Mathematical and computational methods in neuroscience imaging

Spectacular advances in the biomedical imaging field during the last decade, e.g. microscopy, optics, molecular techniques and genetics, have massively increased the availability of data and expanded the range of the possible. High-resolution fluorescent microscopy and high-throughput technologies for instance, enable the accurate and efficient visualization of neurons, glia and their subcellular compartments both in vitro and in vivo. Despite these impressive technological advances though, there is still a critical need to develop more powerful conceptual and computational methods to extract meaningful quantitative information and provide reliable interpretation of the data. In this talk, I will illustrate state-of-the art methods for the analysis and interpretation of neuroimaging data including advanced multiscale representations and emerging learning-based algorithms targeted to problems from neuroscience. The research presented in this talk includes contributions from several graduate and undergraduate students who have worked under my direction at the University of Houston during the past 10 years.