Abstract
Physical connections between nodes in a complex network are constrained by limiting factors, such as the cost of establishing links and maintaining them, which can hinder network capability in terms of signal propagation speed and processing power. Trade-off mechanisms between cost constraints and performance requirements are reflected in the topology of a network and, ultimately, on the dependence of connectivity on geometric distance. This issue, though rarely addressed, is crucial in neuroscience, where physical links between brain regions are associated with a metabolic cost. In this work we investigate brain connectivity—estimated by means of a recently developed method that evaluates time scales of cross-correlation observability—and its dependence on geometric distance by analyzing resting state magnetoencephalographic recordings collected from a large set of healthy subjects. We identify three regimes of distance each showing a specific behavior of connectivity. This identification makes up a new tool to study the mechanisms underlying network formation and sustainment, with possible applications to the investigation of neuroscientific issues, such as aging and neurodegenerative diseases.
Highlights
The application of network analysis methods on structural and functional brain connectivity is a widely used tool to investigate the topology of complex brain networks emerging during both resting state and cognitive engagement in a task
Instantiating and running a functional brain network has a metabolic cost in term of glucose and oxygen consumption required to sustain information processing and circulation. This wiring cost is related to the distance between communicating brain regions and is supposed to be minimized by the brain, while preserving the crucial computational advantages conferred by network complexity (Bullmore and Sporns, 2012; Gollo et al, 2018)
While actual anatomical link length does not coincide with a straight segment, Euclidean distance appears to be a relevant parameter in the study of brain topology and connectivity (Ghosh et al, 2008; Supekar et al, 2009; Kaiser, 2011; Cabral et al, 2014), making up a lower bound to the real anatomical distance (AvenaKoenisberger et al, 2018)
Summary
The application of network analysis methods on structural and functional brain connectivity is a widely used tool to investigate the topology of complex brain networks emerging during both resting state and cognitive engagement in a task. Instantiating and running a functional brain network has a metabolic cost in term of glucose and oxygen consumption required to sustain information processing and circulation This wiring cost is related to the distance between communicating brain regions and is supposed to be minimized by the brain, while preserving the crucial computational advantages conferred by network complexity (Bullmore and Sporns, 2012; Gollo et al, 2018). Despite the relevance of the topic, to our best knowledge only a few studies addressed directly the dependence of connectivity on geometric distance in the brain
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