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One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted 'gold-standard' subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in

Climate change is with us. As professionals who place value on evidence-based practice, climate change is something we cannot ignore. The current pandemic of the novel coronavirus, SARS-CoV-2, has demonstrated how global crises can arise suddenly and have a significant impact on public health. Global warming, a chronic process punctuated by acute episodes of extreme weather events, is an insidious global health crisis needing at least as much attention. Many neurological diseases are complex chronic conditions influenced at many levels by changes in the environment. This review aimed to collate and evaluate reports from clinical and basic science about the relationship between climate change and epilepsy. The keywords climate change, seasonal variation, temperature, humidity, thermoregulation, biorhythm, gene, circadian rhythm, heat, and weather were used to search the published evidence. A number of climatic variables are associated with increased seizure frequency in people with epilepsy. Climate change-induced increase in seizure precipitants such as fevers, stress, and sleep deprivation (e.g. as a result of more frequent extreme weather events) or vector-borne infections may trigger or exacerbate seizures, lead to deterioration of seizure control, and affect neurological, cerebrovascular, or cardiovascular comorbidities and risk of sudden unexpected death in epilepsy. Risks are likely to be modified by many factors, ranging from individual genetic variation and temperature-dependent channel function, to housing quality and global supply chains. According to the results of the limited number of experimental studies with animal models of seizures or epilepsy, different seizure types appear to have distinct susceptibility to seasonal influences. Increased body temperature, whether in the context of fever or not, has a critical role in seizure threshold and seizure-related brain damage. Links between climate change and epilepsy are likely to be multifactorial, compl

Objective: To assess seizure and cognitive outcomes and their predictors in children (<16 years at surgery) and adults undergoing temporal lobe epilepsy (TLE) surgery in eight Italian centers. Methods: This is a retrospective multicenter study. We performed a descriptive analysis and subsequently carried out multivariable mixed-effect models corrected for multiple comparisons. Results: We analyzed data from 511 patients (114 children) and observed significant differences in several clinical features between adults and children. The possibility of achieving Engel class IA outcome and discontinuing antiepileptic drugs (AEDs) at last follow-up (FU) was significantly higher in children (P =.006 and <.0001). However, percentages of children and adults in Engel class I at last FU (mean ± SD, 45.9 ± 17 months in children; 45.9 ± 20.6 months in adults) did not differ significantly. We identified different predictors of seizure outcome in children vs adults and at short- vs long-term FU. The only variables consistently associated with class I outcome over time were postoperative electroencephalography (EEG) in adults (abnormal, improved,odds ratio [OR] = 0.414, P =.023, Q = 0.046 vs normal, at 2-year FU and abnormal, improved, OR = 0.301, P =.001, Q = 0.002 vs normal, at last FU) and the completeness of resection of temporal magnetic resonance (MR) abnormalities other than hippocampal sclerosis in children (OR = 7.93, P =.001, Q = 0.003, at 2-year FU and OR = 45.03, P <.0001, Q < 0.0001, at last FU). Cognitive outcome was best predicted by preoperative performances in either age group. Significance: Clinical differences between adult and pediatric patients undergoing TLE surgery are reflected in differences in long-term outcomes and predictors of failures. Children are more likely to achieve sustained seizure freedom and withdraw AEDs after TLE surgery. E

Fibroblast growth-factor homologous factor (FHF1) gene variants have recently been associated with developmental and epileptic encephalopathy (DEE). FHF1 encodes a cytosolic protein that modulates neuronal sodium channel gating. We aim to refine the electroclinical phenotypic spectrum of patients with pathogenic FHF1 variants. We retrospectively collected clinical, genetic, neurophysiologic, and neuroimaging data of 17 patients with FHF1-DEE. Sixteen patients had recurrent heterozygous FHF1 missense variants: 14 had the recurrent p.Arg114His variant and two had a novel likely pathogenic variant p.Gly112Ser. The p.Arg114His variant is associated with an earlier onset and more severe phenotype. One patient carried a chromosomal microduplication involving FHF1. Twelve patients carried a de novo variant, five (29.5%) inherited from parents with gonadic or somatic mosaicism. Seizure onset was between 1 day and 41 months; in 76.5% it was within 30 days. Tonic seizures were the most frequent seizure type. Twelve patients (70.6%) had drug-resistant epilepsy, 14 (82.3%) intellectual disability, and 11 (64.7%) behavioral disturbances. Brain magnetic resonance imaging (MRI) showed mild cerebral and/or cerebellar atrophy in nine patients (52.9%). Overall, our findings expand and refine the clinical, EEG, and imaging phenotype of patients with FHF1-DEE, which is characterized by early onset epilepsy with tonic seizures, associated with moderate to severe ID and psychiatric features.

Noonan syndrome (NS) is a congenital autosomic dominant condition characterized by a variable spectrum from a clinical and genetical point of view. Germline mutations in more than ten genes involved in RAS–MAPK signal pathway have been demonstrated to cause the disease. An higher risk for leukemia and solid malignancies, including brain tumors, is related to NS. A review of the published literature concerning low grade gliomas (LGGs) in NS is presented. We described also a 13-year-old girl with NS associated with a recurrent mutation in PTPN11, who developed three different types of brain tumors, i.e., an optic pathway glioma, a glioneuronal neoplasm of the left temporal lobe and a cerebellar pilocytic astrocytoma. Molecular characterization of the glioneuronal tumor allowed to detect high levels of phosphorylated MTOR (pMTOR); therefore, a therapeutic approach based on an mTOR inhibitor (everolimus) was elected. The treatment was well tolerated and proved to be effective, leading to a stabilization of the tumor, which was surgical removed. The positive outcome of the present case suggests considering this approach for patients with RASopathies and brain tumors with hyperactivated MTOR signaling.

The authors regret to inform that one of the co-author “Simona Pellacani” is affiliated to IRCCS Stella Maris, Pisa, Italy The authors would like to apologize for any inconvenience caused.