Patterns In Schizophrenia Research Articles

EEG Techniques with Brain Activity Localization, Specifically LORETA, and Its Applicability in Monitoring Schizophrenia.

Background/Objectives: Electroencephalography (EEG) is considered a standard but powerful tool for the diagnosis of neurological and psychiatric diseases. With modern imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and magnetoencephalography (MEG), source localization can be improved, especially with low-resolution brain electromagnetic tomography (LORETA). The aim of this review is to explore the variety of modern techniques with emphasis on the efficacy of LORETA in detecting brain activity patterns in schizophrenia. The study's novelty lies in the comprehensive survey of EEG methods and detailed exploration of LORETA in schizophrenia research. This evaluation aligns with clinical objectives and has been performed for the first time. Methods: The study is split into two sections. Part I examines different EEG methodologies and adjuncts to detail brain activity in deep layers in articles published between 2018 and 2023 in PubMed. Part II focuses on the role of LORETA in investigating structural and functional changes in schizophrenia in studies published between 1999 and 2024 in PubMed. Results: Combining imaging techniques and EEG provides opportunities for mapping brain activity. Using LORETA, studies of schizophrenia have identified hemispheric asymmetry, especially increased activity in the left hemisphere. Cognitive deficits were associated with decreased activity in the dorsolateral prefrontal cortex and other areas. Comparison of the first episode of schizophrenia and a chronic one may help to classify structural change as a cause or as a consequence of the disorder. Antipsychotic drugs such as olanzapine or clozapine showed a change in P300 source density and increased activity in the delta and theta bands. Conclusions: Given the relatively low spatial resolution of LORETA, the method offers benefits such as accessibility, high temporal resolution, and the ability to map depth layers, emphasizing the potential of LORETA in monitoring the progression and treatment response in schizophrenia.

Altered excitatory and inhibitory ionotropic receptor subunit expression in the cortical visuospatial working memory network in schizophrenia.

Dysfunction of the cortical dorsal visual stream and visuospatial working memory (vsWM) network in individuals with schizophrenia (SZ) likely reflects alterations in both excitatory and inhibitory neurotransmission within nodes responsible for information transfer across the network, including primary visual (V1), visual association (V2), posterior parietal (PPC), and dorsolateral prefrontal (DLPFC) cortices. However, the expression patterns of ionotropic glutamatergic and GABAergic receptor subunits across these regions, and alterations of these patterns in SZ, have not been investigated. We quantified transcript levels of key subunits for excitatory N-methyl-D-aspartate receptors (NMDARs), excitatory alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptors (AMPARs), and inhibitory GABAA receptors (GABAARs) in postmortem total gray matter from V1, V2, PPC, and DLPFC of unaffected comparison (UC) and matched SZ subjects. In UC subjects, levels of most AMPAR and NMDAR mRNAs exhibited opposite rostral-to-caudal gradients, with AMPAR GRIA1 and GRIA2 mRNA levels highest in DLPFC and NMDAR GRIN1 and GRIN2A mRNA levels highest in V1. GABRA5 and GABRA1 mRNA levels were highest in DLPFC and V1, respectively. In SZ, most transcript levels were lower relative to UC subjects, with these differences largest in V1, intermediate in V2 and PPC, and smallest in DLPFC. In UC subjects, these distinct patterns of receptor transcript levels across the cortical vsWM network suggest that the balance between excitation and inhibition is achieved in a region-specific manner. In SZ subjects, the large deficits in excitatory and inhibitory receptor transcript levels in caudal sensory regions suggest that abnormalities early in the vsWM pathway might contribute to altered information processing in rostral higher-order regions.

Natural language processing for defining linguistic features in schizophrenia: A sample from Turkish speakers

Natural language processing (NLP) provides fast and accurate extraction of features related to the language of schizophrenia. We utilized NLP methods to test the hypothesis that schizophrenia is associated with altered linguistic features in Turkish, a non-Indo-European language, compared to controls. We also explored whether these possible altered linguistic features were language-dependent or -independent. We extracted and compared speech in schizophrenia (SZ, N = 38) and healthy well-matched control (HC, N = 38) participants using NLP. The analysis was conducted in two parts. In the first one, mean sentence length, total completed words, moving average type-token ratio to measure the lexical diversity, and first-person singular pronoun usage were calculated. In the second one, we used parts-of-speech tagging (POS) and Word2Vec in schizophrenia and control. We found that SZ had lower mean sentence length and moving average type-token ratio but higher use of first-person singular pronoun. All these significant results were correlated with the Thought and Language Disorder Scale score. The POS approach demonstrated that SZ used fewer coordinating conjunctions. Our methodology using Word2Vec detected that SZ had higher semantic similarity than HC and K-Means could differentiate between SZ and HC into two distinct groups with high accuracy, 86.84 %. Our findings showed that altered linguistic features in SZ are mostly language-independent. They are promising to describe language patterns in schizophrenia which proposes that NLP measurements may allow for rapid and objective measurements of linguistic features.

Individualized Texture Similarity Network in Schizophrenia

BackgroundStructural covariance network disruption has been considered an important pathophysiological indicator for schizophrenia. Here, we introduced a novel individualized structural covariance network measure, referred to as a texture similarity network (TSN), and hypothesized that the TSN could reliably reveal unique intersubject heterogeneity and complex dysconnectivity patterns in schizophrenia. MethodsThe TSN was constructed by measuring the covariance of 180 three-dimensional voxelwise gray-level co-occurrence matrix feature maps between brain areas in each participant. We first tested the validity and reproducibility of the TSN in characterizing the intersubject variability in 2 longitudinal test-retest healthy cohorts. The TSN was further applied to elucidate intersubject variability and dysconnectivity patterns in 10 schizophrenia case-control datasets (609 schizophrenia cases vs. 579 controls) as well as in a first-episode depression dataset (69 patients with depression vs. 69 control participants). ResultsThe test-retest analysis demonstrated higher TSN intersubject than intrasubject variability. Moreover, the TSN reliably revealed higher intersubject variability in both chronic and first-episode schizophrenia, but not in depression. The TSN also reproducibly detected coexistent increased and decreased TSN strength in widespread brain areas, increased global small-worldness, and the coexistence of both structural hyposynchronization in the central networks and hypersynchronization in peripheral networks in patients with schizophrenia but not in patients with depression. Finally, aberrant intersubject variability and covariance strength patterns revealed by the TSN showed a missing or weak correlation with other individualized structural covariance network measures, functional connectivity, and regional volume changes. ConclusionsThese findings support the reliability of a TSN in revealing unique structural heterogeneity and complex dysconnectivity in patients with schizophrenia.

Evaluating Gene Expression and Methylation Profiles of TCF4, MBP, and EGR1 in Peripheral Blood of Drug-Free Patients with Schizophrenia: Correlations with Psychopathology, Intelligence, and Cognitive Impairment.

Discovery and validation of new, reliable diagnostic and predictive biomarkers for schizophrenia (SCZ) are an ongoing effort. Here, we assessed the mRNA expression and DNA methylation of the TCF4, MBP, and EGR1 genes in the blood of patients with SCZ and evaluated their relationships to psychopathology and cognitive impairments. Quantitative real-time PCR and quantitative methylation-specific PCR methods were used to assess the expression level and promoter DNA methylation status of these genes in 70 drug-free SCZ patients and 72 healthy controls. The correlation of molecular changes with psychopathology and cognitive performance of participants was evaluated. We observed downregulation of TCF4 and upregulation of MBP mRNA levels in SCZ cases, relative to controls in our study. DNA methylation status at the promoter region of TCF4 demonstrated an altered pattern in SCZ as well. Additionally, TCF4 mRNA levels were inversely correlated with PANSS and Stroop total errors and positively correlated with WAIS total score and working memory, consistent with previous studies by our group. In contrast, MBP mRNA level was significantly positively correlated with PANSS and Stroop total errors and inversely correlated with WAIS total score and working memory. These epigenetic and expression signatures can help to assemble a peripheral biomarker-based diagnostic panel for SCZ.

Pineal Abnormalities in Psychosis and Mood Disorders: A Systematic Review.

The pineal gland (PG) is a small interhemispheric brain structure that influences human physiology in many ways, most importantly via secretion of the hormone melatonin which is known to regulate sleep and wakefulness. Here, we systematically reviewed existing neuroimaging studies of PG structure, and/or melatonin release (MLT) in psychosis and mood disorders. Medline, PubMed, and Web of Science databases were searched (on 3 February 2023), yielding 36 studies (8 PG volume, 24 MLT). The findings showed smaller-than-normal PG volume in people with schizophrenia, regardless of symptom severity and illness stage; and smaller-than-normal PG volume in major depression, with some indication of this being present only in certain subgroups, or in those with high scores on the 'loss of interest' symptom. There was considerable evidence of lower-than-normal MLT as well as aberrant MLT secretion pattern in schizophrenia. A similar picture, though less consistent than that seen in schizophrenia, emerged in major depression and bipolar disorder, with some evidence of a transient lowering of MLT following the initiation of certain antidepressants in drug-withdrawn patients. Overall, PG and MLT aberrations appear to represent transdiagnostic biomarkers for psychosis and mood disorders, but further work is needed to establish their clinical correlates and treatment implications.

A method for capturing dynamic spectral coupling in resting fMRI reveals domain-specific patterns in schizophrenia.

Resting-state functional magnetic resonance imaging (rs-fMRI) is a powerful tool for assessing functional brain connectivity. Recent studies have focused on shorter-term connectivity and dynamics in the resting state. However, most of the prior work evaluates changes in time-series correlations. In this study, we propose a framework that focuses on time-resolved spectral coupling (assessed via the correlation between power spectra of the windowed time courses) among different brain circuits determined via independent component analysis (ICA). Motivated by earlier work suggesting significant spectral differences in people with schizophrenia, we developed an approach to evaluate time-resolved spectral coupling (trSC). To do this, we first calculated the correlation between the power spectra of windowed time-courses pairs of brain components. Then, we subgrouped each correlation map into four subgroups based on the connectivity strength utilizing quartiles and clustering techniques. Lastly, we examined clinical group differences by regression analysis for each averaged count and average cluster size matrices in each quartile. We evaluated the method by applying it to resting-state data collected from 151 (114 males, 37 females) people with schizophrenia (SZ) and 163 (117 males, 46 females) healthy controls (HC). Our proposed approach enables us to observe the change of connectivity strength within each quartile for different subgroups. People with schizophrenia showed highly modularized and significant differences in multiple network domains, whereas males and females showed less modular differences. Both cell count and average cluster size analysis for subgroups indicate a higher connectivity rate in the fourth quartile for the visual network in the control group. This indicates increased trSC in visual networks in the controls. In other words, this shows that the visual networks in people with schizophrenia have less mutually consistent spectra. It is also the case that the visual networks are less spectrally correlated on short timescales with networks of all other functional domains. The results of this study reveal significant differences in the degree to which spectral power profiles are coupled over time. Importantly, there are significant but distinct differences both between males and females and between people with schizophrenia and controls. We observed a more significant coupling rate in the visual network for the healthy controls and males in the upper quartile. Fluctuations over time are complex, and focusing on only time-resolved coupling among time-courses is likely to miss important information. Also, people with schizophrenia are known to have impairments in visual processing but the underlying reasons for the impairment are still unknown. Therefore, the trSC approach can be a useful tool to explore the reasons for the impairments.

The cortical hypogyrification pattern in antipsychotic-naive first-episode schizophrenia.

Schizophrenia is thought to be a neurodevelopmental disease with high genetic heritability, and evidence from neuroimaging studies has consistently shown widespread cortical local gyrification index (LGI) alterations; however, genes accounting for LGI alterations in schizophrenia remain unknown. The present study examined the LGI alterations in first-episode antipsychotic-naive schizophrenia compared with controls (235 patients and 214 controls); transcription-neuroimaging association analysis was used to evaluate the relationship between LGI deficits and specific risk genes. The expression profiles of 232 schizophrenia risk genes were extracted from six donated normal brains from the Allen Human Brain Atlas database. The correlation between LGI alterations and clinical symptoms was also tested. We found lower LGI values involved in frontotemporal regions and limbic systems. Nonparametric correlation analysis showed that 83 risk genes correlated with the hypogyrification pattern in schizophrenia. These identified risk genes were functionally enriched for the development of the central nervous system. The LGI in the left superior temporal gyrus was negatively associated with Positive and Negative Syndrome Scale negative symptoms. In summary, the present study provides a set of risk genes possibly related to the hypogyrification pattern in antipsychotic-naive first-episode schizophrenia, which could help to unveil the neurobiological underpinnings of cortical impairments in early-stage schizophrenia.

Convergence of Brain Transcriptomic and Neuroimaging Patterns in Schizophrenia, Bipolar Disorder, Autism Spectrum Disorder, and Major Depressive Disorder

BackgroundPsychiatric conditions show overlap in their symptoms, genetics, and involvement in brain areas and circuits. Structural alterations in the brain have been found to run in parallel with expression profiles of risk genes at the level of the brain transcriptome, which may point toward a potential transdiagnostic vulnerability of the brain to disease processes. MethodsWe characterized the transcriptomic vulnerability of the cortex across 4 major psychiatric disorders based on collated data from patients with psychiatric disorders (n = 390) and matched control participants (n = 293). We compared normative expression profiles of risk genes linked to schizophrenia, bipolar disorder, autism spectrum disorder, and major depressive disorder to examine cross-disorder overlap in spatial expression profiles across the cortex and their concordance with a magnetic resonance imaging–derived cross-disorder profile of structural brain alterations. ResultsWe showed high expression of psychiatric risk genes converging on multimodal cortical regions of the limbic, ventral attention, and default mode networks versus primary somatosensory networks. Risk genes were found to be enriched among genes associated with the magnetic resonance imaging cross-disorder profile, suggestive of a common link between brain anatomy and the transcriptome in psychiatric conditions. Characterization of this cross-disorder structural alteration map further shows enrichment for gene markers of astrocytes, microglia, and supragranular cortical layers. ConclusionsOur findings suggest that normative expression profiles of disorder risk genes confer a shared and spatially patterned vulnerability of the cortex across multiple psychiatric conditions. Transdiagnostic overlap in transcriptomic risk suggests a common pathway to brain dysfunction across psychiatric disorders.

Load-dependent inverted U-shaped connectivity of the default mode network in schizophrenia during a working-memory task: evidence from a replication functional MRI study.

Background:Working-memory deficit is associated with aberrant degree distribution of the brain connectome in schizophrenia. However, the brain neural mechanism underlying the degree redistribution pattern in schizophrenia is still uncertain.Methods:We examined the functional degree distribution of the connectome in 81 patients with schizophrenia and 77 healthy controls across different working-memory loads during an n-back task. We tested the associations between altered degree distribution and clinical symptoms, and we conducted functional connectivity analyses to investigate the neural mechanism underlying altered degree distribution. We repeated these analyses in a second independent data set of 96 participants. In the second data set, we employed machine-learning analysis to study whether the degree distribution pattern of one data set could be used to discriminate between patients with schizophrenia and controls in the other data set.Results:Patients with schizophrenia showed decreased centrality in the dorsal posterior cingulate cortex (dPCC) for the “2-back versus 0-back” contrast compared to healthy controls. The dPCC centrality pattern across all working-memory loads was an inverted U shape, with a left shift of this pattern in patients with schizophrenia. This reduced centrality was correlated with the severity of delusions and related to reduced functional connectivity between the dPCC and the dorsal precuneus. We replicated these results with the second data set, and the machine-learning analyses achieved an accuracy level of 71%.Limitations:We used a limited n-back paradigm that precluded the examination of higher working-memory loads.Conclusion:Schizophrenia is characterized by a load-dependent reduction of centrality in the dPCC, related to the severity of delusions. We suggest that restoring dPCC centrality in the presence of cognitive demands might have a therapeutic effect on persistent delusions in people with schizophrenia.

Investigation of systemic immune-inflammation index, neutrophil/high-density lipoprotein ratio, lymphocyte/high-density lipoprotein ratio, and monocyte/high-density lipoprotein ratio as indicators of inflammation in patients with schizophrenia and bipolar disorder.

BackgroundThe systemic immune-inflammation index (SII), system inflammation response index (SIRI), neutrophil/high-density lipoprotein (HDL) ratio (NHR), lymphocyte/HDL ratio (LHR), monocyte/HDL ratio (MHR), and platelet/HDL ratio (PHR) have been recently investigated as new markers for inflammation. The purpose of this research is to use large-scale clinical data to discuss and compare the predictive ability of the SII, SIRI, NHR, LHR, MHR, and PHR in patients with schizophrenia (SCZ) and bipolar disorder (BD), to investigate potential biomarkers.Materials and methodsIn this retrospective, naturalistic, cross-sectional study, we collected the hematological parameter data of 13,329 patients with SCZ, 4,061 patients with BD manic episodes (BD-M), and 1,944 patients with BD depressive episodes (BD-D), and 5,810 healthy subjects served as the healthy control (HC) group. The differences in the SII, SIRI, NHR, LHR, MHR, and PHR were analyzed, and a receiver operating characteristic (ROC) curve was used to analyze the diagnostic potential of these parameters.ResultsCompared with the HC group, the values of the SII, SIRI, NHR, LHR, MHR, and PHR and the levels of neutrophils, monocytes, and triglycerides (TG) were higher in SCZ and BD groups, and levels of platelets, cholesterol (CHO), HDL, low-density lipoprotein (LDL), and apoprotein B (Apo B) were lower in SCZ and BD groups. Compared to the BD group, the values of the SIRI, lymphocytes, monocytes, and HDL were lower and the values of the SII, NHR, PHR, and platelet were higher in the SCZ group. In contrast to the BD-D group, the values of the SII; SIRI; NHR; and MHR; and levels of neutrophils, monocytes, and platelets were higher in the BD-M group, and the levels of CHO, TG, LDL, and Apo B were lower in the BD-M group. The MHR and NHR were predictors for differentiating the SCZ group from the HC group; the SIRI, NHR, and MHR were predictors for differentiating the BD-M group from the HC group; and the MHR was a predictor for differentiating the BD-D group from the HC group. The combination model of the indicators improved diagnostic effectiveness.ConclusionOur study highlights the role of systemic inflammation in the pathophysiology of SCZ, BD-M, and BD-D, the association between inflammation and lipid metabolism, and these inflammation and lipid metabolism indicators showed different variation patterns in SCZ, BD-D, and BD-M.

Different Heschl's Gyrus Duplication Patterns in Deficit and Non-deficit Subtypes of Schizophrenia.

Deficit syndrome schizophrenia is a characteristic subtype defined by persistent negative symptoms and poor functional outcomes; however, the biological mechanisms underlying this specific subtype have not yet been elucidated in detail. The present magnetic resonance imaging study examined the prevalence of duplicated Heschl’s gyrus (HG), a potential neurodevelopmental marker, in schizophrenia patients with (N = 38) and without (N = 37) the deficit syndrome. The prevalence of the HG duplication pattern bilaterally was higher in the whole schizophrenia group than in 59 matched healthy controls. Furthermore, the prevalence of right HG duplication was significantly higher in the deficit schizophrenia group than in the non-deficit schizophrenia group. The HG pattern in schizophrenia was not associated with clinical variables, including illness duration, medication, and symptom severity, while right HG duplication correlated with higher scores for Proxy for the Deficit Syndrome. The present results suggest that the prominent neurodevelopmental pathology associated with gyral formation of HG may contribute to enduring negative symptomatology in schizophrenia.

Different Frequency of Heschl's Gyrus Duplication Patterns in Neuropsychiatric Disorders: An MRI Study in Bipolar and Major Depressive Disorders.

An increased prevalence of duplicated Heschl’s gyrus (HG) has been repeatedly demonstrated in various stages of schizophrenia as a potential neurodevelopmental marker, but it remains unknown whether other neuropsychiatric disorders also exhibit this macroscopic brain feature. The present magnetic resonance imaging study aimed to examine the disease specificity of the established finding of altered HG patterns in schizophrenia by examining independent cohorts of bipolar disorder (BD) and major depressive disorder (MDD). Twenty-six BD patients had a significantly higher prevalence of HG duplication bilaterally compared to 24 age- and sex-matched controls, while their clinical characteristics (e.g., onset age, number of episodes, and medication) did not relate to HG patterns. No significant difference was found for the HG patterns between 56 MDD patients and 33 age- and sex-matched controls, but the patients with a single HG were characterized by more severe depressive/anxiety symptoms compared to those with a duplicated HG. Thus, in keeping with previous findings, the present study suggests that neurodevelopmental pathology associated with gyral formation of the HG during the late gestation period partly overlaps between schizophrenia and BD, but that HG patterns may make a somewhat distinct contribution to the phenomenology of MDD.

Multi-spatial-scale dynamic interactions between functional sources reveal sex-specific changes in schizophrenia.

We introduce an extension of independent component analysis (ICA), called multiscale ICA, and design an approach to capture dynamic functional source interactions within and between multiple spatial scales. Multiscale ICA estimates functional sources at multiple spatial scales without imposing direct constraints on the size of functional sources, overcomes the limitation of using fixed anatomical locations, and eliminates the need for model-order selection in ICA analysis. We leveraged this approach to study sex-specific and sex-common connectivity patterns in schizophrenia. Results show dynamic reconfiguration and interaction within and between multi-spatial scales. Sex-specific differences occur (a) within the subcortical domain, (b) between the somatomotor and cerebellum domains, and (c) between the temporal domain and several others, including the subcortical, visual, and default mode domains. Most of the sex-specific differences belong to between-spatial-scale functional interactions and are associated with a dynamic state with strong functional interactions between the visual, somatomotor, and temporal domains and their anticorrelation patterns with the rest of the brain. We observed significant correlations between multi-spatial-scale functional interactions and symptom scores, highlighting the importance of multiscale analyses to identify potential biomarkers for schizophrenia. As such, we recommend such analyses as an important option for future functional connectivity studies.

Evaluating Big-Neuroimaging-Data Informed Vulnerability Indices for Neuropsychiatric Disorder

Big Data Neuroimaging collaborations integrated worldwide data to identify regional brain deficit patterns in schizophrenia (SSD), bipolar depression (BD), major depressive disorder (MDD), Alzheimer's Disorder (AD). We evaluated the sensitivity and specificity, effects on symptom severity and treatment resistance and risk-transmission abilities of two approaches that translate deficit patterns to the individual level: Regional Vulnerability Index (RVI) and Enigma Dot Product (EDP).

Validation of ketamine as a pharmacological model of thalamic dysconnectivity across the illness course of schizophrenia

N-methyl-D-aspartate receptor (NMDAR) hypofunction is a leading pathophysiological model of schizophrenia. Resting-state functional magnetic resonance imaging (rsfMRI) studies demonstrate a thalamic dysconnectivity pattern in schizophrenia involving excessive connectivity with sensory regions and deficient connectivity with frontal, cerebellar, and thalamic regions. The NMDAR antagonist ketamine, when administered at sub-anesthetic doses to healthy volunteers, induces transient schizophrenia-like symptoms and alters rsfMRI thalamic connectivity. However, the extent to which ketamine-induced thalamic dysconnectivity resembles schizophrenia thalamic dysconnectivity has not been directly tested. The current double-blind, placebo-controlled study derived an NMDAR hypofunction model of thalamic dysconnectivity from healthy volunteers undergoing ketamine infusions during rsfMRI. To assess whether ketamine-induced thalamic dysconnectivity was mediated by excess glutamate release, we tested whether pre-treatment with lamotrigine, a glutamate release inhibitor, attenuated ketamine’s effects. Ketamine produced robust thalamo-cortical hyper-connectivity with sensory and motor regions that was not reduced by lamotrigine pre-treatment. To test whether the ketamine thalamic dysconnectivity pattern resembled the schizophrenia pattern, a whole-brain template representing ketamine’s thalamic dysconnectivity effect was correlated with individual participant rsfMRI thalamic dysconnectivity maps, generating “ketamine similarity coefficients” for people with chronic (SZ) and early illness (ESZ) schizophrenia, individuals at clinical high-risk for psychosis (CHR-P), and healthy controls (HC). Similarity coefficients were higher in SZ and ESZ than in HC, with CHR-P showing an intermediate trend. Higher ketamine similarity coefficients correlated with greater hallucination severity in SZ. Thus, NMDAR hypofunction, modeled with ketamine, reproduces the thalamic hyper-connectivity observed in schizophrenia across its illness course, including the CHR-P period preceding psychosis onset, and may contribute to hallucination severity.

Large-scale real-world data analysis identifies comorbidity patterns in schizophrenia

Schizophrenia affects >3.2 million people in the USA. However, its comorbidity patterns have not been systematically characterized in real-world populations. To address this gap, we conducted an observational study using a cohort of 86 million patients in a nationwide health insurance dataset. We identified participants with schizophrenia and those without schizophrenia matched by age, sex, and the first three digits of zip code. For each phenotype encoded in phecodes, we compared their prevalence in schizophrenia patients and the matched non-schizophrenic participants, and we performed subgroup analyses stratified by age and sex. Results show that anxiety, posttraumatic stress disorder, and substance abuse commonly occur in adolescents and young adults prior to schizophrenia diagnoses. Patients aged 60 and above are at higher risks of developing delirium, alcoholism, dementia, pelvic fracture, and osteomyelitis than their matched controls. Type 2 diabetes, sleep apnea, and eating disorders were more prevalent in women prior to schizophrenia diagnosis, whereas acute renal failure, rhabdomyolysis, and developmental delays were found at higher rates in men. Anxiety and obesity are more commonly seen in patients with schizoaffective disorders compared to patients with other types of schizophrenia. Leveraging a large-scale insurance claims dataset, this study identified less-known comorbidity patterns of schizophrenia and confirmed known ones. These comorbidity profiles can guide clinicians and researchers to take heed of early signs of co-occurring diseases.

A new aspect on the correlation of ten SNPs in MIR and their target genes in dopaminergic pathways in schizophrenia

BackgroundSchizophrenia (SCZ) is a severe mental disorder in which people interpret reality abnormally. Different studies indicated a complex polygenic control over SCZ. In the present study, we investigated the potential correlation between ten SNPs among MicroRNA (MIR) and their target genes; rs369770942, rs143525573, rs200982455, rs530404895, rs753764536, rs374732351, rs4680, rs165599, rs340597269, and rs10759, and schizophrenia in the Iranian population.ResultsThe results revealed that the T allele in rs200982455 increased the risk factor by 3.19 times. We obtained a significant association between rs165599 and schizophrenia in codominant, dominant, and overdominant inheritance models (P = 0.016, P = 0.01, P = 0.004, respectively). Moreover, the risk of schizophrenia increased in the presence of the G allele in rs165599 up to 2.12, 2.35, and 2.28 times, respectively. The A allele in rs10759 increased the risk factor up to 1.05 times.ConclusionOur finding showed that some of the studied SNPs within the genes and MIRs involved in the dopaminergic pathway may consider as a biomarker in the diagnostic patterns in Schizophrenia.

Altered functional connectivity patterns in schizophrenia – a possible link between psychopathology and decreased physical fitness?

Altered functional connectivity patterns in schizophrenia – a possible link between psychopathology and decreased physical fitness?

Structural network alterations and their association with neurological soft signs in schizophrenia: Evidence from clinical patients and unaffected siblings

BackgroundGrey matter abnormalities and neurological soft signs (NSS) have been found in schizophrenia patients and their unaffected relatives. Evidence suggested that NSS are associated with grey matter morphometrical alterations in multiple regions in schizophrenia. However, the association between NSS and structural abnormalities at network level remains largely unexplored, especially in the schizophrenia and unaffected siblings. MethodWe used source-based morphometry (SBM) to examine the association of structural brain network characteristics with NSS in 62 schizophrenia patients, 25 unaffected siblings, and 60 healthy controls. ResultsTwo components, namely the IC-5 (superior temporal gyrus, inferior frontal gyrus and insula network) and the IC-10 (parahippocampal gyrus, fusiform, thalamus and insula network) showed significant grey matter reductions in schizophrenia patients compared to healthy controls and unaffected siblings. Further association analysis demonstrated separate NSS-related grey matter covarying patterns in schizophrenia, unaffected siblings and healthy controls. Specifically, NSS were negatively associated with IC-1 (hippocampus, caudate and thalamus network) and IC-5 in schizophrenia, but with IC-3 (caudate, superior and middle frontal cortices network) in unaffected siblings and with IC-5 in healthy controls. ConclusionOur results confirmed the key cortical and subcortical network abnormalities and NSS-related grey matter covarying patterns in the schizophrenia and unaffected siblings. Our findings suggest that brain regions implicating genetic liability to schizophrenia are partly separated from brain regions implicating neural abnormalities.