2017/07/05 by Daniel Fraiman, Fraiman, Daniel, Ricardo Fraiman +1
Chemistry · Neuroscience · #Applications (stat.AP) #Disordered Systems and Neural Networks (cond-mat.dis-nn) #Electrochemical Analysis and Applications #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Functional Brain Connectivity Studies #Methodology (stat.ME) #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC)
paper · pdf · doi:10.48550/arxiv.1707.01585
openalex publication_date 2017/07/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The study of random networks in a neuroscientific context 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. 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.