Student Posters Repository

White matter refers to the myelin that covers axons in the brain and helps regulate conduction velocity in the transmission of neural signals. It plays a vital role in neural communication and maintaining neural synchrony across brain areas; when disrupted it can lead to neurological disorders and cognitive deficits. In this research, I study the role of myelin in shaping human brain dynamics through computational simulation of biophysical neural network models based on anatomical connectome data. I present a computational pipeline for estimating certain network characteristics by combining neural population activity measurements such as EEG/MEG, neural modeling and an evolutionary optimization algorithm. The goal is to find the optimal parameters of the network model to replicate the brain activity measured in vivo. Thus, the physiological representation of the parameters will give insight on the white matter structure in the clinical participants’ brain networks. For optimization of my model parameters, I use a modified version of the differential evolution algorithm. Traditional machine learning approaches are not employable because my model lacks a gradient. The search space is very large due to the high dimensionality of the problem. As such, I parallelize the searches using the MPI framework (mpi4py for Python) to decrease computation time and increase the breadth of search. I have applied this approach to estimate network weights and also isolate conduction velocities along myelin tracts in the network model. Currently I am working on estimating temporal delays in interactions of brain regions. The use of this computational pipeline has the potential to be useful for understanding various neural disorders such as Alzheimer’s, and be used as a diagnostic tool.