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Nat Neurosci. 2018 Jul;21(7):903-919. doi: 10.1038/s41593-018-0171-8. Epub 2018 Jun 25.

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

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Center for Neural Science, New York University, New York, NY, USA.
NYU Neuroscience Institute, New York University Langone Health, New York, NY, USA.
Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt, Germany.
Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway.
Department of Physics, University of Oslo, Oslo, Norway.
Bernstein Center for Computational Neuroscience Munich, Munich Cluster of Systems Neurology (SyNergy), Faculty of Medicine, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany.
Donders Institute for Brain, Cognition, and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands.
Centre for Integrative Neuroscience & MEG Center, University of Tübingen, Tübingen, Germany.
Department of Neuroscience and Institute for Brain Science, Brown University, Providence, RI, USA.
Center for Neurorestoration and Neurotechnology, U.S. Department of Veterans Affairs, Providence, RI, USA.
Translational Neuroscience Division, Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA.
Department of Neurosurgery, Columbia College of Physicians and Surgeons, New York, NY, USA.
Department of Cognitive Sciences, Department of Biomedical Engineering, University of California, Irvine, CA, USA.


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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