Regional anatomical structures of the brain are intimately connected to functions corresponding to specific regions and the temporospatial pattern of genetic expression and their functions from the fetal period to old age. Therefore, quantitative brain morphometry has often been employed in neuroscience investigations, while controlling for the scanner effect of the scanner is a critical issue for ensuring accuracy in brain morphometric studies of rare orphan diseases due to the lack of normal reference values available for multicenter studies. This study aimed to provide across-site normal reference values of global and regional brain volumes for each sex and age group in children and adolescents. We collected magnetic resonance imaging (MRI) examinations of 846 neurotypical participants aged 6.0–17.9 years (339 male and 507 female participants) from 5 institutions comprising healthy volunteers or neurotypical patients without neurological disorders, neuropsychological disorders, or epilepsy. Regional-based analysis using the CIVET 2.1.0. pipeline provided regional brain volumes, and the measurements were across-site combined using ComBat-GAM harmonization. The normal reference values of global and regional brain volumes and lateral indices in our study could be helpful for evaluating the characteristics of the brain morphology of each individual in a clinical setting and investigating the brain morphology of ultra-rare diseases.

Infantile epileptic spasm syndrome (IESS) is developmental epileptic encephalopathy occurring in childhood. It is often difficult to predict long-term seizure outcomes at the time of onset, particularly in patients with unknown etiology. Predicting long-term seizure outcomes from electroencephalograms (EEG) at onset may be helpful for decision-making on therapeutic strategy by clinicians and families. This study included twenty-two patients with IESS of unknown etiology with a median follow-up period of 11 years (range 5-14 years). We categorized patients diagnosed with IESS of unknown etiology into good and poor outcome groups according to the presence or absence of seizures at the time of the last follow-up. Fifteen patients were categorized into the good outcome group, and the rest into the poor outcome group. A machine learning (ML) model was developed using scale EEG data during sleep to identify patients in the poor outcome group. A self-attention autoencoder (SA-AE), which is an anomaly detection method for time-series data, was adopted because the numbers of patients in the poor and good outcome groups were unbalanced. The ML model achieved a sensitivity of $1.00\pm 0.05$, specificity of $0.88\pm 0.11$, and accuracy of $0.95\pm 0.06$. In addition, the AUROC was $0.91\pm 0.09$. Thus, the SA-AE model under development enabled highly accurate prediction of long-term prognosis in patients with IESS of unknown etiology. We confirmed that trained attention among the EEG channels could be explained from the viewpoint of pathophysiology of IESS. This study contributes to the determination of appropriate treatment for patients with IESS of unknown etiology.