Altered Weibull Degree Distribution in Resting-State Functional Brain Networks Is Associated With Cognitive Decline in Mild Cognitive Impairment

Abstract

The topological organization of human brain networks can be mathematically characterized by the connectivity degree distribution of network nodes. However, there is no clear consensus on whether the topological structure of brain networks follows a power law or other probability distributions, and whether it is altered in Alzheimer's disease (AD). Here we employed resting-state functional MRI and graph theory approaches to investigate the fitting of degree distributions of the whole-brain functional networks and seven subnetworks in healthy subjects and individuals with amnestic mild cognitive impairment (aMCI), i.e., the prodromal stage of AD, and whether they are altered and correlated with cognitive performance in patients. Forty-one elderly cognitively healthy controls and 30 aMCI subjects were included. We constructed functional connectivity matrices among brain voxels and examined nodal degree distributions that were fitted by maximum likelihood estimation. In the whole-brain networks and all functional subnetworks, the connectivity degree distributions were fitted better by the Weibull distribution [f(x)~x(β−1)e(−λxβ)] than power law or power law with exponential cutoff. Compared with the healthy control group, the aMCI group showed lower Weibull β parameters (shape factor) in both the whole-brain networks and all seven subnetworks (false-discovery rate-corrected, p < 0.05). These decreases of the Weibull β parameters in the whole-brain networks and all subnetworks except for ventral attention were associated with reduced cognitive performance in individuals with aMCI. Thus, we provided a short-tailed model to capture intrinsic connectivity structure of the human brain functional networks in health and disease.

Brain rewiring during developmental transitions: A Comparative Analysis of Larva and AdultDrosophila melanogaster

“…The distribution parameters λ and β are determined to be 0.0247 and 1.2409 for in-degree and 0.0244 and 1.3348 for the out-degree. Weibull has also been found to be a better fit for the degree distribution of the functional human brain network [32]. The parameter β > 1 for both the in-and-out-degree distributions indicates that the failure rate of this fit increases as the network grows.…”

Edge‐centric functional network reveals new spatiotemporal biomarkers of early mild cognitive impairment

“…A common feature of network neuroscience research is that it relies on network analysis to study the characteristics of each brain region and subnetwork. The destruction of FCs in patients with EMCI has been demonstrated in previous studies [6,7] . However, most of the current FCs are still centered on the brain regions as nodes, and the edges are calculated according to the Pearson correlation coefficient of the fMRI BOLD time series of two brain regions, which represents the correlation between the brain regions.…”

“…The destruction of FCs in patients with EMCI has been demonstrated in previous studies. [6,7] However, most of the current FCs are still centered on the brain regions as nodes, and the edges are calculated according to the Pearson correlation coefficient of the fMRI BOLD time series of two brain regions, which represents the correlation between the brain regions. However, such features of brain networks may not be effective biomarkers of disease, and how low-order correlations between different pairs of brain regions interact should be considered.…”

Virtual external stimulation promotes the transformation of the brain state from early mild cognitive impairment to health

“…[16][17][18] Different features of functional connectivity in the resting state have been found to correlate with cognitive and memory performances. [19,20] Abnormal topological changes are noted in the brain networks of patients with MCI compared with healthy people, [21] along with a decrease in the strength of functional connectivity in regions within the default mode network. [22] In addition, the pattern of functional brain connectivity fluctuates over time and space.…”