An ANOVA approach for statistical comparisons of brain networks

Daniel Fraiman and Ricardo Fraiman

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.

High, diverse impact for NeuroMat research

Scientific articles produced by members of the Research, Innovation and Dissemination Center for Neuromathematics (RIDC NeuroMat) were cited 1,371 times since 2013. In 2014, articles from this center were cited 45 times; in 2018, citations rose to 509. Since its inception, the RIDC NeuroMat has collectively produced 272 publications, roughly 55 items per year.

Computational models of memory consolidation and long-term synaptic plasticity during sleep

César Rennó-Costa, Ana Cláudia Costa da Silva, Wilfredo Blanco and Sidarta Ribeiro

Estimating the interaction graph of stochastic neural dynamics

Aline Duarte, Antonio Galves, Eva Löcherbach and Guilherme Ost

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