Data-Driven Approaches to Intrinsic Connectivity Networks in the Brain
These researchers are addressing the challenge of finding brain-symptom relations in psychiatric disorders by using a data-driven approach to resolve the neural foundations of heterogeneity within the cognitive processes, symptoms, and community functioning of individuals afflicted by severe mental illness (SMI). Specifically, they use frequent pattern mining, a set of algorithms that have unique power for identifying complex non-linear patterns. To build a nosological structure from the properties of neural systems, the researchers apply frequent pattern-mining algorithms to empirically derived, stable brain connectivity networks, known as intrinsic connectivity networks (ICNs) from magnetic resonance imaging (MRI).
The first step in this project is to establish the validity and utility of fundamental elements of brain circuitry for understanding individual differences in cognition, symptom expression and functioning in SMI using archival data on healthy and SMI samples. Next, the researchers will test the relationships among empirically derived ICNs psychiatric symptoms constructs. The third step will build an explanatory model of symptoms and functioning from their relationships to behavioral measures and empirically derived spatial and temporal brain connectivity networks. This will allow the researchers to test the hypothesis that ICNs have both a direct effect and an effect mediated through behavioral tasks on symptoms and functioning in SMI.
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