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Multicenter Study
. 2017 Jul 1;43(4):914-924.
doi: 10.1093/schbul/sbw145.

Consistent Functional Connectivity Alterations in Schizophrenia Spectrum Disorder: A Multisite Study

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Free PMC article
Multicenter Study

Consistent Functional Connectivity Alterations in Schizophrenia Spectrum Disorder: A Multisite Study

Kristina C Skåtun et al. Schizophr Bull. .
Free PMC article

Abstract

Schizophrenia (SZ) is a severe mental illness with high heritability and complex etiology. Mounting evidence from neuroimaging has implicated disrupted brain network connectivity in the pathophysiology. However, previous findings are inconsistent, likely due to a combination of methodological and clinical variability and relatively small sample sizes. Few studies have used a data-driven approach for characterizing pathological interactions between regions in the whole brain and evaluated the generalizability across independent samples. To overcome this issue, we collected resting-state functional magnetic resonance imaging data from 3 independent samples (1 from Norway and 2 from Sweden) consisting of 182 persons with a SZ spectrum diagnosis and 348 healthy controls. We used a whole-brain data-driven definition of network nodes and regularized partial correlations to evaluate and compare putatively direct brain network node interactions between groups. The clinical utility of the functional connectivity features and the generalizability of effects across samples were evaluated by training and testing multivariate classifiers in the independent samples using machine learning. Univariate analyses revealed 14 network edges with consistent reductions in functional connectivity encompassing frontal, somatomotor, visual, auditory, and subcortical brain nodes in patients with SZ. We found a high overall accuracy in classifying patients and controls (up to 80%) using independent training and test samples, strongly supporting the generalizability of connectivity alterations across different scanners and heterogeneous samples. Overall, our findings demonstrate robust reductions in functional connectivity in SZ spectrum disorders, indicating disrupted information flow in sensory, subcortical, and frontal brain regions.

Keywords: brain networks; independent component analysis; machine learning; psychosis; resting-state fMRI.

Figures

Fig. 1.
Fig. 1.
Effect of group on functional connectivity. Colored squares depict edges with a significant effect of group, represented as F-values. Upper triangle shows Bonferroni corrected edges (P < .00003), while lower triangle show FDR-corrected edges (P < .0021). Cluster colors broadly represents parietal (dark blue), frontoparietal/cingulum (blue), subcortical (yellow), somatomotor/auditory (light green), default mode (red), frontotemporal (pink), and visual/occipital components (green).
Fig. 2.
Fig. 2.
(A) Correlations and their location between the 14 edges showing an effect of group. (B) Spider plot displaying the F-values for the group differences for each significant edge for the different samples. (C) Plots displaying the mean and standard error for all samples.
Fig. 3.
Fig. 3.
Functional connectivity based classification of patients and controls, where the classifier is trained on one group and tested on data from another group. HC, healthy controls; SZ, schizophrenia.

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