PT - JOURNAL ARTICLE AU - Maria Inez Falcon AU - Jeffrey D. Riley AU - Viktor Jirsa AU - Anthony R McIntosh AU - E Elinor Chen AU - Ana Solodkin TI - Functional mechanisms of recovery after chronic stroke: modeling with The Virtual Brain AID - 10.1523/ENEURO.0158-15.2016 DP - 2016 Mar 23 TA - eneuro PG - ENEURO.0158-15.2016 4099 - http://www.eneuro.org/content/early/2016/03/22/ENEURO.0158-15.2016.short 4100 - http://www.eneuro.org/content/early/2016/03/22/ENEURO.0158-15.2016.full AB - 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 employs 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 twenty individuals with stroke and 11 controls we obtained rest fMRI, T1w, and 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, 4) Multivariate linear regression of model parameters with clinical phenotype.Compared to 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.Significance Statement: The development of schemes to acquire neuroimaging big data is fostering a greater understanding of brain function. Yet we are lacking quantitative tools to translate these insights to the individual level, particularly associated with neurological disease. We address this challenge using the neuroinformatics platform, The Virtual Brain, to model individualized brain activity. This approach enables the linkage of macroscopic brain dynamics with mesoscopic biophysical parameters, wherein we demonstrate the capacity of large-scale brain models to track and predict long-term recovery after stroke. Our results establish the basis for a deliberate integration of computational biology and neuroscience into clinical approaches for elucidating cellular mechanisms of disease, opening new venues for the development of individualized therapeutic interventions.