Molecular mechanisms of quetiapine bidirectional regulation of bipolar depression and mania based on network pharmacology and molecular docking: Evidence from computational biology.

BACKGROUND: Quetiapine monotherapy is recommended as the first-line option for acute mania and acute bipolar depression. However, the mechanism of action of quetiapine is unclear. Network pharmacology and molecular docking were employed to determine the molecular mechanisms of quetiapine bidirectional regulation of bipolar depression and mania. METHODS: Putative target genes for quetiapine were collected from the GeneCard, SwissTargetPrediction, and DrugBank databases. Targets for bipolar depression and bipolar mania were identified from the DisGeNET and GeneCards databases. A protein-protein interaction (PPI) network was generated using the String database and imported into Cytoscape. DAVID and the Bioinformatics platform were employed to perform the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of the top 15 core targets. The drug-pathway-target-disease network was constructed using Cytoscape. Finally, molecular docking was performed to evaluate the interactions between quetiapine and potential targets. RESULTS: Targets for quetiapine actions against bipolar depression (126 targets) and bipolar mania (81 targets) were identified. Based on PPI and KEGG pathway analyses, quetiapine may affect bipolar depression by targeting the MAPK and PI3K/AKT insulin signaling pathways via BDNF, INS, EGFR, IGF1, and NGF, and it may affect bipolar mania by targeting the neuroactive ligand-receptor interaction signaling pathway via HTR1A, HTR1B, HTR2A, DRD2, and GRIN2B. Molecular docking revealed good binding affinity between quetiapine and potential targets. LIMITATIONS: Pharmacological experiments should be conducted to verify and further explore these results. CONCLUSIONS: Our findings suggest that quetiapine affects bipolar depression and bipolar mania through distinct biological core targets, and thus through different mechanisms. Furthermore, our results provide a theoretical basis for the clinical use of quetiapine and possible directions for new drug development.

Homotopic functional connectivity disruptions in schizophrenia and their associated gene expression.

It has been revealed that abnormal voxel-mirrored homotopic connectivity (VMHC) is present in patients with schizophrenia, yet there are inconsistencies in the relevant findings. Moreover, little is known about their association with brain gene expression profiles. In this study, transcription-neuroimaging association analyses using gene expression data from Allen Human Brain Atlas and case-control VMHC differences from both the discovery (meta-analysis, including 9 studies with a total of 386 patients and 357 controls) and replication (separate group-level comparisons within two datasets, including a total of 258 patients and 287 controls) phases were performed to identify genes associated with VMHC alterations. Enrichment analyses were conducted to characterize the biological functions and specific expression of identified genes, and Neurosynth decoding analysis was performed to examine the correlation between cognitive-related processes and VMHC alterations in schizophrenia. In the discovery and replication phases, patients with schizophrenia exhibited consistent VMHC changes compared to controls, which were correlated with a series of cognitive-related processes; meta-regression analysis revealed that illness duration was negatively correlated with VMHC abnormalities in the cerebellum and postcentral/precentral gyrus. The abnormal VMHC patterns were stably correlated with 1287 genes enriched for fundamental biological processes like regulation of cell communication, nervous system development, and cell communication. In addition, these genes were overexpressed in astrocytes and immune cells, enriched in extensive cortical regions and wide developmental time windows. The present findings may contribute to a more comprehensive understanding of the molecular mechanisms underlying VMHC alterations in patients with schizophrenia.

Insulin Resistance/Diabetes and Schizophrenia: Potential Shared Genetic Factors and Implications for Better Management of Patients with Schizophrenia.

Schizophrenia is a complex psychotic disorder with co-occurring conditions, including insulin resistance and type 2 diabetes (T2D). It is well established that T2D and its precursors (i.e., insulin resistance) are more prevalent in patients with schizophrenia who are treated with antipsychotics, as well as in antipsychotic-naïve patients experiencing their first episode of psychosis, compared with the general population. However, the mechanism(s) underlying the increased susceptibility, shared genetics, and possible cause-effect relationship between schizophrenia and T2D remain largely unknown. The objective of this narrative review was to synthesize important studies, including Mendelian randomization (MR) analyses that have integrated genome-wide association studies (GWAS), as well as results from in vitro models, in vivo models, and observational studies of patients with schizophrenia. Both GWAS and MR studies have found that schizophrenia and T2D/insulin resistance share genetic risk factors, and this may mediate a connection between peripheral or brain insulin resistance and T2D with cognition impairment and an increased risk of developing prediabetes and T2D in schizophrenia. Moreover, accumulating evidence supports a causal role for insulin resistance in schizophrenia and emphasizes the importance of a genetic basis for susceptibility to T2D in patients with schizophrenia before they receive psychotic treatment. The present findings and observations may have clinical implications for the development of better strategies to treat patients with schizophrenia, with both pharmacological (i.e., samidorphan, empagliflozin) and/or nonpharmacological (i.e., lifestyle changes) approaches. Additionally, this review may benefit the design of future studies by physicians and clinical investigators.

Genomic loci influence patterns of structural covariance in the human brain.

Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain.

Low-dose lithium adjunct to atypical antipsychotic treatment nearly improved cognitive impairment, deteriorated the gray-matter volume, and decreased the interleukin-6 level in drug-naive patients with first schizophrenia symptoms: a follow-up pilot study.

This study was conducted to investigate the effects of long-term low-dose lithium adjunct to antipsychotic agent use on the cognitive performance, whole-brain gray-matter volume (GMV), and interleukin-6 (IL-6) level in drug-naive patients with first-episode schizophrenia, and to examine relationships among these factors. In this double-blind randomized controlled study, 50 drug-naive patients with first-episode schizophrenia each took low-dose (250 mg/day) lithium and placebo (of the same shape and taste) adjunct to antipsychotic agents (mean, 644.70 ± 105.58 and 677.00 ± 143.33 mg/day chlorpromazine equivalent, respectively) for 24 weeks. At baseline and after treatment completion, the MATRICS Consensus Cognitive Battery (MCCB) was used to assess cognitive performance, 3-T magnetic resonance imaging was performed to assess structural brain alterations, and serum IL-6 levels were quantified by immunoassay. Treatment effects were assessed within and between patient groups. Relationships among cognitive performance, whole-brain GMVs, and the IL-6 level were investigated by partial correlation analysis. Relative to baseline, patients in the lithium group showed improved working memory, verbal learning, processing speed, and reasoning/problem solving after 24 weeks of treatment; those in the placebo group showed only improved working memory and verbal learning. The composite MCCB score did not differ significantly between groups. The whole-brain GMV reduction was significantly lesser in the lithium group than in the placebo group (0.46% vs. 1.03%; P < 0.001). The GMV and IL-6 reduction ratios correlated with each other in both groups (r = -0.17, P = 0.025). In the lithium group, the whole-brain GMV reduction ratio correlated with the working memory improvement ratio (r = -0.15, P = 0.030) and processing speed (r = -0.14, P = 0.036); the IL-6 reduction ratio correlated with the working memory (r = -0.21, P = 0.043) and verbal learning (r = -0.30, P = 0.031) improvement ratios. In the placebo group, the whole-brain GMV reduction ratio correlated only with the working memory improvement ratio (r = -0.24, P = 0.019); the IL-6 reduction ratio correlated with the working memory (r = -0.17, P = 0.022) and verbal learning (r = -0.15, P = 0.011) improvement ratios. Both treatments implemented in this study nearly improved the cognitive performance of patients with schizophrenia; relative to placebo, low-dose lithium had slightly greater effects on several aspects of cognition. The patterns of correlation among GMV reduction, IL-6 reduction, and cognitive performance improvement differed between groups.

Dissociation between neuroanatomical and symptomatic subtypes in schizophrenia.

BACKGROUND: Schizophrenia is a complex and heterogeneous syndrome with high clinical and biological stratification. Identifying distinctive subtypes can improve diagnostic accuracy and help precise therapy. A key challenge for schizophrenia subtyping is understanding the subtype-specific biological underpinnings of clinical heterogeneity. This study aimed to investigate if the machine learning (ML)-based neuroanatomical and symptomatic subtypes of schizophrenia are associated. METHODS: A total of 314 schizophrenia patients and 257 healthy controls from four sites were recruited. Gray matter volume (GMV) and Positive and Negative Syndrome Scale (PANSS) scores were employed to recognize schizophrenia neuroanatomical and symptomatic subtypes using K-means and hierarchical methods, respectively. RESULTS: Patients with ML-based neuroanatomical subtype-1 had focally increased GMV, and subtype-2 had widespread reduced GMV than the healthy controls based on either K-means or Hierarchical methods. In contrast, patients with symptomatic subtype-1 had severe PANSS scores than subtype-2. No differences in PANSS scores were shown between the two neuroanatomical subtypes; similarly, no GMV differences were found between the two symptomatic subtypes. Cohen's Kappa test further demonstrated an apparent dissociation between the ML-based neuroanatomical and symptomatic subtypes (P > 0.05). The dissociation patterns were validated in four independent sites with diverse disease progressions (chronic vs. first episodes) and ancestors (Chinese vs. Western). CONCLUSIONS: These findings revealed a replicable dissociation between ML-based neuroanatomical and symptomatic subtypes of schizophrenia, which provides a new viewpoint toward understanding the heterogeneity of schizophrenia.

Validation of a modified Chinese-language version of the Davos Assessment of Cognitive Biases Scale (MCL-DACOBS) in a sample of Chinese patients with schizophrenia.

INTRODUCTION: The Davos Assessment of Cognitive Biases Scale (DACOBS) is widely used to assess cognitive biases in patients who have schizophrenia. However, the lack of a modified Chinese-language version of the DACOBS (MCL-DACOBS) precludes Chinese schizophrenic patients from treatment aimed at normalizing cognitive biases, impacting their prognosis. Here, we aimed to produce a DACOBS for China and test the validity and reliability of the resultant MCL-DACOBS. METHODS: Eighteen researchers collaborated to develop the MCL-DACOBS: A total of 15 researchers modified and translated the English version of the DACOBS, 1 native-English-speaking researcher back-translated the scale, and 2 Chinese sinologists localized and optimized the language of the MCL-DACOBS. Forty-two volunteers checked the scale items' comprehensibility, and the two sinologists performed further localization and optimization based on their feedback. The final version of the MCL-DACOBS used in this study was thus derived from the harmonized English-language version of the scale. Confirmatory factor analyses (CFAs) were used to examine the best latent structure of the MCL-DACOBS. Cronbach's α and intraclass correlation coefficients (ICCs) were used to check the reliability. The discriminative ability of the MCL-DACOBS was assessed according to the area under the receiver operating characteristic curve. RESULTS: The CFA showed that all items loaded onto factors with loadings >0.400. A two-factor structure showed a good model fit (root mean square error of approximation = .018, Tucker-Lewis index = .978, comparative fit index = .984). Promax rotation demonstrated that each item had a high factor load (0.432-0.774). Cronbach's α coefficient and ICC for the MCL-DOCABS were .965 and .957, respectively, indicating that the scale has ideal reliability. CONCLUSION: The MCL-DACOBS has good validity and good reliability, and its psychometric properties indicate that it is a valid tool for measuring cognitive biases in Chinese patients with schizophrenia.

Low-dose lithium adjunct to quetiapine improves cognitive task performance in mice with MK801-induced long-term cognitive impairment: Evidence from a pilot study.

BACKGROUND: Low-dose lithium (LD-Li) has been shown to rescue cognitive impairment in mouse models of short-term mild cognitive impairment, dementia, and schizophrenia. However, few studies have characterized the effects of LD-Li, alone or in conjunction with anti-psychotics, in the mouse model of MK801-induced long term cognitive impairment. METHODS: The present study used in vivo Ca2+ imaging and a battery of cognitive function assessments to investigate the long-term effects of LD-Li on cognition in mice exposed to repeated injections of MK801. Prefrontal Ca2+ activity was visualized to estimate alterations in neural activity in the model mice. Pre-pulse inhibition (PPI), novel object recognition (NOR), Morris water maze (MWM), and fear conditioning (FC) tasks were used to characterize cognitive performance; open field activity (OFA) testing was used to observe psychotic symptoms. Two treatment strategies were tested: LD-Li [250 mg/d human equivalent dose (HED)] adjunct to quetiapine (QTP; 600 mg/d HED); and QTP-monotherapy (mt; 600 mg/d HED). RESULTS: Compared to the QTP-mt group, the LD-Li + QTP group showed greatly improved cognitive performance on all measures between experimental days 29 and 85. QTP-mt improved behavioral measures compared to untreated controls, but the effects persisted only from day 29 to day 43. These data suggest that LD-Li + QTP is superior to QTP-mt for improving long-term cognitive impairments in the MK801 mouse model. LIMITATIONS: There is no medical consensus regarding lithium use in patients with schizophrenia. CONCLUSION: More pre-clinical and clinical studies are needed to further investigate effective treatment strategies for patients with long-term cognitive impairments, such as chronic schizophrenia.

Microglia and cognitive impairment in schizophrenia: translating scientific progress into novel therapeutic interventions.

Cognitive impairment is a core clinical feature of schizophrenia, exerting profound adverse effects on social functioning and quality of life in a large proportion of patients with schizophrenia. However, the mechanisms underlying the pathogenesis of schizophrenia-related cognitive impairment are not well understood. Microglia, the primary resident macrophages in the brain, have been shown to play important roles in psychiatric disorders, including schizophrenia. Increasing evidence has revealed excessive microglial activation in cognitive deficits related to a broad range of diseases and medical conditions. Relative to that about age-related cognitive deficits, current knowledge about the roles of microglia in cognitive impairment in neuropsychiatric disorders, such as schizophrenia, is limited, and such research is in its infancy. Thus, we conducted this review of the scientific literature with a focus on the role of microglia in schizophrenia-associated cognitive impairment, aiming to gain insight into the roles of microglial activation in the onset and progression of such impairment and to consider how scientific advances could be translated to preventive and therapeutic interventions. Research has demonstrated that microglia, especially those in the gray matter of the brain, are activated in schizophrenia. Upon activation, microglia release key proinflammatory cytokines and free radicals, which are well-recognized neurotoxic factors contributing to cognitive decline. Thus, we propose that the inhibition of microglial activation holds potential for the prevention and treatment of cognitive deficits in patients with schizophrenia. This review identifies potential targets for the development of new treatment strategies and eventually the improvement of care for these patients. It might also help psychologists and clinical investigators in planning future research.

Validation and reliability test of Chinese language patient-reported impact of symptoms in schizophrenia scale.

Background: Patient-reported outcomes, or subjective evaluations directly reflecting the patient's views, feelings, and judgments, are now being used to evaluate the outcomes of care and treatment of people with schizophrenia. In this study, we used an updated tool, the patient-reported impact of symptoms in schizophrenia scale (PRISS), translated into Chinese languages to assess the subjective experiences of schizophrenia patients. Objective: This study aimed to test the psychometrics of the Chinese languages PRISS (CL-PRISS). Method: This study used the Chinese version of PRISS (CL-PRISS), acquired from the harmonized English-language version. A total of 280 patients enrolled in this study were asked to complete the CL-PRISS, the positive and negative syndrome scale (PANSS), and the World Health Organization Disability Assessment Schedule (WHO-DAS). Construct and concurrent validity was tested using the confirmatory factor analysis (CFA) and Spearman correlation coefficient, respectively. The reliability of CL-PRISS was tested using Cronbach's α coefficient and the internal correlation coefficient. Results: Confirmatory factor analysis (CFA) analysis demonstrated three major factors in CL_PRISS: the first factor is productive experiences, the second factor is affective-negative, and the third factor experiences. The factor loadings between items and factors ranged from 0.436 to 0.899 (RMSEA = 0.029, TLI = 0.940, CFI = 0.921). The correlation coefficient between the CL_PRISS and PANSS was 0.845, and between the CL-PRISS and WHO-DAS was 0.886. The ICC of the total CL_PRISS was 0.913, and Cronbach's α was 0.903. Conclusion: The Chinese version of the PRISS (CL_PRISS) can be effectively used for assessing the subjective experience of Chinese patients with schizophrenia.

Psychosis brain subtypes validated in first-episode cohorts and related to illness remission: results from the PHENOM consortium.

Using machine learning, we recently decomposed the neuroanatomical heterogeneity of established schizophrenia to discover two volumetric subgroups-a 'lower brain volume' subgroup (SG1) and an 'higher striatal volume' subgroup (SG2) with otherwise normal brain structure. In this study, we investigated whether the MRI signatures of these subgroups were also already present at the time of the first-episode of psychosis (FEP) and whether they were related to clinical presentation and clinical remission over 1-, 3-, and 5-years. We included 572 FEP and 424 healthy controls (HC) from 4 sites (Sao Paulo, Santander, London, Melbourne) of the PHENOM consortium. Our prior MRI subgrouping models (671 participants; USA, Germany, and China) were applied to both FEP and HC. Participants were assigned into 1 of 4 categories: subgroup 1 (SG1), subgroup 2 (SG2), no subgroup membership ('None'), and mixed SG1 + SG2 subgroups ('Mixed'). Voxel-wise analyses characterized SG1 and SG2 subgroups. Supervised machine learning analyses characterized baseline and remission signatures related to SG1 and SG2 membership. The two dominant patterns of 'lower brain volume' in SG1 and 'higher striatal volume' (with otherwise normal neuromorphology) in SG2 were identified already at the first episode of psychosis. SG1 had a significantly higher proportion of FEP (32%) vs. HC (19%) than SG2 (FEP, 21%; HC, 23%). Clinical multivariate signatures separated the SG1 and SG2 subgroups (balanced accuracy = 64%; p < 0.0001), with SG2 showing higher education but also greater positive psychosis symptoms at first presentation, and an association with symptom remission at 1-year, 5-year, and when timepoints were combined. Neuromorphological subtypes of schizophrenia are already evident at illness onset, separated by distinct clinical presentations, and differentially associated with subsequent remission. These results suggest that the subgroups may be underlying risk phenotypes that could be targeted in future treatment trials and are critical to consider when interpreting neuroimaging literature.

Low-dose lithium mono- and adjunctive therapies improve MK-801-induced cognitive impairment and schizophrenia-like behavior in mice - Evidence from altered prefrontal lobe Ca2+ activity.

BACKGROUND: Few studies have evaluated lithium either as monotherapy or in combination with anti-psychotic agents to improve cognition in murine models of schizophrenia. METHODS: Visualization of Ca2+ activity in the prefrontal cortex was used to characterize brain neural activity. Novel object recognition (NOR), Morris water maze (MWM), and fear conditioning (FCT) tests were used to characterize cognitive performance; while pre-pulse inhibition (PPI), elevated plus maze (EPM) and the open field test (OFT) were used to characterize schizophrenia-like behavior. RESULTS: A 28-day course of low-dose lithium (human equivalent dose of 250 mg/day) combined with moderate-dose quetiapine (human equivalent dose of 600 mg/day) improved Ca2+ ratio by 70.10 %, PPI by 69.28 %, NOR by 70.09 %, MWM by 71.28 %, FCT by 68.56 %, EPM by 70.95 % and OFT by 75.23 % compared to the results of positive controls. Unexpectedly, moderate-dose lithium (human equivalent dose of 500 mg/day) used either as monotherapy or as an adjunct with quetiapine worsened Ca2+ activity, PPI, MWM, FCT, EPM, and OPT. LIMITATIONS: Our study cannot explain the contrasting positive and negative effects of low-dose and moderate-dose lithium, respectively, when used either as monotherapies or as adjuncts. Further studies, especially Western blotting, may reveal molecular mechanisms of action. CONCLUSIONS: Low-dose lithium (human equivalent dose of 250 mg/day) combined with moderate-dose quetiapine (human equivalent dose of 600 mg/day) provided the best improvements. Furthermore, benefits persisted for 14 days post-treatment. Our data provide directions for further research of therapeutic alternatives to mitigate schizophrenia-related cognopathy.

Validity and reliability of a Chinese version of the Bergen Work Addiction Scale.

Background: Work addiction (WA), which can impair personal relationships, engagement in recreational activities, and/or health, is a behavioral addiction. A tool for the early detection of WA in China is needed. Objective: The aim of this study was to develop and determine the validity and reliability of a Chinese version of the Bergen Work Addiction Scale (C-BWAS). Methods: Two hundred social workers who provided post-discharge services for adolescents with non-suicidal self-injury (NSSI) were enrolled in this study. The construct validity of the C-BWAS was assessed by confirmatory factor analysis (CFA). Criterion validity was assessed by conducting Pearson correlation analyses of C-CWAS scores with Hamilton Depression Scale (HAM-D) and Hamilton Anxiety Scale (HAM-A) scores. Cronbach's α and the intra-class correlation coefficient (ICC) were used to evaluate the reliability of the C-BWAS. Results: CFA confirmed a one-dimensional structure of the C-BWAS with good construct validity indices [comparative fit index (CFI) = 0.964, Tucker-Lewis index (TLI) = 0.951, root-mean-square error of approximation (RMSEA) = 0.079, and minimum discrepancy C/degrees of freedom (Cmin/DF) = 0.362]. The standardized regression weights ranged from 0.523 to 0.753. All C-BWAS items loaded on one major factor (loading weights, 0.646-0.943). Coefficients of correlation between C-BWAS scores and HAM-D and HAM-A scores were 0.889 and 0.933, respectively. The Cronbach's α coefficient and ICC for the instrument was 0.837 and 0.905, respectively. Conclusion: The presently developed C-BWAS showed very good reliability and acceptably validity. It can be employed as a useful tool for assessing WA severity in social workers who provide post-discharge services for adolescents with NSSI.

Validity and reliability of a Chinese language suicide screening questionnaire-observer rating (CL-SSQ-OR) assessment for children/adolescents.

Background: A Suicide Screening Questionnaire-Observer Rating (SSQ-OR) has been used to assess risk of suicide among individuals and to help clinicians identify and rescue individuals attempting suicide. To prevent the risk of suicide in China, a Chinese language SSQ-OR (CL-SSQ-OR) needs to be introduced. Objective: To test the validity and reliability of a CL-SSQ-OR. Method: A total of 250 individuals were enrolled in this study. Each completed a CL-SSQ-OR assessment, Patient Health Questionnaire-9, and the Beck Scale for Suicide Ideation. Confirmatory factor analysis (CFA) was adopted to determine structural validity. Spearman correlation coefficients were adopted to determine criterion validity. An internal correlation coefficient (ICC) was used to test inter-consistency and Cronbach's α coefficient was used to test split-half reliability. Results: CFA was conducted with use of the maximum variance method to evaluate the item results. All of the items received scores >0.40. In addition, good model fit indices were observed for the two-factor structure RMSEA = 0.046, TLI = 0.965, CFI = 0.977. The items' factor loading of the CL-SSQ-OR in the first factor ranged from 0.443 to 0.878. The items' factor loading of the CL-SSQ-OR in the second factor ranged from 0.400 to 0.810. The ICC of the total CL-SSQ-OR was 0.855. Cronbach's α was 0.873. Conclusion: The CL-SSQ-OR described here demonstrates ideal psychometric properties and is found to be a suitable tool for screening Chinese children/adolescents who are at risk of suicide.

Assessment of Neuroanatomical Endophenotypes of Autism Spectrum Disorder and Association With Characteristics of Individuals With Schizophrenia and the General Population.

Importance: Autism spectrum disorder (ASD) is associated with significant clinical, neuroanatomical, and genetic heterogeneity that limits precision diagnostics and treatment. Objective: To assess distinct neuroanatomical dimensions of ASD using novel semisupervised machine learning methods and to test whether the dimensions can serve as endophenotypes also in non-ASD populations. Design, Setting, and Participants: This cross-sectional study used imaging data from the publicly available Autism Brain Imaging Data Exchange (ABIDE) repositories as the discovery cohort. The ABIDE sample included individuals diagnosed with ASD aged between 16 and 64 years and age- and sex-match typically developing individuals. Validation cohorts included individuals with schizophrenia from the Psychosis Heterogeneity Evaluated via Dimensional Neuroimaging (PHENOM) consortium and individuals from the UK Biobank to represent the general population. The multisite discovery cohort included 16 internationally distributed imaging sites. Analyses were performed between March 2021 and March 2022. Main Outcomes and Measures: The trained semisupervised heterogeneity through discriminative analysis models were tested for reproducibility using extensive cross-validations. It was then applied to individuals from the PHENOM and the UK Biobank. It was hypothesized that neuroanatomical dimensions of ASD would display distinct clinical and genetic profiles and would be prominent also in non-ASD populations. Results: Heterogeneity through discriminative analysis models trained on T1-weighted brain magnetic resonance images of 307 individuals with ASD (mean [SD] age, 25.4 [9.8] years; 273 [88.9%] male) and 362 typically developing control individuals (mean [SD] age, 25.8 [8.9] years; 309 [85.4%] male) revealed that a 3-dimensional scheme was optimal to capture the ASD neuroanatomy. The first dimension (A1: aginglike) was associated with smaller brain volume, lower cognitive function, and aging-related genetic variants (FOXO3; Z = 4.65; P = 1.62 × 10-6). The second dimension (A2: schizophrenialike) was characterized by enlarged subcortical volumes, antipsychotic medication use (Cohen d = 0.65; false discovery rate-adjusted P = .048), partially overlapping genetic, neuroanatomical characteristics to schizophrenia (n = 307), and significant genetic heritability estimates in the general population (n = 14 786; mean [SD] h2, 0.71 [0.04]; P < 1 × 10-4). The third dimension (A3: typical ASD) was distinguished by enlarged cortical volumes, high nonverbal cognitive performance, and biological pathways implicating brain development and abnormal apoptosis (mean [SD] ß, 0.83 [0.02]; P = 4.22 × 10-6). Conclusions and Relevance: This cross-sectional study discovered 3-dimensional endophenotypic representation that may elucidate the heterogeneous neurobiological underpinnings of ASD to support precision diagnostics. The significant correspondence between A2 and schizophrenia indicates a possibility of identifying common biological mechanisms across the 2 mental health diagnoses.

Validity and reliability of a Chinese version of the self-evaluation of negative symptoms.

The negative symptoms of schizophrenia can be present at any clinical stage, but evaluating the negative symptoms always remains challenging. To screen the negative symptoms effectively, self-evaluation should be introduced. To date, professional psychiatrists used almost all of the scales available to screen the negative symptoms but could not obtain an accurate outcome. At the same time, an advanced self-assessment scale is needed to accompany the patients' self-feeling-based treatment strategies to understand their feelings about their symptoms. Hence, Chinese self-evaluation of negative symptoms (SNS) should be introduced in China. This study aims to examine the validity and reliability of the Chinese version of SNS. Two hundred patients with schizophrenia were included in this study and were evaluated entirely with the self-assessed negative symptoms by the Chinese version. The correlation analysis was performed between SNS and the Scale for Assessment of Negative Symptoms (SANS) to assess the criterion validity of SNS for screening negative symptoms. Exploratory factor analysis was used to determine the constructive validity of the SNS. Two senior professional psychiatrists were involved in this assessment based on their clinical experience and capability to define the severity of the negative symptoms. Receiver operating characteristic curve (ROC) analysis was performed to assess the cutoff point of SNS. Cronbach's alpha coefficient and intraclass correlation (ICC) coefficient were used to determine the reliability of SNS. We have the following findings: The Chinese version of SNS demonstrated a significant correlation with the SANS (r = .774, p < .05). Exploratory factor analysis demonstrated that the factor loading varies from .442 to .788. ROC analysis demonstrated that at SNS ≥ 8, the patients demonstrated a mild severity of negative symptoms, and at SNS ≥ 15, the patients demonstrated a severe severity of negative symptoms. Subsequently, 9 < SNS < 14 was defined as a moderate severity of negative symptoms. The Cronbach's alpha and ICC coefficients of the Chinese version SNS were .877 and .774, respectively. Our results showed that the acceptable validity and reliability of the Chinese version of SNS confirmed that SNS is an ideal tool for self-assessment of the negative symptoms in patients with schizophrenia.

Heterogeneous brain dynamic functional connectivity patterns in first-episode drug-naive patients with major depressive disorder.

It remains challenging to identify depression accurately due to its biological heterogeneity. As people suffering from depression are associated with functional brain network alterations, we investigated subtypes of patients with first-episode drug-naive (FEDN) depression based on brain network characteristics. This study included data from 91 FEDN patients and 91 matched healthy individuals obtained from the International Big-Data Center for Depression Research. Twenty large-scale functional connectivity networks were computed using group information guided independent component analysis. A multivariate unsupervised normative modeling method was used to identify subtypes of FEDN and their associated networks, focusing on individual-level variability among the patients for quantifying deviations of their brain networks from the normative range. Two patient subtypes were identified with distinctive abnormal functional network patterns, consisting of 10 informative connectivity networks, including the default mode network and frontoparietal network. 16% of patients belonged to subtype I with larger extreme deviations from the normal range and shorter illness duration, while 84% belonged to subtype II with weaker extreme deviations and longer illness duration. Moreover, the structural changes in subtype II patients were more complex than the subtype I patients. Compared with healthy controls, both increased and decreased gray matter (GM) abnormalities were identified in widely distributed brain regions in subtype II patients. In contrast, most abnormalities were decreased GM in subtype I. The informative functional network connectivity patterns gleaned from the imaging data can facilitate the accurate identification of FEDN-MDD subtypes and their associated neurobiological heterogeneity.

Understanding the heterogeneity of dynamic functional connectivity patterns in first-episode drug naïve depression using normative models.

BACKGROUND: The heterogeneity of the clinical symptoms and presumptive neural pathologies has stunted progress toward identifying reproducible biomarkers and limited therapeutic interventions' effectiveness for the first episode drug-naïve major depressive disorders (FEDN-MDD). This study combined the dynamic features of fMRI data and normative modeling to quantitative and individualized metrics for delineating the biological heterogeneity of FEDN-MDD. METHOD: Two hundred seventy-four adults with FEDN-MDD and 832 healthy controls from International Big-Data Center for Depression Research were included. Subject-specific dynamic brain networks and network fluctuation characteristics were computed for each subject using the group information-guided independent component analysis. Then, we mapped the heterogeneity of the dynamic features (network fluctuation characteristics and dynamic functional connectivity within brain networks) in the patients group via normative modeling. RESULTS: The FEDN-MDD whose network fluctuation characteristics deviate from the normative model also showed significant differences within the default mode network, executive control network, and limbic network compared with healthy controls. Furthermore, the network fluctuation characteristics are significantly increased in patients with FEDN-MDD. About 4.74 % of the patients showed a deviation of dynamic functional connectivity, and only 3.35 % of the controls deviated from the normative model in above 100 connectivities. More patients than healthy controls showed extreme dynamic variabilities in above 100 connectivities. CONCLUSIONS: This work evaluates the efficacy of an individualized approach based on normative modeling for understanding the heterogeneity of abnormal dynamic functional connectivity patterns in FEDN-MDD, and could be used as complementary to classical case-control comparisons.

Computing personalized brain functional networks from fMRI using self-supervised deep learning.

A novel self-supervised deep learning (DL) method is developed to compute personalized brain functional networks (FNs) for characterizing brain functional neuroanatomy based on functional MRI (fMRI). Specifically, a DL model of convolutional neural networks with an encoder-decoder architecture is developed to compute personalized FNs directly from fMRI data. The DL model is trained to optimize functional homogeneity of personalized FNs without utilizing any external supervision in an end-to-end fashion. We demonstrate that a DL model trained on fMRI scans from the Human Connectome Project can identify personalized FNs and generalizes well across four different datasets. We further demonstrate that the identified personalized FNs are informative for predicting individual differences in behavior, brain development, and schizophrenia status. Taken together, the self-supervised DL allows for rapid, generalizable computation of personalized FNs.