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 journalArticle

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
JournalJournal of Neuroscience Methods
Early online date9 Nov 2020
DOIs
Publication statusE-pub ahead of print - 9 Nov 2020

Keywords

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

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