Experimental and Computational Study on Motor Control and Recovery After Stroke: Toward a Constructive Loop Between Experimental and Virtual Embodied Neuroscience

Absolutely no clue how this could possibly help in stroke recovery. Lots of pages to read on your own, unless you are under the mistaken impression that your doctor is responsible and will do something useful with this. 

Experimental and Computational Study on Motor Control and Recovery After Stroke: Toward a Constructive Loop Between Experimental and Virtual Embodied Neuroscience

Anna Letizia Allegra Mascaro^1,2*†, Egidio Falotico^3†, Spase Petkoski^4†, Maria Pasquini³, Lorenzo Vannucci³, Núria Tort-Colet⁵, Emilia Conti^2,6, Francesco Resta^2,6, Cristina Spalletti¹, Shravan Tata Ramalingasetty⁷, Axel von Arnim⁸, Emanuele Formento⁹, Emmanouil Angelidis^8,10, Camilla H. Blixhavn¹¹, Trygve B. Leergaard¹¹, Matteo Caleo^1,12, Alain Destexhe⁵, Auke Ijspeert⁷, Silvestro Micera^3,9, Cecilia Laschi³, Viktor Jirsa⁴, Marc-Oliver Gewaltig^13† and Francesco S. Pavone^2,6†

• ¹Neuroscience Institute, National Research Council, Pisa, Italy
• ²European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Italy
• ³Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
• ⁴Aix-Marseille Université, Inserm, INS UMR_S 1106, Marseille, France
• ⁵Paris-Saclay University, Institute of Neuroscience, CNRS, Gif-sur-Yvette, France
• ⁶Department of Physics and Astronomy, University of Florence, Florence, Italy
• ⁷Biorobotics Laboratory, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
• ⁸Fortiss GmbH, Munich, Germany
• ⁹Bertarelli Foundation Chair in Translational NeuroEngineering, Institute of Bioengineering, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
• ¹⁰Chair of Robotics, Artificial Intelligence and Embedded Systems, Department of Informatics, Technical University of Munich, Munich, Germany
• ¹¹Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
• ¹²Department of Biomedical Sciences, University of Padua, Padua, Italy
• ¹³Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland

Being able to replicate real experiments with computational simulations is a unique opportunity to refine and validate models with experimental data and redesign the experiments based on simulations. However, since it is technically demanding to model all components of an experiment, traditional approaches to modeling reduce the experimental setups as much as possible. In this study, our goal is to replicate all the relevant features of an experiment on motor control and motor rehabilitation after stroke. To this aim, we propose an approach that allows continuous integration of new experimental data into a computational modeling framework. First, results show that we could reproduce experimental object displacement with high accuracy via the simulated embodiment in the virtual world by feeding a spinal cord model with experimental registration of the cortical activity. Second, by using computational models of multiple granularities, our preliminary results show the possibility of simulating several features of the brain after stroke, from the local alteration in neuronal activity to long-range connectivity remodeling. Finally, strategies are proposed to merge the two pipelines. We further suggest that additional models could be integrated into the framework thanks to the versatility of the proposed approach, thus allowing many researchers to achieve continuously improved experimental design.

1. Introduction

In nature, the activity of the brain of an individual interacting with the environment is conditioned by the response of the environment itself, in that the output of the brain is relevant only if it has the ability to impact the future and hence the input the brain receives. This “closed-loop” can be simulated in a virtual world, where simulated experiments reproduce actions (output from the brain) that have consequences (future input to the brain) (Zrenner et al., 2016). To the aim of reproducing in silico the complexity of real experiments, different levels of modeling shall be integrated. However, since modeling all components of an experiment is very difficult, traditional approaches of computational neuroscience reduce the experimental setups as much as possible. An “Embodied brain” (or “task dynamics,” see Zrenner et al., 2016) approach could overcome these limits by associating the modeled brain activity with the generation of behavior within a virtual or real environment, i.e., an entailment between an output of the brain and a feedback signal into the brain (Reger et al., 2000; DeMarse et al., 2001; Tessadori et al., 2012). The experimenter can interfere with the flow of information between the neural system and environment on the one hand and the state and transition dynamics of the environment on the other. Closing the loop can be performed effectively by (i) validating the models on experimental data, and (ii) designing new experiments based on the hypotheses formulated by the simulations. On the example shown in Figure 1, data on brain activity (be it, for instance, from electrophysiological recordings or imaging) and on the environment (e.g., by means of kinematic or dynamic measures) from the real experiment are used to feed the models of the in silico representation of the experiment. From a comparison of the real and model-based data, the features that are most important to replicate the real experiment are identified, and thus novel insights are generated (Figure 1). To realize such a complex virtual system, many choices can be made, for instance on the brain model or spinal cord model that best represent the salient features of experimental measures to be replicated. The ideal framework shall comprise a library of tools to choose from, to reproduce a variety of experimental paradigms in the virtual environment. By briefly introducing the state of the art in brain and spinal cord modeling, we will discuss few classes of models to pick from an ideal library.

FIGURE 1

Figure 1. Scheme of the proposed Embodied brain framework. The picture suggests a closed-loop workflow linking real and simulated experiment. The different types of data obtained from the experiments, from brain activity to dynamic and kinematics of goal-directed movement, are used to feed the whole brain and spinal cord model, in addition to the virtual mouse and environment. The loop is closed by validation of in silico results on real data. Eventually, the simulated experiment raises novel hypotheses, to be validated on new real experiments.

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