Multivariate linear regression of high-dimensional fMRI data with multiple target variables

Authors

  • Giancarlo Valente,

    Corresponding author
    1. Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
    2. Maastricht Brain Imaging Center, Maastricht, The Netherlands
    • Correspondence to: Giancarlo Valente, Department of Cognitive Neurosciences, University of Maastricht, Faculty of Psychology and Neuroscience, P.O. Box 616, 6200 MD Maastricht, The Netherlands. E-mail: giancarlo.valente@maastrichtuniversity.nl

    Search for more papers by this author
  • Agustin Lage Castellanos,

    1. Department of Neuroimaging, Cuban Neuroscience Center, Havana, Cuba
    Search for more papers by this author
  • Gianluca Vanacore,

    1. Department of Electrical Engineering, University of Rome “La Sapienza”, Rome, Italy
    Search for more papers by this author
  • Elia Formisano

    1. Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
    2. Maastricht Brain Imaging Center, Maastricht, The Netherlands
    Search for more papers by this author

Abstract

Multivariate regression is increasingly used to study the relation between fMRI spatial activation patterns and experimental stimuli or behavioral ratings. With linear models, informative brain locations are identified by mapping the model coefficients. This is a central aspect in neuroimaging, as it provides the sought-after link between the activity of neuronal populations and subject's perception, cognition or behavior. Here, we show that mapping of informative brain locations using multivariate linear regression (MLR) may lead to incorrect conclusions and interpretations. MLR algorithms for high dimensional data are designed to deal with targets (stimuli or behavioral ratings, in fMRI) separately, and the predictive map of a model integrates information deriving from both neural activity patterns and experimental design. Not accounting explicitly for the presence of other targets whose associated activity spatially overlaps with the one of interest may lead to predictive maps of troublesome interpretation. We propose a new model that can correctly identify the spatial patterns associated with a target while achieving good generalization. For each target, the training is based on an augmented dataset, which includes all remaining targets. The estimation on such datasets produces both maps and interaction coefficients, which are then used to generalize. The proposed formulation is independent of the regression algorithm employed. We validate this model on simulated fMRI data and on a publicly available dataset. Results indicate that our method achieves high spatial sensitivity and good generalization and that it helps disentangle specific neural effects from interaction with predictive maps associated with other targets. Hum Brain Mapp 35:2163–2177, 2014. © 2013 Wiley Periodicals, Inc.

Ancillary