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.

TLE lateralization using whole brain structural connectivity

“…So far, most of the studies employing DWMRI in TLE have investigated the alternation of diffusion tensor imaging (DTI) scalar parameters such as apparent diffusion coefficient (ADC) or fractional anisotropy (FA) in specific tracts or regions. Some studies have used this information in lateralizing TLE patients [6–8]. However, these studies either combined the information acquired from DTI and other imaging modalities or used the DTI information of specific tracts to literalize the disease.…”

Use of an Automated Quantitative Analysis of Hippocampal Volume, Signal, and Glucose Metabolism to Detect Hippocampal Sclerosis

“…Based on our results, Q-PET was more sensitive than Q-volume, although the difference was not statistically significant. The sensitivity of FDG-PET in detecting epileptogenic lesions has been well-recognized not only for MTLE cases but also for neocortical epilepsy cases ( 13 , 19 ). However, we want to emphasize that FDG-PET is a functional imaging modality that characterizes the different parts of the brain according to metabolic activity.…”

“…MRI post-processing techniques, including quantification of the hippocampal volume, signal and shape, have been developed to improve the detection of HS ( 8 – 11 ). Several studies have quantitatively analyzed the asymmetry of hippocampal volume, signal and glucose uptake in patients with HS ( 12 , 13 ); however, bilateral HS with symmetrical abnormities might be overlooked due to a lack of comparison with normal control subjects. In the present study, we compared the MRI and PET data from individual patients with HS with data from healthy controls to determine the lateralizing values of quantitative analyses of hippocampal volume (Q-volume), signal (Q-FLAIR) and glucose metabolism (Q-PET).…”

Neuroanatomic markers of post-traumatic epilepsy based on magnetic resonance imaging and machine learning

“…This heterogeneity needs to be accounted for in statistical analyses due to its potentially confounding effect. The prediction of post-traumatic seizure onset and frequency based on neurological and radiological examinations has been only moderately successful and more research is needed to understand the relationship between TBI and PTE [46,45,17,14,24]. Thus, the identification of imaging biomarkers can help in developing better PTE prediction strategies.…”