Abstract
Network neuroscience investigates brain functioning through the prism of connectivity, and graph theory has been the main framework to understand brain networks. Recently, an alternative framework has gained attention: topological data analysis. It provides a set of metrics that go beyond pairwise connections and offer improved robustness against noise. Here, our goal is to provide an easy-to-grasp theoretical and computational tutorial to explore neuroimaging data using these frameworks, facilitating their accessibility, data visualisation, and comprehension for newcomers to the field. We provide a concise (and by no means complete) theoretical overview of the two frameworks and a computational guide on the computation of both well-established and newer metrics using a publicly available resting-state functional magnetic resonance imaging dataset. Moreover, we have developed a pipeline for three-dimensional (3-D) visualisation of high order interactions in brain networks.
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Notes
- 1.
Notice that the notion of metric in mathematics defines distance between two points in a set [16], which is distinct from what we are using in this work. We denote as metric any quantity that can be computed, i.e., “measured”, in a brain network or simplicial complex.
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Centeno, E.G.Z., Moreni, G., Vriend, C., Douw, L., Santos, F.A.N. (2021). A Python Hands-on Tutorial on Network and Topological Neuroscience. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2021. Lecture Notes in Computer Science(), vol 12829. Springer, Cham. https://doi.org/10.1007/978-3-030-80209-7_71
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