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We construct embedded functional connectivity networks (FCN) from benchmarkresting-state functional magnetic resonance imaging (rsfMRI) data acquired frompatients with schizophrenia and healthy controls based on linear and nonlinearmanifold learning algorithms, namely, Multidimensional Scaling (MDS), IsometricFeature Mapping (ISOMAP) and Diffusion Maps. Furthermore, based on key globalgraph-theoretical properties of the embedded FCN, we compare theirclassification potential using machine learning techniques. We also assess theperformance of two metrics that are widely used for the construction of FCNfrom fMRI, namely the Euclidean distance and the lagged cross-correlationmetric. We show that the FCN constructed with Diffusion Maps and the laggedcross-correlation metric outperform the other combinations.

Construction of embedded fMRI resting state functional connectivity networks using manifold learning