The Virtual Brain: Modeling Biological Correlates of Recovery after Chronic Stroke

Abstract

There currently remains considerable variability in stroke survivor recovery. To address this, developing individualized treatment has become an important goal in stroke treatment. As a first step, it is necessary to determine brain dynamics associated with stroke and recovery. While recent methods have made strides in this direction, we still lack physiological biomarkers. The Virtual Brain (TVB) is a novel application for modeling brain dynamics that simulates an individual’s brain activity by integrating their own neuroimaging data with local biophysical models. Here, we give a detailed description of the TVB modeling process and explore model parameters associated with stroke. In order to establish a parallel between this new type of modeling and those currently in use, in this work we establish an association between a specific TVB parameter (long-range coupling) that increases after stroke with metrics derived from graph analysis. We used TVB to simulate the individual BOLD signals for 20 patients with stroke and 10 healthy controls. We performed graph analysis on their structural connectivity matrices calculating degree centrality, betweenness centrality, and global efficiency. Linear regression analysis demonstrated that long-range coupling is negatively correlated with global efficiency (P = 0.038), but is not correlated with degree centrality or betweenness centrality. Our results suggest that the larger influence of local dynamics seen through the long-range coupling parameter is closely associated with a decreased efficiency of the system. We thus propose that the increase in the long-range parameter in TVB (indicating a bias toward local over global dynamics) is deleterious because it reduces communication as suggested by the decrease in efficiency. The new model platform TVB hence provides a novel perspective to understanding biophysical parameters responsible for global brain dynamics after stroke, allowing the design of focused therapeutic interventions.

Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain

“…In contrast to previous computational modeling studies that found increased global coupling values in chronic stroke patients (Falcon et al, 2015, 2016), our results revealed no significant differences in global coupling between glioma patients, meningioma patients, and healthy controls.…”

“…First, results showed a strong negative relation between global efficiency of the structural network and global coupling, corroborating the finding of Falcon et al (2015) in stroke patients. This implies that higher global coupling values are required in subjects whose structural connectome is less efficiently organized, to achieve the same amount of functional connectivity between cortical areas.…”

“…Different approaches can be used to simulate activity of nodes (i.e., brain areas) in large-scale brain network models varying from detailed spiking neuron models (Deco and Jirsa, 2012), over so-called neural mass or mean-field models that describe the collective activity of cell populations (Deco and Jirsa, 2012), down to abstract models such as the Ising model (Deco et al, 2012; Haimovici et al, 2013; Marinazzo et al, 2014; we refer the interested reader to Deco et al [2008] for an in-depth explanation of the general principle behind these approaches and the mechanism allowing a wide class of models to bridge temporal and spatial scales). In a clinical context, biologically interpretable dynamical models are of special interest, as their activity and parameters can allow inference of internal states and processes of the large-scale model, which cannot be measured with noninvasive neuroimaging techniques (Falcon et al, 2015; Schirner et al, 2018). Hence, they may provide an entry point for understanding brain disorders at a causal mechanistic level, which might lead to novel, more effective therapeutic interventions (Deco and Kringelbach, 2014; Proix et al, 2017).…”

“…Hence, they may provide an entry point for understanding brain disorders at a causal mechanistic level, which might lead to novel, more effective therapeutic interventions (Deco and Kringelbach, 2014; Proix et al, 2017). The first proof-of-concept studies have indicated that global and local biophysical model parameters derived from large-scale brain network modeling can be related to motor recovery after stroke (Falcon et al, 2015) and the generation and progression of epileptic seizures (Jirsa et al, 2014; Bernard and Jirsa, 2017), demonstrating the potential clinical utility of computational modeling.…”

A new neuroinformatics approach to personalized medicine in neurology: The Virtual Brain

“…Our previous work using graph analysis [52] has shown that it is negatively correlated with global efficiency, or a network’s capacity for communication [18]. Because this still does not provide a clear biological interpretation, our current efforts are focusing on possible biophysical factors to provide better grounding to this parameter.…”