Digital Gait Measures Capture 1-Year Progression in Early-Stage Spinocerebellar Ataxia Type 2.

BACKGROUND: With disease-modifying drugs in reach for cerebellar ataxias, fine-grained digital health measures are highly warranted to complement clinical and patient-reported outcome measures in upcoming treatment trials and treatment monitoring. These measures need to demonstrate sensitivity to capture change, in particular in the early stages of the disease. OBJECTIVE: Our aim is to unravel gait measures sensitive to longitudinal change in the-particularly trial-relevant-early stage of spinocerebellar ataxia type 2 (SCA2). METHODS: We performed a multicenter longitudinal study with combined cross-sectional and 1-year interval longitudinal analysis in early-stage SCA2 participants (n = 23, including nine pre-ataxic expansion carriers; median, ATXN2 CAG repeat expansion 38 ± 2; median, Scale for the Assessment and Rating of Ataxia [SARA] score 4.8 ± 4.3). Gait was assessed using three wearable motion sensors during a 2-minute walk, with analyses focused on gait measures of spatio-temporal variability that have shown sensitivity to ataxia severity (eg, lateral step deviation). RESULTS: We found significant changes for gait measures between baseline and 1-year follow-up with large effect sizes (lateral step deviation P = 0.0001, effect size rprb = 0.78), whereas the SARA score showed no change (P = 0.67). Sample size estimation indicates a required cohort size of n = 43 to detect a 50% reduction in natural progression. Test-retest reliability and minimal detectable change analysis confirm the accuracy of detecting 50% of the identified 1-year change. CONCLUSIONS: Gait measures assessed by wearable sensors can capture natural progression in early-stage SCA2 within just 1 year-in contrast to a clinical ataxia outcome. Lateral step deviation represents a promising outcome measure for upcoming multicenter interventional trials, particularly in the early stages of cerebellar ataxia. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Perceptual encoding of emotions in interactive bodily expressions.

For social species, e.g., primates, the perceptual analysis of social interactions is an essential skill for survival, emerging already early during development. While real-life emotional behavior includes predominantly interactions between conspecifics, research on the perception of emotional body expressions has primarily focused on perception of single individuals. While previous studies using point-light or video stimuli of interacting people suggest an influence of social context on the perception and neural encoding of interacting bodies, it remains entirely unknown how emotions of multiple interacting agents are perceptually integrated. We studied this question using computer animation by creating scenes with two interacting avatars whose emotional style was independently controlled. While participants had to report the emotional style of a single agent, we found a systematic influence of the emotion expressed by the other, which was consistent with the social interaction context. The emotional styles of interacting individuals are thus jointly encoded.

Toward a unifying framework for the modeling and identification of motor primitives.

A large body of evidence suggests that human and animal movements, despite their apparent complexity and flexibility, are remarkably structured. Quantitative analyses of various classes of motor behaviors consistently identify spatial and temporal features that are invariant across movements. Such invariant features have been observed at different levels of organization in the motor system, including the electromyographic, kinematic, and kinetic levels, and are thought to reflect fixed modules-named motor primitives-that the brain uses to simplify the construction of movement. However, motor primitives across space, time, and organization levels are often described with ad-hoc mathematical models that tend to be domain-specific. This, in turn, generates the need to use model-specific algorithms for the identification of both the motor primitives and additional model parameters. The lack of a comprehensive framework complicates the comparison and interpretation of the results obtained across different domains and studies. In this work, we take the first steps toward addressing these issues, by introducing a unifying framework for the modeling and identification of qualitatively different classes of motor primitives. Specifically, we show that a single model, the anechoic mixture model, subsumes many popular classes of motor primitive models. Moreover, we exploit the flexibility of the anechoic mixture model to develop a new class of identification algorithms based on the Fourier-based Anechoic Demixing Algorithm (FADA). We validate our framework by identifying eight qualitatively different classes of motor primitives from both simulated and experimental data. We show that, compared to established model-specific algorithms for the identification of motor primitives, our flexible framework reaches overall comparable and sometimes superior reconstruction performance. The identification framework is publicly released as a MATLAB toolbox (FADA-T, https://tinyurl.com/compsens) to facilitate the identification and comparison of different motor primitive models.

Multimodal Sensor-Based Identification of Stress and Compulsive Actions in Children with Obsessive-Compulsive Disorder for Telemedical Treatment.

In modern psychotherapy, digital health technology offers advanced and personalized therapy options, increasing availability as well as ecological validity. These aspects have proven to be highly relevant for children and adolescents with obsessive-compulsive disorder (OCD). Exposure and Response Prevention therapy, which is the state-of-the-art treatment for OCD, builds on the reconstruction of everyday life exposure to anxious situations. However, while compulsive behavior pre-dominantly occurs in home environments, exposure situations during therapy are limited to clinical settings. Telemedical treatment allows to shift from this limited exposure reconstruction to exposure situations in real life. In the SSTeP KiZ study (smart sensor technology in telepsychotherapy for children and adolescents with OCD), we combine video therapy with wearable sensors delivering physiological and behavioral measures to objectively determine the stress level of patients. The setup allows to gain information from exposure to stress in a realistic environment both during and outside of therapy sessions. In a first pilot study, we explored the sensitivity of individual sensor modalities to different levels of stress and anxiety. For this, we captured the obsessive-compulsive behavior of five adolescents with an ECG chest belt, inertial sensors capturing hand movements, and an eye tracker. Despite their prototypical nature, our results deliver strong evidence that the examined sensor modalities yield biomarkers allowing for personalized detection and quantification of stress and anxiety. This opens up future possibilities to evaluate the severity of individual compulsive behavior based on multi-variate state classification in real-life situations. Clinical Relevance- Our results demonstrate the potential for efficient personalized psychotherapy by monitoring physiological and behavioral changes with multiple sensor modalities in ecologically valid real-life scenarios.

Shape-invariant encoding of dynamic primate facial expressions in human perception.

Dynamic facial expressions are crucial for communication in primates. Due to the difficulty to control shape and dynamics of facial expressions across species, it is unknown how species-specific facial expressions are perceptually encoded and interact with the representation of facial shape. While popular neural network models predict a joint encoding of facial shape and dynamics, the neuromuscular control of faces evolved more slowly than facial shape, suggesting a separate encoding. To investigate these alternative hypotheses, we developed photo-realistic human and monkey heads that were animated with motion capture data from monkeys and humans. Exact control of expression dynamics was accomplished by a Bayesian machine-learning technique. Consistent with our hypothesis, we found that human observers learned cross-species expressions very quickly, where face dynamics was represented largely independently of facial shape. This result supports the co-evolution of the visual processing and motor control of facial expressions, while it challenges appearance-based neural network theories of dynamic expression recognition.

A Dynamical Generative Model of Social Interactions.

The ability to make accurate social inferences makes humans able to navigate and act in their social environment effortlessly. Converging evidence shows that motion is one of the most informative cues in shaping the perception of social interactions. However, the scarcity of parameterized generative models for the generation of highly-controlled stimuli has slowed down both the identification of the most critical motion features and the understanding of the computational mechanisms underlying their extraction and processing from rich visual inputs. In this work, we introduce a novel generative model for the automatic generation of an arbitrarily large number of videos of socially interacting agents for comprehensive studies of social perception. The proposed framework, validated with three psychophysical experiments, allows generating as many as 15 distinct interaction classes. The model builds on classical dynamical system models of biological navigation and is able to generate visual stimuli that are parametrically controlled and representative of a heterogeneous set of social interaction classes. The proposed method represents thus an important tool for experiments aimed at unveiling the computational mechanisms mediating the perception of social interactions. The ability to generate highly-controlled stimuli makes the model valuable not only to conduct behavioral and neuroimaging studies, but also to develop and validate neural models of social inference, and machine vision systems for the automatic recognition of social interactions. In fact, contrasting human and model responses to a heterogeneous set of highly-controlled stimuli can help to identify critical computational steps in the processing of social interaction stimuli.

Detecting and Quantifying Ataxia-Related Motor Impairments in Rodents Using Markerless Motion Tracking With Deep Neural Networks.

In this study we evaluate the application of video-based markerless motion tracking based on deep neural networks for the analysis of ataxia-specific movement abnormalities in rodent models of cerebellar ataxia. Based on a small amount (<100) of manually labeled video frames, markerless motion tracking enabled the extraction of movement trajectories and parameters characterizing ataxia-specific movement abnormalities. In the first experiment, we analyzed videos of 6 shaker and 4 wildtype rats and were able to reproduce thê5 Hz tremor frequency in the shaker rat without the usage of a force plate. In the second experiment, we investigated a spinocerebellar ataxia type 3 (SCA3) mouse model (6 mice aged 3 months and 3 mice aged 9 months) in a beam-balancing task. By establishing a parameter for the assessment of rhythmicity of gait (RoG), we not only found a significantly higher RoG in wildtype mice compared to affected SCA3 mice aged 9 months, but were also able to reveal a significantly lower than typical RoG in SCA3 mice aged 3 months which exhibit no abnormalities in visual inspection. These prototypical results suggest the capability of the presented methods for the application in upcoming therapeutic intervention trials to identify subtle changes in movement behavior.

Representation of the observer's predicted outcome value in mirror and nonmirror neurons of macaque F5 ventral premotor cortex.

In the search for the function of mirror neurons, a previous study reported that F5 mirror neuron responses are modulated by the value that the observing monkey associates with the grasped object. Yet we do not know whether mirror neurons are modulated by the expected reward value for the observer or also by other variables, which are causally dependent on value (e.g., motivation, attention directed at the observed action, arousal). To clarify this, we trained two rhesus macaques to observe a grasping action on an object kept constant, followed by four fully predictable outcomes of different values (2 outcomes with positive and 2 with negative emotional valence). We found a consistent order in population activity of both mirror and nonmirror neurons that matches the order of the value of this predicted outcome but that does not match the order of the above-mentioned value-dependent variables. These variables were inferred from the probability not to abort a trial, saccade latency, modulation of eye position during action observation, heart rate, and pupil size. Moreover, we found subpopulations of neurons tuned to each of the four predicted outcome values. Multidimensional scaling revealed equal normalized distances of 0.25 between the two positive and between the two negative outcomes suggesting the representation of a relative value, scaled to the task setting. We conclude that F5 mirror neurons and nonmirror neurons represent the observer's predicted outcome value, which in the case of mirror neurons may be transferred to the observed object or action.NEW & NOTEWORTHY Both the populations of F5 mirror neurons and nonmirror neurons represent the predicted value of an outcome resulting from the observation of a grasping action. Value-dependent motivation, arousal, and attention directed at the observed action do not provide a better explanation for this representation. The population activity's metric suggests an optimal scaling of value representation to task setting.

A Naturalistic Dynamic Monkey Head Avatar Elicits Species-Typical Reactions and Overcomes the Uncanny Valley.

Research on social perception in monkeys may benefit from standardized, controllable, and ethologically valid renditions of conspecifics offered by monkey avatars. However, previous work has cautioned that monkeys, like humans, show an adverse reaction toward realistic synthetic stimuli, known as the "uncanny valley" effect. We developed an improved naturalistic rhesus monkey face avatar capable of producing facial expressions (fear grin, lip smack and threat), animated by motion capture data of real monkeys. For validation, we additionally created decreasingly naturalistic avatar variants. Eight rhesus macaques were tested on the various videos and avoided looking at less naturalistic avatar variants, but not at the most naturalistic or the most unnaturalistic avatar, indicating an uncanny valley effect for the less naturalistic avatar versions. The avoidance was deepened by motion and accompanied by physiological arousal. Only the most naturalistic avatar evoked facial expressions comparable to those toward the real monkey videos. Hence, our findings demonstrate that the uncanny valley reaction in monkeys can be overcome by a highly naturalistic avatar.

Adaptation aftereffects reveal representations for encoding of contingent social actions.

A hallmark of human social behavior is the effortless ability to relate one's own actions to that of the interaction partner, e.g., when stretching out one's arms to catch a tripping child. What are the behavioral properties of the neural substrates that support this indispensable human skill? Here we examined the processes underlying the ability to relate actions to each other, namely the recognition of spatiotemporal contingencies between actions (e.g., a "giving" that is followed by a "taking"). We used a behavioral adaptation paradigm to examine the response properties of perceptual mechanisms at a behavioral level. In contrast to the common view that action-sensitive units are primarily selective for one action (i.e., primary action, e.g., 'throwing"), we demonstrate that these processes also exhibit sensitivity to a matching contingent action (e.g., "catching"). Control experiments demonstrate that the sensitivity of action recognition processes to contingent actions cannot be explained by lower-level visual features or amodal semantic adaptation. Moreover, we show that action recognition processes are sensitive only to contingent actions, but not to noncontingent actions, demonstrating their selective sensitivity to contingent actions. Our findings show the selective coding mechanism for action contingencies by action-sensitive processes and demonstrate how the representations of individual actions in social interactions can be linked in a unified representation.

Two Ways to Facial Expression Recognition? Motor and Visual Information Have Different Effects on Facial Expression Recognition.

Motor-based theories of facial expression recognition propose that the visual perception of facial expression is aided by sensorimotor processes that are also used for the production of the same expression. Accordingly, sensorimotor and visual processes should provide congruent emotional information about a facial expression. Here, we report evidence that challenges this view. Specifically, the repeated execution of facial expressions has the opposite effect on the recognition of a subsequent facial expression than the repeated viewing of facial expressions. Moreover, the findings of the motor condition, but not of the visual condition, were correlated with a nonsensory condition in which participants imagined an emotional situation. These results can be well accounted for by the idea that facial expression recognition is not always mediated by motor processes but can also be recognized on visual information alone.

Author Correction: Low-dimensional organization of angular momentum during walking on a narrow beam.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Lighting-from-above prior in biological motion perception.

The visual system is able to recognize body motion from impoverished stimuli. This requires combining stimulus information with visual priors. We present a new visual illusion showing that one of these priors is the assumption that bodies are typically illuminated from above. A change of illumination direction from above to below flips the perceived locomotion direction of a biological motion stimulus. Control experiments show that the underlying mechanism is different from shape-from-shading and directly combines information about body motion with a lighting-from-above prior. We further show that the illusion is critically dependent on the intrinsic luminance gradients of the most mobile parts of the moving body. We present a neural model with physiologically plausible mechanisms that accounts for the illusion and shows how the illumination prior might be encoded within the visual pathway. Our experiments demonstrate, for the first time, a direct influence of illumination priors in high-level motion vision.

Low-dimensional organization of angular momentum during walking on a narrow beam.

Walking on a beam is a challenging motor skill that requires the regulation of upright balance and stability. The difficulty in beam walking results from the reduced base of support compared to that afforded by flat ground. One strategy to maintain stability and hence avoid falling off the beam is to rotate the limb segments to control the body's angular momentum. The aim of this study was to examine the coordination of the angular momentum variations during beam walking. We recorded movement kinematics of participants walking on a narrow beam and computed the angular momentum contributions of the body segments with respect to three different axes. Results showed that, despite considerable variability in the movement kinematics, the angular momentum was characterized by a low-dimensional organization based on a small number of segmental coordination patterns. When the angular momentum was computed with respect to the beam axis, the largest fraction of its variation was accounted for by the trunk segment. This simple organization was robust and invariant across all participants. These findings support the hypothesis that control strategies for complex balancing tasks might be easier to understand by investigating angular momentum instead of the segmental kinematics.

Apraxia of object-related action does not depend on visual feedback.

Pantomime of tool use is typically affected in neurological patients with apraxia, while at the same time these patients are able to perform the use of the actual tool with less or no errors. This discrepancy is commonly explained by differences in afferent input, in particular a lack of visual online feedback from the object in pantomime. The present study investigated the role of visual feedback in apraxia of pantomime by testing neurological patients with apraxia and healthy controls in a task requiring the pantomime of tool use as well as real tool use. Visual feedback was systematically removed at different phases of the action using shutter glasses that were controlled online based on real-time motion-capturing. Data analyses revealed more errors in pantomime than in real tool use. These differences were similar in patients as well as in controls. Removal of visual feedback did not affect apractic errors specifically; it neither increased patients' apractic errors during pantomime of tool use nor transformed the patients' normal movements with a real tool into movements with apractic errors. Our findings contradict the hypothesis that apraxia patients pathologically over-rely on visual feedback. Instead, we propose that pantomime of tool use requires cognitive processes that are not necessary for real tool use and independent of visual online feedback.

Motor expertise facilitates the accuracy of state extrapolation in perception.

Predicting the behavior of objects in the environment is an important requirement to overcome latencies in the sensorimotor system and realize precise actions in rapid situations. Internal forward models that were acquired during motor training might not only be used for efficiently controlling fast motor behavior but also to facilitate extrapolation performance in purely perceptual tasks. In this study, we investigated whether preceding virtual cart-pole balancing training facilitates the ability to extrapolate the virtual pole motion. Specifically, subjects had to report the expected pole orientation after an occlusion of the pole of 900ms duration. We compared a group of 10 subjects, proficient in performing the virtual cart-pole balancing task, to 10 naïve subjects without motor experience in cart-pole balancing task. Our results demonstrate that preceding motor training increases the accuracy of pole movement extrapolation, although extrapolation is not trained explicitly. Additionally, we modelled subjects' behaviors and show that the difference in extrapolation performance can be explained by individual differences in the accuracy of internal forward models. When subjects are provided with feedback about the true orientation of the pole after the occlusion in a second phase of the experiment, both groups improve rapidly. The results indicate that the perceptual capability to extrapolate the state of the cart-pole system accurately is implicitly trained during motor learning. We discuss these results in the context of shared representations and action-perception transfer.

Lifting the veil on the dynamics of neuronal activities evoked by transcranial magnetic stimulation.

Transcranial magnetic stimulation (TMS) is a widely used non-invasive tool to study and modulate human brain functions. However, TMS-evoked activity of individual neurons has remained largely inaccessible due to the large TMS-induced electromagnetic fields. Here, we present a general method providing direct in vivo electrophysiological access to TMS-evoked neuronal activity 0.8-1 ms after TMS onset. We translated human single-pulse TMS to rodents and unveiled time-grained evoked activities of motor cortex layer V neurons that show high-frequency spiking within the first 6 ms depending on TMS-induced current orientation and a multiphasic spike-rhythm alternating between excitation and inhibition in the 6-300 ms epoch, all of which can be linked to various human TMS responses recorded at the level of spinal cord and muscles. The advance here facilitates a new level of insight into the TMS-brain interaction that is vital for developing this non-invasive tool to purposefully explore and effectively treat the human brain.

Interacting Learning Processes during Skill Acquisition: Learning to control with gradually changing system dynamics.

There is increasing evidence that sensorimotor learning under real-life conditions relies on a composition of several learning processes. Nevertheless, most studies examine learning behaviour in relation to one specific learning mechanism. In this study, we examined the interaction between reward-based skill acquisition and motor adaptation to changes of object dynamics. Thirty healthy subjects, split into two groups, acquired the skill of balancing a pole on a cart in virtual reality. In one group, we gradually increased the gravity, making the task easier in the beginning and more difficult towards the end. In the second group, subjects had to acquire the skill on the maximum, most difficult gravity level. We hypothesized that the gradual increase in gravity during skill acquisition supports learning despite the necessary adjustments to changes in cart-pole dynamics. We found that the gradual group benefits from the slow increment, although overall improvement was interrupted by the changes in gravity and resulting system dynamics, which caused short-term degradations in performance and timing of actions. In conclusion, our results deliver evidence for an interaction of reward-based skill acquisition and motor adaptation processes, which indicates the importance of both processes for the development of optimized skill acquisition schedules.

Individualized exergame training improves postural control in advanced degenerative spinocerebellar ataxia: A rater-blinded, intra-individually controlled trial.

BACKGROUND: Treatment options are rare in degenerative ataxias, especially in advanced, multisystemic disease. Exergame training might offer a novel treatment strategy, but its effectiveness has not been investigated in advanced stages. METHODS: We examined the effectiveness of a 12-week home-based training with body-controlled videogames in 10 young subjects with advanced degenerative ataxia unable or barely able to stand. Training was structured in two 6-weeks phases, allowing to adapt the training according to individual training progress. Rater-blinded clinical assessment (Scale for the Assessment and Rating of Ataxia; SARA), individual goal-attainment scoring (GAS), and quantitative movement analysis were performed two weeks before training, immediately prior to training, and after training phases 1 and 2 (intra-individual control design). This study is registered with ClinicalTrials.gov, NCT02874911). RESULTS: After intervention, ataxia symptoms were reduced (SARA -2.5 points, p < 0.01), with benefits correlating to the amount of training (p = 0.04). Goal attainment during daily living was higher than expected (GAS: 0.45). Movement analysis revealed reduced body sway while sitting (p < 0.01), which correlated with improvements in SARA posture and gait (p = 0.005), indicating training-induced improvements in posture control mechanisms. CONCLUSION: This study provides first evidence that, even in advanced stages, subjects with degenerative ataxia may benefit from individualized training, with effects translating into daily living and improving underlying control mechanisms. The proposed training strategy can be performed at home, is motivating and facilitates patient self-empowerment.