Predicting the laterality of temporal lobe epilepsy from PET, MRI, and DTI: A multimodal study

Abstract

Pre-surgical evaluation of patients with temporal lobe epilepsy (TLE) relies on information obtained from multiple neuroimaging modalities. The relationship between modalities and their combined power in predicting the seizure focus is currently unknown. We investigated asymmetries from three different modalities, PET (glucose metabolism), MRI (cortical thickness), and diffusion tensor imaging (DTI; white matter anisotropy) in 28 left and 30 right TLE patients (LTLE and RTLE). Stepwise logistic regression models were built from each modality separately and from all three combined, while bootstrapped methods and split-sample validation verified the robustness of predictions. Among all multimodal asymmetries, three PET asymmetries formed the best predictive model (100% success in full sample, >95% success in split-sample validation). The combinations of PET with other modalities did not perform better than PET alone. Probabilistic classifications were obtained for new clinical cases, which showed correct lateralization for 7/7 new TLE patients (100%) and for 4/5 operated patients with discordant or non-informative PET reports (80%). Metabolism showed closer relationship with white matter in LTLE and closer relationship with gray matter in RTLE. Our data suggest that metabolism is a powerful modality that can predict seizure laterality with high accuracy, and offers high value for automated predictive models. The side of epileptogenic focus can affect the relationship of metabolism with brain structure. The data and tools necessary to obtain classifications for new TLE patients are made publicly available.

Keywords: Asymmetry; Classification; Machine learning; Metabolism; Resection.

Supplementary Fig. 2

Fig. 1

Multimodal asymmetries of the mean (A), variance (B), kurtosis (C), and skewness (D) in LTLE (red boxes) and RTLE (green boxes). Boxplots extend between 25th and 75th percentile of the data, the central line indicates the median value. Whiskers extend to the most extreme non-outlier data point (outliers values = 1.5 × interquartile range value). The main dataset consists in data from A, the extended dataset consists in data from A–B–C–D. Asterisks mark significant group differences (Wilcoxon tests) after multiple comparison correction.

Fig. 2

Bootstrapped validation of model 2 (upper panels) and model 3 (lower panels). Left panels: split-sample validation accuracy. Middle panels: split-sample posterior probability. Right panels: distribution of posterior probability from 10,000 full-sample bootstraps. Note, the posterior probability of LTLE was flipped for comparison with RTLE (i.e., 0.03 became 0.97).