Network-level analysis of cortical thickness of the epileptic brain

Temporal lobe epilepsy (TLE) characterized by an epileptogenic focus in the medial temporal lobe is the most common form of focal epilepsy. However, the seizures are not confined to the temporal lobe but can spread to other, anatomically connected brain regions where they can cause similar structural abnormalities as observed in the focus. The aim of this study was to derive whole-brain networks from volumetric data and obtain network-centric measures, which can capture cortical thinning characteristic of TLE and can be used for classifying a given MRI into TLE or normal, and to obtain additional summary statistics that relate to the extent and spread of the disease. T1-weighted whole-brain images were acquired on a 4-T magnet in 13 patients with TLE with mesial temporal lobe sclerosis (TLE-MTS), 14 patients with TLE with normal MRI (TLE-no), and 30 controls. Mean cortical thickness and curvature measurements were obtained using the FreeSurfer software. These values were used to derive a graph, or network, for each subject. The nodes of the graph are brain regions, and edges represent disease progression paths. We show how to obtain summary statistics like mean, median, and variance defined for these networks and to perform exploratory analyses like correlation and classification. Our results indicate that the proposed network approach can improve accuracy of classifying subjects into two groups (control and TLE) from 78% for non-network classifiers to 93% using the proposed approach. We also obtain network "peakiness" values using statistical measures like entropy and complexity-this appears to be a good characterizer of the disease and may have utility in surgical planning.