, Volume 12, Issue 2, pp 229–244

A Review of Feature Reduction Techniques in Neuroimaging


DOI: 10.1007/s12021-013-9204-3

Cite this article as:
Mwangi, B., Tian, T.S. & Soares, J.C. Neuroinform (2014) 12: 229. doi:10.1007/s12021-013-9204-3


Machine learning techniques are increasingly being used in making relevant predictions and inferences on individual subjects neuroimaging scan data. Previous studies have mostly focused on categorical discrimination of patients and matched healthy controls and more recently, on prediction of individual continuous variables such as clinical scores or age. However, these studies are greatly hampered by the large number of predictor variables (voxels) and low observations (subjects) also known as the curse-of-dimensionality or small-n-large-p problem. As a result, feature reduction techniques such as feature subset selection and dimensionality reduction are used to remove redundant predictor variables and experimental noise, a process which mitigates the curse-of-dimensionality and small-n-large-p effects. Feature reduction is an essential step before training a machine learning model to avoid overfitting and therefore improving model prediction accuracy and generalization ability. In this review, we discuss feature reduction techniques used with machine learning in neuroimaging studies.


Feature reductionFeature selectionDimensionality reductionMachine learningMultivariatePredictive ModelingNeuroimaging

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Benson Mwangi
    • 1
  • Tian Siva Tian
    • 2
  • Jair C. Soares
    • 1
  1. 1.UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral SciencesUT Houston Medical SchoolHoustonUSA
  2. 2.Department of PsychologyUniversity of HoustonHoustonUSA
  3. 3.Department of Psychiatry and Behavioral SciencesUniversity of Texas Health Science Center at HoustonHoustonUSA