Imaging methods have become the main approach for identifying dysfunctional neuronal networks in schizophrenia. This review article presents recent results of disorders of neuronal networks at ...structural and functional levels and summarizes the current developments. Large multicenter analyses have further established patterns of regional brain alterations, while novel methods in magnetic resonance (MR) morphometry have contributed to differentiating early from delayed brain structural changes. The use of machine learning approaches has not only enabled the establishment of classification models using biological data for future differential diagnostic use, it has also facilitated multivariate models for outcome prediction following therapeutic interventions. Novel methods, such as BrainAGE, a surrogate marker of accelerated brain aging processes, have added to longitudinal studies to gain insights into the brain structural dynamics from early brain developmental alterations to progressive structural brain changes after disease onset.
Abstract BrainAGE (brain age gap estimation) is a novel morphometric parameter providing a univariate score derived from multivariate voxel-wise analyses. It uses a machine learning approach and can ...be used to analyse deviation from physiological developmental or aging-related trajectories. Using structural MRI data and BrainAGE quantification of acceleration or deceleration of in individual aging, we analysed data from 45 schizophrenia patients, 22 bipolar I disorder patients (mostly with previous psychotic symptoms / episodes), and 70 healthy controls. We found significantly higher BrainAGE scores in schizophrenia, but not bipolar disorder patients. Our findings indicate significantly accelerated brain structural aging in schizophrenia. This suggests, that despite the conceptualisation of schizophrenia as a neurodevelopmental disorder, there might be an additional progressive pathogenic component.
We currently observe a disconcerting phenomenon in machine learning studies in psychiatry: While we would expect larger samples to yield better results due to the availability of more data, larger ...machine learning studies consistently show much weaker performance than the numerous small-scale studies. Here, we systematically investigated this effect focusing on one of the most heavily studied questions in the field, namely the classification of patients suffering from Major Depressive Disorder (MDD) and healthy controls based on neuroimaging data. Drawing upon structural MRI data from a balanced sample of N = 1868 MDD patients and healthy controls from our recent international Predictive Analytics Competition (PAC), we first trained and tested a classification model on the full dataset which yielded an accuracy of 61%. Next, we mimicked the process by which researchers would draw samples of various sizes (N = 4 to N = 150) from the population and showed a strong risk of misestimation. Specifically, for small sample sizes (N = 20), we observe accuracies of up to 95%. For medium sample sizes (N = 100) accuracies up to 75% were found. Importantly, further investigation showed that sufficiently large test sets effectively protect against performance misestimation whereas larger datasets per se do not. While these results question the validity of a substantial part of the current literature, we outline the relatively low-cost remedy of larger test sets, which is readily available in most cases.
•Use of a novel brain structural parameter, the BrainAGE score (brain age estimation gap), based on a machine-learning approach to estimate subjects’ age based on T1 MRIs.•No evidence for accelerated ...brain aging in MDD patients.•Pilot cohort as a reference for further studies on brain-ageing in MDD.
Molecular biological findings indicate that affective disorders are associated with processes akin to accelerated aging of the brain. The use of the BrainAGE (brain age estimation gap) framework allows machine-learning based detection of a gap between age estimated from high-resolution MRI scans an chronological age, and thus an indicator of systems-level accelerated aging. We analysed 3T high-resolution structural MRI scans in 38 major depression patients (without co-morbid axis I or II disorders) and 40 healthy controls using the BrainAGE method to test the hypothesis of accelerated aging in (non-psychotic) major depression. We found no significant difference (or trend) for elevated BrainAGE in this pilot sample. Unlike previous findings in schizophrenia (and partially bipolar disorder), unipolar depression per se does not seem to be associated with accelerated aging patterns across the brain. However, given the limitations of the sample, further study is needed to test for effects in subgroups with comorbidities, as well as longitudinal designs.
•Schizotypal traits in healthy subjects are associated with brain structure•Positive schizotypy correlates with superior prefrontal and anterior cingulate cortex•Negative schizotypy correlates ...inversely with medial / orbital prefrontal cortex•Results support a fronto-striatal neurobiological continuum model of psychosis
Schizotypy is a multidimensional construct of subclinical schizophrenia-like behavioural traits and cognition. The recently developed multidimensional schizotypy scale (MSS) provides an improved psychometric assessment of the three main dimensions (positive, negative, and disorganised). We tested the hypothesis that the three dimensions are related to brain structural variation in the precuneus and fronto-thalamo-striatal system in a new non-clinical healthy cohort to support a dimensional model of the psychosis spectrum. We analysed data from 104 subjects with Multidimensional Schizotypy Scale (MSS) phenotyping and 3 Tesla magnetic resonance images using voxel-based morphometry (VBM) applying CAT12 software, and diffusion-tensor imaging (DTI) with TBSS in FSL to test for correlations with MSS scores. MSS subscales and total score were negatively associated with GMV in brain areas including the medial prefrontal cortex, anterior cingulate cortex, and lateral prefrontal and orbital cortex. MSS schizotypy was associated with white matter integrity in anterior thalamic radiation, uncinate fasciculus, and superior longitudinal fasciculus. Our findings provide first direct evidence for an association of schizotypy (as a psychosis risk phenotype) and the fronto-thalamo-striatal system, in both grey and white matter with regionally diverging effects across single dimensions. This provides new evidence arguing for the fronto-striatal system (rather than precuneus) in schizotypy.
Impulsivity as a trait modulates a range of cognitive functions, e.g. planning, decision-making, or response inhibition. Recent behavioural and psychometric findings challenge both the ...neurobiological models as well as the conceptualisation of psychometric measures of impulsivity. In the present study, we aimed to test the association of brain structure with the Barratt Impulsiveness Scale (BIS-11), a commonly applied self-rating instrument for impulsivity, using both the classical three-factor-model for impulsive behaviour (motor (IM), attentional (IA) and non-planning impulsivity (INP)), as well as the recently proposed alternative model contrasting inability to wait for reward (IWR) as an index of impulsive choice and rapid response style (RRS) as an index of impulsive action. We analysed brain structural data in a community sample of 85 healthy individuals, who completed the BIS-11, using voxel-based morphometry (CAT12: Computational Anatomy Toolbox 12). Regional volumes were correlated with the three traditional BIS-11 subscales, as well as IWR and RRS. BIS-11 total score was positively correlated with right inferior parietal, postcentral, and supramarginal grey matter (p < 0.05, FWE cluster-level corrected). Attentional impulsivity (IA) was also positively correlated with right inferior and superior parietal and supramarginal gyri. Comparison of the other scales did show some divergence, but most correlations did not survive correction for multiple comparisons. Our findings suggest that difference facets of trait impulsivity might be related to different brain areas, and might thus dissociate along distinct but overlapping neural networks. In contrast to lesion or patient studies, these analyses delineate physiological variance, and can thus help to conceptualise network models in the absence of pathology.
•Trait impulsivity is associated with brain structural variation in healthy subjects.•Conceptualisation of trait impulsivity impacts on regional associations.•Impulsive choice is related to inferior parietal and superior temporal cortices.•Credit author statement.
Psychotic disorders and schizophrenia-spectrum personality disorders (PD) with psychotic/psychotic-like symptoms are considerably linked both historically and phenomenologically. In particular with ...regard to schizotypal and schizotypal personality disorder (SPD), this is evidenced by their placement in a joint diagnostic category of non-affective psychoses in the InternationaI Classification of Diseases 10th Revision, (CD-10) and, half-heartedly, the fifth edition of Diagnostic and Statistical Manual of Mental Disorders, (DSM-5). Historically, this close link resulted from observations of peculiarities that resembled subthreshold features of psychosis in the (premorbid) personality of schizophrenia patients and their biological relatives. These personality organizations were therefore called "borderline (schizophrenia)" in the first half of the 20th century. In the 1970s, they were renamed to "schizotypal" and separated from psychotic disorders on axis-I and from other PD on axis-II, including modern borderline PD, in the DSM. The phenomenological and historical overlap, however, has led to the common assumption that the main difference between psychotic disorders and SPD in particular was mainly one of severity or trajectory, with SPD representing a latent form of schizophrenia and/or a precursor of psychosis. Thus, psychosis proneness and schizotypy are often assessed using SPD questionnaires. In this perspective-piece, we revisit these assumptions in light of recent evidence. We conclude that schizotypy, SPD (and other schizophrenia-spectrum PD) and psychotic disorder are not merely states of different severity on one common but on qualitatively different dimensions, with the negative dimension being predictive of SPD and the positive of psychosis. Consequently, in light of the merits of early diagnosis, the differential early detection of incipient psychosis and schizophrenia-spectrum PD should be guided by the assessment of different schizotypy dimensions.
Schizophrenia is a highly heritable, neuropsychiatric disorder characterized by episodic psychosis and altered cognitive function. Despite success in identifying genetic variants associated with ...schizophrenia, there remains uncertainty about the causal genes involved in disease pathogenesis and how their function is regulated.
We performed a multi-stage epigenome-wide association study, quantifying genome-wide patterns of DNA methylation in a total of 1714 individuals from three independent sample cohorts. We have identified multiple differentially methylated positions and regions consistently associated with schizophrenia across the three cohorts; these effects are independent of important confounders such as smoking. We also show that epigenetic variation at multiple loci across the genome contributes to the polygenic nature of schizophrenia. Finally, we show how DNA methylation quantitative trait loci in combination with Bayesian co-localization analyses can be used to annotate extended genomic regions nominated by studies of schizophrenia, and to identify potential regulatory variation causally involved in disease.
This study represents the first systematic integrated analysis of genetic and epigenetic variation in schizophrenia, introducing a methodological approach that can be used to inform epigenome-wide association study analyses of other complex traits and diseases. We demonstrate the utility of using a polygenic risk score to identify molecular variation associated with etiological variation, and of using DNA methylation quantitative trait loci to refine the functional and regulatory variation associated with schizophrenia risk variants. Finally, we present strong evidence for the co-localization of genetic associations for schizophrenia and differential DNA methylation.
•Seed-based amygdala functional connectivity is altered in borderline personality disorder (BPD).•Amygdala functional connectivity changes were not associated with overall symptoms.•Graph-theory ...based measures indicate cerebellar changes in BPD.
Borderline personality disorder (BPD) is characterised by structural and functional brain alterations. Yet, there is little data on functional connectivity (FC) across different levels of brain networks and parameters. In this study, we applied a multi-level approach to analyse abnormal functional connectivity. We analysed resting-state functional magnetic resonance imaging (fMRI) data sets of 69 subjects: 17 female BPD patients and 51 age-matched psychiatrically healthy female controls. fMRI was analysed using CONN toolbox including: a) seed-based FC analysis of amygdala connectivity, b) independent component analysis (ICA) based network analysis of intra- and inter-network FC of selected resting-state networks (DMN, SN, FPN), as well as c) graph-theory based measures of network-level characteristics. We show group-level seed FC differences with higher amygdala to contralateral (superior) occipital cortex connectivity in BPD, which correlated with schema-therapy derived measures of symptoms/traits across the entire cohort. While there was no significant group effect on DMN, SN, or FPN intra-network or inter-network FC, we show a significant group difference for local efficiency and cluster coefficient for a DMN-linked cerebellum cluster. Our findings demonstrate BPD-linked changes in FC across multiple levels of observation, which supports a multi-level analysis for future studies to consider different aspects of functional connectome alterations.
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