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

Cristiana Dimulescu,Klaus Obermayer,Serdar Gareayaghi,Christoph Metzner,Sophie Fromm,Fabian Kamp

doi:10.3389/fpsyt.2021.669783

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.

Highlights

Ample evidence has emerged that dysconnectivity, i.e., networklevel abnormalities in the connectivity between brain regions, might play a central role in the pathophysiology of schizophrenia [1,2,3,4]
Since the affective symptom components of patients with schizoaffective disorder are more similar to patients with bipolar disorder than to patients with schizophrenia and since difference in the central hub structure between patients with schizophrenia and bipolar disorder have been identified [17], we further hypothesized that patients with schizoaffective disorder and patients with schizophrenia might show differences in network and control measures, especially in centrality measures
Psychiatric disorders have been associated with disturbances in these dynamical interactions between brain regions

Summary

Introduction

Ample evidence has emerged that dysconnectivity, i.e., networklevel abnormalities in the connectivity between brain regions, might play a central role in the pathophysiology of schizophrenia [1,2,3,4]. Moving beyond a graph theoretical perspective, Gu et al [10] employed a controltheoretic framework to gain a deeper understanding of the dynamic interactions between large-scale brain networks and their relation to cognitive abilities This framework sees the brain as a dynamic network that shows a repertoire of potential brain states, where a brain state can be viewed as a distinct spatio-temporal pattern of brain activity, that is continually revisited against a background of noisy neural activity [11, 12]. The results of Gu et al [10] highlight that changes to large-scale brain dynamics can, at least to some degree, be predicted from controllability measures calculated from the structural, anatomical connectivity alone. Since the affective symptom components of patients with schizoaffective disorder are more similar to patients with bipolar disorder than to patients with schizophrenia and since difference in the central hub structure between patients with schizophrenia and bipolar disorder have been identified [17], we further hypothesized that patients with schizoaffective disorder and patients with schizophrenia might show differences in network and control measures, especially in centrality measures

Methods

Results

Conclusion