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Aim Previous studies have reported different brain morphologies in different cognitive subgroups of patients with schizophrenia. We aimed to examine the brain structures and functional connectivity in these cognitive subgroups of schizophrenia. Methods We compared brain structures among healthy controls and cognitively deteriorated and preserved subgroups of patients with schizophrenia according to the decline in IQ. Connectivity analyses between subcortical regions and other brain areas were performed using resting-state functional magnetic resonance imaging among the groups. Results Whole brain and total cortical gray matter, right fusiform gyrus, left pars orbitalis gyrus, right pars triangularis, left superior temporal gyrus and left insula volumes, and bilateral cortical thickness were decreased in the deteriorated group compared to the control and preserved groups. Both schizophrenia subgroups had increased left lateral ventricle, right putamen and left pallidum, and decreased bilateral hippocampus, left precentral gyrus, right rostral middle frontal gyrus, and bilateral superior frontal gyrus volumes compared with controls. Hyperconnectivity between the thalamus and a broad range of brain regions was observed in the deteriorated group compared to connectivity in the control group, and this hyperconnectivity was less evident in the preserved group. We also found hyperconnectivity between the accumbens and the superior and middle frontal gyri in the preserved group compared with connectivity in the deteriorated group. Conclusion These findings provide evidence of prominent structural and functional brain abnormalities in deteriorated patients with schizophrenia, suggesting that cognitive subgroups in schizophrenia might be useful biotypes to elucidate brain pathophysiology for new diagnostic and treatment strategies.

The underlying pathologies of psychiatric disorders, which cause substantial personal and social losses, remain unknown, and their elucidation is an urgent issue. To clarify the core pathological mechanisms underlying psychiatric disorders, in addition to laboratory-based research that incorporates the latest findings, it is necessary to conduct large-sample-size research and verify reproducibility. For this purpose, it is critical to conduct multicenter collaborative research across various fields, such as psychiatry, neuroscience, molecular biology, genomics, neuroimaging, cognitive science, neurophysiology, psychology and pharmacology. Moreover, collaborative research plays an important role in the development of young researchers. In this respect, the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium and Cognitive Genetics Collaborative Research Organization (COCORO) have played important roles. In this review, we first overview the importance of multicenter collaborative research and our target psychiatric disorders. Then, we introduce research findings on the pathophysiology of psychiatric disorders from neurocognitive, neurophysiological, neuroimaging, genetic, and basic neuroscience perspectives, focusing mainly on the findings obtained by COCORO. It is our hope that multicenter collaborative research will contribute to the elucidation of the pathological basis of psychiatric disorders. This article is protected by copyright. All rights reserved.

Aim Increased brain gyrification in diverse cortical regions has been reported in patients with schizophrenia, possibly reflecting deviations in early neurodevelopment. However, it remains unknown whether patients with schizotypal disorder exhibit similar changes. Methods This magnetic resonance imaging study investigated brain gyrification in 46 patients with schizotypal disorder (29 male, 17 female), 101 patients with schizophrenia (55 male, 46 female), and 77 healthy controls (44 male, 33 female). T1-weighted magnetic resonance images were obtained for each participant. Using FreeSurfer software, the local gyrification index (LGI) of the entire cortex was compared across the groups. Results Both schizophrenia and schizotypal disorder patients showed a significantly higher LGI in diverse cortical regions, including the bilateral prefrontal and left parietal cortices, as compared with controls, but its extent was broader in schizophrenia especially for the right prefrontal and left occipital regions. No significant correlations were found between the LGI and clinical variables (e.g., symptom severity, medication) for either of the patient groups. Conclusion Increased LGI in the frontoparietal regions was common to both patient groups and might represent vulnerability to schizophrenia, while more diverse changes in schizophrenia patients might be associated with the manifestation of florid psychosis.

Aim Suicide victims have various distresses or motives. There are few studies on how these motives toward suicide relate with each other. We used network analyses to extract the structures of correlations among the motives for suicide. Methods We obtained datasets of suicide victims from 2007–2009 in Japan in cooperation with Ibaraki Prefectural Police Headquarters. The data were analyzed by network centrality measures and a structural analysis by block modeling. Results Among the motives, depression and physical illness showed relatively high scores of ‘degree centrality’, whereas depression and unemployment showed relatively high scores of ‘betweenness centrality’. Structural analysis by block modeling resulted in eight blocks. The most important block comprised eight motives, including conflict between parent and child, marital conflict, economic hardship, and overloaded with debt. Conclusion Depression and physical illness were important and priority areas for completed suicides, although these two motives had different influences on suicide behaviors. Furthermore, structural analysis revealed the important role of a block, including some familial and financial motives, which induced hopelessness. Our results suggest that it might be useful to consider the common ways in which motivations for suicide are tied together when suicide intervention is launched from a social model point of view.

Aim Although schizophrenia and control subjects differ on a variety of neuroanatomical measures, the specificity and sensitivity of any one measure for differentiating between the two groups are low. To identify the correlative pattern of brain changes that best discriminate schizophrenia patients from healthy subjects, discriminant analysis techniques using voxel-based morphometry were applied. Methods The first analysis was conducted to obtain a statistical model that classified 105 female healthy subjects and 38 female schizophrenia patients. First, the differences in gray matter and cerebrospinal fluid volume between the patients and healthy subjects were evaluated using optimized voxel-based morphometry. Then, a discriminant analysis reflecting the results of this evaluation was adopted. The second analysis was performed to prospectively validate the statistical model by successfully classifying a new group that consisted of 23 female healthy subjects and 23 female schizophrenia patients. Results The use of these variables resulted in correct classification rates of 0.72 in the control subjects and 0.76 in the schizophrenia patients. In the second validation analysis using these variables, correct classification rates of 0.70 in the control subjects and 0.74 in the schizophrenia patients were achieved. Conclusion Schizophrenia patients have structural deviations in multiple brain areas, and a combination of structural brain measures can distinguish between patients and controls.

Aim: The purpose of the present study was to investigate the regional cerebral blood flow (rCBF) of patients with delusional disorder, somatic type (DDST) exhibiting somatic hallucination. Methods: Five patients diagnosed with DDST, as well as 20 control subjects, were examined. All subjects underwent technetium-99m ethyl cysteinate dimer brain perfusion single-photon emission computed tomography. Statistical analysis was performed with SPM5, using a two-sample t-test model to test the regional population effect on rCBF. Results: Patients with DDST had a significant increase in perfusion in the left post-central gyrus and the right paracentral lobule, both of which are involved in somatic sensory processing. Conclusion: Somatic hallucination might be associated with increased perfusion in the primary somatosensory regions.

Although the Internet has been widely used in Japan, the quality of information on mental health-related issues has not been evaluated so far. The purpose of the present study was to investigate the current status of Japanese websites that deal with mental health, mental disorders, and their associated matters. Using a search engine, Google, 37 websites were identified that exclusively contain information on mental health or mental disorders. The characteristics of the sites were then examined, along with variety of mental disorders mentioned, and quality of contents for each of the 37 websites. More than half of the websites were set up, at least in part, for commercial purposes and only 27% of sites were owned by professionals. Mood disorder, panic disorder, and schizophrenia were the three most commonly referred disorders on websites. Aside from some exceptions, the quality of information was inadequate, especially that regarding treatment. Most of the websites on mental health and mental disorders examined in the present study have scope for improvement. The challenge is to establish a system to evaluate the sites and to motivate each webmaster to improve the sites.

It is well known that genetic factors contribute to the susceptibility for schizophrenia. Recent advances in the molecular genetics of schizophrenia strongly suggest several susceptibility genes (e.g. dysbindin, neuregulin-1, DISC1, COMT, G72, RGS4 and Akt1). We discuss the evidence and biology of these genes. As glutamate transmission is especially implicated in these genes, neurobiological basis of schizophrenia might be elucidated by investigation of functional interactions between susceptibility genes for schizophrenia and the glutamatergic system.

Suicide victims have various distresses or motives. There are few studies on how these motives toward suicide relate with each other. We used network analyses to extract the structures of correlations among the motives for suicide.We obtained datasets of suicide victims from 2007-2009 in Japan in cooperation with Ibaraki Prefectural Police Headquarters. The data were analyzed by network centrality measures and a structural analysis by block modeling.Among the motives, depression and physical illness showed relatively high scores of 'degree centrality', whereas depression and unemployment showed relatively high scores of 'betweenness centrality'. Structural analysis by block modeling resulted in eight blocks. The most important block comprised eight motives, including conflict between parent and child, marital conflict, economic hardship, and overloaded with debt.Depression and physical illness were important and priority areas for completed suicides, although these two motives had different influences on suicide behaviors. Furthermore, structural analysis revealed the important role of a block, including some familial and financial motives, which induced hopelessness. Our results suggest that it might be useful to consider the common ways in which motivations for suicide are tied together when suicide intervention is launched from a social model point of view.

Although schizophrenia and control subjects differ on a variety of neuroanatomical measures, the specificity and sensitivity of any one measure for differentiating between the two groups are low. To identify the correlative pattern of brain changes that best discriminate schizophrenia patients from healthy subjects, discriminant analysis techniques using voxel-based morphometry were applied.The first analysis was conducted to obtain a statistical model that classified 105 female healthy subjects and 38 female schizophrenia patients. First, the differences in gray matter and cerebrospinal fluid volume between the patients and healthy subjects were evaluated using optimized voxel-based morphometry. Then, a discriminant analysis reflecting the results of this evaluation was adopted. The second analysis was performed to prospectively validate the statistical model by successfully classifying a new group that consisted of 23 female healthy subjects and 23 female schizophrenia patients.The use of these variables resulted in correct classification rates of 0.72 in the control subjects and 0.76 in the schizophrenia patients. In the second validation analysis using these variables, correct classification rates of 0.70 in the control subjects and 0.74 in the schizophrenia patients were achieved.Schizophrenia patients have structural deviations in multiple brain areas, and a combination of structural brain measures can distinguish between patients and controls.