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Robust Detection of Impaired Resting State Functional Connectivity Networks in Alzheimer's Disease Using Elastic Net Regularized Regression
2017
Frontiers in Aging Neuroscience
The large number of multicollinear regional features that are provided by resting state (rs) fMRI data requires robust feature selection to uncover consistent networks of functional disconnection in Alzheimer's disease (AD). Here, we compared elastic net regularized and classical stepwise logistic regression in respect to consistency of feature selection and diagnostic accuracy using rs-fMRI data from four centers of the "German resting-state initiative for diagnostic biomarkers" (psymri.org),
doi:10.3389/fnagi.2016.00318
pmid:28101051
pmcid:PMC5209379
fatcat:c5n77bhfnjb5vnofjuxgxwv3vy