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Paolo Avesani: Poster presentation at the NIPS Workshop Machine Learning in Neuroimaging (MLINI)

News date: 
Saturday, 13 December, 2014
At the MLINI workshop, Paolo Avesani presented joint work by M. Kia, E. Olivetti, S. Vega Pons and P. Avesani in a poster entitled "Multi-Task Learning for Interpretation of Brain Decoding Models".
Improving the interpretability of multivariate models is of primary interest for many neuroimaging studies. In this study, we present an application of multi-task learning (MTL) to enhance the interpretability of linear classifiers once applied to neuroimaging data. To attain our goal, we propose to divide the data into spatial fractions and define the temporal data of each spatial unit as a task in MTL paradigm. Our result on magnetoencephalography (MEG) data reveals preliminary evidence that 1)dividing the brain recordings into spatial fractions basedon spatial units of data; and 2) considering each spatial fraction as a taskand learning patterns of activities within and between tasks simultaneously;are two factors that provide more stability and consequently more interpretability for brain decoding models.