The application of statistical machine learning techniques to neuroimaging data has allowed researchers to decode the cognitive and disease states of participants. The majority of studies using these techniques have focused on pattern classification to decode the type of object a participant is viewing, the type of cognitive task a participant is completing, or the disease state of a participant’s brain. However, an emerging body of literature is extending these classification studies to the decoding of values of continuous variables (such as age, cognitive characteristics, or neuropsychological state) using high-dimensional regression methods. This review details the methods used in such analyses and describes recent results. We provide specific examples of studies which have used this approach to answer novel questions about age and cognitive and disease states. We conclude that while there is still much to learn about these methods, they provide useful information about the relationship between neural activity and age, cognitive state, and disease state, which could not have been obtained using traditional univariate analytical methods.
Keywords: predictive analysis, fMRI, high-dimensional regression, multivariate decoding, machine learning
Citation: Cohen JR, Asarnow RF, Sabb FW, Bilder RM, Bookheimer SY, Knowlton BJ and Poldrack RA (2011) Decoding continuous variables from neuroimaging data: basic and clinical applications. Front. Neurosci. 5:75. doi: 10.3389/fnins.2011.00075
Received: 01 February 2011;
Accepted: 16 May 2011;
Published online: 15 June 2011.
Edited by:Silvia A. Bunge, University of California Berkeley, USA
Copyright: © 2011 Cohen, Asarnow, Sabb, Bilder, Bookheimer, Knowlton and Poldrack. This is an open-access article subject to a non-exclusive license between the authors and Frontiers Media SA, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and other Frontiers conditions are complied with.
*Correspondence: Jessica R. Cohen, University of California, Berkeley, Helen Wills Neuroscience Institute, 132 Barker Hall, Berkeley, 94720, CA, USA, email@example.com