The effect of feature selection on multivariate pattern analysis of structural brain MR images


PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS, vol.47, pp.103-111, 2018 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 47
  • Publication Date: 2018
  • Doi Number: 10.1016/j.ejmp.2018.03.002
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.103-111
  • Keywords: Brain MR images, Multivariate pattern analysis, Feature selection, Neurodegenerative disorders, CLINICAL-PREDICTION, DIAGNOSIS, CLASSIFICATION, SCHIZOPHRENIA, ALGORITHM, NETWORKS, SCANS
  • Gazi University Affiliated: Yes


Clinical predictions performed using structural magnetic resonance (MR) images are crucial in neuroimaging studies and can be used as a successful complementary method for clinical decision making. Multivariate pattern analysis (MVPA) is a significant tool that helps correct predictions by exhibiting a compound relationship between disease-related features. In this study, the effectiveness of determining the most relevant features for MVPA of the brain MR images are examined using ReliefF and minimum Redundancy Maximum Relevance (mRMR) algorithms to predict the Alzheimer's disease (AD), schizophrenia, autism, and attention deficit and hyperactivity disorder (ADHD). Three state-of-the-art MVPA algorithms namely support vector machines (SVM), k-nearest neighbor (kNN) and backpropagation neural network (BP-NN) are employed to analyze the images from five different datasets that include 1390 subjects in total. Feature selection is performed on structural brain features such as volumes and thickness of anatomical structures and selected features are used to compare the effect of feature selection on different MVPA algorithms. Selecting the most relevant features for differentiating images of healthy controls from the diseased subjects using both ReliefF and mRMR methods significantly increased the performance. The most successful MVPA method was SVM for all classification tasks.