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The functional and structural representation of the brain as a complexnetwork is marked by the fact that the comparison of noisy and intrinsicallycorrelated high-dimensional structures between experimental conditions orgroups shuns typical mass univariate methods. Furthermore most networkestimation methods cannot distinguish between real and spurious correlationarising from the convolution due to nodes' interaction, which thus introducesadditional noise in the data. We propose a machine learning pipeline aimed atidentifying multivariate differences between brain networks associated todifferent experimental conditions. The pipeline (1) leverages the deconvolvedindividual contribution of each edge and (2) maps the task into a sparseclassification problem in order to construct the associated "sparse deconvolvedpredictive network", i.e., a graph with the same nodes of those compared butwhose edge weights are defined by their relevance for out of sample predictionsin classification. We present an application of the proposed method by decodingthe covert attention direction (left or right) based on the single-trialfunctional connectivity matrix extracted from high-frequencymagnetoencephalography (MEG) data. Our results demonstrate how networkdeconvolution matched with sparse classification methods outperforms typicalapproaches for MEG decoding.

Sparse Predictive Structure of Deconvolved Functional Brain Networks