Reproducing a decision-making network in a virtual visual discrimination task

We reproduced a decision-making network model using the neural simulator software neural simulation tool (NEST), and we embedded the spiking neural network in a virtual robotic agent performing a simulated behavioral task. The present work builds upon the concept of replicability in neuroscience, preserving most of the computational properties in the initial model although employing a different software tool. The proposed implementation successfully obtains equivalent results from the original study, reproducing the salient features of the neural processes underlying a binary decision. Furthermore, the resulting network is able to control a robot performing an in silico visual discrimination task, the implementation of which is openly available on the EBRAINS infrastructure through the neuro robotics platform (NRP)

Rehabilitation of Post-COVID Patients: A Virtual Reality Home-Based Intervention Including Cardio-Respiratory Fitness Training

The post-COVID syndrome is emerging as a new chronic condition, characterized by symptoms of breathlessness, fatigue, and decline of neurocognitive functions. Rehabilitation programs that include physical training seem to be beneficial to reduce such symptoms and improve patients' quality of life. Given this, and considering the limitations imposed by the pandemic on rehabilitation services, it emerged the need to integrate telerehabilitation programs into clinical practice. Some telerehabilitation solutions, also based on virtual reality (VR), are available in the market. Still, they mainly focus on rehabilitation of upper limbs, balance, and cognitive training, while exercises like cycling or walking are usually not considered. The presented work aims to fill this gap by integrating a VR application to provide cardio-respiratory fitness training to post-COVID patients in an existing telerehabilitation platform. The ARTEDIA application allows patients to perform a cycling exercise and a concurrent cognitive task. Patients can cycle in a virtual park while performing a "go/no-go" task by selecting only specific targets appearing along the way. The difficulty of the practice can be adjusted by the therapists, while the physiological response is continuously monitored through wearable sensors to ensure safety. The application has been integrated into the VRRS system by Khymeia. In the next months, a study to assess the feasibility of a complete telerehabilitation program based on physical and cognitive training will take place. Such a program will combine the existing VRRS exercises and the cardio-respiratory fitness exercise provided by the ARTEDIA application. Feasibility, acceptance, and usability will be assessed from both the patients' and the therapists' sides

Topologically protected localised states in spin chains

We consider spin chain families inspired by the Su, Schrieffer and Hegger (SSH) model. We demonstrate explicitly the topologically induced spatial localisation of quantum states in our systems. We present detailed investigations of the effects of random noise, showing that these topologically protected states are very robust against this type of perturbation. Systems with such topological robustness are clearly good candidates for quantum information tasks and we discuss some potential applications. Thus, we present interesting spin chain models which show promising applications for quantum devices

Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review

Background: The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology. Materials and methods: We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data. Results: A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data. Conclusion: AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice

Adaptive Robotic Control Driven by a Versatile Spiking Cerebellar Network

The cerebellum is involved in a large number of different neural processes, especially in associative learning and in fine motor control. To develop a comprehensive theory of sensorimotor learning and control, it is crucial to determine the neural basis of coding and plasticity embedded into the cerebellar neural circuit and how they are translated into behavioral outcomes in learning paradigms. Learning has to be inferred from the interaction of an embodied system with its real environment, and the same cerebellar principles derived from cell physiology have to be able to drive a variety of tasks of different nature, calling for complex timing and movement patterns. We have coupled a realistic cerebellar spiking neural network (SNN) with a real robot and challenged it in multiple diverse sensorimotor tasks. Encoding and decoding strategies based on neuronal firing rates were applied. Adaptive motor control protocols with acquisition and extinction phases have been designed and tested, including an associative Pavlovian task (Eye blinking classical conditioning), a vestibulo-ocular task and a perturbed arm reaching task operating in closed-loop. The SNN processed in real-time mossy fiber inputs as arbitrary contextual signals, irrespective of whether they conveyed a tone, a vestibular stimulus or the position of a limb. A bidirectional long-term plasticity rule implemented at parallel fibers-Purkinje cell synapses modulated the output activity in the deep cerebellar nuclei. In all tasks, the neurorobot learned to adjust timing and gain of the motor responses by tuning its output discharge. It succeeded in reproducing how human biological systems acquire, extinguish and express knowledge of a noisy and changing world. By varying stimuli and perturbations patterns, real-time control robustness and generalizability were validated. The implicit spiking dynamics of the cerebellar model fulfill timing, prediction and learning functions.European Union (Human Brain Project) REALNET FP7-ICT270434 CEREBNET FP7-ITN238686 HBP-60410

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

Robotic Exoskeleton Gait Training in Stroke: An Electromyography-Based Evaluation

The recovery of symmetric and efficient walking is one of the key goals of a rehabilitation program in patients with stroke. The use of overground exoskeletons alongside conventional gait training might help foster rhythmic muscle activation in the gait cycle toward a more efficient gait. About twenty-nine patients with subacute stroke have been recruited and underwent either conventional gait training or experimental training, including overground gait training using a wearable powered exoskeleton alongside conventional therapy. Before and after the rehabilitation treatment, we assessed: (i) gait functionality by means of clinical scales combined to obtain a Capacity Score, and (ii) gait neuromuscular lower limbs pattern using superficial EMG signals. Both groups improved their ability to walk in terms of functional gait, as detected by the Capacity Score. However, only the group treated with the robotic exoskeleton regained a controlled rhythmic neuromuscular pattern in the proximal lower limb muscles, as observed by the muscular activation analysis. Coherence analysis suggested that the control group (CG) improvement was mediated mainly by spinal cord control, while experimental group improvements were mediated by cortical-driven control. In subacute stroke patients, we hypothesize that exoskeleton multijoint powered fine control overground gait training, alongside conventional care, may lead to a more fine-tuned and efficient gait pattern

Development and Electromyographic Validation of a Compliant Human-Robot Interaction Controller for Cooperative and Personalized Neurorehabilitation

Background: Appropriate training modalities for post-stroke upper-limb rehabilitation are key features for effective recovery after the acute event. This study presents a cooperative control framework that promotes compliant motion and implements a variety of high-level rehabilitation modalities with a unified low-level explicit impedance control law. The core idea is that we can change the haptic behavior perceived by a human when interacting with the rehabilitation robot by tuning three impedance control parameters. Methods: The presented control law is based on an impedance controller with direct torque measurement, provided with positive-feedback compensation terms for disturbances rejection and gravity compensation. We developed an elbow flexion-extension experimental setup as a platform to validate the performance of the proposed controller to promote the desired high-level behavior. The controller was first characterized through experimental trials regarding joint transparency, torque, and impedance tracking accuracy. Then, to validate if the controller could effectively render different physical human-robot interaction according to the selected rehabilitation modalities, we conducted tests on 14 healthy volunteers and measured their muscular voluntary effort through surface electromyography (sEMG). The experiments consisted of one degree-of-freedom elbow flexion/extension movements, executed under six high-level modalities, characterized by different levels of (i) corrective assistance, (ii) weight counterbalance assistance, and (iii) resistance. Results: The unified controller demonstrated suitability to promote good transparency and render both compliant and stiff behavior at the joint. We demonstrated through electromyographic monitoring that a proper combination of stiffness, damping, and weight assistance could induce different user participation levels, render different physical human-robot interaction, and potentially promote different rehabilitation training modalities. Conclusion: We proved that the proposed control framework could render a wide variety of physical human-robot interaction, helping the user to accomplish the task while exploiting physiological muscular activation patterns. The reported results confirmed that the control scheme could induce different levels of the subject's participation, potentially applicable to the clinical practice to adapt the rehabilitation treatment to the subject's progress. Further investigation is needed to validate the presented approach to neurological patients