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Evolution of Hierarchical Complexity in Brain Networks S&T25

  • School: School of Science and Technology
  • Study mode(s): Full-time / Part-time
  • Starting: 2022
  • Funding: UK student / EU student (non-UK) / International student (non-EU) / Fully-funded


NTU's Fully-funded PhD Studentship Scheme 2022

Project ID: S&T25

This project is for an ambitious PhD student to work on a new state-of-the-art paradigm in brain networks at the frontiers of network science, complexity science, and evolutionary systems neuroscience. We want to tackle challenging questions on the link between the evolution of animal brains and the complexity of their brain networks.

Hierarchical complexity allows us to directly measure the complexity of a network. Applied to the human brain, it characterises the diversity of connectivity patterns across the brain and provides a new systems-level way of understanding brain structure in health and disease. It has been shown to be a strong marker of the human brain but not of other kinds of networks- such as social and infrastructure networks. Further, it has been used to show that preterm birth is associated with less complexity in peripheral brain network nodes, while lupus is associated with greater complexity in hub regions of the adult brain, linking the complexity of brain connectivity patterns with development and disease.

This project will tackle both the methodological challenges of hierarchical complexity as well as advance our understanding of the complexity of brain structure across species-- from simple insects to mammals and humans. In application, the student will take on the challenge of acquiring and applying hierarchical complexity across different animal species and across different scales-- from detailed neuron to neuron level connectivity data, to macro-scale regions of interest processed from MRI scans. From a methodological perspective, the student will study hierarchical complexity metric normalisations, adaptations to directed and weighted networks, and considerations of null models among others.  Ultimately, the project aims to come to a solid conclusion on whether hierarchical complexity is linked to animal intelligence or whether it is a more general property of brains. The result of which would allow us to move forward with our understanding of the evolution of connective diversity in the brain and its links to development and disease.

The student will work closely with Dr Keith Smith and Dr Jason Smith as well as with collaborators at the University of Edinburgh. The student will make use of data supplied by collaborators at the Blue Brain project and human neuroimaging supplied by collaborators at the University of Edinburgh. Work carried out by the student will be expected to be published in high quality, peer-reviewed, interdisciplinary journals and to disseminate research at relevant seminars and international conferences.

School strategic research priority

This project goes aligns with both the CHAUD and CIRC research priorities.

For CHAUD, the development of hierarchical complexity as the measurement of complexity for complex biological systems aligns with the biomathematics theme, while the applications to brain health and development are of a broader significance to CHAUD.

For CIRC, the project aligns with the Computational Neuroscience and Cognitive Robotics theme by boosting our understanding of the particularity of how brain networks are structured and the links between brain network complexity and animal intelligence, we can expect new insights and developments into artificial intelligence.

Entry qualifications

For the eligibility criteria, visit our studentship application page.

How to apply

For guidance and to make an application, please visit our studentship application page. The application deadline is Friday 14 January 2022.

Fees and funding

This is part of NTU's 2022 fully-funded PhD Studentship Scheme.

Guidance and support

Download our full applicant guidance notes for more information.

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