The impact of clinical heterogeneity in schizophrenia on genomic analyses

Unraveling the role of the renin-angiotensin system in severe mental illnesses: An insight into psychopathology and cognitive deficits

Severe mental illnesses (SMI), especially schizophrenia and bipolar disorder (BD), are associated with significant distress to patients, reduced life expectancy and a higher cost of care. There is growing evidence that SMI may increase the risk of dementia in later life, posing an additional challenge in the management of these patients. SMI present a complex and highly heterogeneous pathophysiology, which has hampered the understanding of its underlying pathological mechanisms and limited the success of the available therapies. Despite the advances in therapeutic approaches in psychiatry over the past decades, treatment resistance is still a common problem in clinical practice, highlighting the urgent need for novel therapeutic targets for SMI. The discovery that renin-angiotensin system (RAS) components are expressed in the central nervous system opened new possibilities for investigating a potential role for this system in the neurobiology of SMI. The safety and efficacy of AT1 receptor blockers and angiotensin-converting enzyme inhibitors in cardiovascular and metabolic diseases, common medical comorbidities among SMI patients and well-known risk factors for dementia, suggest the potential scalability of these strategies for the management of SMI outcomes including the risk of subsequent dementia. This review aimed to discuss the available evidence from animal models and human studies of the potential involvement of RAS in the pathophysiology of SMI. We also provided a reflection on drawbacks and perspectives that can foster the development of new related therapeutic strategies.

Peering into the mind: unraveling schizophrenia's secrets using models

Schizophrenia (SCZ) is a complex mental disorder characterized by a range of symptoms, including positive and negative symptoms, as well as cognitive impairments. Despite the extensive research, the underlying neurobiology of SCZ remain elusive. To overcome this challenge, the use of diverse laboratory modeling techniques, encompassing cellular and animal models, and innovative approaches like induced pluripotent stem cell (iPSC)-derived neuronal cultures or brain organoids and genetically engineered animal models, has been crucial. Immortalized cellular models provide controlled environments for investigating the molecular and neurochemical pathways involved in neuronal function, while iPSCs and brain organoids, derived from patient-specific sources, offer significant advantage in translational research by facilitating direct comparisons of cellular phenotypes between patient-derived neurons and healthy-control neurons. Animal models can recapitulate the different psychopathological aspects that should be modeled, offering valuable insights into the neurobiology of SCZ. In addition, invertebrates' models are genetically tractable and offer a powerful approach to dissect the core genetic underpinnings of SCZ, while vertebrate models, especially mammals, with their more complex nervous systems and behavioral repertoire, provide a closer approximation of the human condition to study SCZ-related traits. This narrative review provides a comprehensive overview of the diverse modeling approaches, critically evaluating their strengths and limitations. By synthesizing knowledge from these models, this review offers a valuable source for researchers, clinicians, and stakeholders alike. Integrating findings across these different models may allow us to build a more holistic picture of SCZ pathophysiology, facilitating the exploration of new research avenues and informed decision-making for interventions.

Alteration of DNA Methylation and Epigenetic Scores Associated With Features of Schizophrenia and Common Variant Genetic Risk

BACKGROUND

Unpacking molecular perturbations associated with features of schizophrenia is a critical step toward understanding phenotypic heterogeneity in this disorder. Recent epigenome-wide association studies have uncovered pervasive dysregulation of DNA methylation in schizophrenia; however, clinical features of the disorder that account for a large proportion of phenotypic variability are relatively underexplored.

METHODS

We comprehensively analyzed patterns of DNA methylation in a cohort of 381 individuals with schizophrenia from the deeply phenotyped Australian Schizophrenia Research Bank. Epigenetic changes were investigated in association with cognitive status, age of onset, treatment resistance, Global Assessment of Functioning scores, and common variant polygenic risk scores for schizophrenia. We subsequently explored alterations within genes previously associated with psychiatric illness, phenome-wide epigenetic covariance, and epigenetic scores.

RESULTS

Epigenome-wide association studies of the 5 primary traits identified 662 suggestively significant (p < 6.72 × 10-5) differentially methylated probes, with a further 432 revealed after controlling for schizophrenia polygenic risk on the remaining 4 traits. Interestingly, we uncovered many probes within genes associated with a variety of psychiatric conditions as well as significant epigenetic covariance with phenotypes and exposures including acute myocardial infarction, C-reactive protein, and lung cancer. Epigenetic scores for treatment-resistant schizophrenia strikingly exhibited association with clozapine administration, while epigenetic proxies of plasma protein expression, such as CCL17, MMP10, and PRG2, were associated with several features of schizophrenia.

CONCLUSIONS

Our findings collectively provide novel evidence suggesting that several features of schizophrenia are associated with alteration of DNA methylation, which may contribute to interindividual phenotypic variation in affected individuals.

Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions

Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case-control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data-driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case-control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.

Molecular Mechanism Biomarkers Predict Diagnosis in Schizophrenia and Schizoaffective Psychosis, with Implications for Treatment

Diagnostic uncertainty and relapse rates in schizophrenia and schizoaffective disorder are relatively high, indicating the potential involvement of other pathological mechanisms that could serve as diagnostic indicators to be targeted for adjunctive treatment. This study aimed to seek objective evidence of methylenetetrahydrofolate reductase MTHFR C677T genotype-related bio markers in blood and urine. Vitamin and mineral cofactors related to methylation and indolamine-catecholamine metabolism were investigated. Biomarker status for 67 symptomatically well-defined cases and 67 asymptomatic control participants was determined using receiver operating characteristics, Spearman's correlation, and logistic regression. The 5.2%-prevalent MTHFR 677 TT genotype demonstrated a 100% sensitive and specific case-predictive biomarkers of increased riboflavin (vitamin B2) excretion. This was accompanied by low plasma zinc and indicators of a shift from low methylation to high methylation state. The 48.5% prevalent MTHFR 677 CC genotype model demonstrated a low-methylation phenotype with 93% sensitivity and 92% specificity and a negative predictive value of 100%. This model related to lower vitamin cofactors, high histamine, and HPLC urine indicators of lower vitamin B2 and restricted indole-catecholamine metabolism. The 46.3%-prevalent CT genotype achieved high predictive strength for a mixed methylation phenotype. Determination of MTHFR C677T genotype dependent functional biomarker phenotypes can advance diagnostic certainty and inform therapeutic intervention.

Cost-Effectiveness of Aripiprazole Tablets with Sensor versus Oral Atypical Antipsychotics for the Treatment of Schizophrenia Using a Patient-Level Microsimulation Modeling Approach

Objective

Strategies designed to track drug ingestion may improve medication adherence and clinical outcomes in adults with schizophrenia. This study aimed to estimate the cost-effectiveness of aripiprazole tablets with sensor (AS; Abilify MyCite^®) versus generic oral atypical antipsychotics (AAPs) in schizophrenia from the United States payer and societal perspectives over 12 months.

Methods

An individual-level microsimulation was developed to generate individual trajectories using data from a phase 3b multicenter, open-label, mirror-image trial in adults with schizophrenia treated prospectively for 6 months with AS. The patient's clinical characteristics and outcomes were computed as a function of the Positive and Negative Syndrome Scale (PANSS) scores. Direct and indirect medical cost estimates were sourced from the literature; EuroQol 5-Dimensions (EQ-5D) utilities were derived using risk equations based on patient and clinical characteristics. Scenario analyses were also conducted to assess outcomes under the assumption of treatment durability over 12 months.

Results

Over 12 months, AS showed a 12.2% improvement in PANSS score. AS had an incremental cost of $2168 and incremental cost savings of $22,343 from the payer and societal perspectives, respectively, with an incremental quality-adjusted life-year (QALY) gain of 0.0298 versus oral AAPs. Further, AS resulted in a 28.2% reduction in hospitalizations over 12 months. At a willingness-to-pay of $100,000 per QALY, the net monetary benefit over 12 months was $25,323 from the payer perspective. Under the assumption of the durability of the treatment effect of AS, the findings were similar to those of the base case analyses, though with greater cost savings and QALYs gained with AS. The results from the sensitivity analyses were consistent with those of the base case analysis.

Conclusion

AS may be a cost-effective strategy, with lower costs and improved quality of life among patients with schizophrenia over 12 months, from the payer and societal perspectives.

Reprioritising global mental health: psychoses in sub-Saharan Africa

Arthur Kleinman's 2009 Lancet commentary described global mental health as a "moral failure of humanity", asserting that priorities should be based not on the epidemiological and utilitarian economic arguments that tend to favour common mental health conditions like mild to moderate depression and anxiety, but rather on the human rights of those in the most vulnerable situations and the suffering that they experience. Yet more than a decade later, people with severe mental health conditions like psychoses are still being left behind. Here, we add to Kleinman's appeal a critical review of the literature on psychoses in sub-Saharan Africa, highlighting contradictions between local evidence and global narratives surrounding the burden of disease, the outcomes of schizophrenia, and the economic costs of mental health conditions. We identify numerous instances where the lack of regionally representative data and other methodological shortcomings undermine the conclusions of international research carried out to inform decision-making. Our findings point to the need not only for more research on psychoses in sub-Saharan Africa, but also for more representation and leadership in the conduct of research and in international priority-setting more broadly-especially by people with lived experience from diverse backgrounds. This paper aims to encourage debate about how this chronically under-resourced field, as part of wider conversations in global mental health, can be reprioritised.

Single-nucleus RNA sequencing of midbrain blood-brain barrier cells in schizophrenia reveals subtle transcriptional changes with overall preservation of cellular proportions and phenotypes

The midbrain is an extensively studied brain region in schizophrenia, in view of its reported dopamine pathophysiology and neuroimmune changes associated with this disease. Besides the dopaminergic system, the midbrain contains other cell types that may be involved in schizophrenia pathophysiology. The neurovascular hypothesis of schizophrenia postulates that both the neurovasculature structure and the functioning of the blood-brain barrier (BBB) are compromised in schizophrenia. In the present study, potential alteration in the BBB of patients with schizophrenia was investigated by single-nucleus RNA sequencing of post-mortem midbrain tissue (15 schizophrenia cases and 14 matched controls). We did not identify changes in the relative abundance of the major BBB cell types, nor in the sub-populations, associated with schizophrenia. However, we identified 14 differentially expressed genes in the cells of the BBB in schizophrenia as compared to controls, including genes that have previously been related to schizophrenia, such as FOXP2 and PDE4D. These transcriptional changes were limited to the ependymal cells and pericytes, suggesting that the cells of the BBB are not broadly affected in schizophrenia.

Genetic Influences on Cognitive Dysfunction in Schizophrenia

Schizophrenia is a severe and debilitating psychotic disorder that is highly heritable and relatively common in the population. The clinical heterogeneity associated with schizophrenia is substantial, with patients exhibiting a broad range of deficits and symptom severity. Large-scale genomic studies employing a case-control design have begun to provide some biological insight. However, this strategy combines individuals with clinically diverse symptoms and ignores the genetic risk that is carried by many clinically unaffected individuals. Consequently, the majority of the genetic architecture underlying schizophrenia remains unexplained, and the pathways by which the implicated variants contribute to the clinically observable signs and symptoms are still largely unknown. Parsing the complex, clinical phenotype of schizophrenia into biologically relevant components may have utility in research aimed at understanding the genetic basis of liability. Cognitive dysfunction is a hallmark symptom of schizophrenia that is associated with impaired quality of life and poor functional outcome. Here, we examine the value of quantitative measures of cognitive dysfunction to objectively target the underlying neurobiological pathways and identify genetic variants and gene networks contributing to schizophrenia risk. For a complex disorder, quantitative measures are also more efficient than diagnosis, allowing for the identification of associated genetic variants with fewer subjects. Such a strategy supplements traditional analyses of schizophrenia diagnosis, providing the necessary biological insight to help translate genetic findings into actionable treatment targets. Understanding the genetic basis of cognitive dysfunction in schizophrenia may thus facilitate the development of novel pharmacological and procognitive interventions to improve real-world functioning.

NRN1 Gene as a Potential Marker of Early-Onset Schizophrenia: Evidence from Genetic and Neuroimaging Approaches

Included in the neurotrophins family, the Neuritin 1 gene (NRN1) has emerged as an attractive candidate gene for schizophrenia (SZ) since it has been associated with the risk for the disorder and general cognitive performance. In this work, we aimed to further investigate the association of NRN1 with SZ by exploring its role on age at onset and its brain activity correlates. First, we developed two genetic association analyses using a family-based sample (80 early-onset (EO) trios (offspring onset ≤ 18 years) and 71 adult-onset (AO) trios) and an independent case–control sample (120 healthy subjects (HS), 87 EO and 138 AO patients). Second, we explored the effect of NRN1 on brain activity during a working memory task (N-back task; 39 HS, 39 EO and 39 AO; matched by age, sex and estimated IQ). Different haplotypes encompassing the same three Single Nucleotide Polymorphisms(SNPs, rs3763180–rs10484320–rs4960155) were associated with EO in the two samples (GCT, TCC and GTT). Besides, the GTT haplotype was associated with worse N-back task performance in EO and was linked to an inefficient dorsolateral prefrontal cortex activity in subjects with EO compared to HS. Our results show convergent evidence on the NRN1 association with EO both from genetic and neuroimaging approaches, highlighting the role of neurotrophins in the pathophysiology of SZ.

Immune-Related Genomic Schizophrenic Subtyping Identified in DLPFC Transcriptome

Well-documented evidence of the physiologic, genetic, and behavioral heterogeneity of schizophrenia suggests that diagnostic subtyping may clarify the underlying pathobiology of the disorder. Recent studies have demonstrated that increased inflammation may be a prominent feature of a subset of schizophrenics. However, these findings are inconsistent, possibly due to evaluating schizophrenics as a single group. In this study, we segregated schizophrenic patients into two groups ("Type 1", "Type 2") by their gene expression in the dorsolateral prefrontal cortex and explored biological differences between the subgroups. The study included post-mortem tissue samples that were sequenced in multiple, publicly available gene datasets using different sequencing methods. To evaluate the role of inflammation, the expression of genes in multiple components of neuroinflammation were examined: complement cascade activation, glial cell activation, pro-inflammatory mediator secretion, blood-brain barrier (BBB) breakdown, chemokine production and peripheral immune cell infiltration. The Type 2 schizophrenics showed widespread abnormal gene expression across all the neuroinflammation components that was not observed in Type 1 schizophrenics. Our results demonstrate the importance of separating schizophrenic patients into their molecularly defined subgroups and provide supporting evidence for the involvement of the immune-related pathways in a schizophrenic subset.

Going deep into schizophrenia with artificial intelligence

Despite years of research, the mechanisms governing the onset, relapse, symptomatology, and treatment of schizophrenia (SZ) remain elusive. The lack of appropriate analytic tools to deal with the heterogeneity and complexity of SZ may be one of the reasons behind this situation. Deep learning, a subfield of artificial intelligence (AI) inspired by the nervous system, has recently provided an accessible way of modeling and analyzing complex, high-dimensional, nonlinear systems. The unprecedented accuracy of deep learning algorithms in classification and prediction tasks has revolutionized a wide range of scientific fields and is rapidly permeating SZ research. Deep learning has the potential of becoming a valuable aid for clinicians in the prediction, diagnosis, and treatment of SZ, especially in combination with principles from Bayesian statistics. Furthermore, deep learning could become a powerful tool for uncovering the mechanisms underlying SZ thanks to a growing number of techniques designed for improving model interpretability and causal reasoning. The purpose of this article is to introduce SZ researchers to the field of deep learning and review its latest applications in SZ research. In general, existing studies have yielded impressive results in classification and outcome prediction tasks. However, methodological concerns related to the assessment of model performance in several studies, the widespread use of small training datasets, and the little clinical value of some models suggest that some of these results should be taken with caution.

HSPB1 Gene Variants and Schizophrenia: A Case-Control Study in a Polish Population

Schizophrenia (SCZ) is a severe psychiatric disorder that has a significant genetic component. HSPB1 (HSP27) is known for its neuroprotective functions under stress conditions and appears to play an important role during the development of the central nervous system, which is in agreement with the neurodevelopmental hypothesis of SCZ. The aim of the present case-control study was to investigate whether HSPB1 variants contribute to the risk and clinical features (age of onset, symptoms, and suicidal behavior) of SCZ in a Polish population. To the best of our knowledge, this is the first study that investigated the association between the HSPB1 polymorphisms and SCZ. Three SNPs of HSPB1 (rs2868370, rs2868371, and rs7459185) were genotyped in a total of 1082 (403 patients and 679 controls) unrelated subjects using TaqMan assays. The results showed that the genotypes, alleles, and haplotypes of the three SNPs were not significantly different between the schizophrenic patients and healthy controls either in the overall analysis or in the gender-stratified analysis (all p > 0.05). However, we did find a significant effect of the rs2868371 genotype on the age of onset, negative symptoms, and disorganized symptoms in the five-factor model of PANSS (all p < 0.01). Post hoc comparisons showed that carriers of the rs2868371 G/G genotype had significantly higher negative and disorganized factor scores than those with the C/G and C/C genotypes, respectively. Further investigations with other larger independent samples are required to confirm our findings and to better explore the effect of the HSPB1 polymorphisms on the risk and symptomatology of SCZ.

Proteomic Analysis of Plasma Markers in Patients Maintained on Antipsychotics: Comparison to Patients Off Antipsychotics and Normal Controls

BACKGROUND

Schizophrenia (SZ) and bipolar disorder (BD) share many features: overlap in mood and psychotic symptoms, common genetic predisposition, treatment with antipsychotics (APs), and similar metabolic comorbidities. The pathophysiology of both is still not well defined, and no biomarkers can be used clinically for diagnosis and management. This study aimed to assess the plasma proteomics profile of patients with SZ and BD maintained on APs compared to those who had been off APs for 6 months and to healthy controls (HCs).

METHODS

We analyzed the data using functional enrichment, random forest modeling to identify potential biomarkers, and multivariate regression for the associations with metabolic abnormalities.

RESULTS

We identified several proteins known to play roles in the differentiation of the nervous system like NTRK2, CNTN1, ROBO2, and PLXNC1, which were downregulated in AP-free SZ and BD patients but were "normalized" in those on APs. Other proteins (like NCAM1 and TNFRSF17) were "normal" in AP-free patients but downregulated in patients on APs, suggesting that these changes are related to medication's effects. We found significant enrichment of proteins involved in neuronal plasticity, mainly in SZ patients on APs. Most of the proteins associated with metabolic abnormalities were more related to APs use than having SZ or BD. The biomarkers identification showed specific and sensitive results for schizophrenia, where two proteins (PRL and MRC2) produced adequate results.

CONCLUSIONS

Our results confirmed the utility of blood samples to identify protein signatures and mechanisms involved in the pathophysiology and treatment of SZ and BD.

Heterogeneity in response to repeated intranasal oxytocin in schizophrenia and autism spectrum disorders: A meta-analysis of variance

Intranasal oxytocin (OT) has been suggested as a putative adjunctive treatment for patients with schizophrenia and autism spectrum disorders (ASD). Here, we examine available evidence from trials investigating the effects of repeated administrations of intranasal OT on the core symptoms of patients with schizophrenia and ASD, focusing on its therapeutic efficacy and heterogeneity of response (meta-ANOVA). Repeated administration of intranasal OT does not improve most of the core symptoms of schizophrenia and ASD, beyond a small tentative effect on schizophrenia general symptoms. However, we found significant moderator effects for dose in schizophrenia total psychopathology and positive symptoms, and percentage of included men and duration of treatment in schizophrenia general symptoms. We found evidence of heterogeneity (increased variance) in the response of schizophrenia negative symptoms to intranasal OT compared with placebo, suggesting that subgroups of responsive and non-responsive patients might coexist. For other core symptoms of schizophrenia, or any of the core symptom dimensions in ASD, the response to repeated treatment with intranasal OT did not show evidence of heterogeneity.

Epigenetic Modifications in Schizophrenia and Related Disorders: Molecular Scars of Environmental Exposures and Source of Phenotypic Variability

Epigenetic modifications are increasingly recognized to play a role in the etiology and pathophysiology of schizophrenia and other psychiatric disorders with developmental origins. Here, we summarize clinical and preclinical findings of epigenetic alterations in schizophrenia and relevant disease models and discuss their putative origin. Recent findings suggest that certain schizophrenia risk loci can influence stochastic variation in gene expression through epigenetic processes, highlighting the intricate interaction between genetic and epigenetic control of neurodevelopmental trajectories. In addition, a substantial portion of epigenetic alterations in schizophrenia and related disorders may be acquired through environmental factors and may be manifested as molecular "scars." Some of these scars can influence brain functions throughout the entire lifespan and may even be transmitted across generations via epigenetic germline inheritance. Epigenetic modifications, whether caused by genetic or environmental factors, are plausible molecular sources of phenotypic heterogeneity and offer a target for therapeutic interventions. The further elucidation of epigenetic modifications thus may increase our knowledge regarding schizophrenia's heterogeneous etiology and pathophysiology and, in the long term, may advance personalized treatments through the use of biomarker-guided epigenetic interventions.

Machine Learning-Based Electroencephalographic Phenotypes of Schizophrenia and Major Depressive Disorder

Background: Psychiatric diagnosis is formulated by symptomatic classification; disease-specific neurophysiological phenotyping could help with its fundamental treatment. Here, we investigated brain phenotyping in patients with schizophrenia (SZ) and major depressive disorder (MDD) by using electroencephalography (EEG) and conducted machine-learning-based classification of the two diseases by using EEG components. Materials and Methods: We enrolled healthy controls (HCs) (n = 30) and patients with SZ (n = 34) and MDD (n = 33). An auditory P300 (AP300) task was performed, and the N1 and P3 components were extracted. Two-group classification was conducted using linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Positive and negative symptoms and depression and/or anxiety symptoms were evaluated. Results: Considering both the results of statistical comparisons and machine learning-based classifications, patients and HCs showed significant differences in AP300, with SZ and MDD showing lower N1 and P3 than HCs. In the sum of amplitudes and cortical sources, the findings for LDA with classification accuracy (SZ vs. HCs: 71.31%, MDD vs. HCs: 74.55%), sensitivity (SZ vs. HCs: 77.67%, MDD vs. HCs: 79.00%), and specificity (SZ vs. HCs: 64.00%, MDD vs. HCs: 69.67%) supported these results. The SVM classifier showed reasonable scores between SZ and HCs and/or MDD and HCs. The comparison between SZ and MDD showed low classification accuracy (59.71%), sensitivity (65.08%), and specificity (54.83%). Conclusions: Patients with SZ and MDD showed deficiencies in N1 and P3 components in the sum of amplitudes and cortical sources, indicating attentional dysfunction in both early and late sensory/cognitive gating input. The LDA and SVM classifiers in the AP300 are useful to distinguish patients with SZ and HCs and/or MDD and HCs.

Derivation of poly-methylomic profile scores for schizophrenia

Schizophrenia and bipolar disorder share biological features and environmental risk factors that may be associated with altered DNA methylation. In this study we sought to: 1) construct a novel 'Poly-Methylomic Profile Score (PMPS)' by transforming schizophrenia-associated epigenome-wide methylation from a previously published epigenome-wide association study (EWAS) into a single quantitative metric; and 2) examine associations between the PMPS and clinical status in an independent sample of 57 schizophrenia (SZ) cases, 59 bipolar disorder (BD) cases and 55 healthy controls (HC) for whom blood-derived DNA methylation was quantified using the Illumina 450 K methylation beadchip. We constructed five PMPSs at different p-value thresholds by summing methylation beta-values weighted by individual-CpG effect sizes from the meta-analysis of a previously published schizophrenia EWAS (comprising three separate cohorts with 675 [353 SZ and 322 HC] discovery cohort participants, 847 [414 SZ and 433 HC] replication cohort participants, and 96 monozygotic twin-pairs discordant for SZ). All SZ PMPSs were elevated in SZ participants relative to HCs, with the score calculated at a p-value threshold of 1 × 10^-5 accounting for the greatest amount of variance. All PMPSs were elevated in SZ relative to BD and none of the PMPSs were increased in BD, or in a combined cohort of BD and SZ cases, relative to HCs. PMPSs were also not associated with positive or negative symptom severity. That this SZ-derived PMPSs was elevated in SZ, but not BD, suggests that epigenome-wide methylation patterns may represent distinct pathophysiology that is yet to be elucidated.

Brain Connectome Mapping of Complex Human Traits and Their Polygenic Architecture Using Machine Learning

BACKGROUND

Mental disorders and individual characteristics such as intelligence and personality are complex traits sharing a largely unknown neuronal basis. Their genetic architectures are highly polygenic and overlapping, which is supported by heterogeneous phenotypic expression and substantial clinical overlap. Brain network analysis provides a noninvasive means of dissecting biological heterogeneity, yet its sensitivity, specificity, and validity in assessing individual characteristics relevant for brain function and mental health and their genetic underpinnings in clinical applications remain a challenge.

METHODS

In a machine learning approach, we predicted individual scores for educational attainment, fluid intelligence and dimensional measures of depression, anxiety, and neuroticism using functional magnetic resonance imaging-based static and dynamic temporal synchronization between large-scale brain network nodes in 10,343 healthy individuals from the UK Biobank. In addition to using age and sex to serve as our reference point, we also predicted individual polygenic scores for related phenotypes and 13 different neuroticism traits and schizophrenia.

RESULTS

Beyond high accuracy for age and sex, supporting the biological sensitivity of the connectome-based features, permutation tests revealed above chance-level prediction accuracy for trait-level educational attainment and fluid intelligence. Educational attainment and fluid intelligence were mainly negatively associated with static brain connectivity in frontal and default mode networks, whereas age showed positive correlations with a more widespread pattern. In contrast, prediction accuracy was at chance level for depression, anxiety, neuroticism, and polygenic scores across traits.

CONCLUSIONS

These novel findings provide a benchmark for future studies linking the genetic architecture of individual and mental health traits with functional magnetic resonance imaging-based brain connectomics.

Improving individual predictions: Machine learning approaches for detecting and attacking heterogeneity in schizophrenia (and other psychiatric diseases)

Psychiatric diseases are very heterogeneous both in clinical manifestation and etiology. With the recent rise of using machine learning techniques to attempt to diagnose and prognose these disorders, the issue of heterogeneity becomes increasingly important. With the growing interest in personalized medicine, it becomes even more important to not only classify someone as a patient with a certain disorder, its treatment needs a more precise definition of the underlying neurobiology, since different biological origins of the same disease may require (very) different treatments. We review the possible contributions that machine learning techniques could make to explore the heterogeneous nature of psychiatric disorders with a focus on schizophrenia. First we will review how heterogeneity shows up and how machine learning, or multivariate pattern recognition methods in general, can be used to discover it. Secondly, we will discuss the possible uses of these techniques to attack heterogeneity, leading to improved predictions and understanding of the neurobiological background of the disorder.

Cognitive Profiles and Functional Connectivity in First-Episode Schizophrenia Spectrum Disorders - Linking Behavioral and Neuronal Data

The character of cognitive deficit in schizophrenia is not clear due to the heterogeneity in research results. In heterogeneous conditions, the cluster solution allows the classification of individuals based on profiles. Our aim was to examine the cognitive profiles of first-episode schizophrenia spectrum disorder (FES) subjects based on cluster analysis, and to correlate these profiles with clinical variables and resting state brain connectivity, as measured with magnetic resonance imaging. A total of 67 FES subjects were assessed with a neuropsychological test battery and on clinical variables. The results of the cognitive domains were cluster analyzed. In addition, functional connectivity was calculated using ROI-to-ROI analysis with four groups: Three groups were defined based on the cluster analysis of cognitive performance and a control group with a normal cognitive performance. The connectivity was compared between the patient clusters and controls. We found different cognitive profiles based on three clusters: Cluster 1: decline in the attention, working memory/flexibility, and verbal memory domains. Cluster 2: decline in the verbal memory domain and above average performance in the attention domain. Cluster 3: generalized and severe deficit in all of the cognitive domains. FES diagnoses were distributed among all of the clusters. Cluster comparisons in neural connectivity also showed differences between the groups. Cluster 1 showed both hyperconnectivity between the cerebellum and precentral gyrus, the salience network (SN) (insula cortex), and fronto-parietal network (FPN) as well as between the PreCG and SN (insula cortex) and hypoconnectivity between the default mode network (DMN) and seeds of SN [insula and supramarginal gyrus (SMG)]; Cluster 2 showed hyperconnectivity between the DMN and cerebellum, SN (insula) and precentral gyrus, and FPN and IFG; Cluster 3 showed hypoconnectivity between the DMN and SN (insula) and SN (SMG) and pallidum. The cluster solution confirms the prevalence of a cognitive decline with different patterns of cognitive performance, and different levels of severity in FES. Moreover, separate behavioral cognitive subsets can be linked to patterns of brain functional connectivity.

Endophenotypes in Schizophrenia: Digging Deeper to Identify Genetic Mechanisms

Schizophrenia (SZ) is a severe psychotic disorder that is highly heritable and common in the general population. The genetic heterogeneity of SZ is substantial, with contributions from common, rare, and de novo variants, in addition to environmental factors. Large genome-wide association studies have detected many variants that are associated with SZ, yet the pathways by which these variants influence risk remain largely unknown. SZ is also clinically heterogeneous, with patients exhibiting a broad range of deficits and symptom severity that vary over the course of illness and treatment, which has complicated efforts to identify risk variants. However, the underlying brain dysfunction forms a more stable trait marker that quantitative neurocognitive and neurophysiological endophenotypes may be able to objectively measure. These endophenotypes are less likely to be heterogeneous than the disorder and provide a neurobiological context to detect risk variants and underlying pathways among genes associated with SZ diagnosis. Furthermore, many endophenotypes are translational into animal model systems, allowing for direct evaluation of the neural circuit dysfunctions and neurobiological substrates. We review a selection of the most promising SZ endophenotypes, including prepulse inhibition, mismatch negativity, oculomotor antisaccade, letter-number sequencing, and continuous performance tests. We also highlight recent findings from large consortia that suggest the potential role of genes, particularly in the neuregulin and glutamate pathways, in several of these endophenotypes. Although endophenotypes require additional time and effort to assess, the insight into the underlying neurobiology that they provide may ultimately reveal the underlying genetic architecture for SZ and suggest novel treatment targets.

Alteration of gray matter microstructure in schizophrenia

Neuroimaging studies demonstrate gray matter (GM) macrostructural abnormalities in patients with schizophrenia (SCZ). While ex-vivo and genetic studies suggest cellular pathology associated with abnormal neurodevelopmental processes in SCZ, few in-vivo measures have been proposed to target microstructural GM organization. Here, we use diffusion heterogeneity- to study GM microstructure in SCZ. Structural and diffusion magnetic resonance imaging (MRI) were acquired on a 3 Tesla scanner in 46 patients with SCZ and 37 matched healthy controls (HC). After correction for free water, diffusion heterogeneity as well as commonly used diffusion measures FA and MD and volume were calculated for the four cortical lobes on each hemisphere, and compared between groups. Patients with early course SCZ exhibited higher diffusion heterogeneity in the GM of the frontal lobes compared to controls. Diffusion heterogeneity of the frontal lobe showed excellent discrimination between patients and HC, while none of the commonly used diffusion measures such as FA or MD did. Higher diffusion heterogeneity in the frontal lobes in early SCZ may be due to abnormal brain maturation (migration, pruning) before and during adolescence and early adulthood. Further studies are needed to investigate the role of heterogeneity as potential biomarker for SCZ risk.

Genetic assessment of additional endophenotypes from the Consortium on the Genetics of Schizophrenia Family Study

The Consortium on the Genetics of Schizophrenia Family Study (COGS-1) has previously reported our efforts to characterize the genetic architecture of 12 primary endophenotypes for schizophrenia. We now report the characterization of 13 additional measures derived from the same endophenotype test paradigms in the COGS-1 families. Nine of the measures were found to discriminate between schizophrenia patients and controls, were significantly heritable (31 to 62%), and were sufficiently independent of previously assessed endophenotypes, demonstrating utility as additional endophenotypes. Genotyping via a custom array of 1536 SNPs from 94 candidate genes identified associations for CTNNA2, ERBB4, GRID1, GRID2, GRIK3, GRIK4, GRIN2B, NOS1AP, NRG1, and RELN across multiple endophenotypes. An experiment-wide p value of 0.003 suggested that the associations across all SNPs and endophenotypes collectively exceeded chance. Linkage analyses performed using a genome-wide SNP array further identified significant or suggestive linkage for six of the candidate endophenotypes, with several genes of interest located beneath the linkage peaks (e.g., CSMD1, DISC1, DLGAP2, GRIK2, GRIN3A, and SLC6A3). While the partial convergence of the association and linkage likely reflects differences in density of gene coverage provided by the distinct genotyping platforms, it is also likely an indication of the differential contribution of rare and common variants for some genes and methodological differences in detection ability. Still, many of the genes implicated by COGS through endophenotypes have been identified by independent studies of common, rare, and de novo variation in schizophrenia, all converging on a functional genetic network related to glutamatergic neurotransmission that warrants further investigation.