Connectivity of the anterior insula differentiates participants with first-episode schizophrenia spectrum disorders from controls: a machine-learning study.

BACKGROUND: Early diagnosis of schizophrenia could improve the outcomes and limit the negative effects of untreated illness. Although participants with schizophrenia show aberrant functional connectivity in brain networks, these between-group differences have a limited diagnostic utility. Novel methods of magnetic resonance imaging (MRI) analyses, such as machine learning (ML), may help bring neuroimaging from the bench to the bedside. Here, we used ML to differentiate participants with a first episode of schizophrenia-spectrum disorder (FES) from healthy controls based on resting-state functional connectivity (rsFC). METHOD: We acquired resting-state functional MRI data from 63 patients with FES who were individually matched by age and sex to 63 healthy controls. We applied linear kernel support vector machines (SVM) to rsFC within the default mode network, the salience network and the central executive network. RESULTS: The SVM applied to the rsFC within the salience network distinguished the FES from the control participants with an accuracy of 73.0% (p = 0.001), specificity of 71.4% and sensitivity of 74.6%. The classification accuracy was not significantly affected by medication dose, or by the presence of psychotic symptoms. The functional connectivity within the default mode or the central executive networks did not yield classification accuracies above chance level. CONCLUSIONS: Seed-based functional connectivity maps can be utilized for diagnostic classification, even early in the course of schizophrenia. The classification was probably based on trait rather than state markers, as symptoms or medications were not significantly associated with classification accuracy. Our results support the role of the anterior insula/salience network in the pathophysiology of FES.

White matter changes in first episode psychosis and their relation to the size of sample studied: a DTI study.

BACKGROUND: White matter abnormality has been recently proposed as a pathophysiological feature of schizophrenia (SZ). However, most of the data available has been gathered from chronic patients, and was therefore possibly confounded by factors such as duration of the disease, and treatment received. The extent and localization of these changes is also not clear. METHODS: We examined a population of early stage SZ patients using diffusion tensor imaging (DTI). 77 SZ patients and 60 healthy controls (HCs) were included in the analysis using Tract-Based Spatial Statistics (TBSS). We have also analyzed 250 randomly created subsets of the original cohort, to investigate the relation between the result of TBSS analysis, and the size of the sample studied. RESULTS: We have found a significant decrease in fractional anisotropy (FA) in the patient group. This change is present in most major white matter (WM) tracts including the corpus callosum, superior and inferior longitudinal fasciculi, inferior fronto-occipital fasciculus, and posterior thalamic radiation. Furthermore, we identified a clear trend towards an increase in the number and spatial extent of significant voxels reported, with an increasing number of subjects included in the analysis. CONCLUSION: Our study shows that FA is significantly decreased in patients at an early stage of schizophrenia, and that the extent of this finding is dependent on the size of studied sample; therefore underpowered studies might produce results with false spatial localization.