Development of imaging genetics structural equation modeling for examining gene-brain-behavioural/cognitive relationships

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Researchers in statistical science have increasingly been able to access both genetic and neuroimaging data collected from the same individuals, thus enabling to statistically investigate genetic influence on the variation of brain function which is in turn associated with behavioral/cognitive variation. This emerging field, called statistical imaging genetics, aims to develop and apply statistical methods for integrating genetic and neuroimaging data with behavioral or cognitive outcomes to identify the neuromechanisms linked to individual differences in psychiatric disorders or cognitive tasks. Despite its great potential for studying disease- or task-specific gene-brain-behavior/cognition relationships, the field is faced with an ever-increasing need for statistical tools to examine such relationships in a more unified and confirmatory manner, while taking into account biological complexities (e.g., gene-gene interactions, genetic networks, etc.) and methodological challenges (e.g., high dimensionality, multicollinearity, etc.) embedded in genetic and neuroimaging data. Thus, the long-term objective of the proposed program is to develop a general statistical approach to investigating associations among genetic, brain, and behavioral/cognitive variables in a biologically meaningful and confirmatory manner. This program has two short-term objectives. First, it will develop a novel statistical methodology, named Imaging Genetics Structural Equation Modeling (IGSEM), as a flexible and unified tool that enables researchers to specify various biologically plausible gene-brain-behavior/cognition relationships and examine the relationships in a confirmatory manner. Second, it will further extend IGSEM to address several issues of theoretical and empirical importance in the field. The program will begin by technically developing IGSEM and its extensions, which involves model development, parameter estimation, and computer programming. It will then conduct a series of simulated and real data analyses to rigorously evaluate the empirical performance of the proposed methods. The program will make significant technical contributions to the field in that it will develop a general statistical methodology to investigate various gene-brain-behavior/cognition relationships in a more biologically interpretable and confirmatory manner. It will also make empirical contributions, providing researchers and practitioners with efficient tools for understanding the neurobiological basis of individual behavioral or cognitive differences. In addition, the program will contribute to attracting outstanding students and providing training opportunities for them to gain the anticipated research skills and experience that will enable them to progress in the field, or the natural sciences or engineering in general. Lastly, it will lead to ample interdisciplinary collaborations with national and international researchers as well as potential industrial partnerships.