A Model with Dopamine Depletion in Basal Ganglia and Cerebellum Predicts Changes in Thalamocortical Beta Oscillations.

Parkinsonism is presented as a motor syndrome characterized by rigidity, tremors, and bradykinesia, with Parkinson's disease (PD) being the predominant cause. The discovery that those motor symptoms result from the death of dopaminergic cells in the substantia nigra led to focus most of parkinsonism research on the basal ganglia (BG). However, recent findings point to an active involvement of the cerebellum in this motor syndrome. Here, we have developed a multiscale computational model of the rodent brain's BG-cerebellar network. Simulations showed that a direct effect of dopamine depletion on the cerebellum must be taken into account to reproduce the alterations of neural activity in parkinsonism, particularly the increased beta oscillations widely reported in PD patients. Moreover, dopamine depletion indirectly impacted spike-time-dependent plasticity at the parallel fiber-Purkinje cell synapses, degrading associative motor learning as observed in parkinsonism. Overall, these results suggest a relevant involvement of cerebellum in parkinsonism associative motor symptoms.

Applying the IEEE BRAIN neuroethics framework to intra-cortical brain-computer interfaces.

Objective. Brain-computer interfaces (BCIs) are neuroprosthetic devices that allow for direct interaction between brains and machines. These types of neurotechnologies have recently experienced a strong drive in research and development, given, in part, that they promise to restore motor and communication abilities in individuals experiencing severe paralysis. While a rich literature analyzes the ethical, legal, and sociocultural implications (ELSCI) of these novel neurotechnologies, engineers, clinicians and BCI practitioners often do not have enough exposure to these topics.Approach. Here, we present the IEEE Neuroethics Framework, an international, multiyear, iterative initiative aimed at developing a robust, accessible set of considerations for diverse stakeholders.Main results. Using the framework, we provide practical examples of ELSCI considerations for BCI neurotechnologies. We focus on invasive technologies, and in particular, devices that are implanted intra-cortically for medical research applications.Significance. We demonstrate the utility of our framework in exposing a wide range of implications across different intra-cortical BCI technology modalities and conclude with recommendations on how to utilize this knowledge in the development and application of ethical guidelines for BCI neurotechnologies.

Model simulations unveil the structure-function-dynamics relationship of the cerebellar cortical microcircuit.

The cerebellar network is renowned for its regular architecture that has inspired foundational computational theories. However, the relationship between circuit structure, function and dynamics remains elusive. To tackle the issue, we developed an advanced computational modeling framework that allows us to reconstruct and simulate the structure and function of the mouse cerebellar cortex using morphologically realistic multi-compartmental neuron models. The cerebellar connectome is generated through appropriate connection rules, unifying a collection of scattered experimental data into a coherent construct and providing a new model-based ground-truth about circuit organization. Naturalistic background and sensory-burst stimulation are used for functional validation against recordings in vivo, monitoring the impact of cellular mechanisms on signal propagation, inhibitory control, and long-term synaptic plasticity. Our simulations show how mossy fibers entrain the local neuronal microcircuit, boosting the formation of columns of activity travelling from the granular to the molecular layer providing a new resource for the investigation of local microcircuit computation and of the neural correlates of behavior.

Dual STDP processes at Purkinje cells contribute to distinct improvements in accuracy and speed of saccadic eye movements.

Saccadic eye-movements play a crucial role in visuo-motor control by allowing rapid foveation onto new targets. However, the neural processes governing saccades adaptation are not fully understood. Saccades, due to the short-time of execution (20-100 ms) and the absence of sensory information for online feedback control, must be controlled in a ballistic manner. Incomplete measurements of the movement trajectory, such as the visual endpoint error, are supposedly used to form internal predictions about the movement kinematics resulting in predictive control. In order to characterize the synaptic and neural circuit mechanisms underlying predictive saccadic control, we have reconstructed the saccadic system in a digital controller embedding a spiking neural network of the cerebellum with spike timing-dependent plasticity (STDP) rules driving parallel fiber-Purkinje cell long-term potentiation and depression (LTP and LTD). This model implements a control policy based on a dual plasticity mechanism, resulting in the identification of the roles of LTP and LTD in regulating the overall quality of saccade kinematics: it turns out that LTD increases the accuracy by decreasing visual error and LTP increases the peak speed. The control policy also required cerebellar PCs to be divided into two subpopulations, characterized by burst or pause responses. To our knowledge, this is the first model that explains in mechanistic terms the visual error and peak speed regulation of ballistic eye movements in forward mode exploiting spike-timing to regulate firing in different populations of the neuronal network. This elementary model of saccades could be extended and applied to other more complex cases in which single jerks are concatenated to compose articulated and coordinated movements.

Causal and Associational Language in Observational Health Research: A Systematic Evaluation.

We estimated the degree to which language used in the high-profile medical/public health/epidemiology literature implied causality using language linking exposures to outcomes and action recommendations; examined disconnects between language and recommendations; identified the most common linking phrases; and estimated how strongly linking phrases imply causality. We searched for and screened 1,170 articles from 18 high-profile journals (65 per journal) published from 2010-2019. Based on written framing and systematic guidance, 3 reviewers rated the degree of causality implied in abstracts and full text for exposure/outcome linking language and action recommendations. Reviewers rated the causal implication of exposure/outcome linking language as none (no causal implication) in 13.8%, weak in 34.2%, moderate in 33.2%, and strong in 18.7% of abstracts. The implied causality of action recommendations was higher than the implied causality of linking sentences for 44.5% or commensurate for 40.3% of articles. The most common linking word in abstracts was "associate" (45.7%). Reviewers' ratings of linking word roots were highly heterogeneous; over half of reviewers rated "association" as having at least some causal implication. This research undercuts the assumption that avoiding "causal" words leads to clarity of interpretation in medical research.

Recommendations for empowering early career researchers to improve research culture and practice.

Early career researchers (ECRs) are important stakeholders leading efforts to catalyze systemic change in research culture and practice. Here, we summarize the outputs from a virtual unconventional conference (unconference), which brought together 54 invited experts from 20 countries with extensive experience in ECR initiatives designed to improve the culture and practice of science. Together, we drafted 2 sets of recommendations for (1) ECRs directly involved in initiatives or activities to change research culture and practice; and (2) stakeholders who wish to support ECRs in these efforts. Importantly, these points apply to ECRs working to promote change on a systemic level, not only those improving aspects of their own work. In both sets of recommendations, we underline the importance of incentivizing and providing time and resources for systems-level science improvement activities, including ECRs in organizational decision-making processes, and working to dismantle structural barriers to participation for marginalized groups. We further highlight obstacles that ECRs face when working to promote reform, as well as proposed solutions and examples of current best practices. The abstract and recommendations for stakeholders are available in Dutch, German, Greek (abstract only), Italian, Japanese, Polish, Portuguese, Spanish, and Serbian.

Brain-Inspired Spiking Neural Network Controller for a Neurorobotic Whisker System.

It is common for animals to use self-generated movements to actively sense the surrounding environment. For instance, rodents rhythmically move their whiskers to explore the space close to their body. The mouse whisker system has become a standard model for studying active sensing and sensorimotor integration through feedback loops. In this work, we developed a bioinspired spiking neural network model of the sensorimotor peripheral whisker system, modeling trigeminal ganglion, trigeminal nuclei, facial nuclei, and central pattern generator neuronal populations. This network was embedded in a virtual mouse robot, exploiting the Human Brain Project's Neurorobotics Platform, a simulation platform offering a virtual environment to develop and test robots driven by brain-inspired controllers. Eventually, the peripheral whisker system was adequately connected to an adaptive cerebellar network controller. The whole system was able to drive active whisking with learning capability, matching neural correlates of behavior experimentally recorded in mice.

Leveraging Deep Learning Techniques to Improve P300-Based Brain Computer Interfaces.

Brain-Computer Interface (BCI) has become an established technology to interconnect a human brain and an external device. One of the most popular protocols for BCI is based on the extraction of the so-called P300 wave from electroencephalography (EEG) recordings. P300 wave is an event-related potential with a latency of 300 ms after the onset of a rare stimulus. In this paper, we used deep learning architectures, namely convolutional neural networks (CNNs), to improve P300-based BCIs. We propose a novel BCI classifier, called P3CNET, that improved P300 classification accuracy performances of the best state-of-the-art classifier. In addition, we explored pre-processing and training choices that improved the usability of BCI systems. For the pre-processing of EEG data, we explored the optimal signal interval that would improve classification accuracies. Then, we explored the minimum number of calibration sessions to balance higher accuracy and shorter calibration time. To improve the explainability of deep learning architectures, we analyzed the saliency maps of the input EEG signal leading to a correct P300 classification, and we observed that the elimination of less informative electrode channels from the data did not result in better accuracy. All the methodologies and explorations were performed and validated on two different CNN classifiers, demonstrating the generalizability of the obtained results. Finally, we showed the advantages given by transfer learning when using the proposed novel architecture on other P300 datasets. The presented architectures and practical suggestions can be used by BCI practitioners to improve its effectiveness.

Removal of Partially Deployed Supera Stents: Case-Based Review and Technical Considerations.

PURPOSE: Maldeployment of the Supera stent system can result in primary technical failure, inferior primary patency, and poorer patient outcomes. The purpose of this article is to present a case series illustrating the conditions required to perform optimal stent deployment, and if necessary, undertake successful stent removal following maldeployment. TECHNIQUE: Two key failures of effective Supera deployment are elongation and invagination. Several technical factors should be considered to reduce the risk of maldeployment: aggressive target vessel predilation, the use of multiple fluoroscopic views, slow deployment with controlled forward pressure applied on the delivery device, "sandwich packing" of the stent above and below target lesions, and the "pulling back" of invagination. To successfully retrieve a partially deployed stent, 3 factors should be considered: the percentage of the stent already deployed, the distance from the distal tip of the introducing sheath to the proximal extent of the deployed stent, and the severity of proximal vessel disease. The higher these factors, the greater the risk of stent detachment and failed retrieval. CONCLUSION: In this series of 6 cases of maldeployment, the removal of a partially deployed Supera stent appeared to be feasible and safe, with success dependent on selected technical and anatomical considerations.

Creating clear and informative image-based figures for scientific publications.

Scientists routinely use images to display data. Readers often examine figures first; therefore, it is important that figures are accessible to a broad audience. Many resources discuss fraudulent image manipulation and technical specifications for image acquisition; however, data on the legibility and interpretability of images are scarce. We systematically examined these factors in non-blot images published in the top 15 journals in 3 fields; plant sciences, cell biology, and physiology (n = 580 papers). Common problems included missing scale bars, misplaced or poorly marked insets, images or labels that were not accessible to colorblind readers, and insufficient explanations of colors, labels, annotations, or the species and tissue or object depicted in the image. Papers that met all good practice criteria examined for all image-based figures were uncommon (physiology 16%, cell biology 12%, plant sciences 2%). We present detailed descriptions and visual examples to help scientists avoid common pitfalls when publishing images. Our recommendations address image magnification, scale information, insets, annotation, and color and may encourage discussion about quality standards for bioimage publishing.

Towards a Functional Performance Validation Standard for Industrial Low-Back Exoskeletons: State of the Art Review.

While the research interest for exoskeletons has been rising in the last decades, missing standards for their rigorous evaluation are potentially limiting their adoption in the industrial field. In this context, exoskeletons for worker support have the aim to reduce the physical effort required by humans, with dramatic social and economic impact. Indeed, exoskeletons can reduce the occurrence and the entity of work-related musculoskeletal disorders that often cause absence from work, resulting in an eventual productivity loss. This very urgent and multifaceted issue is starting to be acknowledged by researchers. This article provides a systematic review of the state of the art for functional performance evaluation of low-back exoskeletons for industrial workers. We report the state-of-the-art evaluation criteria and metrics used for such a purpose, highlighting the lack of a standard for this practice. Very few studies carried out a rigorous evaluation of the assistance provided by the device. To address also this topic, the article ends with a proposed framework for the functional validation of low-back exoskeletons for the industry, with the aim to pave the way for the definition of rigorous industrial standards.

User-centred assistive SystEm for arm Functions in neUromuscuLar subjects (USEFUL): a randomized controlled study.

BACKGROUND: Upper limb assistive devices can compensate for muscular weakness and empower the user in the execution of daily activities. Multiple devices have been recently proposed but there is still a lack in the scientific comparison of their efficacy. METHODS: We conducted a cross-over multi-centric randomized controlled trial to assess the functional improvement at the upper limb level of two arms supports on 36 patients with muscular dystrophy. Participants tested a passive device (i.e., Wrex by Jaeco) and a semi-active solution for gravity compensation (i.e., Armon Ayura). We evaluated devices' effectiveness with an externally-assessed scale (i.e., Performance of the Upper Limb-PUL-module), a self-perceived scale (i.e., Abilhand questionnaire), and a usability scale (i.e., System Usability Scale). Friedman's test was used to assess significant functional gain for PUL module and Abilhand questionnaire. Moreover, PUL changes were compared by means of the Friedman's test. RESULTS: Most of the patients improved upper limb function with the use of arm supports (median PUL scores increase of 1-3 points). However, the effectiveness of each device was related to the level of residual ability of the end-user. Slightly impaired patients maintained the same independence without and with assistive devices, even if they reported reduced muscular fatigue for both devices. Moderately impaired patients enhanced their arm functionality with both devices, and they obtained higher improvements with the semi-active one (median PUL scores increase of 9 points). Finally, severely impaired subjects benefited only from the semi-active device (median PUL scores increase of 12 points). Inadequate strength was recognized as a barrier to passive devices. The usability, measured by the System Usability Scale, was evaluated by end-users "good" (70/100 points) for the passive, and "excellent" (80/100 points) for the semi-active device. CONCLUSIONS: This study demonstrated that assistive devices can improve the quality of life of people suffering from muscular dystrophy. The use of passive devices, despite being low cost and easy to use, shows limitations in the efficacy of the assistance to daily tasks, limiting the assistance to a predefined horizontal plane. The addition of one active degree of freedom improves efficacy and usability especially for medium to severe patients. Further investigations are needed to increase the evidence on the effect of arm supports on quality of life and diseases' progression in subjects with degenerative disorders. Trial registration clinicaltrials.gov, NCT03127241, Registered 25th April 2017. The clinical trial was also registered as a post-market study at the Italian Ministry of Health.

A Functional Model for Unifying Brain Computer Interface Terminology.

Brain Computer Interface (BCI) technology is a critical area both for researchers and clinical practitioners. The IEEE P2731 working group is developing a comprehensive BCI lexicography and a functional model of BCI. The glossary and the functional model are inextricably intertwined. The functional model guides the development of the glossary. Terminology is developed from the basis of a BCI functional model. This paper provides the current status of the P2731 working group's progress towards developing a BCI terminology standard and functional model for the IEEE.

Corrigendum to "Control of a Humanoid NAO Robot by an Adaptive Bioinspired Cerebellar Module in 3D Motion Tasks".

[This corrects the article DOI: 10.1155/2019/4862157.].

Test-retest reliability of the Performance of Upper Limb (PUL) module for muscular dystrophy patients.

The Performance of the Upper Limb (PUL) module is an externally-assessed clinical scale, initially designed for the Duchenne muscular dystrophy population. It provides an upper extremity functional score suitable for both weaker ambulatory and non-ambulatory phases up to the severely impaired patients. It is capable of characterizing overall progression and severity of disease and of tracking the stereotypical proximal-to-distal progressive loss of upper limb function in muscular dystrophy. Since the PUL module has been validated only with Duchenne patients, its use also for Becker and Limb-Girdle muscular dystrophy patients has been here evaluated, to verify its reliability and extend its use. In particular, two different assessors performed this scale on 32 dystrophic subjects in two consecutive days. The results showed that the PUL module has high reliability, both absolute and relative, based on the calculation of Pearson's r (0.9942), Intraclass Correlation Coefficient (0.9943), Standard Error of Measurement (1.36), Minimum Detectable Change (3.77), and Coefficient of Variation (3%). The Minimum Detectable Change, in particular, can be used in clinical trials to perform a comprehensive longitudinal evaluation of the effects of interventions with the lapse of time. According to this analysis, an intervention is effective if the difference in the PUL score between subsequent evaluation points is equal or higher than 4 points; otherwise, the observed effect is not relevant. Inter-rater reliability with ten different assessors was evaluated, and it has been demonstrated that deviation from the mean is lower than calculated Minimum Detectable Change. The present work provides evidence that the PUL module is a reliable and valid instrument for measuring upper limb ability in people with different forms of muscular dystrophy. Therefore, the PUL module might be extended to other pathologies and reliably used in multicenter settings.

Control of a Humanoid NAO Robot by an Adaptive Bioinspired Cerebellar Module in 3D Motion Tasks.

A bioinspired adaptive model, developed by means of a spiking neural network made of thousands of artificial neurons, has been leveraged to control a humanoid NAO robot in real time. The learning properties of the system have been challenged in a classic cerebellum-driven paradigm, a perturbed upper limb reaching protocol. The neurophysiological principles used to develop the model succeeded in driving an adaptive motor control protocol with baseline, acquisition, and extinction phases. The spiking neural network model showed learning behaviours similar to the ones experimentally measured with human subjects in the same task in the acquisition phase, while resorted to other strategies in the extinction phase. The model processed in real-time external inputs, encoded as spikes, and the generated spiking activity of its output neurons was decoded, in order to provide the proper correction on the motor actuators. Three bidirectional long-term plasticity rules have been embedded for different connections and with different time scales. The plasticities shaped the firing activity of the output layer neurons of the network. In the perturbed upper limb reaching protocol, the neurorobot successfully learned how to compensate for the external perturbation generating an appropriate correction. Therefore, the spiking cerebellar model was able to reproduce in the robotic platform how biological systems deal with external sources of error, in both ideal and real (noisy) environments.

Implementation of an Advanced Frequency-Based Hebbian Spike Timing Dependent Plasticity.

The brain is provided with an enormous computing capability and exploits neural plasticity to store and elaborate complex information. One of the multiple mechanisms that neural circuits express is the Spike-timing-dependent plasticity (STDP), a form of long-term synaptic plasticity exploiting the time relationship between pre- and post-synaptic action potentials (i.e., neuron spikes). It has been found that in certain cases, for instance at the input stage of the cerebellum, between mossy fibers and granular neurons, the plasticity is not only driven by the timing of the spikes, but also by the oscillation frequency of the inputs. This complex behaviour has been implemented in this work, where we developed a novel form of advanced synaptic plasticity model to be used in a well-established neural network simulator (NEST). The subsequent tests proved the proper functioning of the plasticity and its range of applicability, demonstrating the possibility to adopt it in noisy and variable conditions, similar to the biological settings.

Synergy-Based Myocontrol of a Multiple Degree-of-Freedom Humanoid Robot for Functional Tasks.

In the context of sensor-based human-robot interaction, a particularly promising solution is represented by myoelectric control schemes based on synergy-derived signals. We developed and tested on healthy subjects a synergy-based control to achieve simultaneous, continuous actuation of three degrees of freedom of a humanoid robot, while performing functional reach-to-grasp movements. The control scheme exploits subject-specific muscle synergies extracted from eleven upper limb muscles through an easy semi-supervised calibration phase, and computes online activation coefficients to actuate the robot joints. The humanoid robot was able to well reproduce the subjects' motion, which consisted in free multi-degree-of-freedom reach-to-grasp movements at self-paced speeds. Furthermore, the synergy-based online control significantly outperformed a traditional muscle-pair approach (that uses a pair of antagonist muscles for each joint), in terms of decreased error, increased correlation, and peak correlation between the subjects' and the robot's joint angles. The delay introduced by the two algorithms was comparable. This work is a proof-of-concept for an intuitive and robust myocontrol interface, without the need for any training and practice. It has several potential applications, especially for functional assistive engaging devices in children with social and motor impairments.

The Effectiveness of Wearable Upper Limb Assistive Devices in Degenerative Neuromuscular Diseases: A Systematic Review and Meta-Analysis.

Background: This systematic review summarizes the current evidence about the effectiveness of wearable assistive technologies for upper limbs support during activities of daily living for individuals with neuromuscular diseases. Methods: Fourteen studies have been included in the meta-analysis, involving 184 participants. All included studies compared patients ability to perform functional tasks with and without assistive devices. Results: An overall effect size of 1.06 (95% CI = 0.76-1.36, p < 0.00001) was obtained, demonstrating that upper limbs assistive devices significantly improve the performance in activities of daily living in people with neuromuscular diseases. A significant interaction between studies evaluating functional improvement with externally-assessed outcome measures or self-perceived outcome measures has been detected. In particular, the effect size of the sub-group considering self-perceived scales was 1.38 (95% CI = 1.08-1.68), while the effect size of the other group was 0.77 (95% CI = 0.41-1.11), meaning that patients' perceived functional gain is often higher than the functional gain detectable through clinical scales. Conclusion: Overall, the quality of the evidence ranged from low to moderate, due to low number of studies and participants, limitations in the selection of participants and in the blindness of outcome assessors, and risk of publication bias. Significance: A large magnitude effect and a clear dose-response gradient were found, therefore, a strong recommendation, in favor of the use of assistive devices could be suggested.