My name is Maulik Sarin. I am a senior Neuroscience and Psychology major and Chemistry minor at UNC Chapel Hill. I am from Weddington, NC and knew before matriculating to UNC that I wanted to be a Gil intern.
As a Gil intern this year, I am working with Dr. Martin Styner, codirector of the Neuroimaging and Research Analysis Lab (NIRAL). My work this semester revolves around Extra-Axial Cerebrospinal Fluid (EA-CSF). Until recently, the purpose and functions of EA-CSF have remained mostly undiscovered in the field of Neuroscience. Now however, there is growing evidence that the development of the ventricles and flow of EA-CSF early in development has many long-term impacts on the healthy growth of a brain. As part of my internship, I am using a program called ITK-SNAP (developed by Dr. Styner) to manually segment EA-CSF onto infant MRI data. The process involves working through the T1 and T2 weighted MRI slices of the brain through the coronal, axial, and sagittal viewpoints and labeling what we believe to be EA-CSF. In much simpler and coarse terms, I am painting EA-CSF on infant brains using a three-dimensional version of MS paint (oversimplifying heavily here). Once the segmentations are done manually, we use machine learning algorithms to improve computer-automated segmentation. As the manual segmentations become more precise, the machine learning algorithms have better input data from which to base their automatic segmentation. In the future, it is likely that machine learning algorithms will be able to identify different brain regions of MRI data.
However, teaching the computer to label EA-CSF is only one small part of this internship. Once we have labeled where the EA-CSF is in various infant brains, we can calculate the volume and distribution of EA-CSF in these brains. Using this data, we may find relationships between other physiological variables and behavioral outcomes. The infants analyzed are part of a larger autism study, so we will know which infants go on to develop autism and which do not. We are currently working on finding the trend between EA-CSF distribution and placental vascularization, as this has been proposed as linked and a possible indicator of future autism. Current methods to diagnose autism are mostly behavioral and are only accurate after the advancement of the condition. Our work this semester may (hopefully) bring us closer to a physiological predictor of autism risk that works before the presentation of the condition itself.
Apart from this engaging work, the Gil intern has presented me so many opportunities to grow. It is by far my favorite experience at UNC. I have been able to network with people in the top of their fields and with peers with similar interests and ambitions. The internship has helped me develop professionally and as a person. Interns are given much responsibility, often more than their classes have ever given them. And with this responsibility, I learned much, but most importantly, gained an appreciation of and a confidence in my abilities. Life after graduation appears less daunting and more exciting as a direct result of this internship. I am immensely thankful to Dr. Buzinski and Chelsea Ewing for their commitment to the program. And despite the ongoing pandemic, the Gil program has connected me with awesome friends who I may share my interests and passions with.