Friday, November 8, 2013

URSP Student Alexandra Casillas Measures Iron Accumulation and Brain Atrophy in Multiple Sclerosis Patients using MRI Images

The project that I am currently working on involves measuring iron accumulation and brain atrophy in patients with multiple sclerosis with the use of MRI technology. I am an aspiring radiologist, so the first thing that really peaked my interest was the idea of being able to work with MRI images and medical imaging tools. After further reading about the project and meeting my mentor, I was able to understand how important this project was going to be in the multiple sclerosis (MS) field. There is currently no cure for MS, so any information regarding different aspects of the disease is essential. Not only was this going to be an amazing opportunity to be able to work on, but I also knew that I was going to learn a lot considering I only possessed basic knowledge of MS and MRI technology at the time.

This project is very much related to my long-term goal because I want to become a radiologist and ultimately analyze medical images for a living. There are a variety of different areas to specialize in within the field of radiology, so being able to work with neurology now (and hopefully other areas later on) will help me narrow down what parts of the body I am most passionate about.

I do a lot of similar work from week to week. My mentor and I try to meet once a week and in these meetings we will discuss what is going on with the project right now and then we will analyze an article together and see what type of research others are doing with MS and whether there is other evidence out there to prove what we are doing is headed in the right direction. What I do on a weekly basis has changed since I started this project, but right now I mainly have been working with the data and am trying to find some statistical significances between different variables. We are trying to find correlations between a few of the key aspects involved in MS. This involves trying to figure out which set of data will be the best to use and then running various statistical tests, like one and two sample t-tests, spearman correlations, pearson correlations, etc. Once the tests are run, I analyze the numbers, see if there is any significance, and whether there is significance or not, I have to ask myself what exactly does this mean for the data?

One thing that I have learned this week is the correct way to run spearman correlations in R Studio! (Statistical software.)