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A Review of Feature Reduction Techniques in Neuroimaging

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Abstract

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.

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Acknowledgments

This research was funded by NIMH R01085667 and Pat Rutherford, Jr. Endowed Chair in Psychiatry (UT Medical School) grants to J.C.S.

Conflict of Interest

J.C.S has participated in research funded by Forest, Merck, BMS and GSK. He has been a speaker for Pfizer and Abbot.

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  1. UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, 77054, USA

    Benson Mwangi & Jair C. Soares

  2. Department of Psychology, University of Houston, Houston, TX, 77024, USA

    Tian Siva Tian

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  1. Benson Mwangi
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Mwangi, B., Tian, T.S. & Soares, J.C. A Review of Feature Reduction Techniques in Neuroimaging. Neuroinform 12, 229–244 (2014). https://doi.org/10.1007/s12021-013-9204-3

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