Individualised Clinical Neuroimaging in the Developing Brain: Abnormality Detection

The aim of this study is to be able to provide individualised markers of brain tissue abnormalities in complex disorders of neurodevelopment such as childhood epilepsy. This will not only provide treatment targets, it will guide individually tailored interventions (i.e. precision medicine). Detecting these markers using neuroimaging methods is hampered by the rapid developmental changes in the brain throughout infancy. In practice, this means that sensitivity to neuroanatomical changes in childhood disorders can vary depending on what age the child is. This heterogeneity can either mask true changes in clinical studies and trials or bias studies towards very circumscribed age-ranges.
This project will develop and apply machine learning techniques to model typical brain development using large MRI datasets of >2000 infants, and new data collected in children with epilepsy. The resulting model will be used to maximise detection of abnormal tissue in individual children.
This will include the creation of a 4D atlas of the developing brain from 28 weeks PMA (foetal) to 52 weeks PMA. Regression based algorithms will allow the creation of an individual atlas at any age within the first year PMM and be able incorporate the rapid growth and extensive changes during this period. Statistical/machine learning methods will be used to produce probability maps for the whole brain, regional and voxel locations for each template. These can then be used in conjunction with target scans to provide individual analysis of subject brains and assessment of abnormalities; i.e. variations outside the expected norm for each voxel and/or region.