Structural connectivity of temporal lobe structures detects temporal lobe epilepsy
Temporal lobe epilepsy (TLE) is the most common form of adult epilepsy. Detection of TLE from neuroimaging data is a challenging task. In many cases, patients with findings from standard clinical examination do not show any identifiable abnormalities in magnetic resonance imaging (MRI). In this study, we propose a method to detect TLE from structural connectivity of temporal lobe structures, using an integration of diffusion tensor imaging (DTI), graph theory analysis, and machine learning tools. To this end, first, we apply probabilistic tractography to extract white matter fibers and generate weighted structural connectivity graphs of 17 TLE patients and 17 healthy control subjects. Machine learning of graph theoretical measures are then used to classify TLE patients and control subjects. Using leave-one-out cross-validation, we show that graph theory measures used in logistic regression classifiers achieve accuracy in the range of 76% to 91% for the classification of the 34 subjects into the TLE and control groups. The results suggest that machine learning of the graph theoretical metrics derived from DTI-based networks of temporal lobe regions may be used for the diagnosis of TLE patients. Such a method may improve diagnostic assessment, especially in patients without obvious abnormalities in the conventional, anatomical neuroimaging examinations.