Disentangling age- and disease-related alterations in schizophrenia brain network using structural equation modeling: A graph theoretical study based on minimum spanning tree

Abstract

Functional brain networks have been shown to undergo fundamental changes associated with aging or schizophrenia. However, the mechanism of how these factors exert influences jointly or interactively on brain networks remains elusive. A unified recognition of connectomic alteration patterns was also hampered by heterogeneities in network construction and thresholding methods. Recently, an unbiased network representation method regardless of network thresholding, so called minimal spanning tree algorithm, has been applied to study the critical skeleton of the brain network. In this study, we aimed to use minimum spanning tree (MST) as an unbiased network reconstruction and employed structural equation modeling (SEM) to unravel intertwined relationships among multiple phenotypic and connectomic variables in schizophrenia. First, we examined global and local brain network properties in 40 healthy subjects and 40 schizophrenic patients aged 21-55 using resting-state functional magnetic resonance imaging (rs-fMRI). Global network alterations are measured by graph theoretical metrics of MSTs and a connectivity-transitivity two-dimensional approach was proposed to characterize nodal roles. We found that networks of schizophrenic patients exhibited a more star-like global structure compared to controls, indicating excessive integration, and a loss of regional transitivity in the dorsal frontal cortex (corrected p <.05). Regional analysis of MST network topology revealed that schizophrenia patients had more network hubs in frontal regions, which may be linked to the "overloading" hypothesis. Furthermore, using SEM, we found that the level of MST integration mediated the influence of age on negative symptom severity (indirect effect 95% CI [0.026, 0.449]). These findings highlighted an altered network skeleton in schizophrenia and suggested that aging-related enhancement of network integration may undermine functional specialization of distinct neural systems and result in aggravated schizophrenic symptoms.

Keywords: graph theory; mediation analysis; minimum spanning tree; resting-state FMRI; schizophrenia; structural equation modeling.

FIGURE 1

Overview of the analysis flow in the present study. First, we construct brain connectivity map from interdependencies between BOLD signals measured in resting state. Next, the minimum spanning tree algorithm was used to extract the critical skeleton of the brain network. We then studied global network properties reflected by MST metrics. In the meantime, we used the proposed connector‐hub classification scheme to analyze nodal roles. Finally, the global MST metrics were entered into the mediation model to test the hypothesis that the age‐behavior relationship was mediated by brain network structures

FIGURE 2

Different minimum spanning tree global organizations. On the top is the hypothesized normal structure which should be expected to be seen in healthy adults. The state is an intermediate configuration between two extremes. When nodes become increasingly segregated, the tree would transform into the line‐like structure in the lower‐left corner; on the other hand, the star‐like structure represent highly centralized network arrangement

FIGURE 3

Structural equation models. (a) The two models which could potentially depict interactions between age, network structure and behavior. The first mediation model to the left posits that aging would lead to higher leaf fraction, which represents higher level of integration and decreased segregation, and then induce behavior changes. The moderation model examines whether the relationship between age and network structure is influenced by schizophrenia. (b) Primary parameters of the significant indirect effect between age and negative symptom severity through leaf fraction (CI, credible interval)

FIGURE 4

Within‐group similarity matrix for both healthy and schizophrenia groups. Each element is the value of overlapping rate between two subjects

FIGURE 5

Between‐group differences of tree metrics. HC, healthy control, SZ, schizophrenia; “*” indicates significant difference (corrected p <.05 for 5,000 permutations), ** for p <.01

FIGURE 6

(a) Significantly reduced connector index in dorsal frontal cortex (corrected p = .03). (b) The degree distribution of group‐level MSTs. (c) Fitting plot of the two distribution to exponential distribution, power law distribution and exponential truncated power law distribution. HC, healthy control; SZ, schizophrenia. The fitting and graph were completed using GRETNA toolbox (J. Wang, Wang, Xia, Liao, & Evans, 2015)

FIGURE 7

Locations of network hubs and connectors in the brain. Nodes in green are hubs common to both groups; nodes in red are hubs/connectors specific to schizophrenia group; nodes in blue are hubs/connectors specific to healthy control group. Nodes in yellow are all remaining nodes that are not hubs nor connectors. HC, healthy control; SZ, schizophrenia. The graphs were generated using BrainNet Viewer toolbox (Xia, Wang, & He, 2013)