Predicting MEG resting-state functional connectivity using microstructural information

Messaritaki, Eirini ORCID: https://orcid.org/0000-0002-9917-4160, Foley, Sonya ORCID: https://orcid.org/0000-0002-8390-2709, Schiavi, Simona, Magazzini, Lorenzo ORCID: https://orcid.org/0000-0002-8934-8374, Routley, Bethany, Jones, Derek K. ORCID: https://orcid.org/0000-0003-4409-8049 and Singh, Krish D. ORCID: https://orcid.org/0000-0002-3094-2475 2021. Predicting MEG resting-state functional connectivity using microstructural information. Network Neuroscience 5 (2) , pp. 477-504. 10.1162/netn_a_00187

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Abstract

Understanding how human brain microstructure influences functional connectivity is an important endeavor. In this work, magnetic resonance imaging data from ninety healthy participants were used to calculate structural connectivity matrices using the streamline count, fractional anisotropy, radial diffusivity and a myelin measure (derived from multi-component relaxometry) to assign connection strength. Unweighted binarized structural connectivity matrices were also constructed. Magnetoencephalography resting-state data from those participants were used to calculate functional connectivity matrices, via correlations of the Hilbert envelopes of beamformer timeseries in the delta, theta, alpha and beta frequency bands. Non-negative matrix factorization was performed to identify the components of the functional connectivity. Shortest-path-length and search-information analyses of the structural connectomes were used to predict functional connectivity patterns for each participant. The microstructure-informed algorithms predicted the components of the functional connectivity more accurately than they predicted the total functional connectivity. This provides a methodology to understand functional mechanisms better. The shortest-path-length algorithm exhibited the highest prediction accuracy. Of the weights of the structural connectivity matrices, the streamline count and the myelin measure gave the most accurate predictions, while the fractional anisotropy performed poorly. Overall, different structural metrics paint very different pictures of the structural connectome and its relationship to functional connectivity.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Psychology Cardiff University Brain Research Imaging Centre (CUBRIC)
Publisher:	Massachusetts Institute of Technology Press (MIT Press)
ISSN:	2472-1751
Funders:	Wellcome Trust
Date of First Compliant Deposit:	3 February 2021
Date of Acceptance:	1 February 2021
Last Modified:	11 Oct 2023 20:07
URI:	https://orca.cardiff.ac.uk/id/eprint/138223

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