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.

Exploring global and local processes underlying alterations in resting-state functional connectivity and dynamics in schizophrenia

“…This sample has previously been used by our group to explore structural deficits in patients with ScZ [Dimulescu et al. ( 25 )]. From the sample of 43 patients and 43 healthy control participants, we excluded 5 patients due to missing resting-state functional MRI (rs-fMRI) data or artefacts/excessive motion identified during the pre-processing.…”

“…Computational models of large-scale brain circuits can be used to investigate dynamical circuit mechanisms linking local ScZassociated alterations to global changes in the functional organisation of the brain. Leveraging such computational models, studies have shown that decreases in global inter-regional connectivity strengths can lead to wide-spread functional disruptions [Cabral et al (21)], increased global signal variance [Yang et al (22)] and altered topological characteristics of functional brain networks (Cabral et al (23,24) resembling ScZ. However, except for Yang et al (22), these studies only investigated a global scaling of the inter-regional connectivity.…”

Alterations in Resting-State Functional Connectivity and Dynamics in Schizophrenia as a Result of Global not Local Processes

“…The study sample was collected through the Center for Biomedical Research Excellence (COBRE) led by Dr. Vince Calhoun (more information here: http://fcon1000.projects.nitrc.org/indi/retro/cobre.html ) and obtained from the SchizConnect database ( http://schizconnect.org ). This sample has previously been used by our group to explore structural deficits in patients with ScZ (Dimulescu et al (2021)). From the sample of 43 patients and 43 healthy control participants, we excluded 5 patients due to missing resting-state functional MRI (rs-fMRI) data or artefacts/excessive motion identified during the pre-processing.…”

Basins of attraction of chimera states on networks

“…To characterize the emerged states in brain dynamics, we focus on synchronized patterns on brain systems, where regions are divided into different functional regions with similar cognitive processes. For this network, each region is assigned to one of eight cognitive systems ( Yeo et al, 2011 ), including somatomotor (Som), default mode network (DMF), control (Con), dorsal attention (DA), limbic (Lim), visual (Vis), ventral attention (VA), other (Oth, subcortical regions could not be assigned to any system) ( Dimulescu et al, 2021 ). Figure 7 exhibits the assignment of 94 AAL2 brain regions within eight cognitive systems.…”

“…To investigate how chimera states in brain are influenced by stimulation of various brain regions, we integrate WCOs on brain networks and stimulate single region with three global coupling strengths. Results show the existing of three different states, i.e., the coherent, chimera, and metastable state (Yeo et al, 2011;Dimulescu et al, 2021), and suggest that the Kuramoto order parameter is weakly and negatively correlated with the chimera index. Besides, higher degree nodes have a more centralized and compact distribution of the ranked order parameter compared to lower ones.…”