Lecture

Node Degree and Strength

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Description

This lecture covers the measurement of brain node connectivity, focusing on node degree and strength in network neuroscience. It discusses brain connectomics, diffusion weighted imaging, and the Erdős-Rényi model for random networks. The lecture also delves into the Barabàsi-Albert model, power law distributions, and the weight distribution in the brain, emphasizing the complexity of real networks and the caution needed when testing for power laws.

Official source

Ontological neighbourhood

Mathematics

Discrete mathematics: Graph theory

Related concepts (32)

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Brain mapping is a set of neuroscience techniques predicated on the mapping of (biological) quantities or properties onto spatial representations of the (human or non-human) brain resulting in maps. According to the definition established in 2013 by Society for Brain Mapping and Therapeutics (SBMT), brain mapping is specifically defined, in summary, as the study of the anatomy and function of the brain and spinal cord through the use of imaging, immunohistochemistry, molecular & optogenetics, stem cell and cellular biology, engineering, neurophysiology and nanotechnology.

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In the context of network theory, a complex network is a graph (network) with non-trivial topological features—features that do not occur in simple networks such as lattices or random graphs but often occur in networks representing real systems. The study of complex networks is a young and active area of scientific research (since 2000) inspired largely by empirical findings of real-world networks such as computer networks, biological networks, technological networks, brain networks, climate networks and social networks.

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