Automating the analysis of facial emotion expression dynamics: A computational framework and application in psychotic disorders.

Facial emotion expressions play a central role in interpersonal interactions; these displays are used to predict and influence the behavior of others. Despite their importance, quantifying and analyzing the dynamics of brief facial emotion expressions remains an understudied methodological challenge. Here, we present a method that leverages machine learning and network modeling to assess the dynamics of facial expressions. Using video recordings of clinical interviews, we demonstrate the utility of this approach in a sample of 96 people diagnosed with psychotic disorders and 116 never-psychotic adults. Participants diagnosed with schizophrenia tended to move from neutral expressions to uncommon expressions (e.g., fear, surprise), whereas participants diagnosed with other psychoses (e.g., mood disorders with psychosis) moved toward expressions of sadness. This method has broad applications to the study of normal and altered expressions of emotion and can be integrated with telemedicine to improve psychiatric assessment and treatment.

Trajectories through semantic spaces in schizophrenia and the relationship to ripple bursts.

Human cognition is underpinned by structured internal representations that encode relationships between entities in the world (cognitive maps). Clinical features of schizophrenia-from thought disorder to delusions-are proposed to reflect disorganization in such conceptual representations. Schizophrenia is also linked to abnormalities in neural processes that support cognitive map representations, including hippocampal replay and high-frequency ripple oscillations. Here, we report a computational assay of semantically guided conceptual sampling and exploit this to test a hypothesis that people with schizophrenia (PScz) exhibit abnormalities in semantically guided cognition that relate to hippocampal replay and ripples. Fifty-two participants [26 PScz (13 unmedicated) and 26 age-, gender-, and intelligence quotient (IQ)-matched nonclinical controls] completed a category- and letter-verbal fluency task, followed by a magnetoencephalography (MEG) scan involving a separate sequence-learning task. We used a pretrained word embedding model of semantic similarity, coupled to a computational model of word selection, to quantify the degree to which each participant's verbal behavior was guided by semantic similarity. Using MEG, we indexed neural replay and ripple power in a post-task rest session. Across all participants, word selection was strongly influenced by semantic similarity. The strength of this influence showed sensitivity to task demands (category > letter fluency) and predicted performance. In line with our hypothesis, the influence of semantic similarity on behavior was reduced in schizophrenia relative to controls, predicted negative psychotic symptoms, and correlated with an MEG signature of hippocampal ripple power (but not replay). The findings bridge a gap between phenomenological and neurocomputational accounts of schizophrenia.

Temporal imprecision of phase coherence in schizophrenia and psychosis-dynamic mechanisms and diagnostic marker.

Schizophrenia (SCZ) is a complex disorder in which various pathophysiological models have been postulated. Brain imaging studies using EEG/MEG and fMRI show altered amplitude and, more recently, decrease in phase coherence in response to external stimuli. What are the dynamic mechanisms of such phase incoherence, and can it serve as a differential-diagnostic marker? Addressing this gap in our knowledge, we uniquely combine a review of previous findings, novel empirical data, and computational-dynamic simulation. The main findings are: (i) the review shows decreased phase coherence in SCZ across a variety of different tasks and frequencies, e.g., task- and frequency-unspecific, which is further supported by our own novel data; (ii) our own data demonstrate diagnostic specificity of decreased phase coherence for SCZ as distinguished from major depressive disorder; (iii) simulation data exhibit increased phase offset in SCZ leading to a precision index, in the millisecond range, of the phase coherence relative to the timing of the external stimulus. Together, we demonstrate the key role of temporal imprecision in phase coherence of SCZ, including its mechanisms (phase offsets, precision index) on the basis of which we propose a phase-based temporal imprecision model of psychosis (PTP). The PTP targets a deeper dynamic layer of a basic disturbance. This converges well with other models of psychosis like the basic self-disturbance and time-space experience changes, as discussed in phenomenological and spatiotemporal psychopathology, as well as with the models of aberrant predictive coding and disconnection as in computational psychiatry. Finally, our results show that temporal imprecision as manifest in decreased phase coherence is a promising candidate biomarker for clinical differential diagnosis of SCZ, and more broadly, psychosis.

Transdiagnostic inflexible learning dynamics explain deficits in depression and schizophrenia.

Deficits in reward learning are core symptoms across many mental disorders. Recent work suggests that such learning impairments arise by a diminished ability to use reward history to guide behaviour, but the neuro-computational mechanisms through which these impairments emerge remain unclear. Moreover, limited work has taken a transdiagnostic approach to investigate whether the psychological and neural mechanisms that give rise to learning deficits are shared across forms of psychopathology. To provide insight into this issue, we explored probabilistic reward learning in patients diagnosed with major depressive disorder (n = 33) or schizophrenia (n = 24) and 33 matched healthy controls by combining computational modelling and single-trial EEG regression. In our task, participants had to integrate the reward history of a stimulus to decide whether it is worthwhile to gamble on it. Adaptive learning in this task is achieved through dynamic learning rates that are maximal on the first encounters with a given stimulus and decay with increasing stimulus repetitions. Hence, over the course of learning, choice preferences would ideally stabilize and be less susceptible to misleading information. We show evidence of reduced learning dynamics, whereby both patient groups demonstrated hypersensitive learning (i.e. less decaying learning rates), rendering their choices more susceptible to misleading feedback. Moreover, there was a schizophrenia-specific approach bias and a depression-specific heightened sensitivity to disconfirmational feedback (factual losses and counterfactual wins). The inflexible learning in both patient groups was accompanied by altered neural processing, including no tracking of expected values in either patient group. Taken together, our results thus provide evidence that reduced trial-by-trial learning dynamics reflect a convergent deficit across depression and schizophrenia. Moreover, we identified disorder distinct learning deficits.

Extracellular vesicle biomarkers for complement dysfunction in schizophrenia.

Schizophrenia, a complex neuropsychiatric disorder, frequently experiences a high rate of misdiagnosis due to subjective symptom assessment. Consequently, there is an urgent need for innovative and objective diagnostic tools. In this study, we used cutting-edge extracellular vesicles' (EVs) proteome profiling and XGBoost-based machine learning to develop new markers and personalized discrimination scores for schizophrenia diagnosis and prediction of treatment response. We analysed plasma and plasma-derived EVs from 343 participants, including 100 individuals with chronic schizophrenia, 34 first-episode and drug-naïve patients, 35 individuals with bipolar disorder, 25 individuals with major depressive disorder and 149 age- and sex-matched healthy controls. Our innovative approach uncovered EVs-based complement changes in patients, specific to their disease-type and status. The EV-based biomarkers outperformed their plasma counterparts, accurately distinguishing schizophrenia individuals from healthy controls with an area under curve (AUC) of 0.895, 83.5% accuracy, 85.3% sensitivity and 82.0% specificity. Moreover, they effectively differentiated schizophrenia from bipolar disorder and major depressive disorder, with AUCs of 0.966 and 0.893, respectively. The personalized discrimination scores provided a personalized diagnostic index for schizophrenia and exhibited a significant association with patients' antipsychotic treatment response in the follow-up cohort. Overall, our study represents a significant advancement in the field of neuropsychiatric disorders, demonstrating the potential of EV-based biomarkers in guiding personalized diagnosis and treatment of schizophrenia.

Electrophysiological measures from human iPSC-derived neurons are associated with schizophrenia clinical status and predict individual cognitive performance.

Neurons derived from human induced pluripotent stem cells (hiPSCs) have been used to model basic cellular aspects of neuropsychiatric disorders, but the relationship between the emergent phenotypes and the clinical characteristics of donor individuals has been unclear. We analyzed RNA expression and indices of cellular function in hiPSC-derived neural progenitors and cortical neurons generated from 13 individuals with high polygenic risk scores (PRSs) for schizophrenia (SCZ) and a clinical diagnosis of SCZ, along with 15 neurotypical individuals with low PRS. We identified electrophysiological measures in the patient-derived neurons that implicated altered Na+ channel function, action potential interspike interval, and gamma-aminobutyric acid-ergic neurotransmission. Importantly, electrophysiological measures predicted cardinal clinical and cognitive features found in these SCZ patients. The identification of basic neuronal physiological properties related to core clinical characteristics of illness is a potentially critical step in generating leads for novel therapeutics.

Evaluation of a Nondepleted Plasma Multiprotein-Based Model for Discriminating Psychiatric Disorders Using Multiple Reaction Monitoring-Mass Spectrometry: Proof-of-Concept Study.

Psychiatric evaluation relies on subjective symptoms and behavioral observation, which sometimes leads to misdiagnosis. Despite previous efforts to utilize plasma proteins as objective markers, the depletion method is time-consuming. Therefore, this study aimed to enhance previous quantification methods and construct objective discriminative models for major psychiatric disorders using nondepleted plasma. Multiple reaction monitoring-mass spectrometry (MRM-MS) assays for quantifying 453 peptides in nondepleted plasma from 132 individuals [35 major depressive disorder (MDD), 47 bipolar disorder (BD), 23 schizophrenia (SCZ) patients, and 27 healthy controls (HC)] were developed. Pairwise discriminative models for MDD, BD, and SCZ, and a discriminative model between patients and HC were constructed by machine learning approaches. In addition, the proteins from nondepleted plasma-based discriminative models were compared with previously developed depleted plasma-based discriminative models. Discriminative models for MDD versus BD, BD versus SCZ, MDD versus SCZ, and patients versus HC were constructed with 11 to 13 proteins and showed reasonable performances (AUROC = 0.890-0.955). Most of the shared proteins between nondepleted and depleted plasma models had consistent directions of expression levels and were associated with neural signaling, inflammatory, and lipid metabolism pathways. These results suggest that multiprotein markers from nondepleted plasma have a potential role in psychiatric evaluation.

Protein Profiles and Novel Molecular Biomarkers of Schizophrenia Based on 4D-DIA Proteomics.

Schizophrenia is a severe psychological disorder. The current diagnosis mainly relies on clinical symptoms and lacks laboratory evidence, which makes it very difficult to make an accurate diagnosis especially at an early stage. Plasma protein profiles of schizophrenia patients were obtained and compared with healthy controls using 4D-DIA proteomics technology. Furthermore, 79 DEPs were identified between schizophrenia and healthy controls. GO functional analysis indicated that DEPs were predominantly associated with responses to toxic substances and platelet aggregation, suggesting the presence of metabolic and immune dysregulation in patients with schizophrenia. KEGG pathway enrichment analysis revealed that DEPs were primarily enriched in the chemokine signaling pathway and cytokine receptor interactions. A diagnostic model was ultimately established, comprising three proteins, namely, PFN1, GAPDH and ACTBL2. This model demonstrated an AUC value of 0.972, indicating its effectiveness in accurately identifying schizophrenia. PFN1, GAPDH and ACTBL2 exhibit potential as biomarkers for the early detection of schizophrenia. The findings of our studies provide novel insights into the laboratory-based diagnosis of schizophrenia.

Sleep spindles as neurophysiological biomarkers of schizophrenia.

Schizophrenia (SCZ) is a complex psychiatric disorder characterized by a wide range of clinical symptoms, including disrupted sleep. In recent years, there has been growing interest in assessing alterations in sleep parameters in patients with SCZ. Sleep spindles are brief (0.5-2 s) bursts of 12- to 16-Hz rhythmic electroencephalogram (EEG) oscillatory activity occurring during non-rapid eye movement (NREM) sleep. Spindles have been implicated in several critical brain functions, including learning, memory and plasticity, and are thought to reflect the integrity of underlying thalamocortical circuits. This review aims to provide an overview of the current research investigating sleep spindles in SCZ. After briefly describing the neurophysiological features of sleep spindles, I will discuss alterations in spindle characteristics observed in SCZ, their associations with the clinical symptomatology of these patients and their putative underlying neuronal and molecular mechanisms. I will then discuss the utility of sleep spindle measures as predictors of treatment response and disease progression. Finally, I will highlight future directions for research in this emerging field, including the prospect of utilizing sleep spindles as neurophysiological biomarkers of SCZ.

Is source-resolved magnetoencephalographic mismatch negativity a viable biomarker for early psychosis?

Mismatch negativity (MMN) is an auditory event-related response reflecting the pre-attentive detection of novel stimuli and is a biomarker of cortical dysfunction in schizophrenia (SZ). MMN to pitch (pMMN) and to duration (dMMN) deviant stimuli are impaired in chronic SZ, but it is less clear if MMN is reduced in first-episode SZ, with inconsistent findings in scalp-level EEG studies. Here, we investigated the neural generators of pMMN and dMMN with MEG recordings in 26 first-episode schizophrenia spectrum (FEsz) and 26 matched healthy controls (C). We projected MEG inverse solutions into precise functionally meaningful auditory cortex areas. MEG-derived MMN sources were in bilateral primary auditory cortex (A1) and belt areas. In A1, pMMN FEsz reduction showed a trend towards statistical significance (F(1,50) = 3.31; p = .07), and dMMN was reduced in FEsz (F(1,50) = 4.11; p = .04). Hypothesis-driven comparisons at each hemisphere revealed dMMN reduction in FEsz occurred in the left (t(56) = 2.23; p = .03; d = .61) but not right (t(56) = 1.02; p = .31; d = .28) hemisphere, with a moderate effect size. The added precision of MEG source solution with high-resolution MRI and parcellation of A1 may be requisite to detect the emerging pathophysiology and indicates a critical role for left hemisphere pathology at psychosis onset. However, the moderate effect size in left A1, albeit larger than reported in scalp MMN meta-analyses, casts doubt on the clinical utility of MMN for differential diagnosis, as a majority of patients will overlap with the healthy individual's distribution.

Brain-trait-associated variants impact cell-type-specific gene regulation during neurogenesis.

Interpretation of the function of non-coding risk loci for neuropsychiatric disorders and brain-relevant traits via gene expression and alternative splicing quantitative trait locus (e/sQTL) analyses is generally performed in bulk post-mortem adult tissue. However, genetic risk loci are enriched in regulatory elements active during neocortical differentiation, and regulatory effects of risk variants may be masked by heterogeneity in bulk tissue. Here, we map e/sQTLs, and allele-specific expression in cultured cells representing two major developmental stages, primary human neural progenitors (n = 85) and their sorted neuronal progeny (n = 74), identifying numerous loci not detected in either bulk developing cortical wall or adult cortex. Using colocalization and genetic imputation via transcriptome-wide association, we uncover cell-type-specific regulatory mechanisms underlying risk for brain-relevant traits that are active during neocortical differentiation. Specifically, we identified a progenitor-specific eQTL for CENPW co-localized with common variant associations for cortical surface area and educational attainment.

Simultaneous SERS Sensing of Cysteine and Homocysteine in Blood Based on the CBT-Cys Click Reaction: Toward Precisive Diagnosis of Schizophrenia.

At present, there is a lack of sufficiently specific laboratory diagnostic indicators for schizophrenia. Serum homocysteine (Hcy) levels have been found to be related to schizophrenia. Cysteine (Cys) is a demethylation product in the metabolism of Hcy, and they always coexist with highly similar structures in vivo. There are few reports on the use of Cys as a diagnostic biomarker for schizophrenia in collaboration with Hcy, mainly because the rapid, economical, accurate, and high-throughput simultaneous detection of Cys and Hcy in serum is highly challenging. Herein, a click reaction-based surface-enhanced Raman spectroscopy (SERS) sensor was developed for simultaneous and selective detection of Cys and Hcy. Through the efficient and specific CBT-Cys click reaction between the probe containing cyan benzothiazole and Cys/Hcy, the tiny methylene difference between the molecular structures of Cys and Hcy was converted into the difference between the ring skeletons of the corresponding products that could be identified by plasmonic silver nanoparticle enhanced molecular fingerprint spectroscopy to realize discriminative detection. Furthermore, the SERS sensor was successfully applied to the detection in related patient serum samples, and it was found that the combined analysis of Cys and Hcy can improve the diagnostic accuracy of schizophrenia compared to a single indicator.

Long-term diagnostic stability, predictors of diagnostic change, and time until diagnostic change of first-episode psychosis: a 21-year follow-up study.

BACKGROUND: Although diagnostic instability in first-episode psychosis (FEP) is of major concern, little is known about its determinants. This very long-term follow-up study aimed to examine the diagnostic stability of FEP diagnoses, the baseline predictors of diagnostic change and the timing of diagnostic change. METHODS: This was a longitudinal and naturalistic study of 243 subjects with FEP who were assessed at baseline and reassessed after a mean follow-up of 21 years. The diagnostic stability of DSM-5 psychotic disorders was examined using prospective and retrospective consistencies, logistic regression was used to establish the predictors of diagnostic change, and survival analysis was used to compare time to diagnostic change across diagnostic categories. RESULTS: The overall diagnostic stability was 47.7%. Schizophrenia and bipolar disorder were the most stable diagnoses, with other categories having low stability. Predictors of diagnostic change to schizophrenia included a family history of schizophrenia, obstetric complications, developmental delay, poor premorbid functioning in several domains, long duration of untreated continuous psychosis, spontaneous dyskinesia, lack of psychosocial stressors, longer duration of index admission, and poor early treatment response. Most of these variables also predicted diagnostic change to bipolar disorder but in the opposite direction and with lesser effect sizes. There were no significant differences between specific diagnoses regarding time to diagnostic change. At 10-year follow-up, around 80% of the diagnoses had changed. CONCLUSIONS: FEP diagnoses other than schizophrenia or bipolar disorder should be considered as provisional. Considering baseline predictors of diagnostic change may help to enhance diagnostic accuracy and guide therapeutic interventions.

Dissecting the impact of complement component 4A in bipolar disorder.

The genetic overlap between schizophrenia (SZ) and bipolar disorder (BD) is substantial. Polygenic risk scores have been shown to dissect different symptom dimensions within and across these two disorders. Here, we focused on the most strongly associated SZ risk locus located in the extended MHC region, which is largely explained by copy numbers of the gene coding for complement component 4A (C4A). First, we utilized existing brain tissue collections (N = 1,202 samples) and observed no altered C4A expression in BD samples. The generated C4A seeded co-expression networks displayed no genetic enrichment for BD. To study if genetically predicted C4A expression discriminates between subphenotypes of BD, we applied C4A expression scores to symptom dimensions in a total of 4,739 BD cases with deep phenotypic data. We identified a significant association between C4A expression and psychotic mood episodes in BD type 1 (BDI). No significant association was observed between C4A expression and the occurrence of non-affective psychotic episodes in BDI, the psychosis dimensions in the total BD sample, or any other subphenotype of BD. Overall, these results points to a distinct role of C4A in BD that is restricted to vulnerability for developing psychotic symptoms during mood episodes in BDI.

Clinical characteristics indexing genetic differences in schizophrenia: a systematic review.

Genome-wide studies are among the best available tools for identifying etiologic processes underlying psychiatric disorders such as schizophrenia. However, it is widely recognized that disorder heterogeneity may limit genetic insights. Identifying phenotypes indexing genetic differences among patients with non-affective psychotic disorder will improve genome-wide studies of these disorders. The present study systematically reviews existing literature to identify phenotypes that index genetic differences among patients with schizophrenia and related disorders. We systematically reviewed family-based studies and genome-wide molecular-genetic studies investigating whether phenotypic variation in patients with non-affective psychotic disorders (according to DSM or equivalent systems) was associated with genome-wide genetic variation (PROSPERO number CRD42019136169). An electronic database search of PubMed, EMBASE, and PsycINFO from inception until 17 May 2019 resulted in 4347 published records. These records included a total of 813 relevant analyses from 264 articles. Two independent raters assessed the quality of all analyses based on methodologic rigor and power. We found moderate to strong evidence for a positive association between genetic/familial risk for non-affective psychosis and four phenotypes: early age of onset, negative/deficit symptoms, chronicity, and functional impairment. Female patients also tended to have more affected relatives. Severity of positive symptoms was not associated with genetic/familial risk for schizophrenia. We suggest that phenotypes with the most evidence for reflecting genetic difference in participating patients should be measured in future large-scale genetic studies of schizophrenia to improve power to discover causal variants and to facilitate discovery of modifying genetic variants.

A flexible approach to measure care coordination based on patient-sharing networks.

BACKGROUND: Effective care coordination may increase clinical efficiency, but its measurement remains difficult. The established metric "care density" (CD) measures care coordination based on patient-sharing among physicians, but it may be too rigid to generalize across disorders and countries. Therefore, we propose an extension called fragmented care density (FCD), which allows varying weights for connections between different types of providers. We compare both metrics in their ability to predict hospitalizations due to schizophrenia. METHODS: We conducted a longitudinal cohort study based on German claims data from 2014 through 2017 to predict quarterly hospital admissions. 21,016 patients with schizophrenia from the federal state Baden-Württemberg were included. CD and FCD were calculated based on patient-sharing networks. The weights of FCD were optimized to predict hospital admissions during the first year of a 24-month follow-up. Subsequently, we employed likelihood ratio tests to assess whether adding either CD or FCD improved a baseline model with control variables for the second follow-up year. RESULTS: The inclusion of FCD significantly improved the baseline model, Χ2(1) = 53.30, p < 0.001. We found that patients with lower percentiles in FCD had an up to 21% lower hospitalization risk than those with median or higher values, whereas CD did not affect the risk. CONCLUSIONS: FCD is an adaptive metric that can weight provider relationships based on their relevance for predicting any outcome. We used it to better understand which medical specialties need to be involved to reduce hospitalization risk for patients with schizophrenia. As FCD can be modified for different health conditions and systems, it is broadly applicable and might help to identify barriers and promoting factors for effective collaboration.

Dysregulation of Plasma Growth Factors and Chemokines in Cocaine Use Disorder: Implications for Dual Diagnosis with Schizophrenia and Antisocial Personality Disorder in an Exploratory Study.

INTRODUCTION: Dual diagnosis in individuals with cocaine use disorders (CUDs) presents a mental health challenge marked by an increased susceptibility to disabling morbidities and premature mortality. Despite extensive research on depression and anxiety, other prevalent comorbidities, such as psychotic and personality disorders, have received less attention. This study explores inflammation-related mediators as potential biomarkers for CUD and dual diagnosis with schizophrenia (SCZ) or antisocial personality disorder (APD). METHODS: This exploratory study included 95 participants, comprising 40 healthy subjects and 55 abstinent patients with CUD. Lifetime CUD was diagnosed either as single diagnosis (CUD group, N = 25) or as a dual diagnosis (DD group. N = 30) with SCZ (CUD+SCZ subgroup) or APD (CUD+APD subgroup). Participants were clinically assessed, and the plasma concentrations of growth factors (i.e., G-CSF, BDNF, and VEGF-A) and chemokines (i.e., CCL11/eotaxin-1, CCL2/MCP-1, and CXCL12/SDF-1) were determined and log(10)-transformed for analysis. RESULTS: Growth factors and chemokines were dysregulated by CUD and psychiatric diagnoses. Specifically, patients in the CUD group exhibited significantly lower concentrations of G-CSF and CCL11/eotaxin-1 than the control group. In contrast, the DD group showed significantly higher concentrations of all analytes than both the CUD and control groups. Additionally, no differences in these analytes were observed between the CUD+SCZ and CUD+APD subgroups within the DD group. Regarding cocaine-related variables, significant associations were identified in the CUD group: an inverse correlation between the age at first cocaine use and the concentrations of BDNF and CCL2/MCP-1; and a positive correlation between the duration of the cocaine abstinence and the concentrations of BDNF and CCL11/eotaxin-1. Lastly, a logistic regression model incorporating all these analytes demonstrated high discriminatory power in distinguishing patients with CUD alone from those with dual diagnosis. CONCLUSIONS: Individuals with dual diagnosis of CUD exhibit elevated concentrations of growth factors and chemokines, distinguishing them from those with CUD alone. It is unclear whether the differences in these inflammatory mediators are specific to the presence of SCZ and APD. The study highlights potential biomarkers and associations, providing valuable insights into the intricate interplay of CUD and psychiatric disorders to enhance clinical diagnosis and therapeutics.

Downregulation of miR-29a as a possible diagnostic biomarker for schizophrenia.

BACKGROUND: MicroRNAs (miRNAs) are epigenetic factors regulating many genes involved in brain development. Dysregulation of miRNA could result in dysregulation of genes which may contribute to diseases affecting the brain and behavior (e.g., schizophrenia). miR-29 family is a miRNA family contributing to brain maturation. miR-29 knockout in animal studies is reported to correlate with psychiatric disorders very similar to those seen in schizophrenia. In this study, we aimed to evaluate the miR-29a level in patients with schizophrenia and its potential value in the diagnosis of schizophrenia. MATERIALS AND METHODS: The serum sample of 42 patients with schizophrenia and 40 healthy subjects were obtained from the Azeri Recent onset/Acute phase psychosis Survey (ARAS) Cohort study. After preparations, the expression level of miR-29a was investigated by real-time PCR. The SPSS and GraphPad prism software were used to analyze the relation between miR-29a level and clinical parameters and its potential as a biomarker for the diagnosis of schizophrenia. RESULTS: Our study showed a significantly lower miR-29a level in patients compared to healthy controls (p = 0.0012). Furthermore, miR-29a level was significantly lower in some types of schizophrenia (p = 0.024). miR-29a level was not related to sex, age, or heredity (p > 0.05). miR-29a also showed 80% specificity and 71.43% sensitivity in the diagnosis of schizophrenia. CONCLUSION: Downregulation of miR-29a in schizophrenia is significantly related to the development of this illness. It might have the potential as a biomarker for schizophrenia.

Automated diagnosis of schizophrenia based on spatial-temporal residual graph convolutional network.

BACKGROUND: Schizophrenia (SZ), a psychiatric disorder for which there is no precise diagnosis, has had a serious impact on the quality of human life and social activities for many years. Therefore, an advanced approach for accurate treatment is required. NEW METHOD: In this study, we provide a classification approach for SZ patients based on a spatial-temporal residual graph convolutional neural network (STRGCN). The model primarily collects spatial frequency features and temporal frequency features by spatial graph convolution and single-channel temporal convolution, respectively, and blends them both for the classification learning, in contrast to traditional approaches that only evaluate temporal frequency information in EEG and disregard spatial frequency features across brain regions. RESULTS: We conducted extensive experiments on the publicly available dataset Zenodo and our own collected dataset. The classification accuracy of the two datasets on our proposed method reached 96.32% and 85.44%, respectively. In the experiment, the dataset using delta has the best classification performance in the sub-bands. COMPARISON WITH EXISTING METHODS: Other methods mainly rely on deep learning models dominated by convolutional neural networks and long and short time memory networks, lacking exploration of the functional connections between channels. In contrast, the present method can treat the EEG signal as a graph and integrate and analyze the temporal frequency and spatial frequency features in the EEG signal. CONCLUSION: We provide an approach to not only performs better than other classic machine learning and deep learning algorithms on the dataset we used in diagnosing schizophrenia, but also understand the effects of schizophrenia on brain network features.

Polygenetic risk scores and phenotypic constellations of obsessive-compulsive disorder in clozapine-treated schizophrenia.

Obsessive-compulsive symptoms (OCS) are frequently observed in individuals with schizophrenia (SCZ) treated with clozapine (CLZ). This study aimed to analyze prevalence of OCS and obsessive-compulsive disorder (OCD) in this subgroup and find possible correlations with different phenotypes. Additionally, this is the first study to examine polygenetic risk scores (PRS) in individuals with SCZ and OCS. A multicenter cohort of 91 individuals with SCZ who were treated with CLZ was recruited and clinically and genetically assessed. Symptom severity was examined using the Positive and Negative Symptom Scale (PANSS), Clinical Global Impression Scale (CGI), the Calgary Depression Scale for Schizophrenia (CDSS), Global Assessment of Functioning Scale (GAF) and Yale-Brown Obsessive-Compulsive Scale (Y-BOCS). Participants were divided into subgroups based on phenotypic OCS or OCD using Y-BOCS scores. Genomic-wide data were generated, and PRS analyses were performed to evaluate the association between either phenotypic OCD or OCS severity and genotype-predicted predisposition for OCD, SCZ, cross-disorder, and CLZ/norclozapine (NorCLZ) ratio, CLZ metabolism and NorCLZ metabolism. OCS and OCD were frequent comorbidities in our sample of CLZ-treated SCZ individuals, with a prevalence of 39.6% and 27.5%, respectively. Furthermore, the Y-BOCS total score correlated positively with the duration of CLZ treatment in years (r = 0.28; p = 0.008) and the PANSS general psychopathology subscale score (r = 0.23; p = 0.028). A significant correlation was found between OCD occurrence and PRS for CLZ metabolism. We found no correlation between OCS severity and PRS for CLZ metabolism. We found no correlation for either OCD or OCS and PRS for OCD, cross-disorder, SCZ, CLZ/NorCLZ ratio or NorCLZ metabolism. Our study was able to replicate previous findings on clinical characteristics of CLZ-treated SCZ individuals. OCS is a frequent comorbidity in this cohort and is correlated with CLZ treatment duration in years and PANSS general psychopathology subscale score. We found a correlation between OCD and PRS for CLZ metabolism, which should be interpreted as incidental for now. Future research is necessary to replicate significant findings and to assess possible genetic predisposition of CLZ-treated individuals with SCZ to OCS/OCD. Limitations attributed to the small sample size or the inclusion of subjects on co-medication must be considered. If the association between OCD and PRS for CLZ metabolism can be replicated, it should be further evaluated if CYP1A2 alteration, respectively lower CLZ plasma level, is relevant for OCD development.