Epigenetic status, such as DNA methylation and histone modifications, reflect gene‐environment interactions and are considered to be important for understanding the etiology and pathophysiology of psychiatric disorders. Because the serotonin transporter is subject to transcriptional regulation by polymorphic regions of repeated sequences and DNA methylation, it is considered to be a good model for gene‐environment interaction studies. Here, we summarized our epigenetic research on the serotonin transporter, in which we examined the decline in cognitive function and depressive tendencies associated with normal aging using a cohort of elderly individuals. In addition to the background of the research, we described the current results as well as issues and future prospects.

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Patients with mental disorders rarely visit psychiatrists from the early stages of illness, which tends to delay the introduction of appropriate medical care. On the other hand, it is not uncommon for these patients to visit non‐psychiatric physicians for their physical symptoms. Therefore, development of biomarkers by blood sampling that can be performed outside of psychiatry will lead to early detection and intervention of mental disorders. With this expectation, we are searching for objective blood biomarkers of mental disorders. We herein introduce about blood metabolome analysis and present our recent studies focusing on depression and hikikomori (pathological social withdrawal) . We have found significant associations between depressive severity and 3‐hydroxybutyrate, suicidal ideation and kynurenine metabolites, and hikikomori and acylcarnitine/arginine. Establishment of objective biological evaluation systems for mental disorders is expected to lead to the realization of early detection and intervention of mental disorders as well as to the elimination of prejudice and stigma against mental disorders.

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Early psychosis, which is a concept that evolved in association with early intervention activities for psychiatric disorders, generally includes first episode psychosis (e. g., schizophrenia) and clinical high‐risk state for developing psychosis (at‐risk mental state : ARMS) . Magnetic resonance imaging studies of early psychosis have suggested that brain morphological features, such as gray matter volume changes, contribute to discriminating schizophrenia patients from healthy controls in the early illness stages and to predicting future psychosis onset in ARMS individuals. Further development of brain imaging studies for early psychosis in multicenter design and combination of brain imaging findings with other biological indices, such as event‐related potentials and blood markers, would lead to clinical application of these findings, while there are still remaining issues to be addressed before transferring these research findings to the clinical setting.

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There is an expectation that the findings from human brain magnetic resonance imaging (MRI) for psychiatric disorders could differentiate diagnostic categories and predict future prognosis. However, obtained differences between patients and healthy controls have a considerable overlaps and there has been no developed biological markers from MRI studies. Recently, machine learning and deep learning methodologies which use a lot of variables have been popular, but to utilize the techniques, we need to properly harmonize the MRI data from multiple sites and procedures. We continue to explore for the limitation of the MRI data from multi sites, and build machine learning classifiers which could help to use in clinical settings and to elucidate pathophysiology of psychiatric disorders. For the upcoming large‐sample brain MRI studies, we established a novel MRI protocol with high‐resolution multi‐modal images, and developed a traveling subject method to harmonize the data. In the future, collaborative studies are expected to enable a high‐quality multi‐site MRI harmonization and to promote international collaboration for standardized diagnosis of neuro‐psychiatric diseases with neuroimaging.

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Information technologies such as deep learning and machine learning have made remarkable progress, and their effectiveness has been demonstrated in brain imaging research related to psychiatric disorders. For example, the application of supervised learning to resting‐state functional magnetic resonance imaging (fMRI) data has been used to identify psychiatric disorders based on their biological basis, and unsupervised learning has been used for subtyping of psychiatric disorders. However, there have been problems such as small effect sizes on the relationship between resting brain activity and cognitive functions, inter‐imaging‐site differences in brain imaging data, and development of the technologies based on DSM diagnoses. In this paper, I discuss these problems, the extent to which brain imaging studies have revealed psychiatric disorders, and what needs to be done now. I introduce our efforts to overcome these problems, constructing a large multi‐imaging‐site, multi‐disorder dataset, developing a novel harmonization technique to mitigate inter‐site differences in brain imaging data, and investigating subtypes of psychiatric disorders based on the brain circuit.

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In schizophrenia and other psychiatric disorders, the proximity of various neural indexes to the disease mechanisms has been investigated in order to elucidate the pathophysiology and to establish effective treatments. Neural oscillations, rhythmic cortical activity that is based on the balance in excitatory and inhibitory neurons, are considered to be particularly promising candidates for neurophysiological biomarkers among these indices. Abnormal findings of neural oscillations, including impaired smooth shift between spontaneous and task‐related activity, are consistently observed in schizophrenia. In addition, the upcoming large‐scale EEG data will reinforce these findings and may facilitate further translational research.

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