Last summer I had the privilege to work at the National Institutes of Health and attend numerous lectures by amazing speakers. One reoccurring theme became clear; in order to move scientific research forward, research results and failures, need to be better shared. My brother, attending GMU, mentioned the OSCAR program and that there may be research opportunities at GMU to compliment my research goal examining brain diseases (Alzheimer’s, Parkinson, epilepsy). I did some digging and reached out to Dr. Giorgio Ascoli, at the Krasnow Institue. He put me in touch with one of his PhD graduate students, Dr. Keivan Moradi, M.D. I interviewed with Dr. Moradi and he provided me with an internship while still at NOVA.
In collaboration with GMU, the Krasnow Institute for Advanced Study works to expand scientific understanding of the mind, the brain, and intelligence by conducting research at the intersection of cognitive science, neuroscience, artificial intelligence, and complex adaptive systems. Perfect.
Dr. Ascoli, my OSCAR mentor, is the head of the Computational Neuroanatomy Group. Much of the focus of the lab is to connect the cellular organization of brain networks to cognitive functions, i.e. learning and memory, and to curate a central inventory of digitally reconstructed neurons in NeuroMorpho.Org. The Computational Neuroanatomy Group built a three-dimensional model of the hippocampus based on the classification and connectivity of hippocampal neuron types. The information is being organized into the Hippocampome knowledge base. As an intern, I expressed my interest to Dr. Ascoli and Dr. Moradi in the OSCAR program. Together, we discussed the usefulness to write a computer program that could detect synaptic signal initiation points. Having never written a computer program before, but wishing to do so, it seemed like the perfect challenge. So, now I have.
I spent the past year doing massive literature mining, running full-text searches, extracting data from peer-review neuroscience papers, and annotating large-scale Excel documents. For my OSCAR project, I first learned how to utilize Python. The actual detection of the initiation point was one of the first parts of the program. Now, the majority of the daily work comes from figuring out how to properly save, display, and format the information using Python libraries. The implementation is difficult and time consuming to read through the documentation and identify the best solution for the desired outcome. The process as to what will or will not work is heavily dependent on trial and error. I routinely meet with my mentor to make sure the project is heading towards the end goals and continue to make appropriate changes. The final goal is to process the mined data from the csv files in a user friendly format.
The one thing I discovered this semester is that, for me, everything is about research; research what opportunities are available, consider carefully shared information and where it can lead, and don’t be afraid to hear “no”. Find opportunity to work towards your goal. Don’t wait for it to knock.