A1 Journal article (refereed)
Towards a more robust non-invasive assessment of functional connectivity (2024)

Westner, B. U., Kujala, J., Gross, J., & Schoffelen, J.-M. (2024). Towards a more robust non-invasive assessment of functional connectivity. Imaging Neuroscience, 2. https://doi.org/10.1162/imag_a_00119

JYU authors or editors

Publication details

All authors or editors: Westner, Britta U.; Kujala, Jan; Gross, Joachim; Schoffelen, Jan-Mathijs

Journal or series: Imaging Neuroscience

eISSN: 2837-6056

Publication year: 2024

Publication date: 11/03/2024

Volume: 2

Publisher: MIT Press

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1162/imag_a_00119

Research data link: https://github.com/schoffelen/shared_subsampling

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/96647

Abstract

Non-invasive evaluation of functional connectivity, based on source-reconstructed estimates of phase-difference-based metrics, is notoriously non-robust. This is due to a combination of factors, ranging from a misspecification of seed regions to suboptimal baseline assumptions, and residual signal leakage. In this work, we propose a new analysis scheme of source level phase-difference-based connectivity, which is aimed at optimizing the detection of interacting brain regions. Our approach is based on the combined use of sensor subsampling and dual-source beamformer estimation of all-to-all connectivity on a prespecified dipolar grid. First, a pairwise two-dipole model, to account for reciprocal leakage in the estimation of the localized signals, allows for a usable approximation of the pairwise bias in connectivity due to residual leakage of ‘third party’ noise. Secondly, using sensor array subsampling, the recreation of multiple connectivity maps using different subsets of sensors allows for the identification of consistent spatially localized peaks in the 6-dimensional connectivity maps, indicative of true brain region interactions. These steps are combined with the subtraction of null coherence estimates to obtain the final coherence maps. With extensive simulations, we compared different analysis schemes for their detection rate of connected dipoles, as a function of signal-to-noise ratio, phase difference and connection strength. We demonstrate superiority of the proposed analysis scheme in comparison to single-dipole models, or an approach that discards the zero phase difference component of the connectivity. We conclude that the proposed pipeline allows for a more robust identification of functional connectivity in experimental data, opening up new possibilities to study brain networks with mechanistically inspired connectivity measures in cognition and in the clinic.

Keywords: EEG; MEG; imaging; signal analysis; signal processing; brain research

Free keywords: MEG; EEG; source reconstruction; beamformer; functional connectivity

Contributing organizations

Ministry reporting: Yes

VIRTA submission year: 2024

Preliminary JUFO rating: 0