Underlying pathology, repeated focal seizures, and global insults each contribute to atrophy in specific tracts. Genetic factors make less of a contribution in this cohort. A multifactorial model of white matter atrophy in focal epilepsy is proposed. Ann Neurol 2017;81:240-250.

Individuals with focal epilepsy have heterogeneous sites of seizure origin. However, there may be brain regions that are common to most cases of intractable focal epilepsy. In this study, we aim to identify these using multivariate analysis of task-free functional MRI. Fourteen subjects with extratemporal focal epilepsy and 14 healthy controls were included in the study. Task-free functional MRI data were used to calculate voxel-wise regional connectivity with regional homogeneity (ReHo) and weighted degree centrality (DCw), in addition to regional activity using fraction of amplitude of low-frequency fluctuations (fALFF). Multivariate pattern analysis was applied to each of these metrics to discriminate brain areas that differed between focal epilepsy subjects and healthy controls. ReHo and DCw classified focal epilepsy subjects from healthy controls with high accuracy (89.3% and 75%, respectively). However, fALFF did not significantly classify patients from controls. Increased regional network activity in epilepsy subjects was seen in the ipsilateral piriform cortex, insula, and thalamus, in addition to the dorsal anterior cingulate cortex and lateral frontal cortices. Decreased regional connectivity was observed in the ventromedial prefrontal cortex, as well as lateral temporal cortices. Patients with extratemporal focal epilepsy have common areas of abnormality (ReHo and DCw measures), including the ipsilateral piriform cortex, temporal neocortex, and ventromedial prefrontal cortex. ReHo shows additional increase in the "salience network" that includes anterior insula and anterior cingulate cortex. DCw showed additional effects in the ipsilateral thalamus and striatum. These brain areas may represent key regional network properties underlying focal epilepsy.

Focal epilepsy is characterised by paroxysmal events, reflecting changes in underlying local brain networks. To capture brain network activity at the maximal temporal resolution of the acquired functional magnetic resonance imaging (fMRI) data, we have previously developed a novel analysis framework called Dynamic Regional Phase Synchrony (DRePS). DRePS measures instantaneous mean phase coherence within neighbourhoods of brain voxels. We use it here to examine how the dynamics of the functional connections of regional brain networks are altered in neocortical focal epilepsy. Using task-free fMRI data from 21 subjects with focal epilepsy and 21 healthy controls, we calculated the power spectral density of DRePS, which is a measure of signal variability in local connectivity estimates. Whole-brain averaged power spectral density of DRePS, or signal variability of local connectivity, was significantly higher in epilepsy subjects compared to healthy controls. Maximal increase in DRePS spectral power was seen in bilateral inferior frontal cortices, ipsilateral mid-cingulate gyrus, superior temporal gyrus, caudate head, and contralateral cerebellum. Our results provide further evidence of common brain abnormalities across people with focal epilepsy. We postulate that dynamic changes in specific cortical brain areas may help maintain brain function in the presence of pathological epileptiform network activity in neocortical focal epilepsy.

Objective: Lennox-Gastaut syndrome, and the similar but less tightly defined LennoxGastaut phenotype, describe patients with severe epilepsy, generalized epileptic discharges, and variable intellectual disability. Our previous functional neuroimaging studies suggest that abnormal diffuse association network activity underlies the epileptic discharges of this clinical phenotype. Herein we use a data-driven multivariate approach to determine the spatial changes in local and global networks of patients with severe epilepsy of the Lennox-Gastaut phenotype. Methods: We studied 9 adult patients and 14 controls. In 20 min of task-free blood oxygen level-dependent functional magnetic resonance imaging data, two metrics of functional connectivity were studied: Regional homogeneity or local connectivity, a measure of concordance between each voxel to a focal cluster of adjacent voxels; and eigenvector centrality, a global connectivity estimate designed to detect important neural hubs. Multivariate pattern analysis of these data in a machine-learning framework was used to identify spatial features that classified disease subjects. Results: Multivariate pattern analysis was 95.7% accurate in classifying subjects for both local and global connectivity measures (22/23 subjects correctly classified). Maximal discriminating features were the following: increased local connectivity in frontoinsular and intraparietal areas; increased global connectivity in posterior association areas; decreased local connectivity in sensory (visual and auditory) and medial frontal cortices; and decreased global connectivity in the cingulate cortex, striatum, hippocampus, and pons. Significance: Using a data-driven analysis method in task-free functional magnetic resonance imaging, we show increased connectivity in critical areas of association cortex and decreased connectivity in primary cortex. This supports previous findings of a critical role for these association cortical regions as a final common pathway in generating the Lennox-Gastaut phenotype. Abnormal function of these areas is likely to be important in explaining the intellectual problems characteristic of this disorder.