Association between copy number variations in parkin (PRKN) and schizophrenia and autism spectrum disorder: A case-control study.

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.

Machine learning algorithm-based estimation model for the severity of depression assessed using Montgomery-Asberg depression rating scale.

AIM: Depressive disorder is often evaluated using established rating scales. However, consistent data collection with these scales requires trained professionals. In the present study, the "rater & estimation-system" reliability was assessed between consensus evaluation by trained psychiatrists and the estimation by 2 models of the AI-MADRS (Montgomery-Asberg Depression Rating Scale) estimation system, a machine learning algorithm-based model developed to assess the severity of depression. METHODS: During interviews with trained psychiatrists and the AI-MADRS estimation system, patients responded orally to machine-generated voice prompts from the AI-MADRS structured interview questions. The severity scores estimated from two models of the AI-MADRS estimation system, the max estimation model and the average estimation model, were compared with those by trained psychiatrists. RESULTS: A total of 51 evaluation interviews conducted on 30 patients were analyzed. Pearson's correlation coefficient with the scores evaluated by trained psychiatrists was 0.76 (95% confidence interval 0.62-0.86) for the max estimation model, and 0.86 (0.76-0.92) for the average estimation model. The ANOVA ICC rater & estimation-system reliability with the evaluation scores by trained psychiatrists was 0.51 (-0.09 to 0.79) for the max estimation model, and 0.75 (0.55-0.86) for the average estimation model. CONCLUSION: The average estimation model of AI-MADRS demonstrated substantially acceptable rater & estimation-system reliability with trained psychiatrists. Accumulating a broader training dataset and the refinement of AI-MADRS interviews are expected to improve the performance of AI-MADRS. Our findings suggest that AI technologies can significantly modernize and potentially revolutionize the realm of depression assessments.

Subcortical volumetric alterations in four major psychiatric disorders: a mega-analysis study of 5604 subjects and a volumetric data-driven approach for classification.

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.

Cerebral cortical structural alteration patterns across four major psychiatric disorders in 5549 individuals.

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.

Positron emission tomography studies in adult patients with attention-deficit/hyperactivity disorder.

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by inattention, motor hyperactivity, impulsivity, and psychosocial as well as cognitive dysfunction. Although characteristic clinical manifestations have been described, no definitive biomarkers to diagnose ADHD have been established. In this review article, we summarize positron emission tomography (PET) studies conducted in adult patients with ADHD. We found that, although, disturbances of dopamine, serotonin, and norepinephrine functions have been implicated in ADHD, no characteristic findings have been identified from PET studies in patients with ADHD. Several previous PET studies on the central dopaminergic transmission-related ligands in patients with ADHD have shown altered binding of dopamine markers in the basal ganglia. However, no consistent results were observed in the binding characteristics for dopamine transporters and receptors. Findings from PET studies with ligands related to serotonin and norepinephrine pathways showed either unclear clinical significance or low replicability. Therefore, whether alterations of monoamine function may be involved in the pathophysiological mechanism remains to be clarified. The limitations of previous PET studies include their small sample sizes, focus on several kinds of existing ligands, and a questionable validity of the diagnosis (lack of biological diagnostic criteria). To determine the characteristic findings for diagnosing ADHD, further research is needed, and particularly, studies that evaluate new active ligands with specific binding to monoamine pathways should be undertaken.

Involvement of cerebellar and subcortical connector hubs in schizophrenia.

BACKGROUND: Schizophrenia is considered a brain connectivity disorder in which functional integration within the brain fails. Central to the brain's integrative function are connector hubs, brain regions characterized by strong connections with multiple networks. Given their critical role in functional integration, we hypothesized that connector hubs, including those located in the cerebellum and subcortical regions, are severely impaired in patients with schizophrenia. METHODS: We identified brain voxels with significant connectivity alterations in patients with schizophrenia (n = 76; men = 43) compared to healthy controls (n = 80; men = 43) across multiple large-scale resting state networks (RSNs) using a network metric called functional connectivity overlap ratio (FCOR). From these voxels, candidate connector hubs were identified and verified using seed-based connectivity analysis. RESULTS: We found that most networks exhibited connectivity alterations in the patient group. Specifically, connectivity with the basal ganglia and high visual networks was severely affected over widespread brain areas in patients, affecting subcortical and cerebellar regions and the regions involved in visual and sensorimotor processing. Furthermore, we identified critical connector hubs in the cerebellum, midbrain, thalamus, insula, and calcarine with connectivity to multiple RSNs affected in the patients. FCOR values of these regions were also associated with clinical data and could classify patient and control groups with > 80 % accuracy. CONCLUSIONS: These findings highlight the critical role of connector hubs, particularly those in the cerebellum and subcortical regions, in the pathophysiology of schizophrenia and the potential role of FCOR as a clinical biomarker for the disorder.

Cross-Disorder Analysis of Genic and Regulatory Copy Number Variations in Bipolar Disorder, Schizophrenia, and Autism Spectrum Disorder.

BACKGROUND: We aimed to determine the similarities and differences in the roles of genic and regulatory copy number variations (CNVs) in bipolar disorder (BD), schizophrenia (SCZ), and autism spectrum disorder (ASD). METHODS: Based on high-resolution CNV data from 8708 Japanese samples, we performed to our knowledge the largest cross-disorder analysis of genic and regulatory CNVs in BD, SCZ, and ASD. RESULTS: In genic CNVs, we found an increased burden of smaller (<100 kb) exonic deletions in BD, which contrasted with the highest burden of larger (>500 kb) exonic CNVs in SCZ/ASD. Pathogenic CNVs linked to neurodevelopmental disorders were significantly associated with the risk for each disorder, but BD and SCZ/ASD differed in terms of the effect size (smaller in BD) and subtype distribution of CNVs linked to neurodevelopmental disorders. We identified 3 synaptic genes (DLG2, PCDH15, and ASTN2) as risk factors for BD. Whereas gene set analysis showed that BD-associated pathways were restricted to chromatin biology, SCZ and ASD involved more extensive and similar pathways. Nevertheless, a correlation analysis of gene set results indicated weak but significant pathway similarities between BD and SCZ or ASD (r = 0.25-0.31). In SCZ and ASD, but not BD, CNVs were significantly enriched in enhancers and promoters in brain tissue. CONCLUSIONS: BD and SCZ/ASD differ in terms of CNV burden, characteristics of CNVs linked to neurodevelopmental disorders, and regulatory CNVs. On the other hand, they have shared molecular mechanisms, including chromatin biology. The BD risk genes identified here could provide insight into the pathogenesis of BD.

Transdiagnostic comparisons of intellectual abilities and work outcome in patients with mental disorders: multicentre study.

BACKGROUND: Cognitive impairment is common in people with mental disorders, leading to transdiagnostic classification based on cognitive characteristics. However, few studies have used this approach for intellectual abilities and functional outcomes. AIMS: The present study aimed to classify people with mental disorders based on intellectual abilities and functional outcomes in a data-driven manner. METHOD: Seven hundred and forty-nine patients diagnosed with schizophrenia, bipolar disorder, major depression disorder or autism spectrum disorder and 1030 healthy control subjects were recruited from facilities in various regions of Japan. Two independent k-means cluster analyses were performed. First, intelligence variables (current estimated IQ, premorbid IQ, and IQ discrepancy) were included. Second, number of work hours per week was included instead of premorbid IQ. RESULTS: Four clusters were identified in the two analyses. These clusters were specifically characterised in terms of IQ discrepancy in the first cluster analysis, whereas the work variable was the most salient feature in the second cluster analysis. Distributions of clinical diagnoses in the two cluster analyses showed that all diagnoses were unevenly represented across the clusters. CONCLUSIONS: Intellectual abilities and work outcomes are effective classifiers in transdiagnostic approaches. The results of our study also suggest the importance of diagnosis-specific strategies to support functional recovery in people with mental disorders.

Support vector machine-based classification of schizophrenia patients and healthy controls using structural magnetic resonance imaging from two independent sites.

Structural brain alterations have been repeatedly reported in schizophrenia; however, the pathophysiology of its alterations remains unclear. Multivariate pattern recognition analysis such as support vector machines can classify patients and healthy controls by detecting subtle and spatially distributed patterns of structural alterations. We aimed to use a support vector machine to distinguish patients with schizophrenia from control participants on the basis of structural magnetic resonance imaging data and delineate the patterns of structural alterations that significantly contributed to the classification performance. We used independent datasets from different sites with different magnetic resonance imaging scanners, protocols and clinical characteristics of the patient group to achieve a more accurate estimate of the classification performance of support vector machines. We developed a support vector machine classifier using the dataset from one site (101 participants) and evaluated the performance of the trained support vector machine using a dataset from the other site (97 participants) and vice versa. We assessed the performance of the trained support vector machines in each support vector machine classifier. Both support vector machine classifiers attained a classification accuracy of >70% with two independent datasets indicating a consistently high performance of support vector machines even when used to classify data from different sites, scanners and different acquisition protocols. The regions contributing to the classification accuracy included the bilateral medial frontal cortex, superior temporal cortex, insula, occipital cortex, cerebellum, and thalamus, which have been reported to be related to the pathogenesis of schizophrenia. These results indicated that the support vector machine could detect subtle structural brain alterations and might aid our understanding of the pathophysiology of these changes in schizophrenia, which could be one of the diagnostic findings of schizophrenia.

ARHGAP10, which encodes Rho GTPase-activating protein 10, is a novel gene for schizophrenia risk.

Schizophrenia (SCZ) is known to be a heritable disorder; however, its multifactorial nature has significantly hampered attempts to establish its pathogenesis. Therefore, in this study, we performed genome-wide copy-number variation (CNV) analysis of 2940 patients with SCZ and 2402 control subjects and identified a statistically significant association between SCZ and exonic CNVs in the ARHGAP10 gene. ARHGAP10 encodes a member of the RhoGAP superfamily of proteins that is involved in small GTPase signaling. This signaling pathway is one of the SCZ-associated pathways and may contribute to neural development and function. However, the ARHGAP10 gene is often confused with ARHGAP21, thus, the significance of ARHGAP10 in the molecular pathology of SCZ, including the expression profile of the ARHGAP10 protein, remains poorly understood. To address this issue, we focused on one patient identified to have both an exonic deletion and a missense variant (p.S490P) in ARHGAP10. The missense variant was found to be located in the RhoGAP domain and was determined to be relevant to the association between ARHGAP10 and the active form of RhoA. We evaluated ARHGAP10 protein expression in the brains of reporter mice and generated a mouse model to mimic the patient case. The model exhibited abnormal emotional behaviors, along with reduced spine density in the medial prefrontal cortex (mPFC). In addition, primary cultured neurons prepared from the mouse model brain exhibited immature neurites in vitro. Furthermore, we established induced pluripotent stem cells (iPSCs) from this patient, and differentiated them into tyrosine hydroxylase (TH)-positive neurons in order to analyze their morphological phenotypes. TH-positive neurons differentiated from the patient-derived iPSCs exhibited severe defects in both neurite length and branch number; these defects were restored by the addition of the Rho-kinase inhibitor, Y-27632. Collectively, our findings suggest that rare ARHGAP10 variants may be genetically and biologically associated with SCZ and indicate that Rho signaling represents a promising drug discovery target for SCZ treatment.

Effect of intranasal oxytocin on the core social symptoms of autism spectrum disorder: a randomized clinical trial.

Although small-scale studies have described the effects of oxytocin on social deficits in autism spectrum disorder (ASD), no large-scale study has been conducted. In this randomized, parallel-group, multicenter, placebo-controlled, double-blind trial in Japan, 106 ASD individuals (18-48 y.o.) were enrolled between Jan 2015 and March 2016. Participants were randomly assigned to a 6-week intranasal oxytocin (48IU/day, n = 53) or placebo (n = 53) group. One-hundred-three participants were analyzed. Since oxytocin reduced the primary endpoint, Autism Diagnostic Observation Schedule (ADOS) reciprocity, (from 8.5 to 7.7; P < .001) but placebo also reduced the score (8.3 to 7.2; P < .001), no between-group difference was found (effect size -0.08; 95% CI, -0.46 to 0.31; P = .69); however, plasma oxytocin was only elevated from baseline to endpoint in the oxytocin-group compared with the placebo-group (effect size -1.12; -1.53 to -0.70; P < .0001). Among the secondary endpoints, oxytocin reduced ADOS repetitive behavior (2.0 to 1.5; P < .0001) compared with placebo (2.0 to 1.8; P = .43) (effect size 0.44; 0.05 to 0.83; P = .026). In addition, the duration of gaze fixation on socially relevant regions, another secondary endpoint, was increased by oxytocin (41.2 to 52.3; P = .03) compared with placebo (45.7 to 40.4; P = .25) (effect size 0.55; 0.10 to 1.0; P = .018). No significant effects were observed for the other secondary endpoints. No significant difference in the prevalence of adverse events was observed between groups, although one participant experienced temporary gynecomastia during oxytocin administration. Based on the present findings, we cannot recommend continuous intranasal oxytocin treatment alone at the current dose and duration for treatment of the core social symptoms of high-functioning ASD in adult men, although this large-scale trial suggests oxytocin's possibility to treat ASD repetitive behavior.

White matter microstructural alterations across four major psychiatric disorders: mega-analysis study in 2937 individuals.

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.

Quantitative facial expression analysis revealed the efficacy and time course of oxytocin in autism.

Discrepancies in efficacy between single-dose and repeated administration of oxytocin for autism spectrum disorder have led researchers to hypothesize that time-course changes in efficacy are induced by repeated administrations of the peptide hormone. However, repeatable, objective, and quantitative measurement of autism spectrum disorder's core symptoms are lacking, making it difficult to examine potential time-course changes in efficacy. We tested this hypothesis using repeatable, objective, and quantitative measurement of the core symptoms of autism spectrum disorder. We examined videos recorded during semi-structured social interaction administered as the primary outcome in single-site exploratory (n = 18, crossover within-subjects design) and multisite confirmatory (n = 106, parallel-group design), double-blind, placebo-controlled 6-week trials of repeated intranasal administrations of oxytocin (48 IU/day) in adult males with autism spectrum disorder. The main outcomes were statistical representative values of objectively quantified facial expression intensity in a repeatable part of the Autism Diagnostic Observation Schedule: the maximum probability (i.e. mode) and the natural logarithm of mode on the probability density function of neutral facial expression and the natural logarithm of mode on the probability density function of happy expression. Our recent study revealed that increases in these indices characterize autistic facial expression, compared with neurotypical individuals. The current results revealed that oxytocin consistently and significantly decreased the increased natural logarithm of mode on the probability density function of neutral facial expression compared with placebo in exploratory (effect-size, -0.57; 95% CI, -1.27 to 0.13; P = 0.023) and confirmatory trials (-0.41; -0.62 to -0.20; P < 0.001). A significant interaction between time-course (at baseline, 2, 4, 6, and 8 weeks) and the efficacy of oxytocin on the natural logarithm of mode on the probability density function of neutral facial expression was found in confirmatory trial (P < 0.001). Post hoc analyses revealed maximum efficacy at 2 weeks (P < 0.001, Cohen's d = -0.78; 95% CI, -1.21 to -0.35) and deterioration of efficacy at 4 weeks (P = 0.042, Cohen's d = -0.46; 95% CI, -0.90 to -0.01) and 6 weeks (P = 0.10, Cohen's d = -0.35; 95% CI, -0.77 to 0.08), while efficacy was preserved at 2 weeks post-treatment (i.e. 8 weeks) (P < 0.001, Cohen's d = -1.24; 95% CI, -1.71 to -0.78). Quantitative facial expression analyses successfully verified the positive effects of repeated oxytocin on autistic individuals' facial expressions and demonstrated a time-course change in efficacy. The current findings support further development of an optimized regimen of oxytocin treatment.

Application of eye trackers for understanding mental disorders: Cases for schizophrenia and autism spectrum disorder.

Studies of eye movement have become an essential tool of basic neuroscience research. Measures of eye movement have been applied to higher brain functions such as cognition, social behavior, and higher-level decision-making. With the development of eye trackers, a growing body of research has described eye movements in relation to mental disorders, reporting that the basic oculomotor properties of patients with mental disorders differ from those of healthy controls. Using discrimination analysis, several independent research groups have used eye movements to differentiate patients with schizophrenia from a mixed population of patients and controls. Recently, in addition to traditional oculomotor measures, several new techniques have been applied to measure and analyze eye movement data. One research group investigated eye movements in relation to the risk of autism spectrum disorder several years prior to the emergence of verbal-behavioral abnormalities. Research on eye movement in humans in social communication is therefore considered important, but has not been well explored. Since eye movement patterns vary between patients with mental disorders and healthy controls, it is necessary to collect a large amount of eye movement data from various populations and age groups. The application of eye trackers in the clinical setting could contribute to the early treatment of mental disorders.

Comparative Analyses of Copy-Number Variation in Autism Spectrum Disorder and Schizophrenia Reveal Etiological Overlap and Biological Insights.

Compelling evidence in Caucasian populations suggests a role for copy-number variations (CNVs) in autism spectrum disorder (ASD) and schizophrenia (SCZ). We analyzed 1,108 ASD cases, 2,458 SCZ cases, and 2,095 controls in a Japanese population and confirmed an increased burden of rare exonic CNVs in both disorders. Clinically significant (or pathogenic) CNVs, including those at 29 loci common to both disorders, were found in about 8% of ASD and SCZ cases, which was significantly higher than in controls. Phenotypic analysis revealed an association between clinically significant CNVs and intellectual disability. Gene set analysis showed significant overlap of biological pathways in both disorders including oxidative stress response, lipid metabolism/modification, and genomic integrity. Finally, based on bioinformatics analysis, we identified multiple disease-relevant genes in eight well-known ASD/SCZ-associated CNV loci (e.g., 22q11.2, 3q29). Our findings suggest an etiological overlap of ASD and SCZ and provide biological insights into these disorders.

Genetic and animal model analyses reveal the pathogenic role of a novel deletion of RELN in schizophrenia.

Reelin protein (RELN), an extracellular matrix protein, plays multiple roles that range from embryonic neuronal migration to spine formation in the adult brain. Results from genetic studies have suggested that RELN is associated with the risk of psychiatric disorders, including schizophrenia (SCZ). We previously identified a novel exonic deletion of RELN in a patient with SCZ. High-resolution copy number variation analysis revealed that this deletion included exons 52 to 58, which truncated the RELN in a similar manner to the Reln Orleans mutation (Relnrl-Orl). We examined the clinical features of this patient and confirmed a decreased serum level of RELN. To elucidate the pathophysiological role of the exonic deletion of RELN in SCZ, we conducted behavioral and neurochemical analyses using heterozygous Relnrl-Orl/+ mice. These mice exhibited abnormalities in anxiety, social behavior, and motor learning; the deficits in motor learning were ameliorated by antipsychotics. Methamphetamine-induced hyperactivity and dopamine release were significantly reduced in the Relnrl-Orl/+ mice. In addition, the levels of GABAergic markers were decreased in the brain of these mice. Taken together, our results suggest that the exonic deletion of RELN plays a pathological role, implicating functional changes in the dopaminergic and GABAergic systems, in the pathophysiology of SCZ.

Aberrant functional connectivity between the thalamus and visual cortex is related to attentional impairment in schizophrenia.

Resting-state (rs) functional magnetic resonance imaging (fMRI) studies have revealed dysfunctional thalamocortical functional connectivity (FC) in schizophrenia. However, the relationship between thalamocortical FC and cognitive impairment has not been thoroughly investigated. We hypothesized that aberrant thalamocortical FC is related to attention deficits in schizophrenia. Thirty-eight patients with schizophrenia and 38 matched healthy controls underwent rs-fMRI and task fMRI while performing a Flanker task. We observed decreased left thalamic activation in patients with schizophrenia using task fMRI to determine the thalamic seed. A seed-based analysis using this seed was performed in the whole brain to assess differences in thalamocortical FC between the groups. Significantly worse performance was observed in the patient group. The rs-fMRI analysis revealed significantly increased FC between the left thalamus seed and the occipital cortices/postcentral gyri in patients when compared to controls. In the patient group, significant positive correlations were observed between the degree of FC from the left thalamus to the bilateral occipital gyri, which correspond to the visual cortex, and the Flanker effect. No significant correlation was detected in the control group. These results indicate that aberrant FC between the left thalamus and the visual cortex is related to attention deficits in schizophrenia.

The effect of duration of illness and antipsychotics on subcortical volumes in schizophrenia: Analysis of 778 subjects.

BACKGROUND: The effect of duration of illness and antipsychotic medication on the volumes of subcortical structures in schizophrenia is inconsistent among previous reports. We implemented a large sample analysis utilizing clinical data from 11 institutions in a previous meta-analysis. METHODS: Imaging and clinical data of 778 schizophrenia subjects were taken from a prospective meta-analysis conducted by the COCORO consortium in Japan. The effect of duration of illness and daily dose and type of antipsychotics were assessed using the linear mixed effect model where the volumes of subcortical structures computed by FreeSurfer were used as a dependent variable and age, sex, duration of illness, daily dose of antipsychotics and intracranial volume were used as independent variables, and the type of protocol was incorporated as a random effect for intercept. The statistical significance of fixed-effect of dependent variable was assessed. RESULTS: Daily dose of antipsychotics was positively associated with left globus pallidus volume and negatively associated with right hippocampus. It was also positively associated with laterality index of globus pallidus. Duration of illness was positively associated with bilateral globus pallidus volumes. Type of antipsychotics did not have any effect on the subcortical volumes. DISCUSSION: A large sample size, uniform data collection methodology and robust statistical analysis are strengths of the current study. This result suggests that we need special attention to discuss about relationship between subcortical regional brain volumes and pathophysiology of schizophrenia because regional brain volumes may be affected by antipsychotic medication.

White matter microstructure between the pre-SMA and the cingulum bundle is related to response conflict in healthy subjects.

INTRODUCTION: Response conflict involves selectively attending to relevant information and suppressing distracting, irrelevant information. The medial frontal cortex (MFC) is considered to be involved in response conflict. However, it remains unclear which white matter connectivity is associated with response conflict. This study aimed to delineate the neural connectivity of response conflict in healthy subjects and investigate the association between white matter microstructure and performance of a response conflict task. METHOD: Twenty-eight healthy subjects underwent functional magnetic resonance imaging (fMRI) during the Flanker task and diffusion MRI. We identified the presupplementary motor area (pre-SMA) using fMRI. Furthermore, we delineated the white matter connectivity between the pre-SMA and the cingulum bundle (CB), which is located in the MFC, using probabilistic tractography. We calculated the mean diffusivity (MD), index of white matter microstructure, of this tract and evaluate the association between MD and performance of the Flanker task. RESULT: The mean MD of this tract was significantly and positively associated with performance of the Flanker task. CONCLUSION: The present study suggests the white matter connectivity between the pre-SMA and the CB is related to the response conflict in healthy subjects and finer white matter microstructure is associated with smaller response conflict.