Reconstructing coherent networks from electroencephalography and magnetoencephalography with reduced contamination from volume conduction or magnetic field spread

Drakesmith, Mark ORCID: https://orcid.org/0000-0001-8574-9560, El-Deredy, Wael and Welbourne, Stephen 2013. Reconstructing coherent networks from electroencephalography and magnetoencephalography with reduced contamination from volume conduction or magnetic field spread. PLoS ONE 8 (12) , e81553. 10.1371/journal.pone.0081553

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Abstract

Volume conduction (VC) and magnetic field spread (MFS) induce spurious correlations between EEG/MEG sensors, such that the estimation of functional networks from scalp recordings is inaccurate. Imaginary coherency [1] reduces VC/MFS artefacts between sensors by assuming that instantaneous interactions are caused predominantly by VC/MFS and do not contribute to the imaginary part of the cross-spectral densities (CSDs). We propose an adaptation of the dynamic imaging of coherent sources (DICS) [2] - a method for reconstructing the CSDs between sources, and subsequently inferring functional connectivity based on coherences between those sources. Firstly, we reformulate the principle of imaginary coherency by performing an eigenvector decomposition of the imaginary part of the CSD to estimate the power that only contributes to the non-zero phase-lagged (NZPL) interactions. Secondly, we construct an NZPL-optimised spatial filter with two a priori assumptions: (1) that only NZPL interactions exist at the source level and (2) the NZPL CSD at the sensor level is a good approximation of the projected source NZPL CSDs. We compare the performance of the NZPL method to the standard method by reconstructing a coherent network from simulated EEG/MEG recordings. We demonstrate that, as long as there are phase differences between the sources, the NZPL method reliably detects the underlying networks from EEG and MEG. We show that the method is also robust to very small phase lags, noise from phase jitter, and is less sensitive to regularisation parameters. The method is applied to a human dataset to infer parts of a coherent network underpinning face recognition.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Cardiff University Brain Research Imaging Centre (CUBRIC) Medicine Psychology MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG) Neuroscience and Mental Health Research Institute (NMHRI)
Subjects:	B Philosophy. Psychology. Religion > BF Psychology R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Publisher:	Public Library of Science
ISSN:	1932-6203
Date of First Compliant Deposit:	30 March 2016
Last Modified:	11 May 2023 01:53
URI:	https://orca.cardiff.ac.uk/id/eprint/59461

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