SUMMARYPurpose: Diffusion tensor imaging (DTI) studies have reported substantial white matter abnormalities in patients with temporal lobe epilepsy (TLE). However, limited data exist regarding the extent of white matter tract abnormalities, cognitive effects of these abnormalities, and relationship to clinical factors. The current study examined these issues in subjects with chronic TLE. Methods: DTI data were obtained in 12 TLE subjects and 10 age-matched healthy controls. Voxel-wise statistical analysis of fractional anisotropy (FA) was carried out using tract-based spatial statistics (TBSS). White matter integrity was correlated with cognitive performance and epilepsy-related clinical parameters.Results: Subjects with TLE, as compared to healthy controls, demonstrated four clusters of reduced FA, in anterior temporal lobe, mesial temporal lobe, and cerebellum ipsilateral, as well as frontoparietal lobe contralateral to the side of seizure onset. Mean FA was positively correlated with delayed memory, in anterior temporal lobe; and immediate memory, in mesial temporal lobe. Lower FA values in the posterior region of corpus callosum were related to earlier age of seizure onset. Conclusion: TLE is associated with widespread disturbances in white matter tracts and these changes have important cognitive and clinical consequences.

TLE is associated with abnormal integrity of frontal-temporal white matter tracts, but only on the side of seizure onset. This suggests that frontal-temporal white matter tracts are vulnerable to recurrent seizures and/or the factors precipitating the epilepsy.

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.

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.

Objective Adults with Juvenile Myoclonic Epilepsy (JME) have subtle brain structural abnormalities in the fronto-thalamic-cortical network, poorer cognitive function, and worse long-term social outcomes, even when their seizures are controlled and/or remitted. The natural history of JME and development of abnormalities in brain structure and cognition from epilepsy onset has not been studied. Methods The maturational trajectories of cognitive and brain development were prospectively compared between 19 children with new-onset JME in the first two years after diagnosis and 57 healthy controls. Results Cognitive abilities of children with JME were similar to or worse than healthy controls at baseline but failed to reach the competence level of healthy controls at follow-up across most of the tested cognitive abilities. Abnormal patterns of brain development, as assessed by magnetic resonance imaging studies, were evident in children with JME and included attenuation of age-related decline in cortical volume, thickness, and surface area compared to typically developing children. The altered brain developmental trajectory in the JME group was evident in higher-association fronto-parietal-temporal brain regions (p < 0.05, corrected for multiple comparisons). Interpretation Children with JME have abnormal structural brain development and impaired cognitive development early in the course of their epilepsy.