Gandhi Rohan Pankaj received his MS  Dual Degree in Computer Science and Engineering (CSE). His research work was supervised by Dr. Vinoo A. Here’s a summary of Gandhi Rohan Pankaj’s thesis Identifying Unique Brain Signatures Among Individuals Using Supervised Machine Learning:

Recent studies in neuroscience suggest that a unique individual-specific functional connectivity pattern, or brain signature exists in individuals which can be identified by capturing correlated brain activity using functional Magnetic Resonance Imaging (fMRI). All the previous research done in this subject, brief as it is, has utilized some sort of unsupervised similarity measure to identify the individuals, along with mostly using only a static functional connectivity measure. The objective of our thesis is to utilize the functional connectivity patterns to identify an individual using a supervised machine learning based classification approach. To this end, we use the results of static and dynamic functional connectivity (sFC and dFC, respectively) measures on different fMRI datasets as feature sets for different linear and non-linear classification models to evaluate how well the individuals can be identified. For this, we utilize two previously published datasets comprising of naturalistic music listening task-based fMRI response and resting-state fMRI response. We also aimed to find the neural correlates to the brain signatures for identifying the individuals. We found that the classification models using dynamic FC outperform their counterpart, more specifically, the Instantaneous Phase Synchrony (IPS) method of dFC analysis works better than correlation-based windowed analysis. we also found homotopic connectivity of various regions in brain to be an important part of the brain networks used to identify individuals, while the Olfactory Cortex came up repeatedly as an important brain region as well. For the next part of our study, we observed the effects of band-pass filtering and changing the sample size on identifying individuals using the IPS method of dFC analysis by utilizing the minimally pre-processed HCP dataset. We found that performing a band-pass filter greatly boosts the classification accuracy, which stays stable even when the sample size of individuals is increased. Overall, this study shows a lot of promise on using a machine learning approach to identify individuals while using IPS as a dFC measure and presents a novel direction to investigate further. Keywords: identifying individuals, dynamic functional connectivity, instantaneous phase synchrony, fMRI, machine learning, classification, naturalistic paradigm.