Central and non-central networks, cognition, clinical symptoms, and polygenic risk scores in schizophrenia

Abstract

Schizophrenia is a complex disorder that may be the result of aberrant connections between specific brain regions rather than focal brain abnormalities. Here, we investigate the relationships between brain structural connectivity as described by network analysis, intelligence, symptoms, and polygenic risk scores (PGRS) for schizophrenia in a group of patients with schizophrenia and a group of healthy controls. Recently, researchers have shown an interest in the role of high centrality networks in the disorder. However, the importance of non-central networks still remains unclear. Thus, we specifically examined network-averaged fractional anisotropy (mean edge weight) in central and non-central subnetworks. Connections with the highest betweenness centrality within the average network (>75% of centrality values) were selected to represent the central subnetwork. The remaining connections were assigned to the non-central subnetwork. Additionally, we calculated graph theory measures from the average network (connections that occur in at least 2/3 of participants). Density, strength, global efficiency, and clustering coefficient were significantly lower in patients compared with healthy controls for the average network (p_FDR < 0.05). All metrics across networks were significantly associated with intelligence (p_FDR < 0.05). There was a tendency towards significance for a correlation between intelligence and PGRS for schizophrenia (r = -0.508, p = 0.052) that was significantly mediated by central and non-central mean edge weight and every graph metric from the average network. These results are consistent with the hypothesis that intelligence deficits are associated with a genetic risk for schizophrenia, which is mediated via the disruption of distributed brain networks. Hum Brain Mapp 38:5919-5930, 2017. © 2017 Wiley Periodicals, Inc.

Keywords: connectivity; diffusion tensor MRI; genetics; intelligence; schizophrenia; symptoms.

Figure 1

Medium view of (A) central (>75% of centrality values) and (B) non‐central subnetworks for all participants indicating node location and edge (FA) strength. The nodes which are connected by edges with the highest weights (FA > 0.5) in the central subnetwork are brainstem, left hemisphere precuneus cortex, thalamus, caudate, ventral diencephalon and superior frontal gyrus, bilateral caudal anterior division of the cingulate cortex, and isthmus division of the cingulate gyrus. Nodes are color‐coded to indicate the lobe in which they are situated. [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 2

(A) Schematic representation of relationships where an independent variable (X) and an outcome (Y) are hypothesized to be explained by a mediator (M). The direct effect of X on M is a, the effect of M on Y is b, and the effect of X on Y is c. c′ denotes the effect of X on Y when M is taking into account in the model. (B) Representation of the variables analyzed in this study, where X = polygenic risk score for schizophrenia (PGRS at P ≤ 0.5), Y = IQ and M = mean edge weight (central). (C) X = polygenic risk score for schizophrenia (PGRS at P ≤ 0.5), Y = IQ and M = mean edge weight (non‐central). Asterisks represent statistically significant partial correlations.