Follow-up Studies Of ¹⁸F-FDG PET And Metabolic Network Connectivity In Pediatric Epilepsy

Epilepsy is one of the most common neurological disorders,and a major proportion of patients fall into the pediatric group.Despite the fact that the majority of patients can become seizure-free through the anti-seizure drugs,approximately 20%～30%of individuals are estimated to be drug-resistant.Therefore,early assessment of patients’response to anti-epileptic drugs is of great importance for patient management.¹⁸F-fluorodeoxyglucose positron emission tomography(¹⁸F-FDG PET)has been widely used for presurgical localization of epileptic foci and prognosis estimation by measuring the crerbral glucose metabolism pattern.However,the clinical value of ¹⁸F-FDG PET for assessing the treatment response of pediatric patients to anti-seizure drugs remains unclear.Recently,complex network analysis is a rapidly evolving research field,which combines principles and approaches of systems biology and network science,holding the promise to uncovering the causes and to revolutionize the diagnosis and treatments of human diseases.So far,metabolic connectivity based on ¹⁸F-FDG PET using complex network analysis for evaluating the disease severity of pediatric epilepsy patients has not yet been studied.Therefore,the aim of this study was to evaluate the clinical value of glucose metabolism and metabolic connectivity based on ¹⁸F-FDG PET for assessing the sensitivity to anti-epileptic medication in pediatric epilepsy patients,and to investigate whether the metabolic connectivity varies in pediatric epilepsy patients.Part 1 Glucose metabolic changes between the baseline and follow-up ¹⁸F-FDG PET imagingForty-one pediatric epilepsy patients who underwent the baseline and follow-up ¹⁸F-FDG PET/CT,MRI and EEG examinations were retrospectively included,and 25 age-matched children were selected as the healthy control for statistical parametric mapping（SPM）analysis.Patients were divided into drug-sensitive（n=22）or drug-resistant group（n=19）according to the seizure frequency between the baseline and follow-up PET scans.Visual assessment combined with software was used to analyse the metabolic abnormal regions on PET images,and the absolute asymmetry index（（?）AI（?））was calculated accordingly.The changes of cortical glucose metabolism on baseline and follow-up PET images were calculated.Compared with drug-resistant group,the value of（?）AI（?）in drug-sensitive group was significantly decreased（P=0.030）,while the abnormal metabolism regions of 36.4%（8/22）patients were identified as normal by visual assessment.The value of（?）AI（?）was negatively correlated with age in drug-sensitive group（P=0.020）,while positively correlated with the seizure time in drug-resistent group（P=0.044）.Part 2 Brain metabolic network connectivity changes bewtween the baseline and follow-up ¹⁸F-FDG PET imagingThe standardized uptake values ratio（SUVR）and Pearson correlation coefficient were used to construct the brain metabolic connectivity in epilepsy patients and the healthy control.The Brain Analysis Using Graph Theory（BRAPH）software was used to calculate and compare the modularity,average degree,global efficiency,clustering coefficient and path length between the healthy control and patients of drug sensitive or resistant.Compared to the healthy control,the drug-sensitive group showed significant larger modularity,lower average degree,lower global efficiency,lower clustering coefficient and longer path length before drug threatment（all P（27）0.05）.At the end of follow-up,the metabolic connectivity was significantly improved in the drug-sensitive group（all P（29）0.05）.For the drug-resistant group,the change of metabolic connectivity was not significant after anti-epileptic treatment（all P（29）0.05）.Conclusion（1）¹⁸F-FDG PET is an important imaging biomarker for evaluation of therapeutic response,the value of（?）AI（?）could be used to evaluate the disease severity in pediatric epilepsy patients.（2）¹⁸F-FDG PET metabolic connectivity could be used for evaluating the therapeutic response in pediatric epilepsy patients.