High-resolution pediatric age–specific 18F-FDG PET template: a pilot study in epileptogenic focus localization

Background PET imaging has been widely used in diagnosis of neurological disorders; however, its application to pediatric population is limited due to lacking pediatric age–specific PET template. This study aims to develop a pediatric age–specific PET template (PAPT) and conduct a pilot study of epileptogenic focus localization in pediatric epilepsy. Methods We recruited 130 pediatric patients with epilepsy and 102 age-matched controls who underwent 18F-FDG PET examination. High-resolution PAPT was developed by an iterative nonlinear registration-averaging optimization approach for two age ranges: 6–10 years (n = 17) and 11–18 years (n = 50), respectively. Spatial normalization to the PAPT was evaluated by registration similarities of 35 validation controls, followed by estimation of potential registration biases. In a pilot study, epileptogenic focus was localized by PAPT-based voxel-wise statistical analysis, compared with multi-disciplinary team (MDT) diagnosis, and validated by follow-up of patients who underwent epilepsy surgery. Furthermore, epileptogenic focus localization results were compared among three templates (PAPT, conventional adult template, and a previously reported pediatric linear template). Results Spatial normalization to the PAPT significantly improved registration similarities (P < 0.001), and nearly eliminated regions of potential biases (< 2% of whole brain volume). The PAPT-based epileptogenic focus localization achieved a substantial agreement with MDT diagnosis (Kappa = 0.757), significantly outperforming localization based on the adult template (Kappa = 0.496) and linear template (Kappa = 0.569) (P < 0.05). The PAPT-based localization achieved the highest detection rate (89.2%) and accuracy (80.0%). In postsurgical seizure-free patients (n = 40), the PAPT-based localization also achieved a substantial agreement with resection areas (Kappa = 0.743), and the highest detection rate (95%) and accuracy (80.0%). Conclusion The PAPT can significantly improve spatial normalization and epileptogenic focus localization in pediatric epilepsy. Future pediatric neuroimaging studies can also benefit from the unbiased spatial normalization by PAPT. Trial registration. NCT04725162: https://clinicaltrials.gov/ct2/show/NCT04725162

Hybrid Convolutional Neural Network for Localization of Epileptic Focus Based on iEEG

Epileptic focus localization by analysing intracranial electroencephalogram (iEEG) plays a critical role in successful surgical therapy of resection of the epileptogenic lesion. However, manual analysis and classification of the iEEG signal by clinicians are arduous and time-consuming and excessively depend on the experience. Due to individual differences of patients, the iEEG signal from different patients usually shows very diverse features even if the features belong to the same class. Accordingly, automatic detection of epileptic focus is required to improve the accuracy and to shorten the time for treatment. In this paper, we propose a novel feature fusion-based iEEG classification method, a deep learning model termed Time-Frequency Hybrid Network (TF-HybridNet), in which short-time Fourier transform (STFT) and 1d convolution layers are performed on the input iEEG in parallel to extract features of the time-frequency domain and feature maps. And then, the time-frequency features and feature maps are fused and fed to a 2d convolutional neural network (CNN). We used the Bern-Barcelona iEEG dataset for evaluating the performance of TF-HybridNet, and the experimental results show that our approach is able to differentiate the focal from nonfocal iEEG signal with an average classification accuracy of 94.3% and demonstrates an improved accuracy rate compared to the model using only STFT or one-dimensional convolutional layers as feature extraction.

Clinically Localized Seizure Focus Maybe Not Exactly the Position of Abating Seizures: A Computational Evidence

Seizure focus localization is the key to control seizures. However, in this paper, we show that the clinically localized seizure focus may be not exactly the positions to abate seizures. Firstly, the reliability of a previously proposed methodology employed to estimate the synchronicity and directionality of information flows over time between EEG signals, is numerically assessed with a coupled mass neural model. Then 10 channels' EEG signals from a patient with focal epilepsy are used to reconstruct the dynamical complex network of pathological seizure. This may facilitate to identify the evolution paths of information flows and localize the potential seizure foci. What's more, based on the controllability and observability principles of complex systems, we can focus on the key nodes which is effective to control the network seizure behaviors and the key ones that can allow us to estimate the state of all other variables. Results show that to fully control the epileptic network may not just be related to the focus zone, it may also involves in other non-focus nodes. In addition, we use the spatiotemporal neural network model connected by our modeled dynamical adjacent matrix to successfully reproduce the original EEG signals which can be effectively abated by applying the normal distribution noise stimulation with cathodic phase pulses (cNDNs) on the identified key nodes or resecting them. Our results enrich the clinical results and provide new insights into the seizure resection and electronic stimulation therapies.

Epileptic Slow Wave Activity

In recent years, novel markers for the epileptic network beyond interictal spikes and ictal seizure correlates have been described. Slow activity in theta, delta, and lower frequency ranges have been detected using invasive electroencephalography (EEG) and noninvasive magnetoencephalography (MEG)/EEG. While such activity also occurs that is associated, for example, with large lesions and after intracranial surgery, certain subtypes may be used to localize the epileptic network. This chapter provides an overview of MEG slow frequency markers in patients with focal epilepsy. It covers the application of slow activity–based focus localization in patients undergoing workup for epilepsy surgery and discusses the relation to conventional spike-based analysis as well as the potential value of slow activity analysis in patients with previous surgery and persisting or recurring seizures.