How to Build a Functional Connectomic Biomarker for Mild Cognitive Impairment From Source Reconstructed MEG Resting-State Activity: The Combination of ROI Representation and Connectivity Estimator Matters

@article{Dimitriadis2018HowTB,
  title={How to Build a Functional Connectomic Biomarker for Mild Cognitive Impairment From Source Reconstructed MEG Resting-State Activity: The Combination of ROI Representation and Connectivity Estimator Matters},
  author={Stavros I. Dimitriadis and Mar{\'i}a Eugenia L{\'o}pez and Ricardo Bru{\~n}a and Pablo Cuesta and Alberto Marcos and Fernando Maest{\'u} and Ernesto Pereda},
  journal={Frontiers in Neuroscience},
  year={2018},
  volume={12}
}
Our work aimed to demonstrate the combination of machine learning and graph theory for the designing of a connectomic biomarker for mild cognitive impairment (MCI) subjects using eyes-closed neuromagnetic recordings. The whole analysis based on source-reconstructed neuromagnetic activity. As ROI representation, we employed the principal component analysis (PCA) and centroid approaches. As representative bi-variate connectivity estimators for the estimation of intra and cross-frequency… 
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