Self-Monitoring Performance Correlates with Resting-State Networks in Schizophrenia

The emergence of modern functional imaging techniques and their evaluation methods has given neuroscience deeper insight into the neuro-physiological components of Schizophrenia, specifically of the main symptoms experienced in schizophrenia, auditory hallucinations, and ego disturbances. A current hypothesis proposes that these symptoms may result from the inability to distinguish reafferent stimuli from external input arising from pathological dysconnectivity. In the presented study, we investigated resting-state networks (RSN) as indicators of schizophrenia. We combined resting-state fMRI and event-related fMRI (efMRI) data with task-bound motor-sensory self-monitoring performance and symptom ratings. Using independent component analysis (ICA) we identified seven predefined components: the anterior and posterior default mode network, three visual components, and the motor-sensory and executive function components - which we investigated at voxel- and whole-network level. Motor-sensory self-monitoring performance was expressed by the classic signal detection (SDT) parameters d’ and β. Our main findings indicate significant group differences between schizophrenic patients (SP) and healthy controls (HC) in the default mode network and visual components which were accompanied by significant group differences in signal detection. This study will explore and compare functional connectivity among brain regions in SP and HC at rest and while actively engaged with sensory-motor tasks.