Structural Differences Between Healthy Subjects and Patients With Schizophrenia or Schizoaffective Disorder: A Graph and Control Theoretical Perspective

Abstract

The coordinated dynamic interactions of large-scale brain circuits and networks have been associated with cognitive functions and behavior. Recent advances in network neuroscience have suggested that the anatomical organization of such networks puts fundamental constraints on the dynamical landscape of brain activity, i.e., the different states, or patterns of regional activation, and transition between states the brain can display. Specifically, it has been shown that densely connected, central regions control the transition between states that are "easily" reachable (in terms of expended energy), whereas weakly connected areas control transitions to states that are hard-to-reach. Changes in large-scale brain activity have been hypothesized to underlie many neurological and psychiatric disorders. Evidence has emerged that large-scale dysconnectivity might play a crucial role in the pathophysiology of schizophrenia, especially regarding cognitive symptoms. Therefore, an analysis of graph and control theoretic measures of large-scale brain connectivity in patients offers to give insight into the emergence of cognitive disturbances in the disorder. To investigate these potential differences between patients with schizophrenia (SCZ), patients with schizoaffective disorder (SCZaff) and matched healthy controls (HC), we used structural MRI data to assess the microstructural organization of white matter. We first calculate seven graph measures of integration, segregation, centrality and resilience and test for group differences. Second, we extend our analysis beyond these traditional measures and employ a simplified noise-free linear discrete-time and time-invariant network model to calculate two complementary measures of controllability. Average controllability, which identifies brain areas that can guide brain activity into different, easily reachable states with little input energy and modal controllability, which characterizes regions that can push the brain into difficult-to-reach states, i.e., states that require substantial input energy. We identified differences in standard network and controllability measures for both patient groups compared to HCs. We found a strong reduction of betweenness centrality for both patient groups and a strong reduction in average controllability for the SCZ group again in comparison to the HC group. Our findings of network level deficits might help to explain the many cognitive deficits associated with these disorders.

Keywords: connectome; control theory; dysconnectivity; graph theory; schizophrenia.

Figure 1

Hedge's g for the comparisons of various graph measures for the SCZ and SCZaff groups against the HC group are shown in the above Cumming estimation plot. The raw data is plotted on the upper axes. On the lower axes, mean differences are plotted as bootstrap sampling distributions. Each mean difference is depicted as a dot. Each 95% confidence interval is indicated by the ends of the vertical error bars [see (30)]. Panels show (A) characteristic path length (measure of integration), (B) cluster coefficient (measure of segregation), (C) betweenness centrality (measure of centrality), and (D) closeness centrality (measure of centrality). *Denotes statistically significant differences at a significance level of 0.05 Bonferroni corrected.

Figure 2

Hedge's g for the comparisons of various graph measures for the SCZ and SCZaff groups against the HC group are shown in the above Cumming estimation plot. The raw data is plotted on the upper axes. On the lower axes, mean differences are plotted as bootstrap sampling distributions. Each mean difference is depicted as a dot. Each 95% confidence interval is indicated by the ends of the vertical error bars [see (30)]. Panels show (A) transitivity (measure of segregation), (B) global efficiency (measure of integration), and (C) assortativity (measure of resilience).

Figure 3

Hedge's g for the comparisons of (A) average and (B) modal controllability of weighted graphs of the SCZ and SCZaff groups against the HC group are shown in the above Cumming estimation plot. The raw data is plotted on the upper axes. On the lower axes, Hedge's g is plotted as bootstrap sampling distributions. Each g is depicted as a dot. Each 95% confidence interval is indicated by the ends of the vertical error bars [see (30)]. *Denotes statistically significant differences at a significance level of 0.05 Bonferroni corrected.

Figure 4

Location of cognitive control hubs for controllability across large-scale cognitive systems in the HC group [average controllability (A) and modal controllability (B)], the SCZ group [average controllability (C) and modal controllability (D)] and the SCZaff group [average controllability (E) and modal controllability (F)]. We chose the top 15 regions with highest average and modal controllability (averaged over all subjects), respectively.