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

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dc.contributor.authorLópez García, María Eugenia
dc.contributor.authorBruña Fernández, Ricardo
dc.contributor.authorCuesta Prieto, Pablo
dc.contributor.authorMarcos Dolado, Alberto
dc.contributor.authorMaestu Unturbe, Fernando
dc.date.accessioned2024-02-08T08:15:19Z
dc.date.available2024-02-08T08:15:19Z
dc.date.issued2018-06-01
dc.description.abstractOur 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.
dc.description.departmentDepto. de Psicología Experimental, Procesos Cognitivos y Logopedia
dc.description.departmentDepto. de Radiología, Rehabilitación y Fisioterapia
dc.description.facultyFac. de Psicología
dc.description.facultyFac. de Medicina
dc.description.refereedTRUE
dc.description.sponsorshipComisión Europea
dc.description.sponsorshipMinisterio de Economía y Competitividad
dc.description.sponsorshipMinisterio de Educación
dc.description.statuspub
dc.identifier.citationDimitriadis 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
dc.identifier.doi10.3389/fnins.2018.00306
dc.identifier.essn1662-453X
dc.identifier.issn1662-4548
dc.identifier.officialurlhttps://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2018.00306/full
dc.identifier.pmid29910704
dc.identifier.relatedurlhttps://pubmed.ncbi.nlm.nih.gov/29910704/
dc.identifier.urihttps://hdl.handle.net/20.500.14352/100165
dc.journal.titleFrontiers in Neuroscience
dc.language.isoeng
dc.page.initial306
dc.publisherFrontiers
dc.relation.projectIDMR/K004360/1
dc.relation.projectIDFIS2013-41057-P
dc.relation.projectIDTEC2016-80063-C3-2-R
dc.relation.projectIDPSI-2015-68793-C3-1-R
dc.relation.projectIDFPU13/06009
dc.rightsAttribution 4.0 Internationalen
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject.cdu612.8
dc.subject.keywordConnectomic biomarker
dc.subject.keywordMagnetoencephalography
dc.subject.keywordMild cognitive impairment
dc.subject.keywordVirtual source activity
dc.subject.keywordConnectome data analysis
dc.subject.keywordMultiplexity
dc.subject.keywordCross-frequency-coupling
dc.subject.keywordIntrinsic coupling modes
dc.subject.ucmNeurociencias (Medicina)
dc.subject.unesco2490 Neurociencias
dc.titleHow 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
dc.typejournal article
dc.type.hasVersionVoR
dc.volume.number12
dspace.entity.typePublication
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