Structure and dynamics of functional networks in child-onset schizophrenia

• Cortical networks in healthy and child-onset schizophrenic subjects differ significantly in only a small number of large-scale network properties.
• Classification of networks of healthy and schizophrenic subjects yields sensitivity of 90% and specificity of 74%.
• Our analysis allows reliable automatic diagnostics in patients with child-onset schizophrenia based on cortical network properties and data mining methods.

ObjectiveSchizophrenia is a neuropsychiatric disorder characterized by cognitive and emotional deficits and associated with various abnormalities in the organization of neural circuits. It is currently unclear how and to which extend the global network organization is changed due to such disorder. In this work, we analyzed cortical networks of healthy subjects and patients with child-onset schizophrenia to address this issue.MethodsWe performed a comparison of cortical networks extracted from functional MRI data of patients with schizophrenia and healthy subjects considering their topological and dynamical properties.ResultsAmong 54 network measures tested, only four contributed substantially to a discrimination between the classes of healthy and schizophrenic subjects, with a sensitivity of 90% and specificity of 74%. However, such classes of networks did not differ significantly with respect to the level of network resilience and synchronization.ConclusionsSchizophrenic subjects have cortical regions with higher variance of network centrality, but less modular structure.SignificanceOur findings suggest that it is possible to establish data analysis routines that allow automatic diagnosis of a multifaceted disease like child-onset schizophrenia based on fMRI data of individual subjects and extracted network properties.