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

@article{Pesaran2018InvestigatingLB,
  title={Investigating large-scale brain dynamics using field potential recordings: analysis and interpretation},
  author={Bijan Pesaran and Martin A. Vinck and Gaute T. Einevoll and Anton M. Sirota and Pascal Fries and Markus Siegel and Wilson A. Truccolo and Charles E. Schroeder and Ramesh Srinivasan},
  journal={Nature Neuroscience},
  year={2018},
  volume={21},
  pages={903-919}
}
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… Expand
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References

SHOWING 1-10 OF 260 REFERENCES
Source connectivity analysis with MEG and EEG
TLDR
This article reviews several methods that have been applied to investigate interactions between brain regions in source space, and will mainly focus on the different measures used to quantify connectivity, and on theDifferent strategies adopted to identify regions of interest. Expand
Impedance Spectrum in Cortical Tissue: Implications for Propagation of LFP Signals on the Microscopic Level
TLDR
A detailed investigation of the frequency dependence of the conductivity within cortical tissue at microscopic distances using small current amplitudes within the typical (neuro)physiological micrometer and sub-nanoampere range suggests very weak frequency-dependent effects within the frequency range of physiological LFPs. Expand
NeuroGrid: recording action potentials from the surface of the brain
TLDR
The NeuroGrid constitutes an effective method for large-scale, stable recording of neuronal spikes in concert with local population synaptic activity, enhancing comprehension of neural processes across spatiotemporal scales and potentially facilitating diagnosis and therapy for brain disorders. Expand
Power-Law Scaling in the Brain Surface Electric Potential
TLDR
A new paradigm of non-oscillatory “asynchronous,” scale-free, changes in cortical potentials, corresponding to changes in mean population-averaged firing rate, to complement the prevalent “synchronous” rhythm-based paradigm is suggested. Expand
The Spiking Component of Oscillatory Extracellular Potentials in the Rat Hippocampus
TLDR
It is found that postsynaptic currents exhibit a decreasing ability to generate large-amplitude oscillatory signals at high frequencies, whereas phase-modulated spiking shows the opposite trend. Expand
Independent Components of Neural Activity Carry Information on Individual Populations
TLDR
A large-scale network thalamocortical model was used to compute simultaneous LFP, transmembrane currents, and spiking activity and found that the three most robust components matched well the activity of two dominating cell populations. Expand
Differentially variable component analysis: Identifying multiple evoked components using trial-to-trial variability.
TLDR
The differentially variable component analysis algorithm (dVCA) is introduced, which relies on trial-to-trial variability in response amplitude and latency to identify multiple components and is applied to neural ensemble activity recorded from an awake, behaving macaque, demonstrating that dVCA is an important tool for identifying and characterizing multiple components in the single trial. Expand
Feature Selectivity of the Gamma-Band of the Local Field Potential in Primate Primary Visual Cortex
TLDR
Oscillations in the gamma-band of the LFP in the primary visual cortex of the macaque that dominate during visual stimulation are studied to elucidate the relationship between spiking activity of local neural populations and LFP signals. Expand
Measuring the cortical correlation structure of spontaneous oscillatory activity with EEG and MEG
TLDR
The results show that power correlation of orthogonalized signals is feasible for studying functional connectivity with 64-channel EEG and source co-registered 275-channel MEG, and besides differences in SNR, EEG and MEG measure the same correlation patterns across the entire brain. Expand
Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain
Magnetoencephalography (MEG) is a noninvasive technique for investigating neuronal activity in the living human brain. The time resolution of the method is better than 1 ms and the spatialExpand
...
1
2
3
4
5
...