bioRxivNeurosciencePreprint
Biophysical modeling of the neural origin of EEG and MEG signals
Solveig Næss, Geir HalnesiD, Espen HageniD, Donald J. HagleriD, Anders Martin DaleiD, Gaute T. EinevolliD, Torbjørn V. NessiD
Abstract
Electroencephalography (EEG) and magnetoencephalography (MEG) are among the most important techniques for non-invasively studying cognition and disease in the human brain. These signals are known to originate from cortical neural activity, typically described in terms of current dipoles. While the link between cortical current dipoles and EEG/MEG signals is relatively well understood, surprisingly little is known about the link between different kinds of neural activity and the current dipoles themselves. Detailed biophysical modeling has played an important role in exploring the neural origin of intracranial electric signals, like extracellular spikes and local field potentials. However, this approach has not yet been taken full advantage of in the context of exploring the neural origin of the cortical current dipoles that are causing EEG/MEG signals. Here, we present a method for reducing arbitrary simulated neural activity to single current dipoles. We find that the method is applicable for calculating extracranial signals, but less suited for calculating intracranial electrocorticography (ECoG) signals. We demonstrate that this approach can serve as a powerful tool for investigating the neural origin of EEG/MEG signals. This is done through example studies of the single-neuron EEG contribution, the putative EEG contribution from calcium spikes, and from calculating EEG signals from large-scale neural network simulations. We also demonstrate how the simulated current dipoles can be used directly in combination with detailed head models, allowing for simulated EEG signals with an unprecedented level of biophysical details. In conclusion, this paper presents a framework for biophysically detailed modeling of EEG and MEG signals, which can be used to better our understanding of non-inasively measured neural activity in humans. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/181875v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@47f029org.highwire.dtl.DTLVardef@e8244dorg.highwire.dtl.DTLVardef@a765c1org.highwire.dtl.DTLVardef@96550_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LICurrent dipoles are computed from biophysically detailed simulated neuron and network activity C_LIO_LIExtracted current dipoles allow for accurate computation of EEG and MEG signals in simplified and detailed head models C_LIO_LICurrent-diplole approximation generally not suitable for accurate calculations of ECoG signals C_LIO_LIMethod provides biophysics-based link between detailed neural activity and systems-level signals C_LI
- organism: human
- assay: electronic health records
- research focus: dimensionality reduction
- research focus: benchmarking
- research focus: software / tool
- approach: statistical model
- approach: simulation
- Citations
- 0
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Resolving the mesoscopic missing link: Biophysical modeling of EEG from cortical columns in primates
Beatriz Herrera, Jacob Alan Westerberg, … Jorge J. Riera·NeuroImagePeer-reviewed·Aug 27, 2022
- Simulating human sleep spindle MEG and EEG from ion channel and circuit level dynamics
Burke Q. Rosen, Giri P. Krishnan, … Eric Halgren·Journal of Neuroscience MethodsPeer-reviewed·Oct 6, 2018
- Multi-Scale Neural Sources of EEG: Genuine, Equivalent, and Representative. A Tutorial Review
Paul L. Nunez, Michael D. Nunez, Ramesh Srinivasan·Brain TopographyPeer-reviewed·Jan 25, 2019
- Coupled neuro-fluid dynamics in human bioelectromagnetism
Tzvetan G. Popov, Gregory A. Miller·bioRxiv·Oct 15, 2026
- Towards a mesoscale physical modeling framework for stereotactic-EEG recordings
Borja Mercadal, Edmundo Lopez-Sola, … Giulio Ruffini·Journal of Neural EngineeringPeer-reviewed·Dec 22, 2022
- Multimodal modeling of neural network activity: computing LFP, ECoG, EEG and MEG signals with LFPy2.0
Espen Hagen, Solveig Næss, … Gaute T. Einevoll·Frontiers in NeuroinformaticsPeer-reviewed·Dec 18, 2018