The role of network directionality in the brain

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Padmore, A, 2021. The role of network directionality in the brain. MPhil, Nottingham Trent University.

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Abstract

Network science is a vast interdisciplinary area which connects disparate subjects such as mathematics, the natural sciences, sociology, information technology and more. Network neuroscience, in particular, is a thriving and rapidly expanding field in which graph theory techniques have been deployed to better understand structure-function relations in the brain across multiple temporal and spatial scales. In this thesis, we use large-scale brain network models for a range of different species (cat, Macaque monkey and C.elegans) to simulate important aspects of brain function, such as associative memory and synchrony related activities. Network directionality is a fundamental feature of such models, yet it is typically ignored due to limitations of non-invasive imaging techniques. Here, we explore the role that directionality plays in determining neural activity in the brain. We start by considering a system of Hopfield neural elements with heterogeneous structural connectivity given by range of species and parcellations for which network directionality information is present. We investigate the effect of removing directionality of connections on brain capacity, which we quantify via its ability to store attractor states. In addition to determining large numbers of fixed-point attractor sets, we deploy the recently developed basin stability technique in order to assess the global stability of such brain states as well as their robustness to non-small perturbations. By comparison with standard network models with the same coarse statistics, we find that directionality effects not only the number of fixed-point attractors but also the likelihood that neural systems remain in their most 'desirable' states. These findings suggest that directionality plays an important role in shaping transition routes between different brain networks states. We then go onto consider the impact that network directionality has on the synchrony properties of the brain. We simulate neural dynamics on the aforementioned connectome-based networks deploying a phase delayed Kuramoto Model, which is perhaps the simplest example of a delay coupled oscillatory network and is well-suited to assessing how directed connectomes govern synchronisation properties of the brain. In particular, we find that network directionality profoundly impacts both the time-scale at which coordinated rhythmic activity occurs across large-scale brain networks as well as the stability properties of these synchronised states. We also find that recently observed relations between network structure and directed functional connectivity, as quantified using the directed phase lag index, appear far less conclusive when network directionality is accounted for. This study thereby emphasizes the substantial role network directionality plays in shaping the brain’s ability to both store and process information.

Item Type:	Thesis
Creators:	Padmore, A.
Date:	November 2021
Rights:	This work is the intellectual property of the author. You may copy up to 5% of this work for private study, or personal, non-commercial research. Any re-use of the information contained within this document should be fully referenced, quoting the author, title, university, degree level and pagination. Queries or requests for any other use, or if a more substantial copy is required, should be directed in the owner(s) of the Intellectual Property Rights.
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	16 Jun 2022 13:55
Last Modified:	16 Jun 2022 13:55
URI:	https://irep.ntu.ac.uk/id/eprint/46461