Dynamic causal modelling for schizophrenia

Abstract

Schizophrenia is a complex psychiatric disorder which leads to local abnormalities in brain activity. Functional Magnetic Resonance Imaging (fMRI) technology enables medical doctors to observe brain activity patterns that represent the execution of subject tasks, both physical and mental. In general, each subject exhibits his own activation pattern for a given task, whose intensity is affected by the physiology of the subject's brain, the usage of medications, and the parameters of the scanner used for image acquisition. Since it is possible to co-register the resulting activation map to a standard brain, all activation patterns from the different individuals can be analyzed in terms of consistency on the brain sections or brain coordinates where the activation is observed. The dynamic Causal Model using Bayesian networks (DBNs) extracts causal relationships from functional magnetic resonance imaging (fMRI) data applying HITON-PC, a local causal algorithm. Based on these relationships, a dynamic causal model is to be build that is used to classify patient data as belonging to healthy or ill subjects. Causal Explorer is a Matlab library of computational causal discovery and variable selection algorithms.