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Prediction of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using MRI and Structural Network Features
2016
Frontiers in Aging Neuroscience
Optimized magnetic resonance imaging (MRI) features and abnormalities of brain network architectures may allow earlier detection and accurate prediction of the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). In this study, we proposed a classification framework to distinguish MCI converters (MCIc) from MCI non-converters (MCInc) by using a combination of FreeSurfer-derived MRI features and nodal features derived from the thickness network. At the feature selection
doi:10.3389/fnagi.2016.00076
pmid:27148045
pmcid:PMC4836149
fatcat:ikzulhkrlzbftokpf2z7vfg3pi