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

Abstract

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.

Normalizing the brain connectome for communication through synchronization

“…The simplest and hence most elaborate is the Kuramoto model ( 32 ). It is widely utilized for describing emergent phenomena in complex systems ( 81 ), such as the brain ( 82 – 84 ), with a structure often represented via complex networks ( 85 ). For synchronization at frequency Ω, the model reads ( 33 ) where θ i denotes the phase of the i- th node oscillating with a natural frequency ω i , the dependence on time is implicit, and due to the synchronization θ j ( t − τ ij ) = θ j ( t ) + Ωτ ij .…”

“…The mouse connectome in Fig. 2 (A) was obtained from the Allen Institute Mouse Brain Connectivity data ( 37 ), which is integrated in The Virtual Mouse Brain ( 90 ), and which was already used to model mice brain dynamics ( 10, 84 ).…”

Driving Oscillatory Dynamics: Neuromodulation for Recovery After Stroke

“…In the field of stroke recovery, there is a need for the implementation of these closedloop systems to modulate oscillatory activity and reduce the high variation in treatment efficacy seen between patients. The recently developed "Embodied Brain" closed-loop simulation model may help us to understand the impacts of specific changes to neuronal circuits and oscillatory dynamics before implementing them experimentally (Allegra Mascaro et al, 2020). Future research should combine environmental and direct brain stimulation techniques in animal models to improve our understanding of evoked and enhanced oscillatory dynamics in order to refine the closed-loop systems used in human patients (Figure 2).…”

White-matter degradation and dynamical compensation support age-related functional alterations in human brain

“…As a consequence in the study of its large-scale dynamics, the brain is typically represented as a network of anatomically interacting regions each constrained by inherent dynamics (Sanz-Leon et al, 2015). Using this paradigm, a number of modeling studies have demonstrated that independently of the size of the brain regions and their underlying dynamics, neuroanatomical constraints of the human brain shape and drive its functionality during resting healthy state (Kringelbach et al, 2015; Deco et al, 2009, 2011; Schirner et al, 2018; Courtiol et al, 2020), or during pathologies such as epilepsy (Jirsa et al, 2017), stroke (Allegra Mascaro et al, 2020), or Alzheimer’s disease (Stefanovski et al, 2019).…”

“…Functional alterations are studied through the dFC and MC, which capture temporal and spatial aspects of the FC (Arbabyazd et al, 2020). For the last part, we utilize Kuramoto oscillators, which despite being overtly simple, due to their parsimonious parametrization allow for drawing specific links between network structure and the emergent synchronization patterns of the neuronal activity (Pope et al, 2021; Cabral et al, 2011; Allegra Mascaro et al, 2020).…”