Predicting Cognitive Data from Medical Images Using Sparse Linear Regression

Benjamin M. Kandel^{1, 4}, David A. Wolk², James C. Gee^{3, 4}, and Brian Avants^{3, 4}

¹Department of Bioengineering, University of Pennsylvania, USA

²Department of Neurology and Penn Memory Center, University of Pennsylvania, USA

³Department of Radiology, University of Pennsylvania, USA

⁴Penn Image Computing and Science Laboratory (PICSL), USA

Abstract. We present a new framework for predicting cognitive or other continuous-variable data from medical images. Current methods of probing the connection between medical images and other clinical data typically use voxel-based mass univariate approaches. These approaches do not take into account the multivariate, network-based interactions between the various areas of the brain and do not give readily interpretable metrics that describe how strongly cognitive function is related to neuroanatomical structure. On the other hand, high-dimensional machine learning techniques do not typically provide a direct method for discovering which parts of the brain are used for making predictions. We present a framework, based on recent work in sparse linear regression, that addresses both drawbacks of mass univariate approaches, while preserving the direct spatial interpretability that they provide. In addition, we present a novel optimization algorithm that adapts the conjugate gradient method for sparse regression on medical imaging data. This algorithm produces coefficients that are more interpretable than existing sparse regression techniques.

LNCS 7917, p. 86 ff.

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