In addition to the primary aim of seizure freedom, a key secondary aim of pediatric epilepsy surgery is to stabilize and, potentially, optimize cognitive development. Although the efficacy of surgical treatment for seizure control has been established, the long-term intellectual and developmental trajectories are yet to be delineated. We conducted a systematic review and meta-analysis of studies reporting pre- and postsurgical intelligence or developmental quotients (IQ/DQ) of children with focal lesional epilepsy aged ≤18 years at epilepsy surgery and assessed at >2 years after surgery. We determined the IQ/DQ change and conducted a random-effects meta-analysis and meta-regression to assess its determinants. We included 15 studies reporting on 341 patients. The weighted mean age at surgery was 7.1 years (range = .3-13.8). The weighted mean postsurgical follow-up duration was 5.6 years (range = 2.7-12.8). The overall estimate of the mean presurgical IQ/DQ was 60 (95% confidence interval [CI] = 47-73), the postsurgical IQ/DQ was 61 (95% CI = 48-73), and the change was +.94 IQ/DQ (95% CI = -1.70 to 3.58, p = .486). Children with presurgical IQ/DQ ≥ 70 showed a tendency for higher gains than those with presurgical IQ/DQ < 70 (p = .059). Higher gains were determined by cessation of antiseizure medication (ASM; p = .041), not just seizure freedom. Our findings indicate, on average, stabilization of intellectual and developmental functioning at long-term follow-up after epilepsy surgery. Once seizure freedom has been achieved, ASM cessation enables the optimization of intellectual and developmental trajectories in affected children.

Epilepsy is regarded as a structural and functional network disorder, affecting around 50 million people worldwide. A correct disease diagnosis can lead to quicker medical action, preventing adverse effects. This paper reports the design of a classifier for epilepsy diagnosis in patients after a first ictal episode, using electroencephalogram (EEG) recordings. The dataset consists of resting-state EEG from 629 patients, of which 504 were retained for the study. The patient\'s cohort exists out of 291 patients with epilepsy and 213 patients with other pathologies. The data were split into two sets: 80% training set and 20% test set. The extracted features from EEG included functional connectivity measures, graph measures, band powers and brain asymmetry ratios. Feature reduction was performed, and the models were trained using Machine Learning (ML) techniques. The models\' evaluation was performed with the area under the receiver operating characteristic curve (AUC). When focusing specifically on focal lesional epileptic patients, better results were obtained. This classification task was optimized using a 5-fold cross-validation, where SVM using PCA for feature reduction achieved an AUC of 0.730 ± 0.030. In the test set, the same model achieved 0.649 of AUC. The verified decrease is justified by the considerable diversity of pathologies in the cohort. An analysis of the selected features across tested models shows that functional connectivity and its graph measures have the most considerable predictive power, along with full-spectrum frequency-based features. To conclude, the proposed algorithms, with some refinement, can be of added value for doctors diagnosing epilepsy from EEG recordings after a suspected first seizure.