Week 1: Prelude, Project, and Preparation

Hi all! My name is Aditya Nautiyal, and my project is on Chronic Traumatic Encephalopathy, specifically using Tau Positron Emission Tomography and Machine Learning to detect, define, and differentiate it from other tau-pathological diseases. 

A 2023 Boston University study found that 92% of studied brains of NFL players reported that they had brain trauma of some kind. Chronic Traumatic Encephalopathy, usually referred to as CTE, plagues professional athletes who play contact sports. Currently however, there is no way to diagnose CTE within athletes while they are alive. This stems from a multitude of factors, specifically being the in-depth analysis required of identifying a specific tau protein pattern unique to the disease. Tau proteins stabilize microtubules in the brain, essentially providing a track for transporting nutrients and components. However, trauma to the brain induces mechanical shear, which causes these proteins to misfold and thus hinder their function. This leads to reduced brain function, loss of memory, and other symptoms we commonly see in athletes with long-term exposure to brain contact/trauma/injury.

Alzheimer’s disease, also referred to as ALD, is similar in that it is a tauopathy(tau-related neurodegenerative disease). Currently, there is research that experiments with a process known as Tau Positron Emission Tomography(Tau PET). Tau PET is a hotspot detection system that highlights tau proteins within the brain, helpful for identifying a tau-related neurodegenerative disease. It works by injecting a biomarker, usually 18F-flortaucipir (Tauvid), that tracks and binds to the tau proteins, and then gives an emission detectable by the scan that determines hotspots of Tau. These hotspots are particularly useful as “clumping” is an indicator of the disease. 

With these scans existing in datasets, I want to analyze them and use machine learning to determine what quantifies the difference between ALD and CTE in tau protein buildup. The difference will most likely be in the regional clumping of tau, providing insight to how they stem from different patterns/locations. My final product will be a developed machine learning model that can differentiate CTE from other tau-related neurodegenerative diseases like ALD.

As for my activities, Week 1 helped me gain a baseline understanding of Machine Learning and how I will leverage it in my project. I spent around 10 hours watching videos, taking quizzes, and doing online labs as part of the 3 week online course I have enrolled in. The other 6 hours were spent reading the literature outlined in my syllabus.