ArticleNetwork neuroscience (Cambridge, Mass.)2025
Validating MEG estimated resting-state connectome with intracranial EEG.
Article in Network neuroscience (Cambridge, Mass.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Assessing Strengths and Limitations of Magnetoencephalography Source Imaging With Intracerebral EEG.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Shared spatial and temporal principles govern connectome dynamics across timescales.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- A geometry aware framework enhances noninvasive mapping of whole human brain dynamics.Nature biomedical engineering · 2026Article
- Predicting seizure onset zones from interictal intracranial EEG using functional connectivity and machine learning.Scientific reports · 2025Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Magnetoencephalography (MEG) is widely used for studying resting-state brain connectivity. However, MEG source imaging is ill posed and has limited spatial resolution. This introduces source-leakage issues, making it challenging to interpret MEG-derived connectivity in resting states. To address this, we validated MEG-derived connectivity from 45 healthy participants using a normative intracranial EEG (iEEG) atlas. The MEG inverse problem was solved using the wavelet-maximum entropy on the mean method. We computed four connectivity metrics: amplitude envelope correlation (AEC), orthogonalized AEC (OAEC), phase locking value (PLV), and weighted-phase lag index (wPLI). We compared spatial correlation between MEG and iEEG connectomes across standard canonical frequency bands. We found moderate spatial correlations between MEG and iEEG connectomes for AEC and PLV. However, when considering metrics that correct/remove zero-lag connectivity (OAEC/wPLI), the spatial correlation between MEG and iEEG connectomes decreased. MEG exhibited higher zero-lag connectivity compared with iEEG. The correlations between MEG and iEEG connectomes suggest that relevant connectivity patterns can be recovered from MEG. However, since these correlations are moderate/low, MEG connectivity results should be interpreted with caution. Metrics that correct for zero-lag connectivity show decreased correlations, highlighting a trade-off; while MEG may capture more connectivity due to source-leakage, removing zero-lag connectivity can eliminate true connections.
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