“Tuberous sclerosis complex (TSC) is a genetic disorder of non-cancerous tubers throughout the body, particularly affecting the brain. Neurologically, cortical tubers often cause seizures while TSC-associated neuropsychiatric disorders include autism spectrum disorder, ADHD, anxiety and depression. However, there is wide symptom variability between patients which is not well-understood.
This PhD project will bring together advances in clinical neuroimaging, machine learning and clinical neuroscience to build tools that will detect and analyse brain lesions from MRI scans. These tools will be used to help predict which tubers are likely to be causing seizures and link to the genetic mutation and patient symptoms. Eventually they could be incorporated into personalised interventions including epilepsy surgery planning.
Skills: the student will gain experience in python programming, AI model development and translational neuroimaging. The develop data integration and analysis skills across imaging, genetic and clinical datasets. Finally, they will be working as part of a multidisciplinary team, collaborating with biomedical researchers, doctors and neuropsychologists.
PhD Objectives:
Year 1: Develop and evaluate imaging AI tools for detection and segmentation of MRI brain lesions in TSC.
Year 2: Build tools to identify which tubers are likely to be causing seizures, supporting surgical planning in patients with epilepsy
Year 3: Link brain phenotypes to patients’ genetic, clinical and neurodevelopmental profiles.
Year 4: Integrate tools into clinical tool to support real-world evaluation
3-Month MRes Rotation Project:
The student will explore AI techniques for segmenting tubers in a small dataset of MRI scans. This will help the student to develop AI, neuroimaging and data analysis tools.
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