the contact area arising over 100ms before the motor response, during the loading phase, suggesting that local and partial deformation patterns prior to lift-off are used in the nervous system to adjust the grip force as a function of the frictional condition.

Humans are able to perform very sophisticated reaching movements in a myriad of contexts based on flexible control strategies influenced by the task goal and environmental constraints such as obstacles. However, it remains unknown whether these control strategies can be adjusted online. The objective of this study was to determine whether the factors which determine control strategies during planning also modify the execution of an ongoing movement following sudden changes in task demand. More precisely, we investigated whether, and at which latency, feedback responses to perturbation loads followed the change in the structure of the goal target or environment. We changed the target width (square or rectangle) to alter the task redundancy, or the presence of obstacles to induce different constraints on the reach path, and assessed based on surface recordings when the change in visual display altered the feedback response to mechanical perturbations. Task-related EMG responses were detected within 150 ms of a change in target shape. Considering visuomotor delays of ∼ 100 ms, these results suggest that it takes 50 ms to change control policy within a trial. An additional 30 ms delay was observed when the change in context involved sudden appearance or disappearance of obstacles. Overall, our results demonstrate that the control policy within a reaching movement is not static: contextual factors which influence movement planning also influence movement execution at surprisingly short latencies. Moreover, the additional 30 ms associated with obstacles suggest that these two types of changes may be mediated via distinct processes.New & NoteworthyThe present work demonstrates that the control strategies used to perform reaching movements are adjusted online when the structure of the target or the presence of obstacles are altered during movements. Thus, the properties of goal-directed reaching control are not simply selected during the planning stage of a movement prior to execution. Rather, they are dynamically and rapidly adjusted online, within ∼150ms, according to changes in environment.

Summary Besides relying heavily on sensory and reinforcement feedback, motor skill learning may also depend on the level of motivation experienced during training. Yet, how motivation by reward modulates motor learning remains unclear. In 90 healthy subjects, we investigated the net effect of motivation by reward on motor learning while controlling for the sensory and reinforcement feedback received by the participants. Reward improved motor skill learning beyond performance-based reinforcement feedback. Importantly, the beneficial effect of reward involved a specific potentiation of reinforcement-related adjustments in motor commands, which concerned primarily the most relevant motor component for task success and persisted on the following day in the absence of reward. We propose that the long-lasting effects of motivation on motor learning may entail a form of associative learning resulting from the repetitive pairing of the reinforcement feedback and reward during training, a mechanism that may be exploited in future rehabilitation protocols., Graphical abstract, Highlights • Reward improves motor learning beyond performance-based reinforcement feedback • Reward boosts specifically reinforcement-based adjustments in motor commands • These effects persist on the following day, in the absence of reward, Neuroscience; Behavioral neuroscience; Sensory neuroscience; Cognitive neuroscience

In humans, practically all movements are learnt and performed in a constant gravitational field. Yet, studies on arm movements and object manipulation in parabolic flight have highlighted very fast sensorimotor adaptations to altered gravity environments. Here, we wondered if the motor adjustments observed in those altered gravity environments could also be observed on Earth in a situation where the body is upside-down. To address this question, we asked participants to perform rhythmic arm movements in two different body postures (right-side-up and upside-down) while holding an object in precision grip. Analyses of grip-load force coordination and of movement kinematics revealed distinct adaptation patterns between grip and arm control. Grip force and load force were tightly synchronized from the first movements performed in upside-down posture, reflecting a malleable allocentric grip control. In contrast, velocity profiles showed a more progressive adaptation to the upside-down posture and reflected an egocentric planning of arm kinematics. In addition to suggesting distinct mechanisms between grip dynamics and arm kinematics for adaptation to novel contexts, these results also suggest the existence of general mechanisms underlying gravity-dependent motor adaptation that can be used for fast sensorimotor coordination across different postures on Earth and, incidentally, across different gravitational conditions in parabolic flights, in human centrifuges, or in Space. NEW

A hallmark of human reaching movements is that they are appropriately tuned to the task goal and to the environmental context. This was demonstrated by the way humans flexibly respond to mechanical and visual perturbations that happen during movement. Furthermore, it was previously showed that the properties of goal-directed control can change within a movement, following abrupt changes in the goal structure. Such online adjustment was characterized by a modulation of feedback gains following switches in target shape. However, it remains unknown whether the underlying mechanism merely switches between prespecified policies, or whether it results from continuous and potentially dynamic adjustments. Here, we address this question by investigating participants’ feedback control strategies in presence of various changes in target width during reaching. More specifically, we studied whether the feedback responses to mechanical perturbations were sensitive to the rate of change in target width, which would be inconsistent with the hypothesis of a single, discrete switch. Based on movement kinematics and surface EMG data, we observed a modulation of feedback response clearly dependent on dynamical changes in target width. Together, our results demonstrate a continuous and online transformation of task-related parameters into suitable control policies.Significance statementHumans can adjust their control policy online in response to changes in the goal structure. However, it was unknown whether this adjustment resulted from a switch between two policies, or from dynamic and continuous adjustments. To address this question, we investigated whether online adjustments were tuned to dynamic changes in goal target which varied at different rates. Our results demonstrated that online adjustments were tuned to the rate of change in target width, suggesting that human reaching control policies are derived based on continuous monitoring of task-related parameters supporting online and dynamic adjustments.

It is unclear how sensory information from muscle spindles located within muscles spanning multiple joints influences perception of body position and motion. We address this issue by comparing errors in estimating limb position made by human subjects with predicted errors made by two musculoskeletal models, one with only monoarticulars and one with both monoarticulars and biarticulars. We provide evidence that biarticulars produce coupling of errors between joints, which help to reduce errors.

Describing how the brain anatomical wiring contributes to the emergence of coordinated neural activity underlying complex behavior remains challenging. Indeed, patterns of remote coactivations that adjust with the ongoing task-demand do not systematically match direct, static anatomical links. Here, we propose that observed coactivation patterns, known as functional connectivity (FC), can be explained by a controllable linear diffusion dynamics defined on the brain architecture. Our model, termed structure-informed FC, is based on the hypothesis that different sets of brain regions controlling the information flow on the anatomical wiring produce state-specific functional patterns. We thus introduce a principled framework for the identification of potential control centers in the brain. We find that well-defined, sparse, and robust sets of control regions, partially overlapping across several tasks and resting state, produce FC patterns comparable to empirical ones. Our findings suggest that controllability is a fundamental feature allowing the brain to reach different states., Author Summary Understanding how brain anatomy promotes particular patterns of coactivations among neural regions is a key challenge in neuroscience. This challenge can be addressed using network science and systems theory. Here, we propose that coactivations result from the diffusion of information through the network of anatomical links connecting brain regions, with certain regions controlling the dynamics. We translate this hypothesis into a model called structure-informed functional connectivity, and we introduce a framework for identifying control regions based on empirical data. We find that our model produces coactivation patterns comparable to empirical ones, and that distinct sets of control regions are associated with different functional states. These findings suggest that controllability is an important feature allowing the brain to reach different states.

Humans excel at learning complex tasks, and elite performers such as musicians or athletes develop motor skills that defy biomechanical constraints. All actions require the movement of massive bodies. Of particular interest in the process of sensorimotor learning and control is the impact of gravitational forces on the body. Indeed, efficient control and accurate internal representations of the body configuration in space depend on our ability to feel and anticipate the action of gravity. Here we review studies on perception and sensorimotor control in both normal and altered gravity. Behavioral and modeling studies together suggested that the nervous system develops efficient strategies to take advantage of gravitational forces across a wide variety of tasks. However, when the body was exposed to altered gravity, the rate and amount of adaptation exhibited substantial variation from one experiment to another and sometimes led to partial adjustment only. Overall, these results support the hypothesis that the brain uses a multimodal and flexible representation of the effect of gravity on our body and movements. Future work is necessary to better characterize the nature of this internal representation and the extent to which it can adapt to novel contexts.

Many studies highlight that human movements are highly successful yet display a surprising amount of variability from trial to trial. There is a consistent pattern of variability throughout movement: initial motor errors are corrected by the end of movement, suggesting the presence of a powerful online control process. Here, we analyze the trial-by-trial variability of goal-directed reaching in nonhuman primates (five male Rhesus monkeys) and demonstrate that they display a similar pattern of variability during reaching, including a strong negative correlation between initial and late hand motion. We then demonstrate that trial-to-trial neural variability of primary motor cortex (M1) is positively correlated with variability of future hand motion (τ = ∼160 ms) during reaching. Furthermore, the variability of M1 activity is also correlated with variability of past hand motion (τ = ∼90 ms), but in the opposite polarity (i.e., negative correlation). Partial correlation analysis demonstrated that M1 activity independently reflects the variability of both past and future hand motions. These findings provide support for the hypothesis that M1 activity is involved in online feedback control of motor actions. SIGNIFICANCE STATEMENT Previous studies highlight that primary motor cortex (M1) rapidly responds to either visual or mechanical disturbances, suggesting its involvement in online feedback control. However, these studies required external disturbances to the motor system and it is not clear whether a similar feedback process addresses internal noise/errors generated by the motor system itself. Here, we introduce a novel analysis that evaluates how variations in the activity of M1 neurons covary with variations in hand motion on a trial-to-trial basis. The analyses demonstrate that M1 activity is correlated with hand motion in both the near future and the recent past, but with opposite polarity. These results suggest that M1 is involved in online feedback motor control to address errors/noise within the motor system.

Bimanual tasks involve the coordination of both arms, which often offers redundancy in the ways a task can be completed. The distribution of control across limbs is often considered from the perspective of handedness. In this context, although there are differences across dominant and nondominant arms during reaching control ( Sainburg 2002 ), previous studies have shown that the brain tends to favor the dominant arm when performing bimanual tasks ( Salimpour and Shadmehr 2014 ). However, biomechanical factors known to influence planning and control in unimanual tasks may also generate limb asymmetries in force generation, but their influence on bimanual control has remained unexplored. We investigated this issue in a series of experiments in which participants were instructed to generate a 20-N force with both arms, with or without perturbation of the target force during the trial. We modeled the task in the framework of optimal feedback control of a two-link model with six human-like muscles groups. The biomechanical model predicted a differential contribution of each arm dependent on the orientation of the target force and joint configuration that was quantitatively matched by the participants’ behavior, regardless of handedness. Responses to visual perturbations were strongly influenced by the perturbation direction, such that online corrections also reflected an optimal use of limb biomechanics. These results show that the nervous system takes biomechanical constraints into account when optimizing the distribution of forces generated across limbs during both movement planning and feedback control of a bimanual task. NEW & NOTEWORTHY Here, we studied a bimanual force production task to examine the effects of biomechanical constraints on the distribution of control across limbs. Our findings show that the central nervous system optimizes the distribution of force across the two arms according to the joint configuration of the upper limbs. We further show that the underlying mechanisms influence both movement planning and online corrective responses to sudden changes in the target force.

Long-range autocorrelations (LRA) are a robust feature of rhythmic movements, which may provide important information about neural control and potentially constitute a powerful marker of dysfunction. A clear difficulty associated with the assessment of LRA is that it requires a large number of cycles to generate reliable results. Here we investigate how series length impacts the reliability of LRA assessment. A total of 94 time series extracted from walking or cycling tasks were re-assessed with series length varying from 64 to 512 data points. LRA were assessed using an approach combining the rescaled range analysis or the detrended fluctuation analysis (Hurst exponent, H), along with the shape of the power spectral density (α exponent). The statistical precision was defined as the ability to obtain estimates for H and α that are consistent with their theoretical relationship, irrespective of the series length. The sensitivity consisted of testing whether significant differences between experimental conditions found in the original studies when considering 512 data points persisted with shorter series. We also investigate the use of evenly-spaced diffusion plots as a methodological improvement of original version of methods for short series. Our results show that the reliable assessment of LRA requires 512 data points, or no shorter than 256 data points provided that more robust methods are considered such as the evenly-spaced algorithms. Such series can be reasonably obtained in clinical populations with moderate, or even more severe, gait impairments and open the perspective to extend the use of LRA assessment as a marker of gait stability applicable to a broad range of locomotor disorders.

Tactile and muscle afferents provide critical sensory information for grasp control, yet the contribution of each sensory system during online control has not been clearly identified. More precisely, it is unknown how these two sensory systems participate in online control of digit forces following perturbations to held objects. To address this issue, we investigated motor responses in the context of fingertip loading, which parallels the impact of perturbations to held objects on finger motion and fingerpad deformation, and characterized surface recordings of intrinsic (first dorsal interosseous, FDI) and extrinsic (flexor digitorum superficialis, FDS) hand muscles based on statistical modeling. We designed a series of experiments probing the effects of peripheral stimulation with or without anesthesia of the finger, and of task instructions. Loading of the fingertip generated a motor response in FDI at ~60 ms following the perturbation onset, which was only driven by muscle stretch, as the ring-block anesthesia reduced the gain of the response occurring later than 90 ms, leaving responses occurring before this time unaffected. In contrast, the motor response in FDS was independent of the lateral motion of the finger. This response started at ~90 ms on average and was immediately adjusted to task demands. Altogether these results highlight how a rapid integration of partially distinct sensorimotor circuits supports rapid motor responses to fingertip loading. NEW & NOTEWORTHY To grasp and manipulate objects, the brain uses touch signals related to skin deformation as well as sensory information about motion of the fingers encoded in muscle spindles. Here we investigated how these two sensory systems contribute to feedback responses to perturbation applied to the fingertip. We found distinct response components, suggesting that each sensory system engages separate sensorimotor circuits with distinct functions and latencies.

Background: Functional connectivity quantifies the statistical dependencies between the activity of brain regions, measured using neuroimaging data such as functional magnetic resonance imaging (fMRI) blood-oxygenation-level dependent time series. The network representation of functional connectivity, called a functional connectome (FC), has been shown to contain an individual fingerprint allowing participants identification across consecutive testing sessions. Recently, researchers have focused on the extraction of these fingerprints, with potential applications in personalized medicine., Materials and Methods: In this study, we show that a mathematical operation denominated degree-normalization can improve the extraction of FC fingerprints. Degree-normalization has the effect of reducing the excessive influence of strongly connected brain areas in the whole-brain network. We adopt the differential identifiability framework and apply it to both original and degree-normalized FCs of 409 individuals from the Human Connectome Project, in resting-state and 7 fMRI tasks., Results: Our results indicate that degree-normalization systematically improves three fingerprinting metrics, namely differential identifiability, identification rate, and matching rate. Moreover, the results related to the matching rate metric suggest that individual fingerprints are embedded in a low-dimensional space., Discussion: The results suggest that low-dimensional functional fingerprints lie in part in weakly connected sub-networks of the brain and that degree-normalization helps uncovering them. This work introduces a simple mathematical operation that could lead to significant improvements in future FC fingerprinting studies.

Humans and other animals adapt motor commands to predictable disturbances within tens of trials in laboratory conditions. A central question is how does the nervous system adapt to disturbances in natural conditions when exactly the same movements cannot be practiced several times. Because motor commands and sensory feedback together carry continuous information about limb dynamics, we hypothesized that the nervous system could adapt to unexpected disturbances online., Humans and other animals adapt motor commands to predictable disturbances within tens of trials in laboratory conditions. A central question is how does the nervous system adapt to disturbances in natural conditions when exactly the same movements cannot be practiced several times. Because motor commands and sensory feedback together carry continuous information about limb dynamics, we hypothesized that the nervous system could adapt to unexpected disturbances online. We tested this hypothesis in two reaching experiments during which velocity-dependent force fields (FFs) were randomly applied. We found that within-movement feedback corrections gradually improved, despite the fact that the perturbations were unexpected. Moreover, when participants were instructed to stop at a via-point, the application of a FF prior to the via-point induced mirror-image after-effects after the via-point, consistent with within-trial adaptation to the unexpected dynamics. These findings suggest a fast time-scale of motor learning, which complements feedback control and supports adaptation of an ongoing movement.

Motor learning and adaptation are important functions of the nervous system. Classical studies have characterized how humans adapt to changes in the environment during tasks such as reaching, and have documented improvements in behavior across movements. Yet little is known about how quickly the nervous system adapts to such disturbances. In particular, recent work has suggested that adaptation could be sufficiently fast to alter the control strategies of an ongoing movement. To further address the possibility that learning occurred within a single movement, we designed a series of human reaching experiments to extract in muscles recordings the latency of feedback adaptation. Our results confirmed that participants adapted their feedback responses to unanticipated force fields applied randomly. In addition, our analyses revealed that the feedback response was specifically and finely tuned to the ongoing perturbation not only across trials with the same force field, but also across different kinds of force fields. Finally, changes in muscle activity consistent with feedback adaptation occurred in about 250ms following reach onset. We submit this estimate as the latency of motor adaptation in the nervous system.

Modern control theory highlights strategies that consider a range of factors, such as errors caused by environmental disturbances or inaccurate estimates of body or environmental dynamics. Here we reveal similar diversity in how humans naturally adapt and control their arm movements. We divided participants into groups based on how well they adapted to interaction loads during a single session of reaching movements. This classification revealed differences in how participants controlled their movements and responded to mechanical perturbations. Interestingly, variation in behaviour across good and partial adapters resembled simulations from stochastic and robust optimal feedback control, respectively, where the latter minimizes the effect of disturbances, including those introduced by inaccurate internal models of movement dynamics. In a second experiment, we varied the interaction loads over short time periods making it difficult to adapt. Under these conditions, participants who otherwise adapted well altered their behaviour and more closely resembled those using a robust control strategy. Taken together, the results suggest the diversity of how humans control and adapt their arm movements may reflect the accuracy of (or confidence in) their internal models. Our findings may open novel perspectives for interpreting motor behaviour in uncertain environments, or when neurologic dysfunction compromises motor adaptation.

Recent advances in movement neuroscience have consistently highlighted that the nervous system performs sophisticated feedback control over very short time scales (

Humans adapt to mechanical perturbations such as force fields during reaching within tens of trials. However, recent findings suggested that this adaptation may start within one single trial, i.e., online corrective movements can become tuned to the unanticipated perturbations within a trial. This was highlighted in previous works with a reaching experiment in which participants had to stop at a via-point (VP) located between the start and the goal. A force field was applied during the first and second parts of the movement and then occasionally unexpectedly switched off at the VP during catch trials. The results showed an after-effect during the second part of the movement when participants exited the VP. This behavioural result was interpreted as a standard after-effect, but it remained unclear how it was related to conventional trial-by-trial learning. The current study aimed to investigate how long do such changes in movement representations last in memory. For this, we have studied the same reaching task with VP in two situations: one with very short residing time in the VP and the second with an imposed minimum 500ms dwell time in the VP. In both situations, during the unexpected absence of the force field after VP, after-effects were observed. This suggests that online corrections to the internal representation of reach dynamics can be preserved in memory for around 850ms of resting time on average. Therefore, rapid changes occurring within movements can thus be preserved in memory long enough to influence trial-by-trial motor adaptation. SIGNIFICANCE STATEMENT Recent studies suggested that adaptive feedback control happens within a reach movement and the feedback responses are tuned specifically to the single-trial perturbation. Here we show that these feedback mediated changes in movement representations can last for around 850ms and are available to reproduce the characteristics of the newly acquired correction process. Current data replicate previous studies showing that feedback corrections are associated with changes in online representations, and demonstrate that these changes are preserved in memory long enough to be an important component of standard trial-by-trial adaptation.

Motor learning and adaptation are important functions of the nervous system. Classical studies have characterized how humans adapt to changes in the environment during tasks such as reaching, and have documented improvements in behavior across movements. However, little is known about how quickly the nervous system adapts to such disturbances. In particular, recent work has suggested that adaptation could be sufficiently fast to alter the control strategies of an ongoing movement. To further address the possibility that learning occurred within a single movement, we designed a series of human reaching experiments to extract from muscles recordings the latency of feedback adaptation. Our results confirmed that participants adapted their feedback responses to unanticipated force fields applied randomly. In addition, our analyses revealed that the feedback response was specifically and finely tuned to the ongoing perturbation not only across trials with the same force field, but also across different kinds of force fields. Finally, changes in muscle activity consistent with feedback adaptation occurred in ;250ms following reach onset. The adaptation that we observed across trials presented in a random context was similar to the one observed when the force fields could be anticipated, suggesting that these two adaptive processes may be closely linked to each other. In such case, our measurement of 250ms may correspond to the latency of motor adaptation in the nervous system. Key words: adaptive control; feedback control; motor adaptation; reaching Significance Statement We measure the latency of feedback adaptation in a human reaching experiment by applying force field trials randomly. Despite the fact that these disturbances could not be anticipated, we measured improvement in feedback corrections that paralleled standard adaptation. Correlates in muscle recordings occurred within ;250ms following movement onset. Such a short timescale of adaptation suggested that rapid adaptation complements feedback control of an ongoing movement. To further test this hypothesis, we demonstrate that indeed participants are able to adapt their feedback responses to different kinds of force fields and directions applied randomly. These findings support the existence of very rapid, possibly online, adaptation in the nervous system.

Successful performance in many everyday tasks requires compensating unexpected mechanical disturbance to our limbs and body. The long-latency reflex plays an important role in this process because it is the fastest response to integrate sensory information across several effectors, like when linking the elbow and shoulder or the arm and body. Despite the dozens of studies on inter-effector long-latency reflexes, there has not been a comprehensive treatment of how these reveal the basic control organization that sets constraints on any candidate model of neural feedback control such as optimal feedback control. We considered three contrasting ways that controllers can be organized: multiple independent controllers vs. a multiple-input multiple-output (MIMO) controller, a continuous feedback controller vs. an intermittent feedback controller, and a direct MIMO controller vs. a state feedback controller. Following a primer on control theory and review of the relevant evidence, we conclude that continuous state feedback control best describes the organization of inter-effector coordination by the long-latency reflex.

In order to perform accurate movements, the nervous system must transform sensory feedback into motor commands that compensate for errors caused by motor variability and external disturbances. Recent studies focusing on the importance of sensory feedback in motor control have illustrated that the brain generates highly flexible responses to visual perturbations (hand-cursor or target jumps), or following mechanical loads applied to the limb. These parallel approaches have emphasized sophisticated, goal-directed feedback control, but also reveal that flexible perturbation responses are expressed at different latencies depending on what sensory system is engaged by the perturbation. Across studies, goal-directed visuomotor responses consistently emerge in muscle activity ∼100 ms after a perturbation, while mechanical perturbations evoke goal-directed muscle responses in as little as ∼60 ms (long-latency responses). We discuss the limitation of current models of multisensory integration in light of these asynchronous processing delays, and suggest that understanding how the brain performs real-time multisensory integration is an open question for future studies.

Information from the central and peripheral nervous systems is continuously integrated to produce a stable gait pattern. However, stride duration fluctuates in a complex manner in healthy subjects, exhibiting long-range autocorrelations that can span over hundreds of consecutive strides. The present study was conducted to explore the mechanisms controlling the long-term fluctuation dynamics of gait. In the first part of the study, stride duration variability was evaluated on a treadmill during forward (FW) and backward walking (BW). Despite the modification of the biomechanical constraints imposed on the locomotor system, the characteristics of the long-range autocorrelations remained unchanged in both modes of locomotion (FW: H = 0.79 ± 0.04 and α = 0.58 ± 0.13; BW: H = 0.79 ± 0.11 and α = 0.53 ± 0.25). In the second part of the study, stride duration variability was assessed while the subjects were performing a dual-task paradigm that combined gait and mental calculation. The long-term variability of stride duration was similar during usual walking (H = 0.80 ± 0.06 and α = 0.57 ± 0.13) and in dual-tasking (H = 0.77 ± 0.06 and α = 0.52 ± 0.16), whereas walking altered the performance of the cognitive task. Hence, the biomechanical and cognitive interferences imposed in the present study were not sufficient to induce a modification of the long-range autocorrelations highlighted in walking variability. These observations underline the robustness of the long-range autocorrelations.

Long-range dependency has been found in most rhythmic motor signals. The origin of this property is unknown and largely debated. There is a controversy on the influence of voluntary control induced by requiring a pre-determined pace such as asking subjects to step to a metronome. We studied the cycle duration variability of 15 men pedaling on an ergometer at free pace and at an imposed pace (60 rpm). Revolution time was determined based on accelerometer signals (sample frequency 512 Hz). Revolution time variability was assessed by coefficient of variation (CV). The presence of long-range autocorrelations was based on scaling properties of the series variability (Hurst exponent) and the shape of the power spectral density (α exponent). Mean revolution time was significantly lower at freely chosen cadence, while values of CV were similar between both sessions. Long-range autocorrelations were highlighted in all series of cycling patterns. However, Hurst and α exponents were significantly lower at imposed cadence. This study demonstrates the presence of long-range autocorrelations during cycling and that voluntary intent can modulate the interdependency between consecutive cycles. Therefore, cycling may constitute a powerful paradigm to investigate the influence of central control mechanisms on the long-range interdependency characterizing rhythmic motor tasks.

Objective: Gait instability and fall risk are major concerns in Parkinson’s disease. This study shows that the temporal or- ganization of gait variability can represent a marker of gait instability that complements standard assessment of motor de cits in Parkinson’s disease. Methods: Temporal organization (long-range autocorrelation; LRA) of stride duration variability, collected from 20 persons with Parkinson’s disease walking overground at a comfort- able speed, was studied. The presence of LRA was based on the scaling properties of the series variability and the shape of the power spectral density. Simultaneously, measures of neu- rological impairment (MDS-UPDRS), balance (BESTest), and balance con dence (ABC-Scale) were collected. To precisely identify the relationship between LRA and functional meas- ures, correlation coef cients were applied. Results: Degradation of LRA was strongly correlated with other clinical scores, in such a way that the temporal organi- zation of gait variability was more random for patients pre- senting with greater motor impairments. Importantly, these measures were relatively independent of age, and gait speed, thus they can be applied to a wide clinical population. Conclusion: The ndings of this study emphasize that tem- poral organization of gait variability is related to degree of functional impairment in Parkinson’s disease. LRA may thus be regarded as an objective and quantitative measure of gait stability for both clinical practice and research.