Elastic-net Multiple Kernel Learning for multi-region neuroimaging-based diagnosis

Multiple Kernel Learning (MKL) has been proposed as an approach to simultaneously learn the kernel weights and the associated decision function in supervised learning settings (eg [1]). In the context of neuroimaging-based classification MKL framework can be applied to investigate the relative contributions of different image modalities and/or brain regions. Here we applied the Elastic-net MKL as an exploratory approach to learn the optimal combination of individual brain regions that best discriminate depressed patients versus healthy subjects based on patterns of fMRI activation to sad faces. Our results show that the application of Elastic-net MKL to neuroimaging data can lead to anatomically interpretable classification models and potentially improve the accuracy with respect to the whole-brain accuracy. In addition it can provide insights about how a psychiatric or neurologic disorder affects the brain, ie sparse vs. distributed effects.