2017/03/06 by Mathilde Ménoret, Ménoret, Mathilde, Nicolas Farrugia +5 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Neuroscience · #Advanced Graph Neural Networks #Bioinformatics and Genomic Networks #Functional Brain Connectivity Studies #cs.CV #q-bio.NC #stat.ML
paper · pdf · doi:10.48550/arxiv.1703.01842
5 pages, GlobalSIP 2017
arxiv created 2017/08/28 · arxiv updated 2017/08/29
Graph Signal Processing (GSP) is a promising framework to analyze multi-dimensional neuroimaging datasets, while taking into account both the spatial and functional dependencies between brain signals. In the present work, we apply dimensionality reduction techniques based on graph representations of the brain 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 perform better than classical dimension reduction including Principal Component Analysis (PCA) and Independent Component Analysis (ICA).