Abstract
We applied graph analysis to both anatomical and functional connectivity in the human brain. Anatomical connectivity was acquired from diffusion tensor imaging data by probabilistic fiber tracking, and functional connectivity was extracted from resting-state functional magnetic resonance imaging data by calculating correlation maps of time series. For the same subject, anatomical networks seemed to be disassortative, while functional networks were significantly assortative. Anatomical networks showed higher efficiency and smaller diameters than functional networks. It can be proposed that anatomical connectivity, as a major constraint of functional connectivity, has a relatively stable and efficient structure to support functional connectivity that is more changeable and flexible.
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