The successful candidates will join a dynamic interdisciplinary collaboration between A/Prof Mac Shine (Brain and Mind Centre), A/Prof Joseph Lizier (School of Computer Science) and Dr Ben Fulcher (School of Physics), within the University's Centre for Complex Systems, focused on advancing our understanding of brain function and cognition using cutting-edge computational and neuroimaging techniques at the intersection of network neuroscience, dynamical systems and information theory. The positions are funded by a grant from the Australian Research Council 'Evaluating the Network Neuroscience of Human Cognition to Improve AI'.

Understanding communication and information processing in nervous systems is a central goal of neuroscience. Over the past two decades, advances in connectomics and network neuroscience have opened new avenues for investigating polysynaptic communication in complex brain networks. Recent work has brought into question the mainstay assumption that connectome signalling occurs exclusively via shortest paths, resulting in a sprawling constellation of alternative network communication models. This Review surveys the latest developments in models of brain network communication. We begin by drawing a conceptual link between the mathematics of graph theory and biological aspects of neural signalling such as transmission delays and metabolic cost. We organize key network communication models and measures into a taxonomy, aimed at helping researchers navigate the growing number of concepts and methods in the literature. The taxonomy highlights the pros, cons and interpretations of different conceptualizations of connectome signalling. We showcase the utility of network communication models as a flexible, interpretable and tractable framework to study brain function by reviewing prominent applications in basic, cognitive and clinical neurosciences. Finally, we provide recommendations to guide the future development, application and validation of network communication models.

While time-averaged dynamics of brain functional connectivity are known to reflect the underlying structural connections, the exact relationship between large-scale function and structure remains an unsolved issue in network neuroscience. Large-scale networks are traditionally observed by correlation of fMRI timecourses, and connectivity of source-reconstructed electrophysiological measures are less prominent. Accessing the brain by using multimodal recordings combining EEG, fMRI and diffusion MRI (dMRI) can help to refine the understanding of the spatio-temporal organization of both static and dynamic brain connectivity. In this talk I will discuss our prior findings that whole-brain connectivity derived from source-reconstructed resting-state (rs) EEG is both linked to the rs-fMRI and dMRI connectome. The EEG connectome provides complimentary information to link function to structure as compared to an fMRI-only perspective. I will present an approach extending the multimodal data integration of concurrent rs-EEG-fMRI to the temporal domain by combining dynamic functional connectivity of both modalities to better understand the neural basis of functional connectivity dynamics. The close relationship between time-varying changes in EEG and fMRI whole-brain connectivity patterns provide evidence for spontaneous reconfigurations of the brain’s functional processing architecture. Finally, I will talk about data quality of connectivity derived from concurrent EEG-fMRI recordings and how the presented multimodal framework could be applied to better understand focal epilepsy. In summary this talk will give an overview of how to integrate large-scale EEG networks with MRI-derived brain structure and function. In conclusion EEG-based connectivity measures not only are closely linked to MRI-based measures of brain structure and function over different time-scales, but also provides complimentary information on the function of underlying brain organization.

In this webinar, Dr Ye Tian and A/Prof Andrew Zalesky will present new research on mapping the functional architecture of the human subcortex. They used 3T and 7T functional MRI from more than 1000 people to map one of the most detailed functional atlases of the human subcortex to date. Comprising four hierarchical scales, the new atlas reveals the complex topographic organisation of the subcortex, which dynamically adapts to changing cognitive demands. The atlas enables whole-brain mapping of connectomes and has been used to optimise targeting of deep brain stimulation. This joint work with Professors Michael Breakspear and Daniel Margulies was recently published in Nature Neuroscience. In the second part of the webinar, Dr Ye Tian will present her current research on the biological ageing of different body systems, including the human brain, in health and degenerative conditions. Conducted in more than 30,000 individuals, this research reveals associations between the biological ageing of different body systems. She will show the impact of lifestyle factors on ageing and how advanced ageing can predict the risk of mortality. Associate Professor Andrew Zalesky is a Principal Researcher with a joint appointment between the Faculties of Engineering and Medicine at The University of Melbourne. He currently holds a NHMRC Senior Research Fellowship and serves as Associate Editor for Brain Topography, Neuroimage Clinical and Network Neuroscience. Dr Zalesky is recognised for the novel tools that he has developed to analyse brain networks and their application to the study of neuropsychiatric disorders. Dr Ye Tian is a postdoctoral researcher at the Department of Psychiatry, University of Melbourne. She received her PhD from the University of Melbourne in 2020, during which she established the Melbourne Subcortex Atlas. Dr Tian is interested in understanding brain organisation and using brain imaging techniques to unveil neuropathology underpinning neuropsychiatric disorders.