Abstract

Network analysis of brain imaging data has provided important insights into the global configuration of functional brain networks. This opens up the possibility to study how neural dynamics, in health and disease, are constrained by the brain’s network architecture. In this talk I’ll be presenting an attempt to use this “dynamic network approach” to shed light on a widespread phenomenon observed in neuroimaging studies of patients with schizophrenia: the appearance of areas of increased activation relative to controls, as well as areas of decreased activations. These hyper-activations are usually interpreted as reflecting the involvement of compensatory neural mechanisms in an abnormal brain. However, little is known about these phenomena. We propose to test the idea of these hyper-activations being a response to these hypo-activations, thinking of them as dynamic phenomena that occur in a failing network, similar to a station closure in an underground network. Since compensatory activations in schizophrenia might be present only during cognitive effort and not during rest, I’ll be discussing how we are developing a network analysis of functional neuroimaging meta-data to address this.