Statistical and Machine Learning Methods for Neuroimaging: Examples, Challenges, and Extensions to Diffusion Imaging Data

Lauren O'Donnell and Thomas Schultz
In: Visualization and Processing of Higher Order Descriptors for Multi-Valued Data, pages 299-319, Springer, 2015
 

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.

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Bibtex

@INCOLLECTION{ODonnell:TenDag2015,
     author = {O'Donnell, Lauren and Schultz, Thomas},
      pages = {299--319},
      title = {Statistical and Machine Learning Methods for Neuroimaging: Examples, Challenges, and Extensions to
               Diffusion Imaging Data},
  booktitle = {Visualization and Processing of Higher Order Descriptors for Multi-Valued Data},
     series = {Mathematics and Visualization},
       year = {2015},
  publisher = {Springer},
   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.}
}