Consolidation of human somatosensory memory during motor learning

Abstract Sensorimotor learning is a bidirectional process associated with concurrent neuroplastic changes in the motor and somatosensory system. While motor memory consolidation and retention have been extensively studied during skill acquisition, little is known about the formation and consolidation of somatosensory memory associated with motor learning. Using a robotic exoskeleton, we tracked markers of somatosensory and motor learning while healthy participants trained to make goal-directed wrist reaching movements over five days and evaluated retention for up to 10 days after practice. Markers of somatosensory learning were changes in wrist position sense bias (systematic error) and precision (random error). The main results are as follows: First, somatosensory (proprioceptive) memory consolidation shows signs of cost savings with repeated sensorimotor training – the same feature is known for motor memory formation. Moreover, somatosensory learning generalized to untrained workspace. Second, somatosensory learning over days can be characterized as an early improvement in sensory precision and a later improvement in sensory bias. Third, the time course of learning gains in position sense acuity coincided with improvements in spatial movement accuracy. Finally, the gains of somatosensory learning were retained for several days. Improvements in position sense bias were still visible up to 3 days after the end of practice for the trained workspace positions, but decayed faster in the untrained workspace. Improvements in position sense precision were retained for up to 10 days and were workspace independent. The findings are consistent with the view that an internal model of somatosensory joint space is formed during motor learning

Editorial : Improving Neuroprosthetics Through Novel Techniques for Processing Electrophysiological Human Brain Signals

Non peer reviewe

Neuromechanical Biomarkers for Robotic Neurorehabilitation

: One of the current challenges for translational rehabilitation research is to develop the strategies to deliver accurate evaluation, prediction, patient selection, and decision-making in the clinical practice. In this regard, the robot-assisted interventions have gained popularity as they can provide the objective and quantifiable assessment of the motor performance by taking the kinematics parameters into the account. Neurophysiological parameters have also been proposed for this purpose due to the novel advances in the non-invasive signal processing techniques. In addition, other parameters linked to the motor learning and brain plasticity occurring during the rehabilitation have been explored, looking for a more holistic rehabilitation approach. However, the majority of the research done in this area is still exploratory. These parameters have shown the capability to become the "biomarkers" that are defined as the quantifiable indicators of the physiological/pathological processes and the responses to the therapeutical interventions. In this view, they could be finally used for enhancing the robot-assisted treatments. While the research on the biomarkers has been growing in the last years, there is a current need for a better comprehension and quantification of the neuromechanical processes involved in the rehabilitation. In particular, there is a lack of operationalization of the potential neuromechanical biomarkers into the clinical algorithms. In this scenario, a new framework called the "Rehabilomics" has been proposed to account for the rehabilitation research that exploits the biomarkers in its design. This study provides an overview of the state-of-the-art of the biomarkers related to the robotic neurorehabilitation, focusing on the translational studies, and underlying the need to create the comprehensive approaches that have the potential to take the research on the biomarkers into the clinical practice. We then summarize some promising biomarkers that are being under investigation in the current literature and provide some examples of their current and/or potential applications in the neurorehabilitation. Finally, we outline the main challenges and future directions in the field, briefly discussing their potential evolution and prospective

New Perspectives on the Dialogue between Brains and Machines

Brain-machine interfaces (BMIs) are mostly investigated as a means to provide paralyzed people with new communication channels with the external world. However, the communication between brain and artificial devices also offers a unique opportunity to study the dynamical properties of neural systems. This review focuses on bidirectional interfaces, which operate in two ways by translating neural signals into input commands for the device and the output of the device into neural stimuli. We discuss how bidirectional BMIs help investigating neural information processing and how neural dynamics may participate in the control of external devices. In this respect, a bidirectional BMI can be regarded as a fancy combination of neural recording and stimulation apparatus, connected via an artificial body. The artificial body can be designed in virtually infinite ways in order to observe different aspects of neural dynamics and to approximate desired control policies

Perspectives and Challenges in Robotic Neurorehabilitation

The development of robotic devices for rehabilitation is a fast-growing field. Nowadays, thanks to novel technologies that have improved robots' capabilities and offered more cost-effective solutions, robotic devices are increasingly being employed during clinical practice, with the goal of boosting patients' recovery. Robotic rehabilitation is also widely used in the context of neurological disorders, where it is often provided in a variety of different fashions, depending on the specific function to be restored. Indeed, the effect of robot-aided neurorehabilitation can be maximized when used in combination with a proper training regimen (based on motor control paradigms) or with non-invasive brain machine interfaces. Therapy-induced changes in neural activity and behavioral performance, which may suggest underlying changes in neural plasticity, can be quantified by multimodal assessments of both sensorimotor performance and brain/muscular activity pre/post or during intervention. Here, we provide an overview of the most common robotic devices for upper and lower limb rehabilitation and we describe the aforementioned neurorehabilitation scenarios. We also review assessment techniques for the evaluation of robotic therapy. Additional exploitation of these research areas will highlight the crucial contribution of rehabilitation robotics for promoting recovery and answering questions about reorganization of brain functions in response to disease

Small-World Propensity Reveals the Frequency Specificity of Resting State Networks

Goal: Functional connectivity (FC) is an important indicator of the brain's state in different conditions, such as rest/task or health/pathology. Here we used high-density electroencephalography coupled to source reconstruction to assess frequency-specific changes of FC during resting state. Specifically, we computed the Small-World Propensity (SWP) index to characterize network small-world architecture across frequencies. Methods: We collected resting state data from healthy participants and built connectivity matrices maintaining the heterogeneity of connection strengths. For a subsample of participants, we also investigated whether the SWP captured FC changes after the execution of a working memory (WM) task. Results: We found that SWP demonstrates a selective increase in the alpha and low beta bands. Moreover, SWP was modulated by a cognitive task and showed increased values in the bands entrained by the WM task. Conclusions: SWP is a valid metric to characterize the frequency-specific behavior of resting state networks. ispartof: IEEE Open Journal of Engineering in Medicine and Biology status: accepte

User-centered design and development of TWIN-Acta: A novel control suite of the TWIN lower limb exoskeleton for the rehabilitation of persons post-stroke

Introduction: Difficulties faced while walking are common symptoms after stroke, significantly reducing the quality of life. Walking recovery is therefore one of the main priorities of rehabilitation. Wearable powered exoskeletons have been developed to provide lower limb assistance and enable training for persons with gait impairments by using typical physiological movement patterns. Exoskeletons were originally designed for individuals without any walking capacities, such as subjects with complete spinal cord injuries. Recent systematic reviews suggested that lower limb exoskeletons could be valid tools to restore independent walking in subjects with residual motor function, such as persons post-stroke. To ensure that devices meet end-user needs, it is important to understand and incorporate their perspectives. However, only a limited number of studies have followed such an approach in the post-stroke population. Methods: The aim of the study was to identify the end-users needs and to develop a user-centered-based control system for the TWIN lower limb exoskeleton to provide post-stroke rehabilitation. We thus describe the development and validation, by clinical experts, of TWIN-Acta: a novel control suite for TWIN, specifically designed for persons post-stroke. We detailed the conceived control strategy and developmental phases, and reported evaluation sessions performed on healthy clinical experts and people post-stroke to evaluate TWIN-Acta usability, acceptability, and barriers to usage. At each developmental stage, the clinical experts received a one-day training on the TWIN exoskeleton equipped with the TWIN-Acta control suite. Data on usability, acceptability, and limitations to system usage were collected through questionnaires and semi-structured interviews. Results: The system received overall good usability and acceptability ratings and resulted in a well-conceived and safe approach. All experts gave excellent ratings regarding the possibility of modulating the assistance provided by the exoskeleton during the movement execution and concluded that the TWIN-Acta would be useful in gait rehabilitation for persons post-stroke. The main limit was the low level of system learnability, attributable to the short-time of usage. This issue can be minimized with prolonged training and must be taken into consideration when planning rehabilitation. Discussion: This study showed the potential of the novel control suite TWIN-Acta for gait rehabilitation and efficacy studies are the next step in its evaluation process

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements