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AI-enabled Imaging

Deep Generative Models of Fetal Brain Development: forward modelling of the mechanisms of neurodevelopmental impairment

Project ID: 2022_033

1st Supervisor: Emma Robinson , King’s College London
2nd Supervisor: Jonathan O’Muircheartaigh , King’s College London
Clinical Supervisor: Professor Serena Counsell

Aim of the PhD Project:

  • Develop novel techniques for surface-to-volume image registration consolidating concepts from discrete and deep optimisation  
  • Incorporate biomechanical and biophysical models of brain growth and folding  
  • Build generative forward models of neurodevelopment from fetal and neonatal MRI 
  • Investigate extensive opportunities for clinical translation following preterm birth, congenital heart disease and epilepsy 

Project description/background:

Over the period of late gestation, the fetal brain undergoes a period of rapid development. Neuronal cells make distant connections, laying down a network of communication that will later support complex cognition. The rapid growth of cells in the cortex, the outer layer of the brain, creates biomechanical tensions which cause the surface to fold. 

Disruptions to this process, for example resulting from preterm birth or prenatal conditions such as congenital heart disease, can result in long-term neurodevelopmental impairments such as Autism, ADHE and epilepsy. The objective of this project is to build a forward model of this process, through which pathways of brain injury may be simulated, and the causes of neurodevelopmental impairment be understood. 

This project extends from previous work that built average models of brain growth using a combination of image registration and Gaussian process regression [1]. This model showed strong potential for the modelling the deviation of preterm development from healthy, at the centre of the brain. But struggled to model the cortex, where considerable variation of individual’s brain shape and patterns of cortical organisation make comparison across scans much more difficult. 

The work also incorporates ideas from [2,3] which built a deep generative model for image-to-image translation and used it to derive feature attribution (FA) maps that highlight all evidence of pathology in individual brains. This is achieved by learning a mapping that changes an image backwards from categorised as diseased towards being classified as healthy.  

Neither of these projects presented a forward or mechanistic model of disease; however, in [4,5] we propose a novel biomechanical models of brain atrophy following dementia. This supports simulation of the trajectory of brain atrophy under different disease states or conditions: healthy ageing, mild cognitive impairment or full Alzheimer’s disease. 

Accordingly in this project we seek to integrate these ideas to develop a deep generative biomechanical model of cortical growth from late gestation to birth. This will extend the ideas of [4,5] integrating novel models of cortical folding [5] and techniques from the domain of cortical surface registration [6,7] to learn to model longitudinal mappings between an individual’s fetal and neonatal scans. Then will adapt ideas from image-to-image models for phenotype translation [2,3] to change tissue contrast and appearance. In this way simultaneously simulating changes in shape, size and tissue maturation. 

The expectation will be that the student has (or is willing to develop) a good grasp of biomechanical and biophysical modelling, strong programming and machine learning and expertise in deep generative modelling. A strong mathematical aptitude will be essential for this project. 

References

  1. O’Muircheartaigh, J et al.  Brain 143.2 (2020): 467-479. https://doi.org/10.1093/brain/awz412  
  2. Bass, Cher, et al. Advances in Neural Information Processing Systems  33 (2020) 7697—7709 https://proceedings.neurips.cc/paper/2020/file/56f9f88906aebf4ad985aaec7fa01313-Paper.pdf  
  3. Bass, Cher, et al. arXiv preprint arXiv:2103.02561 (2021). https://arxiv.org/abs/2103.02561  
  4. Da Silva, Mariana, et al. arXiv preprint arXiv:2012.07596 (2020). https://arxiv.org/abs/2012.07596  
  5. Da Silva, Mariana, et al. arXiv preprint arXiv:2108.08214 (2021). https://arxiv.org/abs/2108.08214  
  6. Tallinen, T et al PNAS 11.35 (2014): 12667-12672. doi:10.1073/pnas.1406015111  
  7. Robinson, EC., et al. Neuroimage 100 (2014): 414-426. https://doi.org/10.1016/j.neuroimage.2014.05.069 
  8. Robinson, EC., et al.  Neuroimage 167 (2018): 453-465. https://doi.org/10.1016/j.neuroimage.2017.10.037 

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