High-dimensional brain-wide functional connectivity mapping in magnetoencephalography

Jose Sanchez Bornot, Maria Lopez, Ricardo Bruna, Fernando Maestu, Vahab Youssofzadeh, Su Yang, David Finn, Stephen Todd, Paula McClean, Girijesh Prasad, KongFatt Wong-Lin

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
9 Downloads (Pure)

Abstract

Background:
Brain functional connectivity (FC) analyses based on magneto/electroencephalography (M/EEG) signals have yet to exploit the intrinsic high-dimensional information. Typically, these analyses are constrained to regions of interest to avoid the curse of dimensionality, with the latter leading to conservative hypothesis testing.

New method:
We removed such constraint by estimating high-dimensional source-based M/EEG-FC using cluster-permutation statistic (CPS) and demonstrated the feasibility of this approach by identifying resting-state changes in mild cognitive impairment (MCI), a prodromal stage of Alzheimer’s disease. Particularly, we proposed a unified framework for CPS analysis together with a novel neighbourhood measure to estimate more compact and neurophysiological plausible neural communication. As clusters could more confidently reveal interregional communication, we proposed and tested a cluster-strength index to demonstrate other advantages of CPS analysis.

Results:
We found clusters of increased communication or hypersynchronization in MCI compared to healthy controls in delta (1-4 Hz) and higher-theta (6-8 Hz) bands oscillations. These mainly consisted of interactions between occipitofrontal and occipitotemporal regions in the left hemisphere, which may be critically affected in the early stages of Alzheimer’s disease.

Conclusions:
Our approach could be important to create high-resolution FC maps from neuroimaging studies in general, allowing the multimodal analysis of neural communication across multiple spatial scales. Particularly, FC clusters more robustly represent the interregional communication by identifying dense bundles of connections that are less sensitive to inter-individual anatomical and functional variability. Overall, this approach could help to better understand neural information processing in healthy and disease conditions as needed for developing biomarker research.
Original languageEnglish
Article number108991
JournalJournal of Neuroscience Methods
Volume348
Early online date9 Nov 2020
DOIs
Publication statusPublished - 15 Jan 2021

Bibliographical note

Funding Information:
This work was supported by the EU’s INTERREG VA Programme , managed by the Special EU Programmes Body (SEUPB) (J.M.S.-B., P.L.M. and K.W.-L.), the Northern Ireland Functional Brain Mapping Project ( 1303/101154803 ) funded by Invest NI and Ulster University (S.Y., G.P. and K.W.-L.), the Spanish Ministry of Economy and Competitiveness ( PSI2009-14415-C03-01 ) and Madrid Neurocenter (M.E.L., R.B. and F.M.), Alzheimer’s Research UK (ARUK) Pump Priming Awards (D.P.F, S.T., G.P., P.L.M. and K.W.-L.), and Medical College of Wisconsin (V.Y.). P. M. and K.W.-L. received additional support from Ulster University Research Challenge Fund, and Global Challenges Research Fund, and K.W.-L. from COST Action Open Multiscale Systems Medicine (OpenMultiMed) supported by COST (European Cooperation in Science and Technology). The views and opinions expressed in this paper do not necessarily reflect those of the European Commission or the Special EU Programmes Body (SEUPB).

Publisher Copyright:
© 2020 The Authors

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • functional connectivity
  • cluster permutation statistics
  • nonparametric statistics
  • multiple comparison correction
  • EEG and MEG biomarkers
  • Alzheimer’s disease

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