Abstract
Brain parcellation divides the brain’s spatial domain into small regions, which are represented by nodes within the network analysis framework. While template-based parcellations are widely used, the parcels on the template do not necessarily match individual’s functional nodes. A new method is developed to overcome the inconsistent network analysis results by by-passing the difficulties of parcellating the brain into functionally meaningful areas. First, roughly equal-sized parcellations are obtained. Second, these random parcellations are applied to individual subjects multiple times and a pseudo-bootstrap (PBS) of the network is obtained for statistical inferences. It was found that the variation of mean global network metrics from PBS sampling is smaller compared with inter-subject variation or within-subject variation between two diffusion MRI scans. Using the mean global network metrics from PBS sampling, the intra-class correlation is always higher than the average obtained from using a single random parcellation. As one application, the PBS method was tested on the Human Connectome Project resting state dataset to identify individuals across scan sessions based on the mean functional connectivity (FC)—a trivial network property that has little information about the connectivity between nodes. An accuracy rate of ∼90% was achieved by simply finding the maximum correlation of mean FC of PBS samples between two scan sessions.
Highlights
Network analysis provides a complete new avenue in exploring the function and structure of the brain from a network perspective (Brodmann, 1909)
The PBS sampling resulted in a Gaussian-like distribution of the global metrics, indicating that different parcellation can lead to similar global metric values
While the global network properties of the structural brain network vary across different repetitions of the sized random parcellations, we find that the variability is small and comparable with inter-subject variability and within-subject variations between diffusion MRI (dMRI) scans
Summary
Network analysis provides a complete new avenue in exploring the function and structure of the brain from a network perspective (Brodmann, 1909). One of the biggest challenge of network analysis in neuroimaging is defining the nodes (de Reus and van den Heuvel, 2013; Stanley et al, 2013). The most natural way to define nodes would be to represent individual neurons as nodes. Even a single voxel in the brain image contains millions of neurons. A parcellation in the microscopic level is unrealistic for MRI-based whole brain imaging. People have attempted to divide the brain into different regions with
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