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Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). T1-weighted structural brain MRI scans from 1165 individuals at CHR (CHR-PS+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 228), and 1029 HCs, were obtained from 21 sites. We used ComBat to harmonize measures of subcortical volume, cortical thickness and surface area data and corrected for non-linear effects of age and sex using a general additive model. CHR-PS+ (n = 120) and HC (n = 799) data from 20 sites served as a training dataset, which we used to build a classifier. The remaining samples were used external validation datasets to evaluate classifier performance (test, independent confirmatory, and independent group [CHR-PS- and CHR-UNK] datasets). The accuracy of the classifier on the training and independent confirmatory datasets was 85% and 73% respectively. Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%). We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample. The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis. Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings.

Background: Understanding the etiology of psychosis is essential to the development of preventive interventions. The COVID‐19 pandemic provides a rare natural experiment that can expand our understanding of the role of social factors in the trajectories and etiology of psychosis across adolescence, particularly in Tokyo where the prevalence of actual COVID‐19 infection remained low. We hypothesized that the likelihood of self‐reporting psychotic experiences (PEs) would increase following the onset of the COVID‐19 pandemic. Methods: The Tokyo Teen Cohort (TTC) is a prospective cohort study of adolescents in the general population of the Tokyo metropolitan area, followed from age 10 to 16 years. We used multi‐level linear regression models to test the associations between the phase of the COVID‐19 pandemic and self‐reported PEs. Results: Among 1935 adolescents included in the analysis, a rapid increase in PEs occurred at the onset of the COVID‐19 pandemic, following approximately 6 years of steady decline across prior waves. This association was more pronounced for boys compared to girls. This increase became more pronounced as the pandemic moved into later phases, defined based on contemporaneous sociopolitical changes in Tokyo (i.e. changes to school closure, social distancing guidelines, and the state of emergency status). Conclusions: The steady decline in PEs across adolescence was halted and reversed concurrent with the COVID‐19 pandemic onset, despite very low rates of COVID‐19 infection. This implicates COVID‐19 related socioenvironmental factors as contributory etiological factors in the development of PEs in this adolescent cohort. [ABSTRACT FROM AUTHOR]

Background: Little information is available on the association between gender nonconformity during adolescence and subsequent mental health. While the distress related to gender nonconformity may be socially produced rather than attributed to individual-level factors, further research is needed to better understand the role of psychosocial factors in this context. Method: We analyzed data from the Tokyo Teen Cohort, obtained through random sampling of adolescents born between 2002 and 2004. We used inverse probability weighting to examine the association of gender nonconformity at ages 12 and 14 as a time-varying variable with subsequent mental health at age 16, while accounting for time-fixed and time-varying confounders. Furthermore, we used a weighting approach to investigate the mediating role of modifiable psychosocial factors in this association, addressing exposure-mediator and mediator–mediator interactions. Results: A total of 3171 participants were analyzed. Persistent gender nonconforming behavior at ages 12 and 14 was associated with subsequent depression (β = 2.02, 95% confidence interval [CI] 0.85 to 3.19) and psychotic experiences (β = 0.33, 95% CI 0.14 to 0.52) at age 16. The results remained robust in sensitivity analyses. Approximately 30% of the association between gender nonconformity and depression was consistently mediated by a set of psychosocial factors, namely loneliness, bullying victimization, and relationships with mother, father, and friends. Conclusions: Persistent gender nonconformity during adolescence is associated with subsequent mental health. Psychosocial factors play a vital mediating role in this association, highlighting the essential need for social intervention and change to reduce stigmatization and ameliorate mental health challenges. [ABSTRACT FROM AUTHOR]

Gamma oscillations, thought to arise from the activity of ɣ‐aminobutyric acid (GABA)ergic interneurons, have potential as a biomarker for schizophrenia. Gamma‐band auditory steady‐state responses (ASSRs) are notably reduced in both chronic and early‐stage schizophrenia patients. Furthermore, alterations in gamma‐band ASSRs have been demonstrated in animal models through translational research. However, the 40‐Hz harmonic responses of the 20‐Hz ASSR are not as well‐characterized, despite the possibility that these harmonic oscillatory responses may reflect resonant activity in neural circuits. In this study, we investigated the 40‐Hz harmonic response to the 20‐Hz ASSR in the early stages of schizophrenia. The study recruited 49 participants, including 15 individuals at ultra‐high‐risk (UHR) for psychosis, 13 patients with first‐episode schizophrenia (FES), and 21 healthy controls (HCs). The 40‐Hz harmonic responses of the 20‐Hz ASSR were evident in all groups. Interestingly, while previous report observed reduced 40‐Hz ASSRs, the 40‐Hz harmonic responses of the 20‐Hz ASSR were not reduced in the UHR or FES groups. These findings suggest that the gamma‐band ASSR and its harmonic responses may represent distinct aspects of pathophysiology in the early stages of schizophrenia. [ABSTRACT FROM AUTHOR]

Aim: The present study aimed to examine the association between copy number variations (CNVs) in parkin (PRKN) and schizophrenia (SCZ) and autism spectrum disorder (ASD) in a large case–control sample. Method: Array comparative genomic hybridization was performed on 3111 cases with SCZ, 1236 cases with ASD, and 2713 controls. We systematically prioritized likely pathogenic CNVs (LP‐CNVs) in PRKN and examined their association with SCZ and ASD. Results: In total, 3014 SCZ cases (96.9%), 1205 ASD cases (97.5%), and 2671 controls (98.5%) passed quality control. We found that monoallelic carriers of LP‐CNVs in PRKN were common (70/6890, 1.02%) and were not at higher risk of SCZ (p = 0.29) or ASD (p = 0.72). We observed that the distribution pattern of LP‐CNVs in the Japanese population was consistent with those in other populations. We also identified a patient diagnosed with SCZ and early‐onset Parkinson's disease carrying biallelic pathogenic CNVs in PRKN. The absence of Parkinson's symptoms in 10 other monoallelic carriers of the same pathogenic CNV further reflects the lack of effect of monoallelic pathogenic variants in PRKN in the absence of a second hit. Conclusion: The present findings suggest that monoallelic CNVs in PRKN do not confer a significant risk for SCZ or ASD. However, further studies to investigate the association between biallelic CNVs in PRKN and SCZ and ASD are warranted. [ABSTRACT FROM AUTHOR]

Neuroimaging databases for neuro-psychiatric disorders enable researchers to implement data-driven research approaches by providing access to rich data that can be used to study disease, build and validate machine learning models, and even redefine disease spectra. The importance of sharing large, multi-center, multi-disorder databases has gradually been recognized in order to truly translate brain imaging knowledge into real-world clinical practice. Here, we review MRI databases that share data globally to serve multiple psychiatric or neurological disorders. We found 42 datasets consisting of 23,293 samples from patients with psychiatry and neurological disorders and healthy controls; 1245 samples from mood disorders (major depressive disorder and bipolar disorder), 2015 samples from developmental disorders (autism spectrum disorder, attention-deficit hyperactivity disorder), 675 samples from schizophrenia, 1194 samples from Parkinson's disease, 5865 samples from dementia (including Alzheimer's disease), We recognize that large, multi-center databases should include governance processes that allow data to be shared across national boundaries. Addressing technical and regulatory issues of existing databases can lead to better design and implementation and improve data access for the research community. The current trend toward the development of shareable MRI databases will contribute to a better understanding of the pathophysiology, diagnosis and assessment, and development of early interventions for neuropsychiatric disorders., (© 2024 The Author(s). Psychiatry and Clinical Neurosciences published by John Wiley & Sons Australia, Ltd on behalf of Japanese Society of Psychiatry and Neurology.)

Purpose: Young caregivers experience, on average, poorer mental health outcomes than non-caregiving young people. However, it is unknown to what extent these effects differ with age, or among short-term versus long-term caregivers. Using repeated assessment of young caregiving across multiple waves of a prospective adolescent cohort study, we conducted repeated cross-sectional analyses of caregiver status and contemporaneous depressive symptoms, self-harm, and suicidality in early to middle adolescence., Methods: Four waves of questionnaire data from a large, longitudinal population-based cohort study (Tokyo Teen Cohort) were analyzed. Caregiver status was collected from participants aged 10, 12, 14, and 16 years. Mental health outcomes assessed were depressive symptoms, self-harm and suicidal feelings. Logistic regression analyses were conducted assessing effects of a) young caregiver status and b) new, long-term, and ex-caregiver 2-year categorizations on mental health outcomes at 12, 14, and 16 years, both unadjusted and adjusted for potential confounders (sex, low income, single-parent household, and parental distress)., Results: Depressive symptoms were elevated among long-term caregivers at 14 years (unadjusted odds ratio (uOR): 3.11 [1.33-7.27], adjusted odds ratio (aOR): 2.49 [1.03-5.99]). Borderline associations between long-term caregiving and self-harm (uOR: 3.14 [1.06-9.35], aOR: 2.51 [0.82-7.63]) and suicidal feelings (uOR: 2.49 [0.98-6.34], aOR: 2.06 [0.80-5.33]) were detected at 16 years. No associations were found at age 12 years in primary analyses; sensitivity analyses indicated possible increased depressive symptoms., Discussion: Young caregivers with long-term caregiving roles are at the greatest risk for negative mental health outcomes, with effects concentrated in later adolescence. These findings highlight urgent need for early identification and practical and psychological support for young people shouldering caregiving burdens., (Copyright © 2024 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.)

Background and Hypothesis: Problematic internet use (PIU) is prevalent among adolescents. Past research suggested cross-sectional associations between PIU and psychotic experiences, but little information is available on the longitudinal association. We hypothesized that PIU in adolescence may be longitudinally associated with psychotic experiences, adjusting for confounders., Study Design: We analyzed a random sample of adolescents in the Tokyo Teen Cohort to examine how PIU at ages 10 (2012-2015), 12 (2014-2017), and 16 (2019-2021) was associated with mental health issues at age 16. PIU was evaluated by the modified Compulsive Internet Use Scale, psychotic experiences by the Adolescent Psychotic-like Symptom Screener, and depression by the Short Mood and Feelings Questionnaire. We also examined the mediating role of social withdrawal., Study Results: We analyzed 3171 adolescents; 151 reported psychotic experiences and 327 reported depression at age 16. Compared with the lowest tertile PIU group, the highest tertile PIU group at age 12 showed an increased adjusted risk of psychotic experiences (RD 3.3%, 95% CI 2.9%-3.7%; RR 1.65, 95% CI 1.55-1.73) and depression (RD 5.9%, 95% CI 5.5%-6.3%; RR 1.61, 95% CI 1.55-1.68) at age 16. PIU at age 16 showed analogous results, while PIU at age 10 suggested a smaller impact. Social withdrawal mediated 9.4%-29.0% of the association between PIU and psychotic experiences., Conclusions: PIU is longitudinally associated with psychotic experiences and depression in adolescents. Further longitudinal and intervention studies are warranted to provide robust public health implications and foster a safer digital future., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: Schizophrenia is a complex mental disorder characterized by significant cognitive and neurobiological alterations. Impairments in cognitive function and eye movement have been known to be promising biomarkers for schizophrenia. However, cognitive assessment methods require specialized expertise. To date, data on simplified measurement tools for assessing both cognitive function and eye movement in patients with schizophrenia are lacking., Objective: This study aims to assess the efficacy of a novel tablet-based platform combining cognitive and eye movement measures for classifying schizophrenia., Methods: Forty-four patients with schizophrenia, 67 healthy controls, and 41 patients with other psychiatric diagnoses participated in this study from 10 sites across Japan. A free-viewing eye movement task and 2 cognitive assessment tools (Codebreaker task from the THINC-integrated tool and the CognitiveFunctionTest app) were used for conducting assessments in a 12.9-inch iPad Pro. We performed comparative group and logistic regression analyses for evaluating the diagnostic efficacy of the 3 measures of interest., Results: Cognitive and eye movement measures differed significantly between patients with schizophrenia and healthy controls (all 3 measures; P<.001). The Codebreaker task showed the highest classification effectiveness in distinguishing schizophrenia with an area under the receiver operating characteristic curve of 0.90. Combining cognitive and eye movement measures further improved accuracy with a maximum area under the receiver operating characteristic curve of 0.94. Cognitive measures were more effective in differentiating patients with schizophrenia from healthy controls, whereas eye movement measures better differentiated schizophrenia from other psychiatric conditions., Conclusions: This multisite study demonstrates the feasibility and effectiveness of a tablet-based app for assessing cognitive functioning and eye movements in patients with schizophrenia. Our results suggest the potential of tablet-based assessments of cognitive function and eye movement as simple and accessible evaluation tools, which may be useful for future clinical implementation., (©Kentaro Morita, Kenichiro Miura, Atsuhito Toyomaki, Manabu Makinodan, Kazutaka Ohi, Naoki Hashimoto, Yuka Yasuda, Takako Mitsudo, Fumihiro Higuchi, Shusuke Numata, Akiko Yamada, Yohei Aoki, Hiromitsu Honda, Ryo Mizui, Masato Honda, Daisuke Fujikane, Junya Matsumoto, Naomi Hasegawa, Satsuki Ito, Hisashi Akiyama, Toshiaki Onitsuka, Yoshihiro Satomura, Kiyoto Kasai, Ryota Hashimoto. Originally published in JMIR Mental Health (https://mental.jmir.org), 30.05.2024.)

Introduction: Macronutrient intake can be one of the most influential factors in cognitive and neural development in adolescents. Adolescence is a specific period of cognitive and neural development, and nutritional effects during this period could be life-long. Therefore, understanding the effects of macronutrient intake on cognitive and neural development in adolescents is crucially important. We thus examined the association across macronutrient intake, intelligence, and neural development using population-based cohort data., Methods: We conducted two studies. In study 1, we included a total of 1,734 participants (boys, 907, age [mean ± standard deviation] 171.9 ± 3.44 months; range 163.0-186.0 months) from the Tokyo TEEN Cohort (TTC) to examine the association between macronutrient intake and intelligence quotient (IQ). In study 2, we included a total of 63 participants (boys, 38, age 174.4 ± 7.7 months; range 160.7-191.6 months) to investigate the effect of nutrition intake on neural development using graph theory analysis for resting-state functional magnetic resonance imaging (rs-fMRI) derived from a subset of the TTC., Results: TTC data revealed that a higher IQ was associated in boys with increased protein intake ( β  = 0.068, p  = 0.031), and in girls, with reduced carbohydrate intake ( β  = -0.076, p  = 0.024). Graph theory analysis for rs-fMRI at approximately age 12 has shown that impaired local efficiency in the left inferior frontal gyrus was associated with higher carbohydrate and fat intake ([x, y, z] = [-51, 23, 8], p FDR-corrected  = 0.00018 and 0.02290, respectively), whereas increased betweenness centrality in the left middle temporal gyrus was associated with higher carbohydrate, fat, and protein intake ([x, y, z] = [-61, -43, -13], p FDR-corrected  = 0.0027, 0.0029, and 0.00075, respectively). Moreover, we identified a significant moderating effect of fat and protein intake on the relationship between change in betweenness centrality over a 2-year measurement gap in the left middle temporal gyrus and intelligence ( β  = 12.41, p  = 0.0457; β  = 12.12, p  = 0.0401, respectively)., Conclusion: Our study showed the association between macronutrient intake and neural development related to intelligence in early adolescents. Appropriate nutritional intake would be a key factor for healthy cognitive and neural development., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Nakamura, Yamasaki, Okada, Ando, Nishida, Kasai and Koike.)

Importance: The lack of robust neuroanatomical markers of psychosis risk has been traditionally attributed to heterogeneity. A complementary hypothesis is that variation in neuroanatomical measures in individuals at psychosis risk may be nested within the range observed in healthy individuals., Objective: To quantify deviations from the normative range of neuroanatomical variation in individuals at clinical high risk for psychosis (CHR-P) and evaluate their overlap with healthy variation and their association with positive symptoms, cognition, and conversion to a psychotic disorder., Design, Setting, and Participants: This case-control study used clinical-, IQ-, and neuroimaging software (FreeSurfer)-derived regional measures of cortical thickness (CT), cortical surface area (SA), and subcortical volume (SV) from 1340 individuals with CHR-P and 1237 healthy individuals pooled from 29 international sites participating in the Enhancing Neuroimaging Genetics Through Meta-analysis (ENIGMA) Clinical High Risk for Psychosis Working Group. Healthy individuals and individuals with CHR-P were matched on age and sex within each recruitment site. Data were analyzed between September 1, 2021, and November 30, 2022., Main Outcomes and Measures: For each regional morphometric measure, deviation scores were computed as z scores indexing the degree of deviation from their normative means from a healthy reference population. Average deviation scores (ADS) were also calculated for regional CT, SA, and SV measures and globally across all measures. Regression analyses quantified the association of deviation scores with clinical severity and cognition, and 2-proportion z tests identified case-control differences in the proportion of individuals with infranormal (z < -1.96) or supranormal (z > 1.96) scores., Results: Among 1340 individuals with CHR-P, 709 (52.91%) were male, and the mean (SD) age was 20.75 (4.74) years. Among 1237 healthy individuals, 684 (55.30%) were male, and the mean (SD) age was 22.32 (4.95) years. Individuals with CHR-P and healthy individuals overlapped in the distributions of the observed values, regional z scores, and all ADS values. For any given region, the proportion of individuals with CHR-P who had infranormal or supranormal values was low (up to 153 individuals [<11.42%]) and similar to that of healthy individuals (<115 individuals [<9.30%]). Individuals with CHR-P who converted to a psychotic disorder had a higher percentage of infranormal values in temporal regions compared with those who did not convert (7.01% vs 1.38%) and healthy individuals (5.10% vs 0.89%). In the CHR-P group, only the ADS SA was associated with positive symptoms (β = -0.08; 95% CI, -0.13 to -0.02; P = .02 for false discovery rate) and IQ (β = 0.09; 95% CI, 0.02-0.15; P = .02 for false discovery rate)., Conclusions and Relevance: In this case-control study, findings suggest that macroscale neuromorphometric measures may not provide an adequate explanation of psychosis risk.