With modern imaging techniques, massive imaging data can be observed over both time and space. Such imaging techniques include functional magnetic resonance imag- ing (fMRI), electroencephalography (EEG), diffusion tensor imaging (DTI), positron emission tomography (PET), and single photon emission- computed tomography (SPECT) among many other imaging techniques. The subject of neuroimaging analysis has exploded from simple algebraic operations on imaging data to advanced statistical and mathematical methods on imaging data. This course on statistical methods for NDA is designed to provide students the detailed mathematical and statistical techniques underlying imaging techniques (e.g., imaging cluster) used in the field of medical image analysis, with an emphasis on computer implementation.
This course is designed for researchers and students who wish to analyze and model medical image data quantitatively. The course material is applicable to a wide variety of medical and biological imaging problems. The topics cover basic statistical principle, functional magnetic resonance imaging, diffusion tensor imaging, functional connectivity, image feature, image segmentation, image registration, shape representation, population statistics, imaging genetics, predictive models, data mining, and big data integration. This course will cover the mathematical and statistical fundamentals and implementation of these methods. For instance, participants will learn basics that will help them to understand the methods and tools built into packages like SPM, FSL, Slicers, and others in order to optimally use them.