Striving to Find a Physiological Indicator of Autism Risk at NIRAL – with Elias Mohammad

Hello! My name is Elias Mohammad and I am a junior from Wake Forest, NC. I am currently pursuing a B.S. in both Neuroscience and Exercise Sports Science with a minor in Chemistry. I am very interested in neuroscience and have shadowed and worked with several neurosurgeons at UNC with the goal of attending medical school and diving into the field of neurosurgery.

By attending an early college in high school, I was introduced to many fields of interest at a young age. I always knew I wanted to get into the medical field but never truly knew what specific field I wanted to get into. I initially wanted to become an orthopedic surgeon because as an athlete for many years, I unfortunately had several surgeries on my knees and was always doing physical therapy. It was a field I saw myself get into as it involved sports athletes as well as a way to find a solution to several patients. However, as I started shadowing and working with Dr. Deb Bhowmick, a neurosurgeon at UNC, I started to see how I was much more interested in the brain because of the overall use of medicine. The brain’s functions are so vast and broad and there is so much to learn and is truly what excites me. Moreover, I also started to work with Dr. Peter Gordon, a professor at UNC, in The Language, Cognition, and Brain Lab where I began to do several literature reviews and research about the cognition of the brain and how it works. Also, being a medical scribe in the Neuro ICU truly taught me how the brain really impacts each system of the body. Whether it may be a seizure or a hemorrhage, there was always focus on the other systems of the body because of its consequent effects. Through the various opportunities I was able to partake in such as scribing, working with Dr. Bhomick, and enjoying my neuroscience classes, I decided to complete an honors thesis with Dr. Gordon on the effects of music on the Testing effect. All these opportunities have set me up well to know what I am truly interested especially moving forward in my academic career.

This semester I will be working with Dr. Styner in the NIRAL lab, Neuro imaging and Research Analysis Lab, as a part of the Gil Internship whom I am very appreciative of to allow me to explore my interests even further. The work I will be getting involved in is research that revolves around analyzing an existing autism study (where we study kids at high familial risk for ASD) for associations between myelin content in the white matter (via T1w/T2w ratio) and ASD diagnosis (potentially also ASD symptomology). This study has had similar results to other experiments that showed there was less myelin content in 6-12 month old children that later were diagnosed with autism. However, the way it was done by UNC compared to other known studies such as a Swedish study is much different. In the Swedish study, the calibration of the MRI images used the eye as a point of calibration relative to the interstitial muscle. This works really well because they are both in close proximity as well as the eye being a shaded dark relative to the light shade of the brain which is optimal for an image to be calibrated. The UNC study does an unusual method where the point of calibration is the Bucoul fat (cheek fat) relative to the outside region of the brain. So I will be replicating the Swedish study with some minor changes in procedures and calibrating those MRI images of the UNC study. This will allow us to see how it compares to the Swedish study and evaluate how accurate both methods are through quality control. I will be using a specific software to do this called ITKSNAP to calibrate and segment specific parts of the brain into a template. The process involves working through the T1 and T2 weighted MRI slices of the brain using the coronal, axial, and sagittal viewpoints and labeling. Once the segmentations are done manually, we use machine learning algorithms to improve computer automated segmentation. As the manual segmentations become more precise and accurate, the machine learning algorithms have better input data from which to base their automatic segmentation of future MRI’s that are segmented. The infants and children analyzed are part of a larger autism study, so we will know which infants go on to develop autism and which do not. Currently, the methods employed to diagnose autism is mainly behavioral. With the research being completed this semester, it will hopefully find a a physiological predictor of autism risk so that a child can be diagnosed sooner rather than later as well as there being a physical image representation of the diagnosis rather than just a behavioral assessment.

Apart from the work I do in the lab, the opportunities that this internship has presented to me is extremely helpful especially in the sense of professional development. By being able to update our resumes and CV’s properly to speaking with past alumni that are now in graduate or medical school, I have been given a lot of insight and advice on how I can pursue my unique path. Now I am able to see a future after graduation and the things I know need to be done in order for me to be successful in the field I want to get into. I am extremely grateful to Dr. Buzinski for the opportunity to be apart of this program and all the values I have learned and will carry with me in the future.