Metabolism and Intracranial Epileptogenicity in Temporal Lobe Long-Term Epilepsy-Associated Tumor

Abstract

Brain tumors are common in epilepsy surgery and frequently occur in the temporal lobe, but the optimal surgical strategies to remove the tumor and epileptogenic zone remain controversial. We aim at illustrating the positron emission tomography (PET) metabolism and the stereoelectroencephalography (SEEG) epileptogenicity of temporal lobe long-term epilepsy-associated tumors (LEAT). In this study, 70 patients and 25 healthy controls were included. Our analysis leveraged group-level analysis to reveal the whole-brain metabolic pattern of temporal lobe LEATs. The SEEG-based epileptogenicity mapping was performed to verify the PET findings in the epileptic network. Compared to controls, patients with a temporal lobe LEAT showed a more widespread epileptic network based on 18FDG-PET in patients with a mesial temporal lobe LEAT than in those with a lateral temporal lobe LEAT. The significant brain clusters mainly involved the paracentral lobule (ANOVA F = 9.731, p < 0.001), caudate nucleus (ANOVA F = 20.749, p < 0.001), putamen (Kruskal–Wallis H = 19.258, p < 0.001), and thalamus (ANOVA F = 4.754, p = 0.011). Subgroup analysis and SEEG-based epileptogenicity mapping are similar to the metabolic pattern. Our findings demonstrate the metabolic and electrophysiological organization of the temporal lobe LEAT epileptic network, which may assist in a patient-specific surgical strategy.

Deep Learning Approaches for Imaging-Based Automated Segmentation of Tuberous Sclerosis Complex

“…The three-dimensional convolutional neural network (3D-CNN), DeepMedic [27], was designed and trained for a voxel-wise dual class segmentation of the tuber. The 3D-CNN contains eight convolutional layers (kernel size = 3 3 ) for each resolution pathway, followed by two fully connected layers implemented as 1 3 convolutions and a final classification layer (Figure 1B).…”