Research Project
10378845
Project Summary Much mental health research uses neurophysiological measurements to describe the way neural activity within and across brain regions is related to behavioral function and dysfunction. One kind of signal, known as a spike train, comes from an individual neuron. Another, the local field potential (LFP), is based on activity from large numbers of neurons within specified parts of the brain. For both kinds of data, scientifically rigorous statistical analysis must accommodate unstable fluctuations, associated with movement or thought, known in statistics as non- stationarity. The continuing research program of this grant is to develop methods for analyzing non-stationary neural data. Of particular interest is the description of interactions among two or more brain areas. This application is for an administrative supplement to support an under-represented minority PhD student for two years. As documented by, among others, the National Science Foundation, people of African ancestry are severely under-represented in the sciences. The training provided to the candidate, under this supplement, would serve to elevate the candidate's research profile, and would ultimately contribute to enhancing diversity in the STEM workforce. The candidate's research concerns methods for identifying the flow of information from one brain area to another based on neural spike trains. Two major complications are, first, the noisiness of spike trains as conveyors of information and, second, the large numbers of neurons that must be considered simultaneously. Statistical methods developed very recently, through research supported by this grant, suggest very promising approaches to reducing the effects of noise and grappling with large networks of neurons. Based on these ideas, the candidate will develop a PhD thesis topic, and pursue it, with support from this supplement.
