Connectivity differences in brain networks

Zalesky, Andrew, Cocchi, Luca, Fornito, Alex, Murray, Micah M. and Bullmore, Ed (2012) Connectivity differences in brain networks. NeuroImage, 60 2: 1055-1062. doi:10.1016/j.neuroimage.2012.01.068


Author Zalesky, Andrew
Cocchi, Luca
Fornito, Alex
Murray, Micah M.
Bullmore, Ed
Title Connectivity differences in brain networks
Journal name NeuroImage   Check publisher's open access policy
ISSN 1053-8119
1095-9572
Publication date 2012-04-01
Sub-type Article (original research)
DOI 10.1016/j.neuroimage.2012.01.068
Open Access Status Not Open Access
Volume 60
Issue 2
Start page 1055
End page 1062
Total pages 8
Place of publication Maryland Heights MO, United States
Publisher Academic Press
Language eng
Formatted abstract
The scenario considered here is one where brain connectivity is represented as a network and an experimenter wishes to assess the evidence for an experimental effect at each of the typically thousands of connections comprising the network. To do this, a univariate model is independently fitted to each connection. It would be unwise to declare significance based on an uncorrected threshold of α = 0.05, since the expected number of false positives for a network comprising N = 90 nodes and N(N − 1)/2 = 4005 connections would be 200. Control of Type I errors over all connections is therefore necessary. The network-based statistic (NBS) and spatial pairwise clustering (SPC) are two distinct methods that have been used to control family-wise errors when assessing the evidence for an experimental effect with mass univariate testing. The basic principle of the NBS and SPC is the same as supra-threshold voxel clustering. Unlike voxel clustering, where the definition of a voxel cluster is unambiguous, ‘clusters’ formed among supra-threshold connections can be defined in different ways. The NBS defines clusters using the graph theoretical concept of connected components. SPC on the other hand uses a more stringent pairwise clustering concept. The purpose of this article is to compare the pros and cons of the NBS and SPC, provide some guidelines on their practical use and demonstrate their utility using a case study involving neuroimaging data.
Keyword Brain connectivity
Brain network
Connectome
Network-based statistic
Spatial pairwise clustering
Family-wise errors
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Queensland Brain Institute Publications
Official 2013 Collection
 
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