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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.

BackgroundVideo game loot boxes, which can typically be purchased by players or are given as reward, contain random virtual items, or loot, ranging from simple customization options for a player's avatar or character, to game-changing equipment such as weapons and armor. Loot boxes have drawn concern, as purchasing loot boxes might lead to the development of problematic gambling for adolescents. Although parental problem gambling is associated with adolescent problem gambling, no studies have evaluated the prevalence of loot box purchases in adolescents’ parents. ObjectiveThis study investigated the association between loot box purchasing among adolescents and parents, and problem online gaming in population-based samples. MethodsIn total, 1615 adolescent (aged 14 years) gamers from Japan responded to a questionnaire regarding their loot box purchasing and problem online gaming behaviors. Problem online gaming was defined as four or more of the nine addictive behaviors from the Diagnostic and Statistical Manual of Mental Disorders. The adolescents’ primary caregivers were asked about their loot box purchasing. ResultsOf the 1615 participants, 57 (3.5%) reported loot box purchasing. This prevalence did not differ according to primary caregivers’ loot box purchasing, but adolescents who purchased loot boxes were significantly more likely to exhibit problem online gaming (odds ratio 3.75, 95% CI 2.17-6.48). ConclusionsAdolescent loot box purchasing is linked to problem online gaming, but not with parents’ loot box purchasing. Measures to reduce these behaviors should target reducing addictive symptoms in young video gamers.

Differential diagnosis is sometimes difficult in practical psychiatric settings, in terms of using the current diagnostic system based on presenting symptoms and signs. The creation of a novel diagnostic system using objective biomarkers is expected to take place. Neuroimaging studies and others reported that subcortical brain structures are the hubs for various psycho-behavioral functions, while there are so far no neuroimaging data-driven clinical criteria overcoming limitations of the current diagnostic system, which would reflect cognitive/social functioning. Prior to the main analysis, we conducted a large-scale multisite study of subcortical volumetric and lateralization alterations in schizophrenia, bipolar disorder, major depressive disorder, and autism spectrum disorder using T1-weighted images of 5604 subjects (3078 controls and 2526 patients). We demonstrated larger lateral ventricles volume in schizophrenia, bipolar disorder, and major depressive disorder, smaller hippocampus volume in schizophrenia and bipolar disorder, and schizophrenia-specific smaller amygdala, thalamus, and accumbens volumes and larger caudate, putamen, and pallidum volumes. In addition, we observed a leftward alteration of lateralization for pallidum volume specifically in schizophrenia. Moreover, as our main objective, we clustered the 5,604 subjects based on subcortical volumes, and explored whether data-driven clustering results can explain cognitive/social functioning in the subcohorts. We showed a four-biotype classification, namely extremely (Brain Biotype [BB] 1) and moderately smaller limbic regions (BB2), larger basal ganglia (BB3), and normal volumes (BB4), being associated with cognitive/social functioning. Specifically, BB1 and BB2-3 were associated with severe and mild cognitive/social impairment, respectively, while BB4 was characterized by normal cognitive/social functioning. Our results may lead to the future creation of novel biological data-driven psychiatric diagnostic criteria, which may be expected to be useful for prediction or therapeutic selection.

According to the operational diagnostic criteria, psychiatric disorders such as schizophrenia (SZ), bipolar disorder (BD), major depressive disorder (MDD), and autism spectrum disorder (ASD) are classified based on symptoms. While its cluster of symptoms defines each of these psychiatric disorders, there is also an overlap in symptoms between the disorders. We hypothesized that there are also similarities and differences in cortical structural neuroimaging features among these psychiatric disorders. T1-weighted magnetic resonance imaging scans were performed for 5,549 subjects recruited from 14 sites. Effect sizes were determined using a linear regression model within each protocol, and these effect sizes were meta-analyzed. The similarity of the differences in cortical thickness and surface area of each disorder group was calculated using cosine similarity, which was calculated from the effect sizes of each cortical regions. The thinnest cortex was found in SZ, followed by BD and MDD. The cosine similarity values between disorders were 0.943 for SZ and BD, 0.959 for SZ and MDD, and 0.943 for BD and MDD, which indicated that a common pattern of cortical thickness alterations was found among SZ, BD, and MDD. Additionally, a generally smaller cortical surface area was found in SZ and MDD than in BD, and the effect was larger in SZ. The cosine similarity values between disorders were 0.945 for SZ and MDD, 0.867 for SZ and ASD, and 0.811 for MDD and ASD, which indicated a common pattern of cortical surface area alterations among SZ, MDD, and ASD. Patterns of alterations in cortical thickness and surface area were revealed in the four major psychiatric disorders. To our knowledge, this is the first report of a cross-disorder analysis conducted on four major psychiatric disorders. Cross-disorder brain imaging research can help to advance our understanding of the pathogenesis of psychiatric disorders and common symptoms.

Background: The 22q11.2 deletion syndrome (22q11DS) is characterised by a changing pattern of overlapping intellectual, physical, and mental disabilities along the course of one's life. However, the impact of overlapping disorders (multimorbidity) on educational challenges remains unclear. Method: A survey was conducted with 88 caregivers of individuals with 22q11DS. A quantitative analysis of educational challenges and support needs divided into age groups (7-12, 13-15, 16-18, and 19 years and over) and a qualitative analysis of the free-text items in the questionnaire was conducted. Results: Caregivers were more interested in comprehensive developmental support when their children were younger, and the emphasis shifted to concerns regarding environments that matched individual characteristics at older ages. Furthermore, when there are multiple disabilities or disorders, support is concentrated on the more obvious disabilities, and the lack of support for the less superficially obvious disabilities associated with multiple difficulties, including mental health problems, can be a challenge for people with 22q11DS and their families. Conclusions: This study suggests a need for increased focus on multimorbidity and associated disabilities in school education that are difficult to observe because of their mildness or borderline levels if present alone.

Abstract The gamma-band auditory steady-state response (ASSR), primarily generated from the auditory cortex, has received substantial attention as a potential brain marker indicating the pathophysiology of schizophrenia. Previous studies have shown reduced gamma-band ASSR in patients with schizophrenia and demonstrated correlations with impaired neurocognition and psychosocial functioning. Recent studies in clinical and healthy populations have suggested that the neural substrates of reduced gamma-band ASSR may be distributed throughout the cortices surrounding the auditory cortex, especially in the right hemisphere. This study aimed to investigate associations between the gamma-band ASSR and white matter alterations in the bundles broadly connecting the right frontal, parietal and occipital cortices to clarify the networks underlying reduced gamma-band ASSR in patients with schizophrenia. We measured the 40 Hz ASSR using electroencephalography and diffusion tensor imaging in 42 patients with schizophrenia and 22 healthy comparison subjects. The results showed that the gamma-band ASSR was positively correlated with fractional anisotropy (an index of white matter integrity) in the regions connecting the right frontal, parietal and occipital cortices in healthy subjects (β = 0.41, corrected p = 0.075, uncorrected p = 0.038) but not in patients with schizophrenia (β = 0.17, corrected p = 0.46, uncorrected p = 0.23). These findings support our hypothesis that the generation of gamma-band ASSR is supported by white matter bundles that broadly connect the cortices and that these relationships may be disrupted in schizophrenia. Our study may help characterize and interpret reduced gamma-band ASSR as a useful brain marker of schizophrenia.

Abstract 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 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 Aim The neurodevelopmental delay hypothesis may explain the mechanism of nocturnal enuresis (NE), but whether the hypothesis holds in early adolescence remains unknown, considering NE frequency. Therefore, we aimed to identify whether neurodevelopmental delays in infancy were associated with the frequency of NE in early adolescence. Methods Data were extracted from the Tokyo Early Adolescence Survey, in which 4478 children (mean age: 10.2 years) and their parents participated. Twelve developmental delays (eight motor, four verbal) were extracted from the Maternal and Child Health Handbook. The NE frequency was obtained from a parent‐answered questionnaire, which divided children into three categories according to DSM‐5 criteria (none, infrequent [

Abstract Aims This study investigated fatigue in individuals with 22q11.2 deletion syndrome (22q11.2DS), identifying both physical and psychosocial factors. Methods We conducted a cross‐sectional web‐based survey of 125 parents of individuals with 22q11.2DS. Significant factors of fatigue were identified using bivariate analysis and forward stepwise binary logistic regression. Results Overall, 34% (42 of 125) of the participants reported fatigue. Bivariate analysis identified 37 variables linked with fatigue, of which 11 were significant in multivariable analysis: older age (odds ratio [OR] = 1.28, 95% confidence interval [CI] [1.07, 1.53], P = 0.008), history of auditory hypersensitivity (OR = 36.4, 95% CI [4.08, 323.8], P = 0.001), delayed motor development (OR = 10.4, 95% CI [1.78, 61.0], P = 0.009), difficulty participating in events (OR = 33.5, 95% CI [1.1, 1068.2], P = 0.047), inability to join groups (OR = 47.9, 95% CI [4.8, 481.7], P = 0.001), concerns about marriage (OR = 17.0, 95% CI [2.6, 112.7], P = 0.003), inability to attend facilities (OR = 20.4, 95% CI [1.4, 308.0], P = 0.029), poor coordination when changing medical facilities (OR =

BackgroundSchizophrenia 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. ObjectiveThis study aims to assess the efficacy of a novel tablet-based platform combining cognitive and eye movement measures for classifying schizophrenia. MethodsForty-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. ResultsCognitive and eye movement measures differed significantly between patients with schizophrenia and healthy controls (all 3 measures; P

IntroductionMacronutrient 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.MethodsWe 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.ResultsTTC 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], pFDR-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], pFDR-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).ConclusionOur 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.

Individuals at Clinical High Risk for Psychosis (CHR-P) demonstrate heterogeneity in clinical profiles and outcome features. However, the extent of neuroanatomical heterogeneity in the CHR-P state is largely undetermined. We aimed to quantify the neuroanatomical heterogeneity in structural magnetic resonance imaging measures of cortical surface area (SA), cortical thickness (CT), subcortical volume (SV), and intracranial volume (ICV) in CHR-P individuals compared with healthy controls (HC), and in relation to subsequent transition to a first episode of psychosis. The ENIGMA CHR-P consortium applied a harmonised analysis to neuroimaging data across 29 international sites, including 1579 CHR-P individuals and 1243 HC, offering the largest pooled CHR-P neuroimaging dataset to date. Regional heterogeneity was indexed with the Variability Ratio (VR) and Coefficient of Variation (CV) ratio applied at the group level. Personalised estimates of heterogeneity of SA, CT and SV brain profiles were indexed with the novel Person-Based Similarity Index (PBSI), with two complementary applications. First, to assess the extent of within-diagnosis similarity or divergence of neuroanatomical profiles between individuals. Second, using a normative modelling approach, to assess the 'normativeness' of neuroanatomical profiles in individuals at CHR-P. CHR-P individuals demonstrated no greater regional heterogeneity after applying FDR corrections. However, PBSI scores indicated significantly greater neuroanatomical divergence in global SA, CT and SV profiles in CHR-P individuals compared with HC. Normative PBSI analysis identified 11 CHR-P individuals (0.70%) with marked deviation (>1.5 SD) in SA, 118 (7.47%) in CT and 161 (10.20%) in SV. Psychosis transition was not significantly associated with any measure of heterogeneity. Overall, our examination of neuroanatomical heterogeneity within the CHR-P state indicated greater divergence in neuroanatomical profiles at an individual level, irrespective of psychosis conversion. Further large-scale investigations are required of those who demonstrate marked deviation.

Identifying both the commonalities and differences in brain structures among psychiatric disorders is important for understanding the pathophysiology. Recently, the ENIGMA-Schizophrenia DTI Working Group performed a large-scale meta-analysis and reported widespread white matter microstructural alterations in schizophrenia; however, no similar cross-disorder study has been carried out to date. Here, we conducted mega-analyses comparing white matter microstructural differences between healthy comparison subjects (HCS; N = 1506) and patients with schizophrenia (N = 696), bipolar disorder (N = 211), autism spectrum disorder (N = 126), or major depressive disorder (N = 398; total N = 2937 from 12 sites). In comparison with HCS, we found that schizophrenia, bipolar disorder, and autism spectrum disorder share similar white matter microstructural differences in the body of the corpus callosum; schizophrenia and bipolar disorder featured comparable changes in the limbic system, such as the fornix and cingulum. By comparison, alterations in tracts connecting neocortical areas, such as the uncinate fasciculus, were observed only in schizophrenia. No significant difference was found in major depressive disorder. In a direct comparison between schizophrenia and bipolar disorder, there were no significant differences. Significant differences between schizophrenia/bipolar disorder and major depressive disorder were found in the limbic system, which were similar to the differences in schizophrenia and bipolar disorder relative to HCS. While schizophrenia and bipolar disorder may have similar pathological characteristics, the biological characteristics of major depressive disorder may be close to those of HCS. Our findings provide insights into nosology and encourage further investigations of shared and unique pathophysiology of psychiatric disorders.

Abstract Background The conceptualization of personal recovery began in Europe and North America and has spread worldwide. However, the concept of personal recovery in addition to recovery-promoting factors may be influenced by culture. We explored how users of mental health services in Japan perceive their own personal recovery and the factors that promote it. Methods We conducted semi-structured interviews and focus group interviews with individuals using mental health services. The interview data were analysed using thematic analysis with a grouped framework analysis approach. We used a coding framework based on the existing CHIME framework (connectedness, hope and optimism about the future, identity, meaning in life, and empowerment). Results Data were obtained from 30 users of mental health services (mean age: 40.4 years; 46.7% women; 50.0% with schizophrenia). “Compassion for others” was newly extracted in “Connectedness”, and “Rebuilding/redefining identity not being as shaped by social norms” was newly extracted in “Identity” as personal recovery. “Positive experiences in childhood” (including positive parenting support from neighbours) was newly extracted as a recovery-promoting factor. Conclusions Our unique findings on the rebuilding identity/defining identity free from conformity to social norms due to interactions with familiar people, including peers, may be culture dependent. This study raises overarching questions regarding how socio-cultural values influence the development of identity and personal values and how they are in turn reflected in personal recovery.

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. [ABSTRACT FROM AUTHOR]