Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction

Graph Signal Processing (GSP) is a promising method to analyze high-dimensional neuroimaging datasets while taking into account both the spatial and functional dependencies between brain signals. In the present work, we apply GSP with dimensionality reduction techniques to decode brain activity from real and simulated fMRI datasets. We introduce seven graphs obtained from a) geometric structure and/or b) functional connectivity between brain areas at rest and compare them when performing dimension reduction for classification. We show that mixed graphs using both a) and b) offer the best performance. We also show that graph sampling methods works better than classical dimension reduction methods including Principal Component Analysis (PCA) and Independent Component Analysis (ICA).