Statistical and machine learning methods for neuroimaging: Examples, challenges, and extensions to diffusion imaging data

Abstract

In neuroimaging research, a wide variety of quantitative computational methods enable inference of results regarding the brain’s structure and function. In this chapter, we survey two broad families of approaches to quantitative analysis of neuroimaging data: statistical testing and machine learning. We discuss how methods developed for traditional scalar structural neuroimaging data have been extended to diffusion magnetic resonance imaging data. Diffusion MRI data have higher dimensionality and allow the study of the brain’s connection structure. The intended audience of this chapter includes students or researchers in neuroimage analysis who are interested in a high-level overview of methods for analyzing their data.