Structural neuroimaging as clinical predictor: A review of machine learning applications.

Journal: NeuroImage. Clinical
Published Date: Aug 10, 2018

Abstract

In this paper, we provide an extensive overview of machine learning techniques applied to structural magnetic resonance imaging (MRI) data to obtain clinical classifiers. We specifically address practical problems commonly encountered in the literature, with the aim of helping researchers improve the application of these techniques in future works. Additionally, we survey how these algorithms are applied to a wide range of diseases and disorders (e.g. Alzheimer's disease (AD), Parkinson's disease (PD), autism, multiple sclerosis, traumatic brain injury, etc.) in order to provide a comprehensive view of the state of the art in different fields.

Authors

  • José María Mateos-Pérez
    Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
  • Mahsa Dadar
    Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
  • María Lacalle-Aurioles
    Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
  • Yasser Iturria-Medina
    Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
  • Yashar Zeighami
    Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
  • Alan C Evans
    McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada.

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