Abstract
The majority of network studies of human brain structural connectivity are based on single-shell diffusion-weighted imaging (DWI) data. Recent advances in imaging hardware and software capabilities have made it possible to acquire multishell (b-values) high-quality data required for better characterization of white-matter crossing-fiber microstructures. The purpose of this study was to investigate the extent to which brain structural organization and network topology are affected by the choice of diffusion magnetic resonance imaging (MRI) acquisition strategy and parcellation scale. We performed graph-theoretical network analysis using DWI data from 35 Human Connectome Project subjects. Our study compared four single-shell (b = 1000, 3000, 5000, 10,000 s/mm2) and multishell sampling schemes and six parcellation scales (68, 200, 400, 600, 800, 1000 nodes) using five graph metrics, including small-worldness, clustering coefficient, characteristic path length, modularity and global efficiency. Rich-club analysis was also performed to explore the rich-club organization of brain structural networks. Our results showed that the parcellation scale and imaging protocol have significant effects on the network attributes, with the parcellation scale having a substantially larger effect. Regardless of the parcellation scale, the brain structural networks exhibited a rich-club organization with similar cortical distributions across the parcellation scales involving at least 400 nodes. Compared to single b-value diffusion acquisitions, the deterministic tractography using multishell diffusion imaging data consisting of shells with b-values higher than 5000 s/mm2 resulted in significantly improved fiber-tracking results at the locations where fiber bundles cross each other. Brain structural networks constructed using the multishell acquisition scheme including high b-values also exhibited significantly shorter characteristic path lengths, higher global efficiency and lower modularity. Our results showed that both parcellation scale and sampling protocol can significantly impact the rich-club organization of brain structural networks. Therefore, caution should be taken concerning the reproducibility of connectivity results with regard to the parcellation scale and sampling scheme.
Highlights
Introduction distributed under the terms andThe human brain connectome has greatly expanded our understanding of how cognitive processes emanate from a fundamental structural substrate [1]
We investigated the extent to which the brain structural organization was by the scalethe
We found significant differences between the values of the graph metrics derived from the singleand multi-shell-based brain structural networks across all parcellation scales
Summary
Introduction distributed under the terms andThe human brain connectome has greatly expanded our understanding of how cognitive processes emanate from a fundamental structural substrate [1]. Structural connectivity analysis using graph metrics has been widely used to investigate the topological properties of brain structural networks derived from diffusion-weighted conditions of the Creative Commons. Many studies have focused on graph measures of network segregation (e.g., clustering coefficient and modularity) and measures of network integration (e.g., degree, characteristic path length and global efficiency) to investigate the small-worldness property of the human brain, exhibiting an optimal balance between the segregation and integration of information [2,4]. The existence of a densely connected cortical “rich club” of hubs, playing a crucial role in global brain communication through short communication pathways, has been considered as the key characteristic of brain networks exhibiting a hierarchical structure [5]. It is suggested that any damage to cortical rich-club regions can cause large widespread disruption across large-scale brain networks with a significant impact on cognition [6,7,8,9]
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