Spatially constrained sparse regression for the data-driven discovery of Neuroimaging biomarkers
Spatially constrained sparse regression for the data-driven discovery of Neuroimaging biomarkers
Kumar, Kuldeep and Desrosiers, Christian and Chaddad, Ahmad and Toews, Matthew
Proceedings – International Conference on Pattern Recognition 2016
Abstract : Sparse multivariate regression techniques like Lasso and Elastic Net are among the most popular approaches for the identification of biomarkers related to brain diseases like Alzheimer’s. Because they use L1 norm to enforce sparsity, these approaches are often sensitive to differences in voxel intensities within the same scan or across subjects. Also, when few samples are available, such approaches can select voxels that are only correlated by chance, leading to disconnected features that do not correspond to any significant brain structure. To address these challenges, we propose a novel sparse regression method that uses the L0 norm for sparse regularization, and imposes spatial consistency constraints on the selected features without requiring an atlas of pre-defined regions. This method uses an efficient optimization strategy based on the Alternating Direction Method of Multipliers (ADMM), that can scale to large data matrices. The performance of the proposed method is evaluated using synthetic data and 3429 T1-weighted (MP-RAGE) images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results show our method to outperform Lasso and Elastic Net regression in the recovery of spatially consistent features corresponding to known neuroimaging biomarkers.
