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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.

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.

In thermally activated delayed fluorescence (TADF)‐based organic light‐emitting diodes (OLEDs), acceleration of reverse intersystem crossing (RISC) and suppression of intersystem crossing (ISC) are demanded to shorten a lifetime of triplet excitons. As a system realizing RISC faster than ISC, inverted singlet‐triplet excited states (iST) with a negative energy difference (ΔEST) between the lowest excited singlet and the lowest triplet states have been gathering much attention recently. Here, we have focused on an asymmetric hexa‐azaphenalene (A6AP) core to obtain a new insight into iST. Based on A6AP, we have newly designed A6AP‐Cz with the calculated ΔEST of −44 meV. The experimental studies of a synthesized A6AP‐Cz revealed that the lifetime of delayed fluorescence (τDF) was only 54 ns, which was the shortest among all organic materials. The rate constant of RISC (kRISC=1.9×107 s−1) was greater than that of ISC (kISC=1.0×107 s−1). The negative ΔEST of A6AP‐Cz was experimentally confirmed from 1) the kRISC and kISC (−45 meV) and 2) the temperature‐dependent τDF. 3) The onsets of fluorescence and phosphorescence spectra at 77 K also supported the evidence of negative ΔEST (−73 meV). This study demonstrated the potential of A6AP as an iST core for the first time. [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]

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. [ABSTRACT FROM AUTHOR]