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B1282
Title: Analyzing group fMRI with multilayer network embedding methods Authors:  James Wilson - University of San Francisco (United States) [presenting]
Abstract: Learning interpretable features from complex multilayer networks is a challenging and important problem. The need for such representations is particularly evident in multilayer networks of the brain, where nodal characteristics may help model and differentiate regions of the brain according to individual, cognitive task, or disease. Motivated by this problem, we introduce the multi-node2vec algorithm, an efficient and scalable feature engineering method that automatically learns continuous node feature representations from multilayer networks. A second-order random walk sampling procedure that efficiently explores the inner- and intra- layer ties of the observed multilayer network is utilized to identify multilayer neighborhoods. Maximum likelihood estimators of the nodal features are identified through the use of the Skip-gram neural network model on the collection of sampled neighborhoods. We demonstrate the efficacy of multi-node2vec on a multilayer functional brain network from resting state fMRI scans over a group of 74 healthy individuals and 70 patients with varying degrees of schizophrenia. Findings reveal that multi-node2vec identifies regional characteristics that closely associate with the functional organization of the brain and offer insights into the differences between the patient and healthy groups.