Spatial Regularization for Multitask Learning and Application in fMRI Data Analysis

Functional magnetic resonance imaging (fMRI) has become one of the most widely used techniques in investigating human brain function over the past two decades. However, the analysis of fMRI data is extremely complex due to its difficulties in big data processing, complicated structure of relationship between hemodynamic response and brain activity, and analysis using advanced technology and sophisticated techniques for classification and pattern recognition. Hence, efficient and accurate machine learning models are necessary to interpret fMRI data by incorporating spatial with temporal information. In this paper, we investigate a class of spatial multitask learning models which incorporates spatial information of each task's neighborhood. Simulation and real application results show satisfactory performance from spatial multitask learning algorithms.