4.7 Review

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

Journal

NATURE NEUROSCIENCE
Volume 21, Issue 7, Pages 903-919

Publisher

NATURE PUBLISHING GROUP
DOI: 10.1038/s41593-018-0171-8

Keywords

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Categories

Funding

  1. European Union's Horizon 2020 Framework Programme for Research and Innovation [720270]
  2. DFG [SPP 1665, FOR 1847, FR2557/5-1-CORNET]
  3. European Union [FP7-600730-Magnetrodes]
  4. NIH [1U54MH091657-WU-Minn-Consortium-HCP]
  5. LOEWE (NeFF)
  6. U.S. Department of Veterans Affairs [RX000668]
  7. Pablo J. Salame ' 88 Goldman Sachs endowed Assistant Professorship of Computational Neuroscience
  8. DoD [W911NF-14-2-0043, N66001-17-C-4002]
  9. BrainCom from EU Horizon 2020 program [732032]
  10. Munich Cluster for Systems Neurology (SyNergy) [EXC 1010]
  11. Deutsche Forschungsgemeinschaft Priority Program
  12. Bundesministerium fur Bildung und Forschung
  13. [MH111439]
  14. [DC015780]
  15. [NIH-NINDS R01NS079533]
  16. [NEI R01-EY024067]
  17. [NINDS R01-NS104923]
  18. [ARO MURI 68984-CS-MUR]
  19. [NSF BCS 150236]

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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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