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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.

Herein, we address the time evolution of brain functional networks computed from electroencephalographic activity driven by visual stimuli. We describe how these functional network signatures change in fast scale when confronted with point-light display stimuli depicting biological motion (BM) as opposed to scrambled motion (SM). Whereas global network measures (average path length, average clustering coefficient, and average betweenness) computed as a function of time did not discriminate between BM and SM, local node properties did. Comparing the network local measures of the BM condition with those of the SM condition, we found higher degree and betweenness values in the left frontal (F7) electrode, as well as a higher clustering coefficient in the right occipital (O2) electrode, for the SM condition. Conversely, for the BM condition, we found higher degree values in central parietal (Pz) electrode and a higher clustering coefficient in the left parietal (P3) electrode. These results are discussed in the context of the brain networks involved in encoding BM versus SM.

Objective: To investigate the impact of chronic pain on brain dynamics at rest., Methods: Functional connectivity was examined in patients with fibromyalgia (FM) (n = 9) and healthy controls (n = 11) by calculating partial correlations between low-frequency blood oxygen level-dependent fluctuations extracted from 15 brain regions., Results: Patients with FM had more positive and negative correlations within the pain network than healthy controls. Patients with FM displayed enhanced functional connectivity of the anterior cingulate cortex (ACC) with the insula (INS) and basal ganglia (p values between .01 and .05), the secondary somatosensory area with the caudate (CAU) (p = .012), the primary motor cortex with the supplementary motor area (p = .007), the globus pallidus with the amygdala and superior temporal sulcus (both p values < .05), and the medial prefrontal cortex with the posterior cingulate cortex (PCC) and CAU (both p values < .05). Functional connectivity of the ACC with the amygdala and periaqueductal gray (PAG) matter (p values between .001 and .05), the thalamus with the INS and PAG (both p values < .01), the INS with the putamen (p = .038), the PAG with the CAU (p = .038), the secondary somatosensory area with the motor cortex and PCC (both p values < .05), and the PCC with the superior temporal sulcus (p = .002) was also reduced in FM. In addition, significant negative correlations were observed between depression and PAG connectivity strength with the thalamus (r = -0.64, p = .003) and ACC (r = -0.60, p = .004)., Conclusions: These findings demonstrate that patients with FM display a substantial imbalance of the connectivity within the pain network during rest, suggesting that chronic pain may also lead to changes in brain activity during internally generated thought processes such as occur at rest.

Brain "rest" is defined--more or less unsuccessfully--as the state in which there is no explicit brain input or output. This work focuses on the question of whether such state can be comparable to any known dynamical state. For that purpose, correlation networks from human brain functional magnetic resonance imaging are contrasted with correlation networks extracted from numerical simulations of the Ising model in two dimensions at different temperatures. For the critical temperature Tc, striking similarities appear in the most relevant statistical properties, making the two networks indistinguishable from each other. These results are interpreted here as lending support to the conjecture that the dynamics of the functioning brain is near a critical point.