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

Dimitriadis, Stavros ORCID: https://orcid.org/0000-0002-0000-5392, Lopez, Maria, Bruña, Ricardo, Cuesta, Pablo, Marcos, Alberta, Maestu, Fernando and Pereda, Ernesto 2018. 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. Frontiers in Neuroscience 12 , 306. 10.3389/fnins.2018.00306

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Abstract

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 analysing FCG simultaneously in intra and cross-frequency whole brain interactions with various connectivity estimators in beamformed recordings.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Medicine Psychology Neuroscience and Mental Health Research Institute (NMHRI) Cardiff University Brain Research Imaging Centre (CUBRIC)
Publisher:	Frontiers Media
ISSN:	1662-4548
Date of First Compliant Deposit:	23 April 2018
Date of Acceptance:	20 April 2018
Last Modified:	07 May 2023 21:05
URI:	https://orca.cardiff.ac.uk/id/eprint/110877

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