Computational Models of Dysconnectivity in Large-Scale Resting-State Networks

Abstract Brain activity, on every scale, spontaneously fluctuates, thereby exhibiting complex, dynamic interactions that manifest rich synchronization patterns. In the past decade, many studies have advanced our understanding of the mechanisms behind the dynamic interactions within the brain through the basis of its structural and functional connectivity (FC) structures. Moreover, there is a tremendous effort to unveil the role that these interactions play in psychiatric disorders. Specifically, neuroimaging techniques such as functional magnetic resonance imaging have provided robust evidence regarding large-scale coordinated fluctuations in the brain at rest (i.e., the subject is idled by her own pace, with no explicit physical or mental task being given). Within a short time, scientists around the world have reported complex spatiotemporal connectivity structures (i.e., resting-state networks; RSNs) appearing in resting-state neuroimaging recordings. In parallel, many studies have shown the clinical significance of RSNs in psychiatric disorders. However, the connectivity patterns observed through the neuroimaging modalities are highly complex and interdependent. This makes the interpretation of the results nontrivial; hence the current approaches fail to provide a mechanistic understanding of brain function. Computational modeling studies first addressed the link between anatomical and FC. The success of these models has inspired the computational neuroscience community to develop adequate models that can capture empirically observed dynamic interactions in the brain. Despite being its infancy, these models are key to tackle the complexity of the brain dynamics and to unveil the hidden mechanisms. In this chapter, we provide an overview of how computational modeling approaches might help us to understand brain dynamics at the macroscopic scale.