Multi-scale analysis of schizophrenia risk genes, brain structure, and clinical symptoms reveals integrative clues for subtyping schizophrenia patients

Analysis linking directly genomics, neuroimaging phenotypes and clinical measurements is crucial for understanding psychiatric disorders, but remains rare. Here, we describe a multi-scale analysis using genome-wide SNPs, gene-expression, grey matter volume (GMV) and the Positive and Negative Syndrome Scale scores (PANSS) to explore the etiology of schizophrenia. With 72 drug-naive schizophrenic first episode patients (FEPs) and 73 matched heathy controls, we identified 108 genes, from schizophrenia risk genes, that correlated significantly with GMV, which are highly co-expressed in the brain during development. Among these 108 candidates, 19 distinct genes were found associated with 16 brain regions referred to as hot clusters (HCs), primarily in the frontal cortex, sensory-motor regions and temporal and parietal regions. The patients were subtyped into three groups with distinguishable PANSS scores by the GMV of the identified HCs. Furthermore, we found that HCs with common GMV among patient groups are related to genes that mostly mapped to pathways relevant to neural signaling, which are associated with the risk for schizophrenia. Our results provide an integrated view of how genetic variants may affect brain structures that lead to distinct disease phenotypes. The method of multi-scale analysis that was described in this research, may help to advance the understanding of the etiology of schizophrenia

Individualized differential diagnosis of schizophrenia and mood disorders using neuroanatomical biomarkers

Magnetic resonance imaging-based markers of schizophrenia have been repeatedly shown to separate patients from healthy controls at the single-subject level, but it remains unclear whether these markers reliably distinguish schizophrenia from mood disorders across the life span and generalize to new patients as well as to early stages of these illnesses. The current study used structural MRI-based multivariate pattern classification to (i) identify and cross-validate a differential diagnostic signature separating patients with first-episode and recurrent stages of schizophrenia (n = 158) from patients with major depression (n = 104); and (ii) quantify the impact of major clinical variables, including disease stage, age of disease onset and accelerated brain ageing on the signature's classification performance. This diagnostic magnetic resonance imaging signature was then evaluated in an independent patient cohort from two different centres to test its generalizability to individuals with bipolar disorder (n = 35), first-episode psychosis (n = 23) and clinically defined at-risk mental states for psychosis (n = 89). Neuroanatomical diagnosis was correct in 80% and 72% of patients with major depression and schizophrenia, respectively, and involved a pattern of prefronto-temporo-limbic volume reductions and premotor, somatosensory and subcortical increments in schizophrenia versus major depression. Diagnostic performance was not influenced by the presence of depressive symptoms in schizophrenia or psychotic symptoms in major depression, but earlier disease onset and accelerated brain ageing promoted misclassification in major depression due to an increased neuroanatomical schizophrenia likeness of these patients. Furthermore, disease stage significantly moderated neuroanatomical diagnosis as recurrently-ill patients had higher misclassification rates (major depression: 23%; schizophrenia: 29%) than first-episode patients (major depression: 15%; schizophrenia: 12%). Finally, the trained biomarker assigned 74% of the bipolar patients to the major depression group, while 83% of the first-episode psychosis patients and 77% and 61% of the individuals with an ultra-high risk and low-risk state, respectively, were labelled with schizophrenia. Our findings suggest that neuroanatomical information may provide generalizable diagnostic tools distinguishing schizophrenia from mood disorders early in the course of psychosis. Disease course-related variables such as age of disease onset and disease stage as well alterations of structural brain maturation may strongly impact on the neuroanatomical separability of major depression and schizophrenia

The Core Deficit of Classical Schizophrenia: Implications for Predicting the Functional Outcome of Psychotic Illness and Developing Effective Treatments

© The Author(s) 2019. Many people suffering from psychotic illnesses experience persisting impairment of occupational and social function. Evidence assembled since the classical description of schizophrenia over a century ago indicates that both disorganization and impoverishment of mental activity are associated with persisting impairment. Longitudinal studies of young people at risk of schizophrenia reveal that both mental impoverishment and disorganization predict poor long-term outcome. These clinical features are related to cognitive impairments. Evidence from brain imaging indicates overlap in the brain abnormalities implicated in these phenomena, including impaired function of long-range connections between sensory cortex and the salience network, a network engaged in recruiting cerebral systems for processing of information salient to current circumstances. The evidence suggests that the common features underlying these two groups of symptoms might reflect a core pathological process distinguishing nonaffective from affective psychosis. This pathological process might therefore justifiably be designated the “core deficit” of classical schizophrenia. To develop more effective treatments to prevent persisting disability, we require the ability to identify individuals at risk at an early stage. Recent studies provide pointers toward effective strategies for identifying cases at risk of poor outcome. Accumulating evidence confirms that appreciable potential for neuroplastic change in the brain persists into adult life. Furthermore, brain function can be enhanced by targeted neuromodulation treatments. We now have promising tools not only for investigating the psychological and neural mechanisms that underlie persisting functional impairment but also for identifying individuals at risk and for harnessing brain plasticity to improve treatment

Pattern of neural responses to verbal fluency shows diagnostic specificity for schizophrenia and bipolar disorder

<p>Abstract</p> <p>Background</p> <p>Impairments in executive function and language processing are characteristic of both schizophrenia and bipolar disorder. Their functional neuroanatomy demonstrate features that are shared as well as specific to each disorder. Determining the distinct pattern of neural responses in schizophrenia and bipolar disorder may provide biomarkers for their diagnoses.</p> <p>Methods</p> <p>104 participants underwent functional magnetic resonance imaging (fMRI) scans while performing a phonological verbal fluency task. Subjects were 32 patients with schizophrenia in remission, 32 patients with bipolar disorder in an euthymic state, and 40 healthy volunteers. Neural responses to verbal fluency were examined in each group, and the diagnostic potential of the pattern of the neural responses was assessed with machine learning analysis.</p> <p>Results</p> <p>During the verbal fluency task, both patient groups showed increased activation in the anterior cingulate, left dorsolateral prefrontal cortex and right putamen as compared to healthy controls, as well as reduced deactivation of precuneus and posterior cingulate. The magnitude of activation was greatest in patients with schizophrenia, followed by patients with bipolar disorder and then healthy individuals. Additional recruitment in the right inferior frontal and right dorsolateral prefrontal cortices was observed in schizophrenia relative to both bipolar disorder and healthy subjects. The pattern of neural responses correctly identified individual patients with schizophrenia with an accuracy of 92%, and those with bipolar disorder with an accuracy of 79% in which mis-classification was typically of bipolar subjects as healthy controls.</p> <p>Conclusions</p> <p>In summary, both schizophrenia and bipolar disorder are associated with altered function in prefrontal, striatal and default mode networks, but the magnitude of this dysfunction is particularly marked in schizophrenia. The pattern of response to verbal fluency is highly diagnostic for schizophrenia and distinct from bipolar disorder. Pattern classification of functional MRI measurements of language processing is a potential diagnostic marker of schizophrenia.</p

Biclustered Independent Component Analysis for Complex Biomarker and Subtype Identification from Structural Magnetic Resonance Images in Schizophrenia

Clinical and cognitive symptoms domain-based subtyping in schizophrenia (Sz) has been critiqued due to the lack of neurobiological correlates and heterogeneity in symptom scores. We, therefore, present a novel data-driven framework using biclustered independent component analysis to detect subtypes from the reliable and stable gray matter concentration (GMC) of patients with Sz. The developed methodology consists of the following steps: source-based morphometry (SBM) decomposition, selection and sorting of two component loadings, subtype component reconstruction using group information-guided ICA (GIG-ICA). This framework was applied to the top two group discriminative components namely the insula/superior temporal gyrus/inferior frontal gyrus (I-STG-IFG component) and the superior frontal gyrus/middle frontal gyrus/medial frontal gyrus (SFG-MiFG-MFG component) from our previous SBM study, which showed diagnostic group difference and had the highest effect sizes. The aggregated multisite dataset consisted of 382 patients with Sz regressed of age, gender, and site voxelwise. We observed two subtypes (i.e., two different subsets of subjects) each heavily weighted on these two components, respectively. These subsets of subjects were characterized by significant differences in positive and negative syndrome scale (PANSS) positive clinical symptoms (p = 0.005). We also observed an overlapping subtype weighing heavily on both of these components. The PANSS general clinical symptom of this subtype was trend level correlated with the loading coefficients of the SFG-MiFG-MFG component (r = 0.25; p = 0.07). The reconstructed subtype-specific component using GIG-ICA showed variations in voxel regions, when compared to the group component. We observed deviations from mean GMC along with conjunction of features from two components characterizing each deciphered subtype. These inherent variations in GMC among patients with Sz could possibly indicate the need for personalized treatment and targeted drug development

Detecting neuroimaging biomarkers for schizophrenia:a meta-analysis of multivariate pattern recognition studies

Multivariate pattern recognition approaches have recently facilitated the search for reliable neuroimaging-based biomarkers in psychiatric disorders such as schizophrenia. By taking into account the multivariate nature of brain functional and structural changes as well as their distributed localization across the whole brain, they overcome drawbacks of traditional univariate approaches. To evaluate the overall reliability of neuroimaging-based biomarkers, we conducted a comprehensive literature search to identify all studies that used multivariate pattern recognition to identify patterns of brain alterations that differentiate patients with schizophrenia from healthy controls. A bivariate random-effects meta-analytic model was implemented to investigate the sensitivity and specificity across studies as well as to assess the robustness to potentially confounding variables. In the total sample of n=38 studies (1602 patients and 1637 healthy controls), patients were differentiated from controls with a sensitivity of 80.3% (95% CI: 76.7–83.5%) and a specificity of 80.3% (95% CI: 76.9–83.3%). Analysis of neuroimaging modality indicated higher sensitivity (84.46%, 95% CI: 79.9–88.2%) and similar specificity (76.9%, 95% CI: 71.3–81.6%) of rsfMRI studies as compared with structural MRI studies (sensitivity: 76.4%, 95% CI: 71.9–80.4%, specificity of 79.0%, 95% CI: 74.6–82.8%). Moderator analysis identified significant effects of age (p=0.029), imaging modality (p=0.019), and disease stage (p=0.025) on sensitivity as well as of positive-to-negative symptom ratio (p=0.022) and antipsychotic medication (p=0.016) on specificity. Our results underline the utility of multivariate pattern recognition approaches for the identification of reliable neuroimaging-based biomarkers. Despite the clinical heterogeneity of the schizophrenia phenotype, brain functional and structural alterations differentiate schizophrenic patients from healthy controls with 80% sensitivity and specificity

A Machine-Learning-Based Investigation of Schizophrenia Using Structural MRI

openSchizophrenia is a serious mental health concerns that affects 1% of the population (Jones et al., 2005). This study aimed to create objective tools that can correctly classify people with schizophrenia according to their diagnosis, predominant symptoms, illness duration, and illness severity based on their structural brain imaging variables. 1087 brain images (700=healthy controls, 387=people with schizophrenia) included in the analysis. Support Vector Machines, random forests, logistic regression, and XGBoost were used for diagnostic classification and reached 71% of maximum accuracy. Sulcal width was found to be the most important brain imaging variable that differed between groups. Support vector machines and random forests were used to classify patients according to their predominant symptoms and these classifications reached a maximum accuracy of 66%. Support vector machines could correctly classify people with schizophrenia according to their illness duration with a 75% accuracy and according to their illness severity with 69%. The result of the study shows that using machine learning methods, it is possible to create objective tools for schizophrenia that can be later used in clinics. Keywords: Schizophrenia, Structural MRI, Machine Learning ClassificationSchizophrenia is a serious mental health concerns that affects 1% of the population (Jones et al., 2005). This study aimed to create objective tools that can correctly classify people with schizophrenia according to their diagnosis, predominant symptoms, illness duration, and illness severity based on their structural brain imaging variables. 1087 brain images (700=healthy controls, 387=people with schizophrenia) included in the analysis. Support Vector Machines, random forests, logistic regression, and XGBoost were used for diagnostic classification and reached 71% of maximum accuracy. Sulcal width was found to be the most important brain imaging variable that differed between groups. Support vector machines and random forests were used to classify patients according to their predominant symptoms and these classifications reached a maximum accuracy of 66%. Support vector machines could correctly classify people with schizophrenia according to their illness duration with a 75% accuracy and according to their illness severity with 69%. The result of the study shows that using machine learning methods, it is possible to create objective tools for schizophrenia that can be later used in clinics. Keywords: Schizophrenia, Structural MRI, Machine Learning Classificatio

Neurobiology of schizotypal phenotypes - Schizotypy as a framework for dimensional psychiatry

Complex, dimensional phenotypes represent a valuable framework for the analysis of fundamental neurobiological mechanisms of psychiatric disorders. They facilitate the deconstruction of diagnostic entities and the study of protective processes that prevent progression into clinical domains. Within the psychosis spectrum, schizotypy describes a multidimensional personality construct with behavioural, cognitive, and emotional characteristics similar to key symptoms of schizophrenia, that can equally be grouped into the dimensions positive (magical thinking, unusual perceptions and beliefs), negative (introversion, anhedonia), and disorganised (cognitive disorganisation, eccentricity). Within a continuum model of psychosis, schizotypy is discussed as variation of healthy function, and as risk phenotype of schizophrenia and psychosis proneness, assuming a (partially) overlapping genetic architecture along the spectrum. Current aetiological models propose an impact of genetic liability, in interaction with environmental risk and modulated by protective factors like cognitive function, through disruptions in neuronal development. In fact, recent studies show that schizotypy is associated with brain structural variation, partially overlapping with regions that are also impaired in patients with schizophrenia spectrum disorders. This dissertation characterised neurobiological determinants of schizotypy regarding its genetic basis and neural networks, aiming to develop a multimodal model to integrate those into a joint framework. STUDIES I and IV investigated the genetic structure of schizotypy, demonstrating its association with common variants (single nucleotide polymorphisms, SNPs) in genes (CACNA1C and ZNF804A) involved in processes of neuronal development and identified as risk genes for schizophrenia and other psychiatric disorders (STUDY I). In this association, biological sex has a moderating role. However, a direct association of a polygenic schizophrenia risk score, based on cumulative SNP-risk, was not established (STUDY IV). STUDIES II and III analysed brain structural correlates of schizotypy dimensions, finding an association of the positive dimension (and symptom-associated distress) with grey matter volume in associative brain areas precuneus, striatum and inferior temporal gyrus. STUDY II further indicates that this relationship can be buffered by above average general cognitive function. Study V ultimately integrates the previous results into a joint multivariate model that proves to explain a substantial amount of phenotypic variance. The model shows that the interaction effect of polygenic and poly-environmental risk on positive schizotypy is mediated through brain structural variation in the precuneus, and modulated by the level of executive function. In conclusion, this dissertation shows that schizotypy is associated with genetic polymorphisms involved in neuronal development and function. While those are identified as schizophrenia risk variants, the lack of an association with polygenic schizophrenia risk suggests a limited overlap of the genetic architectures of the phenotypes. The confirmation of the multivariate model, however, indicates an indirect effect through variations in brain structure and modulated by intra- and extrapersonal factors. Accordingly, particularly positive schizotypy is associated with structural alterations in brain regions central for the integration, evaluation, and attribution of perceptual information within associative neuronal networks. Thus, schizotypy is a valuable endophenotype of the schizophrenia spectrum, showing that pathophysiological aberrations lie on a continuum with variation of healthy functioning. Schizotypy, however, also describes the manifestation of interindividual variation in behaviour, cognition, and emotion, with its underlying mechanisms representing an exemplary framework for the study of dimensional, phenotypic spectra