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Functional network connectivity during rest and task: Comparison of healthy controls and schizophrenic patients., and . EMBC, page 4418-4421. IEEE, (2011)ICA order selection based on consistency: Application to genotype data., , and . EMBC, page 360-363. IEEE, (2012)Multimodal Data Fusion of Deep Learning and Dynamic Functional Connectivity Features to Predict Alzheimer's Disease Progression., , , and . EMBC, page 4409-4413. IEEE, (2019)Detecting volumetric changes in fMRI connectivity networks in schizophrenia patients., , , and . EMBC, page 726-729. IEEE, (2014)The chronnectome: Evaluating replicability of dynamic connectivity patterns in 7500 resting fMRI datasets., , , and . EMBC, page 5571-5574. IEEE, (2016)Identification of patterns of gray matter abnormalities in schizophrenia using source-based morphometry and bagging., , , , , and . EMBC, page 1513-1516. IEEE, (2014)A GICA-TVGL framework to study sex differences in resting state fMRI dynamic connectivity., , , , and . Medical Imaging: Image Processing, volume 11313 of SPIE Proceedings, page 113132F. SPIE, (2020)Graph Modularity and Randomness Measures : A Comparative Study., , and . SSIAI, page 33-36. IEEE Computer Society, (2018)Evaluating Models of Dynamic Functional Connectivity Using Predictive Classification Accuracy., , , , , , , and . ICASSP, page 2566-2570. IEEE, (2018)Decentralized independent vector analysis., , , , and . ICASSP, page 826-830. IEEE, (2017)