Functional Mechanisms of Recovery after Chronic Stroke: Modeling with the Virtual Brain

Abstract

We have seen important strides in our understanding of mechanisms underlying stroke recovery, yet effective translational links between basic and applied sciences, as well as from big data to individualized therapies, are needed to truly develop a cure for stroke. We present such an approach using The Virtual Brain (TVB), a neuroinformatics platform that uses empirical neuroimaging data to create dynamic models of an individual’s human brain; specifically, we simulate fMRI signals by modeling parameters associated with brain dynamics after stroke.In 20 individuals with stroke and 11 controls, we obtained rest fMRI, T1w, and diffusion tensor imaging (DTI) data. Motor performance was assessed pre-therapy, post-therapy, and 6–12 months post-therapy. Based on individual structural connectomes derived from DTI, the following steps were performed in the TVB platform: (1) optimization of local and global parameters (conduction velocity, global coupling); (2) simulation of BOLD signal using optimized parameter values; (3) validation of simulated time series by comparing frequency, amplitude, and phase of the simulated signal with empirical time series; and (4) multivariate linear regression of model parameters with clinical phenotype. Compared with controls, individuals with stroke demonstrated a consistent reduction in conduction velocity, increased local dynamics, and reduced local inhibitory coupling. A negative relationship between local excitation and motor recovery, and a positive correlation between local dynamics and motor recovery were seen.TVB reveals a disrupted post-stroke system favoring excitation-over-inhibition and local-over-global dynamics, consistent with existing mammal literature on stroke mechanisms. Our results point to the potential of TVB to determine individualized biomarkers of stroke recovery.

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

“…In our previous work using TVB modeling in stroke patients [44], we found consistent parameter changes associated with poor long-term motor recovery. These included local hyper-excitability, lower conduction velocities in cortico-cortical connectivity, and an imbalance between local and global dynamics.…”

“…In other words, the change in these parameters produced normalization of the balance between global and local dynamics and excessive excitation. Notably, these two parameters were those that correlated with long-term motor recovery in our previous work [44]. …”

“…To test the effects of restored parameter values on brain dynamics, the following steps were performed: Baseline (C bl ) identification: C bl was defined as the Pearson’s correlation between the average empirical functional connectivity matrix from controls and the individual simulated functional matrices of each patient.Virtual therapy: Parameters that changed after stroke [44] were adjusted for each patient to match the average control value (global coupling, conduction velocity, coupling between inhibitory and excitatory populations).Resimulation: Brain signals (rsfMRI) were resimulated with the modified parameter values and the consequent functional connectivity matrices were generated.Correlation post-virtual therapy (C vt ): C vt was defined as the Pearson’s correlation between the average empirical functional connectome from controls with the individual functional connectomes derived post-virtual therapy.Therapeutic effect (TE) of TVT: TE was calculated by subtracting the baseline correlation from that obtained after TVT (TE = C vt − C bl ). We then calculated the percentage final gain (G) from baseline using the following algorithm: $$G=true(\frac{\mathrm{normalT}\mathrm{normalE}}{C_{b1}}true)\times 100$$…”

“…Virtual therapy: Parameters that changed after stroke [44] were adjusted for each patient to match the average control value (global coupling, conduction velocity, coupling between inhibitory and excitatory populations).…”

Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain

“…A previous computational modeling study also reported alterations in inhibitory over excitatory coupling in chronic stroke patients (Falcon et al, 2016). It is difficult, however, to compare these results to those of our study, as Falcon and colleagues did not correct for region size or differentiate between affected and unaffected cortical areas.…”