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Reorganization of the language network from typically left-lateralized frontotemporal regions to bilaterally distributed or right-lateralized networks occurs in anywhere from 25%-30% of patients with focal epilepsy. In patients who have been recently diagnosed with epilepsy, an important question remains as to whether it is the presence of seizures or the underlying epilepsy etiology that leads to atypical language representations. This question becomes even more interesting in pediatric samples, where the typical developmental processes of the language network may confer more variability and plasticity in the language network. We assessed a carefully selected cohort of children with recent-onset epilepsy to examine whether it is the effects of seizures or their underlying cause that leads to atypical language lateralization.We used functional magnetic resonance imaging (fMRI) to compare language laterality in children with recently diagnosed focal unaware epilepsy and age-matched controls. Age at epilepsy onset (age 4 to 6 years vs age 7 to 12 years) was also examined to determine if age at onset influenced laterality.The majority of recent-onset patients and controls exhibited left-lateralized language. There was a significant interaction such that the relationship between epilepsy duration and laterality differed by age at onset. In children with onset after age 6, a longer duration of epilepsy was associated with less left-lateralized language dominance. In contrast, in children with onset between 4 and 6 years of age, a longer duration of epilepsy was not associated with less left language dominance.Our results demonstrate that although language remained largely left-lateralized in children recently diagnosed with epilepsy, the impact of seizure duration depended on age at onset, indicating that the timing of developmental and disease factors are important in determining language dominance.

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 individuals with epilepsy.