Neural Noise and Cognitive Control in Schizophrenia

University Of Georgia, Athens GA

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Abstract

DESCRIPTION (provided by applicant): People with schizophrenia have documented evidence of abnormal prefrontal cortex (PFC) mediated cognitive control. A group of healthy people exist who manifest neither signs nor symptoms of schizophrenia but who nevertheless show impaired cognitive control. In this healthy group, both task performance and brain activities are like those observed in people schizophrenia. Yet, schizophrenia is a unique disorder that must be caused by neural deviations differentiable from those solely associated with manifestations of poor PFC-mediated cognitive control. This project will demonstrate that nonspecific neural noise (as a function of cognitive control requirements) accentuates and characterizes the behavioral and neural manifestations of poor cognitive control in schizophrenia. Long-term Goals: This work will show that cognitive control abnormalities resulting in similar behavioral manifestations have different neural etiologies. It will link seemingly disparate behavioral and brain activity deviations for a unified theory of schizophrenia, thus providing a template for translational studies of the illness. Specific Aims: This project will address 4 critical issues for understanding associations between neural noise, cognitive control, regulation of sensory systems, and behavioral performance in schizophrenia: (i) excess (nonspecific) neural noise as a primary neurophysiological deviation; (ii) PFC activation and PFC-mediated modulation of sensory processing as a function of cognitive control requirements; (iii) regulation of sensory input to support visual attention and perceptual target detection as a function of stimulus density; and (iv) specificity of deviations in (i)-(iii) to schizophrenia. Methods: During visual tasks, eye movements will be recorded using EOG and neural activity data will be collected with combined MEG (143 channel) / EEG (64 sensor) equipment designed for simultaneous measurements. Combining EEG/MEG yields the best spatial accuracy and temporal certainty for solving source localization problems given their shared and unique sensitivities to brain activities because they act synergistically to improve source localizations. The advantages of combining imaging technologies like EEG/MEG to solve source localization problems has been known for some time but it is still an uncommon practice, although we have demonstrated our ability to perform combined EEG/MEG source localizations using saccade tasks (McDowell et al., 2005). This research will have important implications for understanding the neural bases of cognitive control problems inherent to schizophrenia and how those differ from comparison populations. We also aim to develop a more coherent understanding of the role of PFC problems in schizophrenia, which would guide translational research and inform strategies for developing rehabilitation and treatment techniques.