The objectives of this F31 award are to 1) facilitate the applicant's pursuit of advanced training in the clinical science of schizophrenia (SZ), cognitive neuroscience, and advanced neuroimaging and quantitative methods, and 2) identify neural mechanisms of cognitive impairment and cognitive recovery among first-episode SZ patients receiving a cognitive training intervention. SZ is a severe neuropsychiatric disorder that involves profound impairment in the cognitive domains of attention, learning, and memory. Cognitive function is strongly linked to social and occupational outcomes among individuals with SZ, and the period following a first episode of SZ is considered a critical window for cognitive intervention. Cognitive impairment in SZ is associated with disturbances in functional neural network dynamics, but consistent associations between specific network factors and individual domains of cognitive dysfunction have not been identified. Although cognitive training (CT) interventions have shown moderate efficacy in remediating cognitive dysfunction in SZ, improved understanding of neural mechanisms associated with CT may provide insight into ways of refining CT in order to improve its efficacy. Specifically, it remains to be seen whether beneficial effects of CT in SZ are attributable primarily to normalization of neural networks (i.e., reduction of pre-existing disturbances) or to an increase in patients' capacity for compensatory network engagement. In the proposed project, clinical and cognitive measures and resting-state fMRI data from first-episode SZ patients (N=42) and demographically-matched healthy comparison subjects (N=42) will be analyzed in order to 1) identify associations between pre- intervention disturbances in neural network organization and specific domains of cognitive impairment in first- episode SZ and 2) identify changes in neural network organization associated with cognitive improvement following CT so as to determine whether these changes represent normalization or compensation. Whereas most studies of functional neural networks in SZ have characterized networks broadly as over-connected or under-connected, the proposed project will quantify neural network connections and multiple distinct network properties by applying principles from graph theory, a system of techniques designed for analyzing the functional organization of complex networks. A graph-theoretic approach, combined with a focus on differentiating normalization from compensation, is expected to yield novel insights into neural mechanisms of cognitive impairment and recovery in SZ, paving the way for CT interventions that target neural mechanisms most conducive to cognitive recovery. In tandem with these research objectives, the applicant will pursue extensive coursework, mentorship, and clinical science training activities in order to develop as an independent researcher with expertise in applying clinical and cognitive neuroscience to the study of cognitive impairment and intervention in SZ.
The objective of the proposed F31 award is to facilitate the applicant's development of independent research skills and expertise in the clinical science of schizophrenia, cognitive neuroscience, and advanced neuroimaging and quantitative methods. Under the mentorship of expert co-sponsors and consultants, the applicant will apply graph-theory techniques to identify neural network dynamics relevant to cognitive function in first-episode schizophrenia, with a focus on distinguishing between normalizing and compensatory neural mechanisms of change following cognitive training. Through pursuit of these research aims and comprehensive training activities, the applicant will develop as an independent clinical scientist pursuing a program of research positioned to inform the development of effective interventions for improving outcomes among patients with schizophrenia.
