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Cited by 43Functional Brain Connectivity StudiesEEG and Brain-Computer InterfacesNeural dynamics and brain function

Identifying good practices for detecting inter-regional linear functional connectivity from EEG

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Pellegrini, Franziska, Delorme, Arnaud, Nikulin, Vadim, Haufe, Stefan·2023·NeuroImage
DOI 10.1016/j.neuroimage.2023.120218PMC PMC10374983

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Abstract

Aggregating voxel-level statistical dependencies between multivariate time series is an important intermediate step when characterising functional connectivity (FC) between larger brain regions. However, there are numerous ways in which voxel-level data can be aggregated into inter-regional FC, and the advantages of each of these approaches are currently unclear. In this study we generate ground-truth data and compare the performances of various pipelines that estimate directed and undirected linear phase-to-phase FC between regions. We test the ability of several existing and novel FC analysis pipelines to identify the true regions within which connectivity was simulated. We test various inverse modelling algorithms, strategies to aggregate time series within regions, and connectivity metrics. Furthermore, we investigate the influence of the number of interactions, the signal-to-noise ratio, the noise mix, the interaction time delay, and the number of active sources per region on the ability of detecting phase-to-phase FC. Throughout all simulated scenarios, lowest performance is obtained with pipelines involving the absolute value of coherency. Further, the combination of dynamic imaging of coherent sources (DICS) beamforming with directed FC metrics that aggregate information across multiple frequencies leads to unsatisfactory results. Pipelines that show promising results with our simulated pseudo-EEG data involve the following steps: (1) Source projection using the linearly-constrained minimum variance (LCMV) beamformer. (2) Principal component analysis (PCA) using the same fixed number of components within every region. (3) Calculation of the multivariate interaction measure (MIM) for every region pair to assess undirected phase-to-phase FC, or calculation of time-reversed Granger Causality (TRGC) to assess directed phase-to-phase FC. We formulate recommendations based on these results that may increase the validity of future experimental connectivity studies. We further introduce the free ROIconnect plugin for the EEGLAB toolbox that includes the recommended methods and pipelines that are presented here. We show an exemplary application of the best performing pipeline to the analysis of EEG data recorded during motor imagery.

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Authors
Pellegrini, F., Delorme, A., Nikulin, V., & Haufe, S.
Affiliations
  • Bernstein Center for Computational Neuroscience Berlin (DE)
  • Max Planck Institute for Human Cognitive and Brain Sciences (DE)
  • Physikalisch-Technische Bundesanstalt (DE)
Corresponding author
  • Pellegrini, Franziska
  • Haufe, Stefan
ORCID
Venue
NeuroImage
Publisher
Elsevier BV
Published
2023-08
Volume
277
Pages
120218
ISSN
1053-8119 (ISSN-L)
DOI
10.1016/j.neuroimage.2023.120218
PMC
PMC10374983
OpenAlex
W4380200385
Cited by
43
References
1
Citations by year
2026: 12 · 2025: 16 · 2024: 10 · 2023: 5
FWCI
6.44
Type
journal article
Language
en
Open access
Licence
Locations
  • NeuroImage (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Horizon 2020
  • European Research Council (758985)
  • Deutsche Forschungsgemeinschaft (424778381 TRR 295)
  • Horizon 2020 Framework Programme
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Keywords
EEG, directed functional connectivity, LCMV beamformer, principal component analysis, motor imagery
MeSH terms
Brain, Brain Mapping, Computer Simulation, Electroencephalography, Humans, Signal Processing, Computer-Assisted

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