The pathological and clinical characteristics of temporal lobe epilepsy (TLE) tend to be affected by epileptic seizures, specifically represented by seizure lateralization and frequency. Although the lateralization of the epileptogenic zone can be clarified in terms of neuroanatomical damage, there have been conflicting findings on the relationship between seizure frequency and neuroanatomical damage. In this study we sought to examine the relationship in the framework of machine learning-based predictive modeling. We acquired 60 grey matter (GM) anatomical features from structural MRI data and 46 white matter (WM) anatomical features from diffusion-weighted MRI data for 42 TLE patients and 45 healthy controls and applied the random forests method to the neuroanatomical features. We demonstrated that, whereas the neuroanatomical features were promising markers for the discrimination of the TLE patients from the healthy controls, the separation between the TLE patients with low and high seizure frequency on the basis of the neuroanatomical features was challenging. When we applied feature selection techniques for the construction of the predictive models, a greater number of features were selected as being relevant to the distinction of the TLE patients from the healthy controls than to the classification of the TLE patients according to seizure frequency. Furthermore, we adopted model-based clustering analysis and showed that seizure frequency-based subgroups were not matched well with neuroanatomy-based subgroups in the TLE patients. We propose that the challenge of predicting seizure frequency using neuroanatomical features could be attributable to considerable inter-individual variability in neuroanatomical damage among seizure frequency-based subgroups.
Keywords: Machine learning; Model-based clustering; Neuroanatomical damage; Seizure frequency; Temporal lobe epilepsy.
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