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Investigating large-scale brain dynamics using field potential recordings: analysis and interpretation

Nature Neuroscience volume 21, pages 903–919 (2018)Cite this article

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Abstract

New technologies to record electrical activity from the brain on a massive scale offer tremendous opportunities for discovery. Electrical measurements of large-scale brain dynamics, termed field potentials, are especially important to understanding and treating the human brain. Here, our goal is to provide best practices on how field potential recordings (electroencephalograms, magnetoencephalograms, electrocorticograms and local field potentials) can be analyzed to identify large-scale brain dynamics, and to highlight critical issues and limitations of interpretation in current work. We focus our discussion of analyses around the broad themes of activation, correlation, communication and coding. We provide recommendations for interpreting the data using forward and inverse models. The forward model describes how field potentials are generated by the activity of populations of neurons. The inverse model describes how to infer the activity of populations of neurons from field potential recordings. A recurring theme is the challenge of understanding how field potentials reflect neuronal population activity given the complexity of the underlying brain systems.

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Fig. 1: Field potential recording modalities.
Fig. 2: Forward models.
Fig. 3: Time series and spectral estimation.
Fig. 4: Current source density analysis.
Fig. 5: Granger-causal inferences.
Fig. 6: Phase-dependent neuronal coding.

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Acknowledgements

C.S. acknowledges Y. Kajikawa for contributing figure 4b and for editorial comments. C.S. acknowledges grant support from MH111439 and DC015780. G.E. acknowledges grant support from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 720270 (Human Brain Project SGA1). P.F. acknowledges grant support from DFG (SPP 1665, FOR 1847, FR2557/5-1-CORNET), the European Union (FP7-600730-Magnetrodes), NIH (1U54MH091657-WU-Minn-Consortium-HCP), and LOEWE (NeFF). W.T. acknowledges grant support from NIH-NINDS R01NS079533, U.S. Department of Veterans Affairs, Merit Review Award RX000668, and the Pablo J. Salame ’88 Goldman Sachs endowed Assistant Professorship of Computational Neuroscience. B.P. acknowledges grant support from NEI R01-EY024067, NINDS R01-NS104923, ARO MURI 68984-CS-MUR, NSF BCS 150236, and DoD contracts W911NF- 14-2-0043 and N66001-17-C-4002. A.S. acknowledges grant support from BrainCom from EU Horizon 2020 program via grant no. 732032, Munich Cluster for Systems Neurology (SyNergy, EXC 1010), Deutsche Forschungsgemeinschaft Priority Program 1665 and 1392 and Bundesministerium für Bildung und Forschung via grant no. 01GQ0440 (Bernstein Centre for Computational Neuroscience Munich).

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Authors and Affiliations

  1. Center for Neural Science, New York University, New York, NY, USA

    Bijan Pesaran

  2. NYU Neuroscience Institute, New York University Langone Health, New York, NY, USA

    Bijan Pesaran

  3. Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt, Germany

    Martin Vinck & Pascal Fries

  4. Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway

    Gaute T. Einevoll

  5. Department of Physics, University of Oslo, Oslo, Norway

    Gaute T. Einevoll

  6. Bernstein Center for Computational Neuroscience Munich, Munich Cluster of Systems Neurology (SyNergy), Faculty of Medicine, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany

    Anton Sirota

  7. Donders Institute for Brain, Cognition, and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands

    Pascal Fries

  8. Centre for Integrative Neuroscience & MEG Center, University of Tübingen, Tübingen, Germany

    Markus Siegel

  9. Department of Neuroscience and Institute for Brain Science, Brown University, Providence, RI, USA

    Wilson Truccolo

  10. Center for Neurorestoration and Neurotechnology, U.S. Department of Veterans Affairs, Providence, RI, USA

    Wilson Truccolo

  11. Translational Neuroscience Division, Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA

    Charles E. Schroeder

  12. Department of Neurosurgery, Columbia College of Physicians and Surgeons, New York, NY, USA

    Charles E. Schroeder

  13. Department of Cognitive Sciences, Department of Biomedical Engineering, University of California, Irvine, CA, USA

    Ramesh Srinivasan

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Pesaran, B., Vinck, M., Einevoll, G.T. et al. Investigating large-scale brain dynamics using field potential recordings: analysis and interpretation. Nat Neurosci 21, 903–919 (2018). https://doi.org/10.1038/s41593-018-0171-8

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