About the Project
The human brain is a complex network of interconnected neural components with network structures existing at several scales. A deeper understanding of these will allow us to better understand and diagnose a range of brain disorders, many of which are progressive and debilitating. There is therefore a significant, on-going effort in the development of models that describe the brain’s connectivity on a system level. For example, the human connectome project is currently in the process of using cutting-edge methods of non-invasive neuroimaging to collect data on 1200 healthy adults, providing a freely accessible reference data-set which can be used for future studies of brain development, aging, and neurological and psychiatric disorders.
Using pre-publication access to the latest functional Magnetic Resonance Imaging (fMRI) data from the human connectome project as well as data-sets of simulated brain networks, the aim in this study will be to develop better statistical tools to estimate network structures. A recent study comparing a wide range of network modelling algorithms for brain network analysis indicates that one of the most promising approaches is based on Bayesian network models. Bayesian networks are probabilistic network models that conveniently encode statistical dependencies, which can be used to estimate causal information flow within the brain.
Current application of Bayesian network models to the study of brain function assumes static networks. However, there is evidence of dynamic changes in system level brain interaction and this will the aspect you will investigate. To do this, you will study and extend Bayesian network methodologies, build dynamic brain network models and study typical dynamics of fMRI data.
If you wish to discuss any details of the project informally, please contact Dr Thomas Blumensath, ISVR Signal Processing and Control Group, Email: [Email Address Removed], Tel: +44 (0) 2380 59 3224
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