Efficient small-world and scale-free functional brain networks at rest using k-nearest neighbors thresholding

Abstract

indicates a shared first-authorship Highlights. A novel thresholding method for brain networks based on k-nearest neighbors (kNN)  kNN applied on resting state fMRI from a big cohort of healthy subjects BASE-II  kNN built networks present greater small world properties than density threshold  kNN built networks present scale-free properties whereas density threshold did not AbstractIn recent years, there has been a massive effort to analyze the topological properties of brain networks. Yet, one of the challenging questions in the field is how to construct brain networks based on the connectivity values derived from neuroimaging methods. From a theoretical point of view, it is plausible that the brain would have evolved to minimize energetic costs of information processing, and therefore, maximizes efficiency as well as to redirect its function in an adaptive fashion, that is, resilience. A brain network with such features, when characterized using graph analysis, would present small-world and scale-free properties.In this paper, we focused on how the brain network is constructed by introducing and testing an alternative method: k-nearest neighbor (kNN). In addition, we compared the kNN method with one of the most common methods in neuroscience: namely the density threshold. We performed our analyses on functional connectivity matrices derived from resting state fMRI of a big imaging cohort (N=434) of young and older healthy participants. The topology of networks was characterized by the graph measures degree, characteristic path length, clustering coefficient and small world. In addition, we verified whether kNN produces scale-free networks. We showed that networks built by kNN presented advantages over traditional thresholding methods, namely greater values for small-world (linked to efficiency of networks) than those derived by means of density thresholds and moreover, it presented also scale-free properties (linked to the resilience of networks), where density threshold did not. A brain network with such properties would have advantages in terms of efficiency, rapid adaptive reconfiguration and resilience, features of brain networks that are relevant for plasticity and cognition as well as neurological diseases as stroke and dementia.

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

“…The current finding of Weibull distribution fits brain networks better is in line with previous reports of the nodal degree of human brain functional networks that follow short-tailed distribution such as the Weibull distribution and the power law with exponential cutoff (Nakamura et al, 2009;Hayasaka and Laurienti, 2010;Gupta and Rajapakse, 2018;Zucca et al, 2019). In contrast, the heavy-tailed power law distribution (also called scale-free network) has been extensively discussed (Eguíluz et al, 2005;Van Den Heuvel et al, 2008;Ciuciu et al, 2014;Hanson et al, 2016;Forlim et al, 2019). Most of these studies on the power law had a strong hypothesis that brain networks are structured with simple growth mechanisms, such as preferential attachment (Barabási and Albert, 1999).…”

Evolution and Functions of the Cerebral Cortex – A review

“…From a theoretical point of view, it is plausible that the brain has evolved to minimize the energetic costs of information processing and therefore maximize efficiency and redirect its function in an adaptive manner, that is, resilience. A brain network with such characteristics, when characterized by graphical analysis, would present small-world and scale-free network properties (Forlim et al, 2019).…”

Less is more

“…Such networks accurately describe the structure of various knowledge organisations, such as the World Wide Web, and social networks that organise knowledge such as academic citations (Barabási & Bonabeau, 2003). While the structure of the human brain is not fully known, there is evidence that its networks, too, resemble a scale-free network (Eguíluz et al, 2005), which can help to explain the brain's efficiency and plasticity (Forlim et al, 2019).…”