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. 2015 Nov;41(6):1326-35.
doi: 10.1093/schbul/sbv060. Epub 2015 May 4.

Disintegration of Sensorimotor Brain Networks in Schizophrenia

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Free PMC article

Disintegration of Sensorimotor Brain Networks in Schizophrenia

Tobias Kaufmann et al. Schizophr Bull. .
Free PMC article

Abstract

Background: Schizophrenia is a severe mental disorder associated with derogated function across various domains, including perception, language, motor, emotional, and social behavior. Due to its complex symptomatology, schizophrenia is often regarded a disorder of cognitive processes. Yet due to the frequent involvement of sensory and perceptual symptoms, it has been hypothesized that functional disintegration between sensory and cognitive processes mediates the heterogeneous and comprehensive schizophrenia symptomatology.

Methods: Here, using resting-state functional magnetic resonance imaging in 71 patients and 196 healthy controls, we characterized the standard deviation in BOLD (blood-oxygen-level-dependent) signal amplitude and the functional connectivity across a range of functional brain networks. We investigated connectivity on the edge and node level using network modeling based on independent component analysis and utilized the brain network features in cross-validated classification procedures.

Results: Both amplitude and connectivity were significantly altered in patients, largely involving sensory networks. Reduced standard deviation in amplitude was observed in a range of visual, sensorimotor, and auditory nodes in patients. The strongest differences in connectivity implicated within-sensorimotor and sensorimotor-thalamic connections. Furthermore, sensory nodes displayed widespread alterations in the connectivity with higher-order nodes. We demonstrated robustness of effects across subjects by significantly classifying diagnostic group on the individual level based on cross-validated multivariate connectivity features.

Conclusion: Taken together, the findings support the hypothesis of disintegrated sensory and cognitive processes in schizophrenia, and the foci of effects emphasize that targeting the sensory and perceptual domains may be key to enhance our understanding of schizophrenia pathophysiology.

Keywords: functional imaging; machine learning; resting state; schizophrenia.

Figures

Fig. 1.
Fig. 1.
Node- and edgewise differences between patients and healthy controls. (A) Data-driven clustering of independent components based on temporal correlations. (B) 47 independent components (model order 80; 33 noise components removed from the analysis). For a more detailed overview of components, see supplementary table 2 and supplementary figure 1. (C) Edgewise comparison of functional connectivity. Edges that show a significant effect of diagnosis are depicted as colored squares. The color represents partial eta-squared effect sizes that were accompanied with a sign to indicate the direction of the effect (toward blue: reduced connectivity in patients; toward red: increased connectivity in patients). All black squares are nonsignificant. White dots indicate effects at a nominal alpha level. To account for multiple comparisons, the lower half of the matrix displays only those edges that were significant at false discovery rate (FDR) level (21↓ + 11↑ significant edges with P < .0015). The upper half applies a Bonferroni correction (4↓ + 1↑ significant edges with P < .00005). (D) Absolute number of edge effects per node at FDR corrected alpha level (computed from panel C). The background color reflects the cluster a node belongs to (see panel A).
Fig. 2.
Fig. 2.
Classification based on the 46 edges of each single node. The red line depicts obtained Matthews correlation coefficient (MCC). The solid black line indicates chance level (MCC = 0) and the dashed black line indicates MCC level achieved with full set of features (all 1081 edges). The color in the background of each node indicates the level of significance obtained from 10000 permutation tests per node. For details, see supplementary table 6.
Fig. 3.
Fig. 3.
Comparison of mean standard deviation in signal amplitude. The red and black lines depict mean SD of time series within patients and controls, respectively. The color in the background of each node indicates the level of significance of an analysis of covariance testing for differences between schizophrenia and healthy controls while accounting for age and gender.

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