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oai:docta.ucm.es:20.500.14352/1001652025-03-18T12:50:39Zcom_20.500.14352_14col_20.500.14352_15

López García, María Eugenia Bruña Fernández, Ricardo Cuesta Prieto, Pablo Marcos Dolado, Alberto Maestu Unturbe, Fernando 2024-02-08T08:15:19Z 2024-02-08T08:15:19Z 2018-06-01 Dimitriadis SI, López ME, Bruña R,Cuesta P, Marcos A, Maestú F andPereda E (2018) How to Build aFunctional Connectomic Biomarker forMild Cognitive Impairment FromSource Reconstructed MEGResting-State Activity: The Combination of ROI Representationand Connectivity Estimator Matters.Front. Neurosci. 12:306 doi:10.3389/fnins.2018.00306 1662-4548 10.3389/fnins.2018.00306 https://hdl.handle.net/20.500.14352/100165 1662-453X https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2018.00306/full 29910704 https://pubmed.ncbi.nlm.nih.gov/29910704/ 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 interactions, we adopted the phase locking value (PLV), the imaginary part (iPLV) and the correlation of the envelope (CorrEnv). Both intra and cross-frequency interactions (CFC) have been estimated with the three connectivity estimators within the seven frequency bands (intra-frequency) and in pairs (CFC), correspondingly. We demonstrated how different versions of functional connectivity graphs single-layer (SL-FCG) and multi-layer (ML-FCG) can give us a different view of the functional interactions across the brain areas. Finally, we applied machine learning techniques with main scope to build a reliable connectomic biomarker by analyzing both SL-FCG and ML-FCG in two different options: as a whole unit using a tensorial extraction algorithm and as single pair-wise coupling estimations. We concluded that edge-weighed feature selection strategy outperformed the tensorial treatment of SL-FCG and ML-FCG. The highest classification performance was obtained with the centroid ROI representation and edge-weighted analysis of the SL-FCG reaching the 98% for the CorrEnv in α1:α2 and 94% for the iPLV in α2. Classification performance based on the multi-layer participation coefficient, a multiplexity index reached 52% for iPLV and 52% for CorrEnv. Selected functional connections that build the multivariate connectomic biomarker in the edge-weighted scenario are located in default-mode, fronto-parietal, and cingulo-opercular network. Our analysis supports the notion of analyzing FCG simultaneously in intra and cross-frequency whole brain interactions with various connectivity estimators in beamformed recordings. eng http://creativecommons.org/licenses/by/4.0/ open access Attribution 4.0 International 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 journal article