Week 8: Preliminary Training

This week, I transitioned from preparing data and building the pipeline to training models. I started training our machine learning model on two types of neuroimaging data: healthy controls and patients with ALD(Alzheimer’s Disease). We trained our model and measured its accuracy using various classification metrics (based on cross-validation) to evaluate the performance of the different neuroimaging modalities (those being processed through the pipeline). While the model’s accuracy has not yet reached a level sufficient for clinical use, it is producing a significant signal. The model is detecting true patterns within the data and is therefore not randomly guessing. This is a good sign and a solid starting point to work from.

I also spent time developing materials for the final presentation. This included drafting slide decks to communicate the project’s rationale and methodology. This process also helped us refine how we wanted to frame the project’s goals and the biological questions we wanted to answer with it. Once I receive my final results, I will incorporate them into the slideshow.

Looking forward, the most important next step is submitting a data access request to the DIAGNOSE CTE study. If approved, this dataset of tau PET scans from former contact sport athletes will add a third and scientifically critical class to the model, enabling it to distinguish not just healthy versus pathological tau, but also to detect the distinct tau distribution patterns associated with CTE. This is a major addition, completing my dataset search and setting up my model for the last phase of rigorous training.