Physical and Topological Constraints on Growth in Human Brain Networks

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Noninvasive MRI technology enables the mapping of white matter connectivity between cortical regions in the human brain. Graph theoretic analysis has shown the organization of this structural brain network facilitates certain functional properties, such as robust and efficient transfer of information between brain regions. What are the generative mechanisms of such a network? To address this, we analyze a broad range of synthetic networks produced by simple growth rules, and we compare these networks both qualitatively and quantitatively to the brain. We consider a growth model in which a set of three parameters govern the tendency of nodes to form connections with one another. These generated networks are analyzed in terms of direct measures of connectivity, such as the length and number of network connections, as well as higher-order graph metrics, such as hierarchy and clustering, that relate to efficient information transfer and resistance to random errors. We find that different sets of input parameters can greatly affect the final structure of the network, with only some subsets of parameter values creating brain-like networks. This shows that very similar growth rules can produce a diverse set of network structures. In ongoing work, we are investigating the role of known biophysical constraints, such as bilateral symmetry and wiring cost, on network growth. This study lends insight into the biophysical mechanisms that govern the development of human brain networks and ultimately shape robust and efficient network function.