Negative symptoms and longitudinal grey matter tissue loss in adolescents at risk of psychosis: preliminary findings from a 6-year follow-up study.

BACKGROUND: Negative symptoms are perhaps the most disabling feature of schizophrenia. Their pathogenesis remains poorly understood and it has been difficult to assess their development over time with imaging techniques. AIMS: To examine, using tensor-based structural imaging techniques, whether there are regions of progressive grey matter volume change associated with the development of negative symptoms. METHOD: A total of 43 adolescents at risk of psychosis were examined using magnetic resonance imaging and whole brain tensor-based morphometry at two time points, 6 years apart. RESULTS: When comparing the individuals with significant negative symptoms with the remaining participants, we identified five regions of significant grey matter tissue loss over the 6-year period. These regions included the left temporal lobe, the left cerebellum, the left posterior cingulate and the left inferior parietal sulcus. CONCLUSIONS: Negative symptoms are associated with longitudinal grey matter tissue loss. The regions identified include areas associated with psychotic symptoms more generally but also include regions uniquely associated with negative symptoms.

The application of nonlinear Dynamic Causal Modelling for fMRI in subjects at high genetic risk of schizophrenia.

Nonlinear Dynamic Causal Modelling (DCM) for fMRI provides computational modelling of gating mechanisms at the neuronal population level. It allows for estimations of connection strengths with nonlinear modulation within task-dependent networks. This paper presents an application of nonlinear DCM in subjects at high familial risk of schizophrenia performing the Hayling Sentence Completion Task (HSCT). We analysed scans of 19 healthy controls and 46 subjects at high familial risk of schizophrenia, which included 26 high risk subjects without psychotic symptoms and 20 subjects with psychotic symptoms. The activity-dependent network consists of the intra parietal cortex (IPS), inferior frontal gyrus (IFG), middle temporal gyrus (MTG), anterior cingulate cortex (ACC) and the mediodorsal (MD) thalamus. The connections between the MD thalamus and the IFG were gated by the MD thalamus. We used DCM to investigate altered connection strength of these connections. Bayesian Model Selection (BMS) at the group and family level was used to compare the optimal bilinear and nonlinear models. Bayesian Model Averaging (BMA) was used to assess the connection strengths with the gating from the MD thalamus and the IFG. The nonlinear models provided the better explanation of the data. Furthermore, the BMA analysis showed significantly lower connection strength of the thalamocortical connection with nonlinear modulation from the MD thalamus in high risk subjects with psychotic symptoms and those who subsequently developed schizophrenia. These findings demonstrate that nonlinear DCM provides a method to investigate altered connectivity at the level of neural circuits. The reduced connection strength with thalamic gating may be a neurobiomarker implicated in the development of psychotic symptoms. This study suggests that nonlinear DCM could lead to new insights into functional and effective dysconnection at the network level in subjects at high familial risk.

The effects of DISC1 risk variants on brain activation in controls, patients with bipolar disorder and patients with schizophrenia.

Three risk variants (rs1538979, rs821577, and rs821633) in the Disrupted-in-Schizophrenia-1 (DISC1) gene have previously been associated with both schizophrenia and bipolar disorder in a recent collaborative analysis of European cohorts. In this study we examined the effects of these risk variants on brain activation during functional magnetic resonance imaging (fMRI) of the Hayling Sentence Completion Task (HSCT) in healthy volunteers (n=33), patients with schizophrenia (n=20) and patients with bipolar disorder (n=36). In the healthy controls the risk associated allele carriers of SNPs rs1538979 and rs821633 demonstrated decreased activation of the cuneus. Moreover, there was an effect of SNP rs1538979 in the pre/postcentral gyrus with decreased activation in healthy controls and increased activation in patients with schizophrenia. In the bipolar group there was decreased activation in the risk carriers of SNP rs821633 in the inferior parietal lobule and left cingulate cortex. Clusters in the precentral gyrus, left middle temporal gyrus and left cerebellum were found to be significant on examining the group × genotype interactions. These findings may provide a better understanding of the neural effects of DISC1 variants and on the pathophysiology of schizophrenia and bipolar disorder.

Computational neuropsychiatry - schizophrenia as a cognitive brain network disorder.

Computational modeling of functional brain networks in fMRI data has advanced the understanding of higher cognitive function. It is hypothesized that functional networks mediating higher cognitive processes are disrupted in people with schizophrenia. In this article, we review studies that applied measures of functional and effective connectivity to fMRI data during cognitive tasks, in particular working memory fMRI studies. We provide a conceptual summary of the main findings in fMRI data and their relationship with neurotransmitter systems, which are known to be altered in individuals with schizophrenia. We consider possible developments in computational neuropsychiatry, which are likely to further our understanding of how key functional networks are altered in schizophrenia.

Towards the identification of imaging biomarkers in schizophrenia, using multivariate pattern classification at a single-subject level.

Standard univariate analyses of brain imaging data have revealed a host of structural and functional brain alterations in schizophrenia. However, these analyses typically involve examining each voxel separately and making inferences at group-level, thus limiting clinical translation of their findings. Taking into account the fact that brain alterations in schizophrenia expand over a widely distributed network of brain regions, univariate analysis methods may not be the most suited choice for imaging data analysis. To address these limitations, the neuroimaging community has turned to machine learning methods both because of their ability to examine voxels jointly and their potential for making inferences at a single-subject level. This article provides a critical overview of the current and foreseeable applications of machine learning, in identifying imaging-based biomarkers that could be used for the diagnosis, early detection and treatment response of schizophrenia, and could, thus, be of high clinical relevance. We discuss promising future research directions and the main difficulties facing machine learning researchers as far as their potential translation into clinical practice is concerned.

Human brain imaging studies of DISC1 in schizophrenia, bipolar disorder and depression: a systematic review.

Disrupted-in-Schizophrenia 1 (DISC1) is a well researched candidate gene for schizophrenia and affective disorders with a range of functions relating to neurodevelopment. Several human brain imaging studies investigating correlations between common and rare variants in DISC1 and brain structure and function have shown conflicting results. A meta-analysis of case/control data showed no association between schizophrenia and any common SNP in DISC1. Therefore it is timely to review the literature to plan the direction of future studies. Twenty-two human brain imaging studies have examined the influence of DISC1 variants in health, schizophrenia, bipolar disorder or depression. The most studied common SNPs are Ser704Cys (rs821616) and Leu607Phe (rs6675281). Some imaging-genomic studies report effects on frontal, temporal and hippocampal structural indices in health and illness and a volumetric longitudinal study supports a putative role for these common SNPs in neurodevelopment. Callosal agenesis is described in association with rare deletions at 1q42 which include DISC1 and rare sequence variants at DISC1 itself. DISC1 interactions with translin-associated factor X (TRAX) and neuregulin have been shown to influence several regional volumes. In the first study involving neonates, a role for Ser704Cys (rs821616) has been highlighted in prenatal brain development with large clusters of reduced grey matter reported in the frontal lobes. Functional MRI studies examining associations between Ser704Cys (rs821616) and Leu607Phe (rs6675281) with prefrontal and hippocampal activation have also given inconsistent results. Prefrontal function was reported to be associated with interaction between DISC1 and CITRON (CIT) in health. Preliminary magnetic resonance spectroscopy and diffusion tensor data support the influence of Ser704Cys (rs821616) status on grey and white matter integrity. The glutamate system remains uninvestigated. Associations between rare sequence variants and structural changes in brain regions including the corpus callosum and effects of gene-gene interactions on brain structure and function are promising areas for future study.