Uncovering Functional Correlates of Structural Organization in Brain Networks at Multiple Scales : From the worm to the human[HBNI Th177]

Abstract

Networks, i.e., systems whose structure is completely described by specifying a set of vertices (or nodes), and the set of edges (or links) connecting these vertices to each other, are ubiquitous in nature. Among the large number of complex networks seen in the natural world, nervous systems, comprising neurons that communicate with each other through chemical (via synapses) or electrical (via gap junctions) means, are particularly fascinating because of the rapid and precise transfer of information they allow between different parts. Thus, explaining how the structural organization of networks in the brain aids in the attainment of its functional goals has become the aim of “connectomics”, the collective term given to efforts at understanding various facets of the topological arrangement of connections between the cells and regions comprising the brain.

In this thesis, we have analyzed the network underlying the nervous system of different organisms and asked the following questions: i. What are the key features that characterize the connection topology across the entire brain ? ii. Can such structural features of the network be related to the functional goals of the nervous system (i.e., the “structure-function” relationship) ? iii. What constraints ensure that a relatively invariant topological organization of the connections between neurons emerge over the course of development (i.e., the “wiring problem”) ?

The different systems that we have considered in this thesis range between the somatic nervous system of the hermaphrodite nematode Caenorhabditis elegans, the network of cortical and sub-cortical areas in the brain of the rhesus macaque monkey and an ensem- ble of networks of the whole brain reconstructed from diffusion tensor imaging of human subjects. Our key findings include the determination of different kinds of homophily at play in the C. elegans nervous system. These tendencies of neurons with certain shared traits to con- nect to each other over the course of development appear to serve as key constraints during development that shape the structural organization of the nervous system. Our mesoscopic analysis of the macaque brain network reveals a robust modular structure, with each module corresponding to specific sensory modality or motor function. We reveal an underlying pattern of inter- and intra modular connectivity that promotes significantly faster communication across the network compared to other equivalent networks. Through analysis of a cohort of human structural brain networks we obtain a “representative network” characterizing the fundamental wiring patterns of the human brain. More informative than a simple averaging over the networks, this generic diagram indicates the frequency of occurrence and connection weight variability across the population for each link as well as provides better correspondence between the structural and functional connectivity. In this thesis we also propose an entirely new approach to analyze a specific type of mesoscopic organization in networks, viz., hierarchy. Using our hierarchy detection method, we uncover a robust arrangement of hierarchical levels in different nervous systems. Our analysis aims at contributing to a novel framework for understanding the structure-function relationship in networks of the brain.