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A Tensor-Based Framework for rs-fMRI Classification and Functional Connectivity Construction
2020
Frontiers in Neuroinformatics
Recently, machine learning methods have gained lots of attention from researchers seeking to analyze brain images such as Resting-State Functional Magnetic Resonance Imaging (rs-fMRI) to obtain a deeper understanding of the brain and such related diseases, for example, Alzheimer's disease. Finding the common patterns caused by a brain disorder through analysis of the functional connectivity (FC) network along with discriminating brain diseases from normal controls have long been the two
doi:10.3389/fninf.2020.581897
pmid:33328948
pmcid:PMC7734298
fatcat:bikmkhxvg5hc3kk3sxczx3xm7e