Whole-brain anatomical networks : Does the choice of nodes matter?

Zalesky, Andrew, Fornito, Alex, Harding, Ian H., Cocchi, Luca, Yucel, Murat, Pantelis, Christos and Bullmore, Edward T. (2010) Whole-brain anatomical networks : Does the choice of nodes matter?. Neuroimage, 50 3: 970-983. doi:10.1016/j.neuroimage.2009.12.027


Author Zalesky, Andrew
Fornito, Alex
Harding, Ian H.
Cocchi, Luca
Yucel, Murat
Pantelis, Christos
Bullmore, Edward T.
Title Whole-brain anatomical networks : Does the choice of nodes matter?
Journal name Neuroimage   Check publisher's open access policy
ISSN 1053-8119
1095-9572
Publication date 2010-04
Sub-type Article (original research)
DOI 10.1016/j.neuroimage.2009.12.027
Volume 50
Issue 3
Start page 970
End page 983
Total pages 14
Place of publication Maryland Heights, MO, United States
Publisher Academic Press
Language eng
Formatted abstract
Whole-brain anatomical connectivity in living humans can be modeled as a network with diffusion-MRI and tractography. Network nodes are associated with distinct grey-matter regions, while white-matter fiber bundles serve as interconnecting network links. However, the lack of a gold standard for regional parcellation in brain MRI makes the definition of nodes arbitrary, meaning that network nodes are defined using templates employing either random or anatomical parcellation criteria. Consequently, the number of nodes included in networks studied by different authors has varied considerably, from less than 100 up to more than 104. Here, we systematically and quantitatively assess the behavior, structure and topological attributes of whole-brain anatomical networks over a wide range of nodal scales, a variety of grey-matter parcellations as well as different diffusion-MRI acquisition protocols. We show that simple binary decisions about network organization, such as whether small-worldness or scale-freeness is evident, are unaffected by spatial scale, and that the estimates of various organizational parameters (e.g. small-worldness, clustering, path length, and efficiency) are consistent across different parcellation scales at the same resolution (i.e. the same number of nodes). However, these parameters vary considerably as a function of spatial scale; for example small-worldness exhibited a difference of 95% between the widely-used automated anatomical labeling (AAL) template (∼ 100 nodes) and a 4000-node random parcellation (σAAL = 1.9 vs. σ4000 = 53.6 ± 2.2). These findings indicate that any comparison of network parameters across studies must be made with reference to the spatial scale of the nodal parcellation
Keyword State Functional Connectivity
Graph-Theoretical Analysis
Small-World Networks
Cortical Networks
Hierarchical Organization
Fiber Orientations
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Unknown

Document type: Journal Article
Sub-type: Article (original research)
Collections: Queensland Brain Institute Publications
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