Magnetoencephalography (MEG), as a non-invasive brain functional imaging technology, plays an increasingly important role in the diagnosis and treatment of pediatric neurological disorders. In recent years, the emergence of helium-free MEG based on optical pumped magnetometer (OPM) technology (OPM-MEG) has provided a novel tool for pediatric brain research and precise diagnosis and treatment of brain diseases. This article elaborates on the technical principles, clinical application standards, testing protocols, and reporting requirements for pediatric OPM-MEG, aiming to establish a scientific and standardized operational framework to promote its rational application and development in pediatric practice. The content covers key aspects such as core equipment parameters, indications and contraindications, pre-examination preparations, optimized operational workflows, data quality control, and reporting standards, offering comprehensive guidance for conducting pediatric OPM-MEG examinations.
Machine learning methods typically focus on the correlations within data while neglecting the causal relationships that reveal underlying mechanisms. This limitation may restrict the reliability and interpretability of models in decision support and intervention strategies. For this reason, causal discovery methods have gained widespread attention. They can infer causal structures and directions between variables from observational data, thereby providing decision-makers with an interpretable and intervenable analytical framework. This review introduces commonly used causal discovery methods based on observational data. Combined with specific case studies, it demonstrates and practices these methods using the R language, aiming to provide readers with practical references for understanding and applying causal discovery methods.