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EEGs Disclose Significant Brain Activity Correlated with Synaptic Fickleness

Overview
Journal Biology (Basel)
Publisher MDPI
Specialty Biology
Date 2021 Aug 6
PMID 34356502
Citations 3
Authors
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Abstract

We here study a network of synaptic relations mingling excitatory and inhibitory neuron nodes that displays oscillations quite similar to electroencephalogram (EEG) brain waves, and identify abrupt variations brought about by swift synaptic mediations. We thus conclude that corresponding changes in EEG series surely come from the slowdown of the activity in neuron populations due to synaptic restrictions. The latter happens to generate an imbalance between excitation and inhibition causing a quick explosive increase of excitatory activity, which turns out to be a (first-order) transition among dynamic mental phases. Moreover, near this phase transition, our model system exhibits waves with a strong component in the so-called that coexist with fast oscillations. These findings provide a simple explanation for the observed and in actual brains, and open a serious and versatile path to understand deeply large amounts of apparently erratic, easily accessible brain data.

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References
1.
Gemein L, Schirrmeister R, Chrabaszcz P, Wilson D, Boedecker J, Schulze-Bonhage A . Machine-learning-based diagnostics of EEG pathology. Neuroimage. 2020; 220:117021. DOI: 10.1016/j.neuroimage.2020.117021. View

2.
Lisman J, Jensen O . The θ-γ neural code. Neuron. 2013; 77(6):1002-16. PMC: 3648857. DOI: 10.1016/j.neuron.2013.03.007. View

3.
Shadlen M, Newsome W . The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. J Neurosci. 1998; 18(10):3870-96. PMC: 6793166. View

4.
Sohal V, Rubenstein J . Excitation-inhibition balance as a framework for investigating mechanisms in neuropsychiatric disorders. Mol Psychiatry. 2019; 24(9):1248-1257. PMC: 6742424. DOI: 10.1038/s41380-019-0426-0. View

5.
Tsodyks M, Uziel A, Markram H . Synchrony generation in recurrent networks with frequency-dependent synapses. J Neurosci. 2000; 20(1):RC50. PMC: 6774142. View