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Abstract:
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Many challenging issues, such as statistical variability in a population, arise from the study of structural connectome maps by using diffusion MRI tractography data. Addressing these challenges requires the development of fast and reliable approaches for processing high-dimensional diffusion data from hundreds (or even thousands) of subjects. We aim to develop a reliable Population-based Structural Connectome (PSC) Mapping framework to construct population structural connectome maps on a common space (or template), while accounting for individual variabilities. The developed PSC framework allows one to view individual structural connectome on different data level, from binary network to streamline based connnectome, allowing analysis of the structural connectome at different detail levels. At the weighted network level, novel connection strength measures for a pair of brain regions are proposed and extracted. At the streamline level, a new compression method is proposed to efficiently represent the connection. The data analysis on a test-retest data set indicates the high re-producibility of PSC. Preliminary groupwise analysis is demonstrated using HCP dataset.
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