Abstract
The study of brain networks has developed extensively over the last couple of decades. By contrast, techniques for the statistical analysis of these networks are less developed. In this paper, we focus on the statistical comparison of brain networks in a nonparametric framework and discuss the associated detection and identification problems. We tested network differences between groups with an analysis of variance (ANOVA) test we developed specifically for networks. We also propose and analyse the behaviour of a new statistical procedure designed to identify different subnetworks. As an example, we show the application of this tool in resting-state fMRI data obtained from the Human Connectome Project. We identify, among other variables, that the amount of sleep the days before the scan is a relevant variable that must be controlled. Finally, we discuss the potential bias in neuroimaging findings that is generated by some behavioural and brain structure variables. Our method can also be applied to other kind of networks such as protein interaction networks, gene networks or social networks.
Highlights
The study of brain networks has developed extensively over the last couple of decades
In Diffusion Tensor Imaging (DTI), large white-matter fibres are measured to create a connectional neuroanatomy brain network, while in resting-state functional magnetic resonance imaging (rs-fMRI), functional connections are inferred by measuring the BOLD activity at each voxel and creating a whole brain functional network based on functionally-connected voxels
We developed an analysis of variance (ANOVA) test for networks
Summary
The study of brain networks has developed extensively over the last couple of decades. In DTI, large white-matter fibres are measured to create a connectional neuroanatomy brain network, while in rs-fMRI, functional connections are inferred by measuring the BOLD activity at each voxel and creating a whole brain functional network based on functionally-connected voxels (i.e., those with similar behaviour) Despite technical limitations, both techniques are routinely used to provide a structural and dynamic explanation for some aspects of human brain function. Both techniques are routinely used to provide a structural and dynamic explanation for some aspects of human brain function These magnetic resonance neuroimages are typically analysed by applying network theory[3,4], which has gained considerable attention for the analysis of brain data over the last 10 years. We apply the method to resting-state fMRI data from the Human Connectome Project and discuss the potential biases generated by some behavioural and brain structural variables. In the Discussion section, we discuss possible improvements, the impact of sample size, and the effects of confounding variables
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