MSE Research Project Database

Human connectome bioinformatics


Project Leader: Andrew Zalesky
Primary Contact: Andrew Zalesky (azalesky@unimelb.edu.au)
Keywords: Biostatistics; Connectome; neuroimaging; neuroimaging and neuroinformatics
Disciplines: Biomedical Engineering
Domains: Convergence of engineering and IT with the life sciences
Research Centre: Neuroengineering Research Laboratory

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.

The human connectome mapped using diffusion-MRI and tractography.
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