I started my journey as a biomedical engineer focused on neural engineering, where I developed computer vision methods to understand brain activity, cognitive control, and neural activation.
Investigating the Intersession Reliability of Dynamic Brain-State Properties
We analyzed resting-state functional magnetic resonance imaging data from 100 Human Connectome Project subjects were compared across 2 scan days. Brain states (i.e., patterns of coactivity across regions) were identified by classifying each time frame using k means clustering with and without global signal regression (GSR). We investigated the consistency in the brain-state properties across days and GSR attenuated the reliability of the brain states as well as changes in the brain-state properties across the course of the scan were investigated as well. The results demonstrate that summary metrics describing the clustering of individual time frames have adequate test/retest reliability, and thus, these patterns of brain activation may hold promise for individual-difference research.
Published in Brain Connectivity 2018
Reverse-Correlation Analysis of Mechanosensation Circuit in C. elegans Reveals Temporal and Spatial Encoding
We use a custom tracking and optogenetics platform to characterize and compare two mechanosensory systems in C. elegans: the gentle touch sensing TRNs and harsh touch sensing PVDs. Through system modeling and computer vision techniques, we developed linear filters that capture dynamics that are consistent with previous findings, as well as provided new insights on the spatial encoding of the TRN and PVD neurons. Our results capture the overall dynamics of behavior induced by the activation of sensory neurons, providing simple transformations that fully characterize these systems.
Published in Nature Scientific Reports 2019