NeuroEngineering Research Lab

Neuroimaging

Magnetic resonance imaging is an important non-invasive medical imaging modality for clinical diagnosis of neurological disorders and neurodegenerative diseases, and for neuroscience and cognitive psychology research. Medical and behavioural science end-users of the technology rely on experimental physicists and hardware engineers to adjust MRI scanners such that the highest quality images are obtained. Traditionally, ‘highest quality’ has meant maximal image intensity contrast. This focus on image intensity alone severely compromises the ability to infer tissue characteristics. Indeed, such is the disregard for quantitative estimation of the underlying tissue composition and geometry that MRI scanners have routinely discarded the phase component of the complex-valued measurements. We apply signal processing and control theory methodology to the problem of understanding the physics, physiology and engineering involved in MRI.

Research Goals

Our research goals are to apply systems engineering approaches to improve, extend and redesign MRI acquisition and analysis techniques for imaging the brain. We are a research laboratory that is jointly within the Florey Neuroscience Institute’s Imaging Division.

Our current reseach projects include:

  • susceptibility weighted and phase MRI
  • diffusion weighted MRI
  • quantitative parameter mapping
  • functional MRI and connectivity analysis
  • fourier synthesis techniques for pulse excitation design

Projects

Brain network dynamics in health and disease

Researchers: Andrew Zalesky, Rao Kotagiri, Tony Burkitt, David Grayden

Brain activity displays a complex spatiotemporal structure, involving dynamically fluctuating patterns of coordination in neural activity among a large number of brain regions and over a diverse range of timescales. This project aims to investigate the large-scale network dynamics of the human brain in health and disease. Brain imaging data acquired in living humans will be used to develop dynamic network models of neural activity that encompass the whole brain. Network nodes will correspond to cortical and subcortical brain regions and the connectivity between pairs of nodes will be inferred from high-quality resting-state functional-MRI data that has already been acquired in a large group of patients diagnosed with schizophrenia and a matched group of healthy individuals. The student will apply cutting-edge network science techniques to study the dynamic behaviour of the brain’s network hubs, modules and its so-called "rich club". This project will deliver network-based brain biomarkers that can accurately and reliably differentiate healthy and diseased brains using brain imaging data. Depending on the student’s interest, opportunities will be made available to investigate psychiatric disorders other than schizophrenia.

Keywords: Network science, brain connectivity, functional neuroimaging, biological psychiatry.

Suggested Reading:

[1] Graph analysis of the human connectome: Promise, progress, and pitfalls (2013) Fornito A, Zalesky A, Breakspear M. NeuroImage. 80:426-444.

[2] Disrupted axonal fiber connectivity in schizophrenia (2011) Zalesky A, Fornito A, Seal ML, Cocchi L, Westin C-F, Bullmore ET, Egan GF, Pantelis C. Biol Psychiatry. 69(1):80-89.

[3] Network-based statistic: Identifying differences in brain networks (2010) Zalesky A, Fornito A, Bullmore ET. NeuroImage. 53(4):1197-1207

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Human connectome bioinformatics

Researchers: Andrew Zalesky

The connectome refers to a comprehensive network description of the brain’s internal wiring. Advances in magnetic resonance imaging (MRI) have enabled reliable mapping of the large-scale connectome in the living human brain. Comparing the human connectome between healthy and diseased brains has identified disease-specific anomalies in brain circuitry that may provide novel therapeutic targets and potential biomarkers to assess risk and predict patient outcomes. This project aims to develop advanced bioinformatic tools that capitalise on these advances. The student will develop methods to perform statistically valid network-level inference on the connectome.  Overcoming limitations of the widely used network-based statistic (NBS) will be the project’s starting point. This project will deliver powerful bioinformatic tools to enable neuroscientists and psychiatrists to accurately and reliably map connectome pathology in the diseased brain. The field of connectome bioinformatics is expected to grow rapidly in response to the abundance of connectomic data that will be made publicly available as part of the $40 million Human Connectome Project. This project is suited to a student with a background in statistics and algorithm development.  

Keywords: Connectome bioinformatics, biostatistics, network-based statistic (NBS), network inference.

Suggested Reading:

[1] Network-based statistic: Identifying differences in brain networks (2010) Zalesky A, Fornito A, Bullmore ET. NeuroImage. 53(4):1197-1207.

[2] Connectivity differences in brain networks (2012) Zalesky A, Cocchi L, Fornito A, Murray MM, Bullmore E. NeuroImage. 60(2):1055-1062.

[3] Learning and comparing functional connectomes across subjects (2013) Varoquaux G, Craddock RC. NeuroImage. 80:405-415.

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Mapping the human schizophrenia connectome

Researchers: Andrew Zalesky

This project aims to comprehensively map the entire human connectome in schizophrenia. The student will complete one of the largest clinical connectome mapping studies undertaken in the world by analysing high-quality brain imaging data in more than 330 individuals with schizophrenia provided by the Australian Schizophrenia Research Bank (ASRB). The ASRB is the largest brain research project ever undertaken in Australia. This project will apply advanced fibre tracking algorithms to the diffusion-MRI brain imaging data acquired in each patient, with the goal of comprehensively mapping all disrupted connections comprising the entire schizophrenia connectome. VLSCI computational resources may be utilised for this purpose.

Keywords: Schizophrenia, biological psychiatry, connectome, tractography.

Suggested Reading:

[1] Disrupted axonal fiber connectivity in schizophrenia (2011) Zalesky A, Fornito A, Seal ML, Cocchi L, Westin C-F, Bullmore ET, Egan GF, Pantelis C. Biol Psychiatry. 69(1):80-89.

[2] Schizophrenia, neuroimaging and connectomics (2012) Fornito A, Zalesky A, Pantelis C, Bullmore E. NeuroImage. 62(4):2296-2314.

[3] The relationship between regional and inter-regional functional connectivity deficits in schizophrenia (2012) Zalesky A, Fornito A, Egan GF, Pantelis C, Bullmore E. Hum Brain Mapp. 33(11):2535-2549.

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Modelling and simulation of interconnected neuronal populations

Researchers: Andrew Zalesky, David Grayden, Tony Burkitt, Rao Kotagiri

The spatiotemporal coordination of neural activity is constrained by the brain’s anatomy, namely, the network of axonal fibre pathways that enable communication between distant brain regions. This project aims to simulate hundreds of distinct neuronal populations that are interconnected via a biologically realistic network of axonal fibre pathways. The neuroanatomical network will be mapped using diffusion-MRI data acquired in living humans to yield a large-scale “wiring diagram” for the whole brain, telling us which neuronal populations are to be interconnected with each other in the network model. Neuronal population dynamics will be modelled and simulated using the Morris-Lecar neural mass model. The student will use the model to systematically disrupt groups of connections, as is consistent with brain disease, and investigate the consequence of these connectivity disruptions on the spatiotemporal coordination of neural activity and the brain’s functional modules. The student will then optimise and rewire the brain’s actual neuroanatomical network to improve its resilience and robustness to attack. This project will involve numerical solution of large systems of delay differential equations and is suited to a student with a background in numerical computation. VLSCI computational resources may be utilised for this purpose.

Keywords: Neural mass model, brain connectivity, diffusion-MRI, network robustness.

Suggested Reading:

[1] Modulation of excitatory synaptic coupling facilitates synchronization and complex dynamics in a biophysical model of neuronal dynamics (2003) Breakspear M, Terry JR, Friston KJ. Network. 14(4):703-732.

[2] Network structure of cerebral cortex shapes functional connectivity on multiple time scales (2007) Honey CJ, Kotter R, Breakspear M, Sporns O. Proc Natl Acad Sci U S A. 104(24):10240-10245.

[3] Modeling the impact of lesions in the human brain (2009) Alstott J, Breakspear M, Hagmann P, Cammoun L, Sporns O. PLoS Comput Biol. 5(6):e1000408.

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System identification of microstructure in the brain using magnetic resonance imaging

Researchers: Leigh Johnston

There has been recent interest in methods that purport to identify microstructural features of the brain from diffusion-weighted magnetic resonance imaging (MRI) data.  Inference on the axonal and cellular micron scale from images of two orders of magnitude less resolution requires accurate models of the tissue geometry and membrane permeabilities.  We are applying system indentification and Bayesian analysis techniques to determine the veracity of such methods, and aim to characterise the limit of robust estimation that can be inferred from the raw MRI data.  

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The mechanisms of cortical folding in brain development

Researchers: Leigh Johnston

An observation commonly made when looking at the human brain is the convoluted, or folded, appearance of the surface. These folds are essential to brain function, as abnormal folding patterns are implicated in a number of neurological disorders. But how do these folds form during development? We will use a novel, multidisciplinary approach to examine the brain during critical periods of fold formation and will determine the underlying cellular mechanisms involved. Our goal is to determine the contribution of axon innervation, cortical growth and the developing vascular system in governing this process. Such knowledge is fundamental for our understanding of brain function and dysfunction.

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Contact Us

Professor David Grayden

Director, NeuroEngineering

T: +61 3 8344 5234
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