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

By Bijan Pesaran, Martin A. Vinck, Gaute T. Einevoll, Anton M. Sirota, Pascal Fries, Markus Siegel, Wilson A. Truccolo, Charles E. Schroeder, R. Srinivasan. Nature Neuroscience, 2018

356 citations recorded by the source corpus.

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. This article presents best practices on how field potential recordings (EEG, MEG, ECoG and LFP) can be analyzed to identify large-scale brain dynamics, and highlights issues and limitations of interpretation.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/2a454df515c9b0c0d0d4463da58e8759cdbdc13f

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In Cognitive Neuroscience

Citations in the corpus

Speech synthesis from neural decoding of spoken sentences (2019, background): “…network 9 decodes articulatory kinematic features from continuous neural activity (high-gamma amplitude envelope 10 and low frequency component 11,12 , see methods) recorded from ventral sensorimotor cortex (vSMC) 13 , superior temporal gyrus (STG) 14 , and inferior frontal gyrus (IFG) 15…”
Distinct laminar origins of high-gamma and low-frequency ECoG signals revealed by optogenetics (2025, unknown): “Enhancing our understanding of the sources of ECoG signals 73 , especially from high-resolution, large-scale recording devices 74,75 , could enhance performance of neuroprosthetic systems 76 and diagnostic tools.”
Distinct developmental changes in linear and nonlinear neural interactions across infancy and adulthood (2025, unknown): “Traditional measures of functional connectivity (e.g., phase coherence or Granger causality) primarily quantify interactions within the same frequency band and are mainly sensitive to linear signal transmission (Pesaran et al., 2018; Schneider et al., 2021; Vinck et al., 2025, 2023).”
The development of oscillatory and aperiodic resting state activity is linked to a sensitive period in humans (2023, background): “Thus, it is owadays considered as essential to remove the aperiodic components f the power spectrum to identify oscillatory activity in distinct narrow requency bands ( Pesaran, 2018 ; Donoghue et al., 2021 ).”