Longitudinal trajectories of anterior cingulate glutamate and subclinical psychotic experiences in early adolescence: the impact of bullying victimization.

Previous studies reported decreased glutamate levels in the anterior cingulate cortex (ACC) in non-treatment-resistant schizophrenia and first-episode psychosis. However, ACC glutamatergic changes in subjects at high-risk for psychosis, and the effects of commonly experienced environmental emotional/social stressors on glutamatergic function in adolescents remain unclear. In this study, adolescents recruited from the general population underwent proton magnetic resonance spectroscopy (MRS) of the pregenual ACC using a 3-Tesla scanner. We explored longitudinal data on the association of combined glutamate-glutamine (Glx) levels, measured by MRS, with subclinical psychotic experiences. Moreover, we investigated associations of bullying victimization, a risk factor for subclinical psychotic experiences, and help-seeking intentions, a coping strategy against stressors including bullying victimization, with Glx levels. Finally, path analyses were conducted to explore multivariate associations. For a contrast analysis, gamma-aminobutyric acid plus macromolecule (GABA+) levels were also analyzed. Negative associations were found between Glx levels and subclinical psychotic experiences at both Times 1 (n = 219, mean age 11.5 y) and 2 (n = 211, mean age 13.6 y), as well as for over-time changes (n = 157, mean interval 2.0 y). Moreover, effects of bullying victimization and bullying victimization × help-seeking intention interaction effects on Glx levels were found (n = 156). Specifically, bullying victimization decreased Glx levels, whereas help-seeking intention increased Glx levels only in bullied adolescents. Finally, associations among bullying victimization, help-seeking intention, Glx levels, and subclinical psychotic experiences were revealed. GABA+ analysis revealed no significant results. This is the first adolescent study to reveal longitudinal trajectories of the association between glutamatergic function and subclinical psychotic experiences and to elucidate the effect of commonly experienced environmental emotional/social stressors on glutamatergic function. Our findings may deepen the understanding of how environmental emotional/social stressors induce impaired glutamatergic neurotransmission that could be the underpinning of liability for psychotic experiences in early adolescence.

Frontal pole-precuneus connectivity is associated with a discrepancy between self-rated and observer-rated depression severity in mood disorders: a resting-state functional magnetic resonance imaging study.

Discrepancies in self-rated and observer-rated depression severity may underlie the basis for biological heterogeneity in depressive disorders and be an important predictor of outcomes and indicators to optimize intervention strategies. However, the neural mechanisms underlying this discrepancy have been understudied. This study aimed to examine the brain networks that represent the neural basis of the discrepancy between self-rated and observer-rated depression severity using resting-state functional MRI. To examine the discrepancy between self-rated and observer-rated depression severity, self- and observer-ratings discrepancy (SOD) was defined, and the higher and lower SOD groups were selected from depressed patients as participants showing extreme deviation. Resting-state functional MRI analysis was performed to examine regions with significant differences in functional connectivity in the two groups. The results showed that, in the higher SOD group compared to the lower SOD group, there was increased functional connectivity between the frontal pole and precuneus, both of which are subregions of the default mode network that have been reported to be associated with ruminative and self-referential thinking. These results provide insight into the association of brain circuitry with discrepancies between self- and observer-rated depression severity and may lead to more treatment-oriented diagnostic reclassification in the future.

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.

The status of MRI databases across the world focused on psychiatric and neurological disorders.

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.

Generalizable brain network markers of major depressive disorder across multiple imaging sites.

Many studies have highlighted the difficulty inherent to the clinical application of fundamental neuroscience knowledge based on machine learning techniques. It is difficult to generalize machine learning brain markers to the data acquired from independent imaging sites, mainly due to large site differences in functional magnetic resonance imaging. We address the difficulty of finding a generalizable marker of major depressive disorder (MDD) that would distinguish patients from healthy controls based on resting-state functional connectivity patterns. For the discovery dataset with 713 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a machine learning MDD classifier. The classifier achieved an approximately 70% generalization accuracy for an independent validation dataset with 521 participants from 5 different imaging sites. The successful generalization to a perfectly independent dataset acquired from multiple imaging sites is novel and ensures scientific reproducibility and clinical applicability.

Distinctive alterations in the mesocorticolimbic circuits in various psychiatric disorders.

AIM: Increasing evidence suggests that psychiatric disorders are linked to alterations in the mesocorticolimbic dopamine-related circuits. However, the common and disease-specific alterations remain to be examined in schizophrenia (SCZ), major depressive disorder (MDD), and autism spectrum disorder (ASD). Thus, this study aimed to examine common and disease-specific features related to mesocorticolimbic circuits. METHODS: This study included 555 participants from four institutes with five scanners: 140 individuals with SCZ (45.0% female), 127 individuals with MDD (44.9%), 119 individuals with ASD (15.1%), and 169 healthy controls (HC) (34.9%). All participants underwent resting-state functional magnetic resonance imaging. A parametric empirical Bayes approach was adopted to compare estimated effective connectivity among groups. Intrinsic effective connectivity focusing on the mesocorticolimbic dopamine-related circuits including the ventral tegmental area (VTA), shell and core parts of the nucleus accumbens (NAc), and medial prefrontal cortex (mPFC) were examined using a dynamic causal modeling analysis across these psychiatric disorders. RESULTS: The excitatory shell-to-core connectivity was greater in all patients than in the HC group. The inhibitory shell-to-VTA and shell-to-mPFC connectivities were greater in the ASD group than in the HC, MDD, and SCZ groups. Furthermore, the VTA-to-core and VTA-to-shell connectivities were excitatory in the ASD group, while those connections were inhibitory in the HC, MDD, and SCZ groups. CONCLUSION: Impaired signaling in the mesocorticolimbic dopamine-related circuits could be an underlying neuropathogenesis of various psychiatric disorders. These findings will improve the understanding of unique neural alternations of each disorder and will facilitate identification of effective therapeutic targets.

Neuroimaging studies within Cognitive Genetics Collaborative Research Organization aiming to replicate and extend works of ENIGMA.

Reproducibility is one of the most important issues for generalizing the results of clinical research; however, low reproducibility in neuroimaging studies is well known. To overcome this problem, the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium, an international neuroimaging consortium, established standard protocols for imaging analysis and employs either meta- and mega-analyses of psychiatric disorders with large sample sizes. The Cognitive Genetics Collaborative Research Organization (COCORO) in Japan promotes neurobiological studies in psychiatry and has successfully replicated and extended works of ENIGMA especially for neuroimaging studies. For example, (a) the ENIGMA consortium showed subcortical regional volume alterations in patients with schizophrenia (n = 2,028) compared to controls (n = 2,540) across 15 cohorts using meta-analysis. COCORO replicated the volumetric changes in patients with schizophrenia (n = 884) compared to controls (n = 1,680) using the ENIGMA imaging analysis protocol and mega-analysis. Furthermore, a schizophrenia-specific leftward asymmetry for the pallidum volume was demonstrated; and (b) the ENIGMA consortium identified white matter microstructural alterations in patients with schizophrenia (n = 1,963) compared to controls (n = 2,359) across 29 cohorts. Using the ENIGMA protocol, a study from COCORO showed similar results in patients with schizophrenia (n = 696) compared to controls (n = 1,506) from 12 sites using mega-analysis. Moreover, the COCORO study found that schizophrenia, bipolar disorder (n = 211) and autism spectrum disorder (n = 126), but not major depressive disorder (n = 398), share similar white matter microstructural alterations, compared to controls. Further replication and harmonization of the ENIGMA consortium and COCORO will contribute to the generalization of their research findings.

Shared functional impairment in the prefrontal cortex affects symptom severity across psychiatric disorders.

BACKGROUND: The prefrontal deficits in psychiatric disorders have been investigated using functional neuroimaging tools; however, no studies have tested the related characteristics across psychiatric disorders considering various demographic and clinical confounders. METHODS: We analyzed 1558 functional brain measurements using a functional near-infrared spectroscopy during a verbal fluency task from 1200 participants with three disease spectra [196 schizophrenia, 189 bipolar disorder (BPD), and 394 major depressive disorder (MDD)] and 369 healthy controls along with demographic characteristics (age, gender, premorbid IQ, and handedness), task performance during the measurements, clinical assessments, and medication equivalent doses (chlorpromazine, diazepam, biperiden, and imipramine) in a consistent manner. The association between brain functions and demographic and clinical variables was tested using a general linear mixed model (GLMM). Then, the direction of relationship between brain activity and symptom severity, controlling for any other associations, was estimated using a model comparison of structural equation models (SEMs). RESULTS: The GLMM showed a shared functional deficit of brain activity and a schizophrenia-specific delayed activity timing in the prefrontal cortex (false discovery rate-corrected p < 0.05). Comparison of SEMs showed that brain activity was associated with the global assessment of functioning scores in the left inferior frontal gyrus opercularis (IFGOp) in BPD group and the bilateral superior temporal gyrus and middle temporal gyrus, and the left superior frontal gyrus, inferior frontal gyrus triangularis, and IFGOp in MDD group. CONCLUSION: This cross-disease large-sample neuroimaging study with high-quality clinical data reveals a robust relationship between prefrontal function and behavioral outcomes across three major psychiatric disorders.

Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias.

When collecting large amounts of neuroimaging data associated with psychiatric disorders, images must be acquired from multiple sites because of the limited capacity of a single site. However, site differences represent a barrier when acquiring multisite neuroimaging data. We utilized a traveling-subject dataset in conjunction with a multisite, multidisorder dataset to demonstrate that site differences are composed of biological sampling bias and engineering measurement bias. The effects on resting-state functional MRI connectivity based on pairwise correlations because of both bias types were greater than or equal to psychiatric disorder differences. Furthermore, our findings indicated that each site can sample only from a subpopulation of participants. This result suggests that it is essential to collect large amounts of neuroimaging data from as many sites as possible to appropriately estimate the distribution of the grand population. Finally, we developed a novel harmonization method that removed only the measurement bias by using a traveling-subject dataset and achieved the reduction of the measurement bias by 29% and improvement of the signal-to-noise ratios by 40%. Our results provide fundamental knowledge regarding site effects, which is important for future research using multisite, multidisorder resting-state functional MRI data.

Cross-scanner reproducibility and harmonization of a diffusion MRI structural brain network: A traveling subject study of multi-b acquisition.

Characterization of brain networks by diffusion MRI (dMRI) has rapidly evolved, and there are ongoing movements toward data sharing and multi-center studies. To extract meaningful information from multi-center data, methods to correct for the bias caused by scanner differences, that is, harmonization, are urgently needed. In this work, we report the cross-scanner differences in structural network analyses using data from nine traveling subjects (four males and five females, 21-49 years-old) who underwent scanning using four 3T scanners (public database available from the Brain/MINDS Beyond Human Brain MRI project (http://mriportal.umin.jp/)). The reliability and reproducibility were compared to those of data from another set of four subjects (all males, 29-42 years-old) who underwent scan-rescan (interval, 105-147 days) with the same scanner as well as scan-rescan data from the Human Connectome Project database. The results demonstrated that the reliability of the edge weights and graph theory metrics was lower for data including different scanners, compared to the scan-rescan with the same scanner. Besides, systematic differences between scanners were observed, indicating the risk of bias in comparing networks obtained from different scanners directly. We further demonstrate that it is feasible to reduce inter-scanner variabilities while preserving the inter-subject differences among healthy individuals by modeling the scanner effects at the level of network matrices, when traveling-subject data are available for calibration between scanners. The present data and results are expected to serve as a basis for developing and evaluating novel harmonization methods.

Comparison of traveling-subject and ComBat harmonization methods for assessing structural brain characteristics.

Multisite magnetic resonance imaging (MRI) is increasingly used in clinical research and development. Measurement biases-caused by site differences in scanner/image-acquisition protocols-negatively influence the reliability and reproducibility of image-analysis methods. Harmonization can reduce bias and improve the reproducibility of multisite datasets. Herein, a traveling-subject (TS) dataset including 56 T1-weighted MRI scans of 20 healthy participants in three different MRI procedures-20, 19, and 17 subjects in Procedures 1, 2, and 3, respectively-was considered to compare the reproducibility of TS-GLM, ComBat, and TS-ComBat harmonization methods. The minimum participant count required for harmonization was determined, and the Cohen's d between different MRI procedures was evaluated as a measurement-bias indicator. The measurement-bias reduction realized with different methods was evaluated by comparing test-retest scans for 20 healthy participants. Moreover, the minimum subject count for harmonization was determined by comparing test-retest datasets. The results revealed that TS-GLM and TS-ComBat reduced measurement bias by up to 85 and 81.3%, respectively. Meanwhile, ComBat showed a reduction of only 59.0%. At least 6 TSs were required to harmonize data obtained from different MRI scanners, complying with the imaging protocol predetermined for multisite investigations and operated with similar scan parameters. The results indicate that TS-based harmonization outperforms ComBat for measurement-bias reduction and is optimal for MRI data in well-prepared multisite investigations. One drawback is the small sample size used, potentially limiting the applicability of ComBat. Investigation on the number of subjects needed for a large-scale study is an interesting future problem.

Dietary Restraint Related to Body Weight Maintenance and Neural Processing in Value-Coding Areas in Adolescents.

BACKGROUND: There is an alarming increase in the obesity prevalence among children in an environment of increasing availability of preprocessed high-calorie foods. However, some people maintain a healthy weight even in such obesogenic environments. This difference in body weight management could be attributed to individual differences in dietary restraint; however, its underlying neurocognitive mechanisms in adolescents remain unclear. OBJECTIVES: This study aimed to elucidate these neurocognitive mechanisms in adolescents by examining the relationships between dietary restraint and the food-related value-coding region located in the ventromedial prefrontal cortex (vmPFC). METHODS: The association between dietary restraint and BMI was tested using a multilinear regression analysis in a large early adolescent cohort (n = 2554; age, 12.2 ± 0.3 years; BMI, 17.9 ± 2.5 kg/m2; 1354 boys). Further, an fMRI experiment was designed to assess the association between the vmPFC response to food images and dietary restraint in 30 adolescents (age, 17.6 ± 1.9 years; BMI, 20.7 ± 2.2 kg/m2; 13 boys). Additionally, using 54 individuals from the cohort (age, 14.5 ± 0.6 years; BMI, 18.8 ± 2.6 kg/m2; 31 boys), we assessed the association between dietary restraint and intrinsic vmPFC-related functional connectivity. RESULTS: In the cohort, adolescents with increased dietary restraint showed a lower BMI (ß = -0.38; P < 0.001; B = -0.06; SE = 0.003). The fMRI results showed a decreased vmPFC response to high-calorie food were correlated with greater dietary restraint. Moreover, there was an association of attenuated intrinsic vmPFC-related functional connectivity in the superior and middle frontal gyrus and the middle temporal gyrus with greater dietary restraint. CONCLUSIONS: Our findings suggest that dietary restraint in adolescents could be a preventive factor for weight gain; its effect involves modulating the vmPFC, which is associated with food value coding.

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.

Functional connectomes linking child-parent relationships with psychological problems in adolescence.

The child-parent relationship is a significant factor in an adolescent's well-being and functional outcomes. Epidemiological evidence indicates that relationships with the father and mother are differentially associated with specific psychobehavioral problems that manifest differentially between boys and girls. Neuroimaging is expected to bridge the gap in understanding such a complicated mapping between the child-parent relationships and adolescents' problems. However, possible differences in the effects of child-father and child-mother relationships on sexual dimorphism in children's brains and psychobehavioral problems have not been examined yet. This study used a dataset of 10- to 13-year-old children (N â€‹= â€‹93) to reveal the triad of associations among child-parent relationship, brain, and psychobehavioral problems by separately estimating the respective effects of child-father and child-mother relationships on boys and girls. We first fitted general linear models to identify the effects of paternal and maternal relationships in largely different sets of children's resting-state functional connectivity, which we term paternal and maternal functional brain connectomes (FBCs). We then performed connectome-based predictive modeling (CPM) to predict children's externalizing and internalizing problems from these parental FBCs. The models significantly predicted a range of girls' internalizing problems, whereas the prediction of boys' aggression was also significant using a more liberal uncorrected threshold. A series of control analyses confirmed that CPMs using FBCs associated with peer relationship or family socioeconomic status failed to make significant predictions of psychobehavioral problems. Lastly, a causal discovery method identified causal paths from daughter-mother relationship to maternal FBC, and then to daughter's internalizing problems. These observations indicate sex-dependent mechanisms linking child-parent relationship, brain, and psychobehavioral problems in the development of early adolescence.

Association between duration of breastfeeding based on maternal reports and dorsal and ventral striatum and medial orbital gyrus volumes in early adolescence.

Maternal breastfeeding has an impact on motor and emotional development in children of the next generation. Elucidating how breastfeeding during infancy affects brain regional structural development in early adolescence will be helpful for promoting healthy development. However, previous studies that have shown relationships between breastfeeding during infancy and cortical brain regions in adolescence are usually based on maternal retrospective recall of breastfeeding, and the accuracy of the data is unclear. In this study, we investigated the association between breastfeeding duration and brain regional volume in a population-neuroimaging study of early adolescents in Japan (N â€‹= â€‹207; 10.5-13.4 years) using voxel-based morphometry, which enabled us to analyze the whole brain. We evaluated breastfeeding duration as indexed by maternal and child health handbook records during infancy. The results showed a significant positive correlation between the duration of breastfeeding and gray matter volume in the dorsal and ventral striatum and the medial orbital gyrus. Post hoc exploratory analyses revealed that the duration of breastfeeding was significantly correlated with emotional behavior. Additionally, the volume in the medial orbital gyrus mediated an association between breastfeeding duration and emotional behavior. This is the first study to evaluate the effect of breastfeeding during infancy on regional brain volumes in early adolescence based on maternal and child health handbook records. Our findings shed light upon the importance of maternal breastfeeding for brain development related to emotional and motivational processing in early adolescence.

Smaller anterior subgenual cingulate volume mediates the effect of girls' early sexual maturation on negative psychobehavioral outcome.

Early-maturing girls are relatively likely to experience compromised psychobehavioral outcomes. Some studies have explored the association between puberty and brain morphology in adolescents, while the results were non-specific for females or the method was a region-of-interest analysis. To our knowledge, no large-scale study has comprehensively explored the effects of pubertal timing on whole-brain volumetric development or the neuroanatomical substrates of the association in girls between pubertal timing and psychobehavioral outcomes. We collected structural magnetic resonance imaging (MRI) data of a subsample (N â€‹= â€‹203, mean age 11.6 years) from a large-scale population-based birth cohort. Tanner stage, a scale of physical maturation in adolescents, was rated almost simultaneously with MRI scan. The Strengths and Difficulties Questionnaire total difficulties (SDQ-TD) scores were rated by primary parents some duration after MRI scan (mean age 12.1 years). In each sex group, we examined brain regions associated with Tanner stage using whole-brain analysis controlling for chronological age, followed by an exploration of brain regions also associated with the SDQ-TD scores. We also performed mediation analyses. In girls, Tanner stage was significantly negatively correlated with gray matter volumes (GMVs) in the anterior/middle cingulate cortex (ACC/MCC), of which the subgenual ACC (sgACC) showed a negative correlation between GMVs and SDQ-TD scores. Smaller GMVs in the sgACC mediated the association between higher Tanner stages and higher SDQ-TD scores. We found no significant results in boys. Our results from a minimally biased, large-scale sample provide new insights into neuroanatomical correlates of the effect of pubertal timing on developmental psychological difficulties emerging in adolescence.

Neurometabolic underpinning of the intergenerational transmission of prosociality.

Parent-child personality transmission can occur via biological gene-driven processes as well as through environmental factors such as shared environment and parenting style. We recently revealed a negative association between prosociality, a highly valued personality attribute in human society, and anterior cingulate cortex (ACC) γ-aminobutyric acid (GABA) levels in children at the age of 10 years. We thus hypothesized that prosociality would be intergenerationally transmitted, and that transmission would be underwritten by neurometabolic heritability. Here, we collected prosociality data from children aged 10 years and their parents in a large-scale population-based birth cohort study. We also measured ACC GABA+ and glutamate plus glutamine (Glx) levels in a follow-up assessment with a subsample of the participants (aged 11 years) using magnetic resonance spectroscopy. We analyzed the associations among children's and parents' prosociality and GABA+/Glx ratios. We also examined the effect of socioeconomic status (SES) and verbalized parental affection (VPA) on these associations. We found a significant positive parent-child association for prosociality (N â€‹= â€‹3026; children's mean age 10.2 years) and GABA+/Glx ratio (N â€‹= â€‹99; children's mean age 11.4 years). There was a significant negative association between GABA+/Glx ratio and prosociality in both children (N â€‹= â€‹208) and parents (N â€‹= â€‹128). Our model accounting for the effects of neurometabolic heritability on prosociality transmission fitted well. Moreover, in this model, a significant positive effect of VPA but not SES on children's prosociality was observed independently of the effect of neurometabolic transmission, while SES but not VPA was significantly associated with parental prosociality. Our results provide novel insights into the neurometabolic substrates of parent-child transmission of social behavior.

Brain morphological and functional features in cognitive subgroups of schizophrenia.

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.

Differentiation of schizophrenia using structural MRI with consideration of scanner differences: A real-world multisite study.

AIM: Neuroimaging studies have revealed that patients with schizophrenia exhibit reduced gray matter volume in various regions. With these findings, various studies have indicated that structural MRI can be useful for the diagnosis of schizophrenia. However, multisite studies are limited. Here, we evaluated a simple model that could be used to differentiate schizophrenia from control subjects considering MRI scanner differences employing voxel-based morphometry. METHODS: Subjects were 541 patients with schizophrenia and 1252 healthy volunteers. Among them, 95 patients and 95 controls (Dataset A) were used for the generation of regions of interest (ROI), and the rest (Dataset B) were used to evaluate our method. The two datasets were comprised of different subjects. Three-dimensional T1-weighted MRI scans were taken for all subjects and gray-matter images were extracted. To differentiate schizophrenia, we generated ROI for schizophrenia from Dataset A. Then, we determined volume within the ROI for each subject from Dataset B. Using the extracted volume data, we calculated a differentiation feature considering age, sex, and intracranial volume for each MRI scanner. Receiver-operator curve analyses were performed to evaluate the differentiation feature. RESULTS: The area under the curve ranged from 0.74 to 0.84, with accuracy from 69% to 76%. Receiver-operator curve analysis with all samples revealed an area under the curve of 0.76 and an accuracy of 73%. CONCLUSION: We moderately successfully differentiated schizophrenia from control using structural MRI from differing scanners from multiple sites. This could be useful for applying neuroimaging techniques to clinical settings for the accurate diagnosis of schizophrenia.