It has been more than 30 years since magnetic resonance imaging (MRI) was applied to clinical research on psychiatric disorders. From case‐control studies with a few cases, MRI research for psychiatric disorders has progressed to multi‐site studies and meta‐analysis. Currently, the research trend is cross‐disease analysis and normative modeling based on harmonization techniques and non‐linear statistics, handling large‐scale data from multi sites. Under these considerations, machine learning classification and subtyping are relatively easy compared to other research field such as genetics. Since MRI‐based classification and subtypes become popular, a multi‐layer database is needed to utilize the classification. Due to the recent advances in MRI measurement and analysis techniques, it has become more difficult for novice researchers to tackle all analytical steps in clinical MRI research. This situation is also seen in other research fields like genetics. As multi‐layer data continue to progress in both quality and quantity, biological psychiatrists will be limited in the areas they can handle. However, they should organize multi‐disciplinary teams and show the right way to clinical psychiatry.

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Schizophrenia is a complex psychiatric disorder with clinical and genetic heterogeneity. Schizophrenia shows multifactorial inheritance with a heritability of 80%. It is suggested the involvement of disorder‐specific genetic factors and genetic factors shared with other psychiatric disorders in the pathogenesis of schizophrenia. Furthermore, not only genetic factors but also environmental factors contribute to the pathogenesis of schizophrenia. As for genetic factors, a large number of genetic polymorphisms are involved in the development of schizophrenia. Several environmental factors may also influence methylation patterns, and have an impact on development and pathogenesis. Overall, it has been suggested that schizophrenia may be caused by gene‐environment interactions, but the mechanisms are not yet fully understood. The polygenic risk score (PRS) and methylation risk score (MRS) are methods to assess an individual’s risk of disorder based on the accumulation of genetic polymorphisms and DNA methylation patterns at numerous loci across the whole genome, respectively. These indices are independent genetic markers, and both PRS and MRS are expected to contribute to the risk of schizophrenia.

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It is believed that the onset of schizophrenia involves complex interactions between genetic and environmental factors. Epigenetic approaches, such as DNA methylation analysis, provide a method for analyzing the influence of environmental factors at the molecular level. In the field of psychiatric disorders, comprehensive DNA methylation analysis, in addition to candidate gene analysis, has been widely conducted. The accumulation of large‐scale DNA methylation data has facilitated the application of various epigenetic clocks. Here, we summarize the current status of the application of epigenetic clocks in schizophrenia.

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The study of extracellular vesicles, particularly important in cancer research, is now attracting attention in psychiatry. In particular, exosomes, which are critical for intercellular communication, encapsulate a wide variety of biological substances. Brain‐derived exosomes can be extracted from peripheral blood, a process referred to as “brain liquid biopsy”, which is being widely discussed for its diagnostic potential in psychiatry. In autism spectrum disorders, activation of immune cells both inside and outside the brain has been reported, but the underlying pathophysiology remains unclear. This review explores the possibility that exosomes, due to their high permeability across the blood‐brain barrier, could affect immune cells inside and outside the brain, potentially triggering similar immune responses in both regions. The involvement of CNS‐associated macrophages, which are located in brain‐boundary areas more vulnerable to the blood‐brain barrier than in the brain parenchyma, is also discussed. These explanations underscore the potential of exosomes as a key player in the understanding and diagnosis of psychiatric disorders through their interaction with immune processes.

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Electroencephalography (EEG) has garnered increasing attention as a tool for translational research aiming to integrate clinical and basic research insights, offering optimal detection and evaluation of dynamic neural activity deficits in mental disorders. With a rich history in psychiatric research, including recent studies on neural oscillations, EEG research in psychiatric disorders, particularly focused on schizophrenia, has accumulated substantial neurophysiological evidence that could serve as biomarkers. However, owing to the heterogeneity inherent in schizophrenia, applications for diagnosis and treatment remain limited. In this review, we present the significance, current challenges and practical initiatives of a nationwide collaborative multi‐facility clinical EEG research project launched in 2020. Recently, each participating facility has established a framework for conducting research tailored to its characteristics, enabling the collection of transdiagnostic EEG data. Moreover, standardized measurement environments across all facilities and centralized database management have facilitated uniform and high‐quality data collection, supported by interdisciplinary analysis teams for advanced data processing. This collaborative research framework is expected to propel the advancement of neurophysiological investigations in psychiatric disorders, with a particular emphasis on clinical applications.

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Screen time (ST) is the amount of time per day spent viewing digital screens such as smartphones, tablets, games, television, and computers. The authors examined the relationship between ST in infancy and the child’s neurodevelopment through the literature review and the authors’ data. The results showed that the longer the ST in infancy, the more likely there was a delay in neurodevelopment in the preschool years, especially in the development of language and communication domains. However, we found that : 1) ST is not delayed if it is educational or co‐viewing with parents ; 2) there is no association between ST in infancy and socialization, daily life skill, or motor functions ; 3) outdoor activities reduce the total amount of ST and provide a buffer for the association between ST and neurodevelopment ; and 4) The association between ST and neurodevelopment in infancy may not be causal. Rather, ST in infancy may be a manifestation of delayed neurodevelopment and genetic risk for neurodevelopmental disorders.

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