Randomization and resilience of brain functional networks as systems-level endophenotypes of schizophrenia

Abstract

Schizophrenia is increasingly conceived as a disorder of brain network organization or dysconnectivity syndrome. Functional MRI (fMRI) networks in schizophrenia have been characterized by abnormally random topology. We tested the hypothesis that network randomization is an endophenotype of schizophrenia and therefore evident also in nonpsychotic relatives of patients. Head movement-corrected, resting-state fMRI data were acquired from 25 patients with schizophrenia, 25 first-degree relatives of patients, and 29 healthy volunteers. Graphs were used to model functional connectivity as a set of edges between regional nodes. We estimated the topological efficiency, clustering, degree distribution, resilience, and connection distance (in millimeters) of each functional network. The schizophrenic group demonstrated significant randomization of global network metrics (reduced clustering, greater efficiency), a shift in the degree distribution to a more homogeneous form (fewer hubs), a shift in the distance distribution (proportionally more long-distance edges), and greater resilience to targeted attack on network hubs. The networks of the relatives also demonstrated abnormal randomization and resilience compared with healthy volunteers, but they were typically less topologically abnormal than the patients' networks and did not have abnormal connection distances. We conclude that schizophrenia is associated with replicable and convergent evidence for functional network randomization, and a similar topological profile was evident also in nonpsychotic relatives, suggesting that this is a systems-level endophenotype or marker of familial risk. We speculate that the greater resilience of brain networks may confer some fitness advantages on nonpsychotic relatives that could explain persistence of this endophenotype in the population.psychosis | dysconnectivity | graph theory | brain network | hubs S chizophrenia is increasingly conceived as a brain dysconnectivity syndrome or disorder of brain network organization (1-4). Various methods have been used to demonstrate abnormal structural or functional connectivity between brain regions in patients with schizophrenia. Specifically, several recent studies have used graph theory to measure the topological pattern of connections (or edges) between regional nodes in large-scale networks derived from neuroimaging data (5-12).The results to date of graph theoretical studies of schizophrenia are not entirely consistent, but there is some convergence around the concept of topological randomization (9, 13). For example, human brain networks (and many other complex, real-life networks) generally have a small-world topology that can be understood as intermediate between the regular, highly clustered organization of a lattice and the globally efficient organization of a random graph. Three independent functional MRI (fMRI) studies have shown that the functional brain networks of patients with schizophrenia are relatively shifted toward the random end of this small-world spectrum, i.e., the...

Reduced specialized processing in psychotic disorder: a graph theoretical analysis of cerebral functional connectivity

“…A recent graph theoretical network study of patients with schizophrenia and first‐degree relatives of patients did reveal a reduced clustering coefficient in both groups with respect to controls (Lo et al. 2015). In comparison with this study, a smaller sample size ( n  = 79) was used, as well as absolute wavelet correlation matrices to construct binary undirected graphs.…”

“…In addition, a graph theoretical network study of patients with schizophrenia and first‐degree relatives of patients has shown similar functional network randomization between these two groups, suggesting that this represents a marker of familial risk (Lo et al. 2015). The thus far more frequently used traditional methods (seed‐based correlation, ICA) consistently showed that unaffected siblings/first‐degree relatives share functional connectivity network alterations with their affected siblings (e.g., Whitfield‐Gabrieli and Ford 2012; Fornito et al.…”

Resilience research in schizophrenia

“…Finally, two graph theoretical studies (i.e. studies modeling topological patterns of connections in complex networks using nodes and lines) which analysed resting-state fMRI data referred to resilience from the perspective of functional brain networks [8,9]. Lo et al [8] reported an abnormal shift to greater randomization of brain network topology in patients with schizophrenia and their first-degree relatives compared to healthy volunteers, suggesting a marker of genetic or shared environmental risk for disorder.…”

“…studies modeling topological patterns of connections in complex networks using nodes and lines) which analysed resting-state fMRI data referred to resilience from the perspective of functional brain networks [8,9]. Lo et al [8] reported an abnormal shift to greater randomization of brain network topology in patients with schizophrenia and their first-degree relatives compared to healthy volunteers, suggesting a marker of genetic or shared environmental risk for disorder. These findings were associated with greater resilience to targeted attacks in silico, which was assessed as the simulated global efficiency of the remaining network after removing higher-degree nodes, implying a survival advantage of the integrity of brain networks with respect to real-life gray matter volume deficits [8].…”

Functional network study on the wild type and DISC1 transgenic mice

“…Furthermore, graph theory has provided abundant information on the brain's functional organization in both normal and abnormal states (Park and Friston, 2013;Fornito et al, 2016). Accumulative evidence of graph analysis on human rs-fMRI research points to its potential to identify endophenotypes of brain disorders (Bullmore and Sporns, 2009a;Wang et al, 2010b;Lo et al, 2015).…”