Inferring synaptic excitation/inhibition balance from field potentials.

TLDR: A computational model is developed to show that E:I changes can be estimated from the power law exponent (slope) of the electrophysiological power spectrum, and provides evidence thatE:I ratio may be inferred from electrophysics recordings at many spatial scales, ranging from the local field potential to surface electrocorticography.

About: This article is published in NeuroImage.The article was published on 2017-09-01 and is currently open access. It has received 397 citations till now. The article focuses on the topics: Local field potential.

Figures

Figure 1. E:I ratio correlates with PSD slope in simulation.

Figure 3. PSD slope tracks theta-modulated changes in E:I balance.

Table 1. LFP Simulation Parameters

Figure 4. ECoG-PSD slope tracks propofol-induced global inhibition.

Figure 2. LFP-PSD slope varies with E:I synapse density ratio in rat CA1. (A) Example shank spanning across CA1 (rad: stratum radiatum; pyr: stratum pyramidale; or: stratum oriens; adapted from (Mizuseki et al. 2011)). (B) Example PSDs computed from electrodes along one recording shank. (C) Aggregate slope profile across depth, centered to the middle of pyramidal layer (0 μm) (horizontal bars denote standard deviation). (D) Excitatory (AMPA) and inhibitory (GABAA) synapse density varies across CA1 depth. (E and F) LFP-PSD slope correlates positively with E:I synapse density ratio (E) and negatively with GABAA density (F) (vertical bars denote standard deviation).

Frequently asked questions

Q1. What are the contributions mentioned in the paper "Inferring synaptic excitation/inhibition balance from field potentials" ?

Fluctuations in this E: I balance have been shown to influence neural computation, working memory, and information processing. This has limited the ability to examine the full impact that E: I shifts have in neural computation and disease. In this study, the authors develop a computational model to show that E: I ratio can be estimated from the power law exponent ( slope ) of the electrophysiological power spectrum, and validate this relationship using previously published datasets from two species ( rat local field potential and macaque electrocorticography ). 

Q2. What are the key words in this article?

Key Words: excitation-inhibition balance, local field potential, electrocorticography, power spectral density, power law, simulation, high-conductance state. 

Q3. What is the name of the author?

While more drastic shifts and aberrant E:I patterns are implicated in numerous neurological and psychiatric disorders, current methods for measuring E:I dynamics require invasive procedures that are difficult to perform in behaving animals, and nearly impossible in humans. 

Q4. Who is the author of this article?

Richard D. Gao1,*, Erik J. Peterson1, Bradley Voytek1,2,3,41Department of Cognitive Science, 2Neurosciences Graduate Program, 3Institute for Neural Computation, and 4Kavli Institute for Brain and Mind, University of California, San Diego, La Jolla, CA, USA.*Correspondence: rigao@ucsd.eduNeural circuits sit in a dynamic balance between excitation (E) and inhibition (I).