Chronic traumatic encephalopathy(CTE) lacks an in-vivo diagnostic biomarker, limiting the ability for doctors and researchers to detect trauma-related neurodegeneration whilst the patient is still alive. Currently, it is diagnosed by examining the brain post-mortem under a microscope, however there have been developments to identify some characteristics before death. Tau positron emission tomography(tau-PET) provides quantitative, spatially resolved measurements of tau pathology within neurodegenerative diseases, and is mostly used in studies of Alzheimer’s disease(ALD). However, its ability to detect reproducible patterns of tau misfolds/aggregation remains uncertain. This project proposes to develop a multi-dataset machine learning framework to determine whether reproducible tau spatial signatures of tau uptake can be identified in individuals with a history of exposure to brain trauma(playing contact sports at an elevated level for 5-10 years).

Aggregating publicly available tau-PET neuroimaging datasets in three categories will be crucial to this project; regular patients with normal tau structure(control), patients with ALD, and patients with confirmed/suspected CTE. Both region-based machine-learning classifiers and three-dimensional convolutional neural networks will then be trained using cross-validated, dataset-stratified evaluation to ensure generalizable pattern learning. Model interpretability techniques, including saliency mapping and feature attribution, will be applied to localize brain regions contributing most strongly to classification and to assess biological plausibility relative to known tau distribution patterns in neurodegenerative disease. The central research question guiding this work is: Can machine learning identify statistically significant and biologically meaningful tau-PET spatial patterns associated with repetitive head impact exposure, despite the absence of a definitive in-vivo biomarker for CTE?