Information dynamics of in silico EEG Brain Waves: Insights into oscillations and functions
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Show full item recordEditorial
Public Library of Science
Date
2024-09-05Referencia bibliográfica
Menesse, G. & Torres Agudo, J. PLoS Comput Biol 20(9): e1012369. [https://doi.org/10.1371/journal.pcbi.1012369]
Sponsorship
Project of I+D+i, Spain Ref. PID2020 113681GBI00, funded by MICIU/AEI/10.13039/501100011033; Consejería de Transformación Económica, Industria, Conocimiento y Universidades, Spain, Junta de Andalucía, Spain; European Regional Development Funds, Ref. P20_00173Abstract
The relation between electroencephalography (EEG) rhythms, brain functions, and behavioral
correlates is well-established. Some physiological mechanisms underlying rhythm generation
are understood, enabling the replication of brain rhythms in silico. This offers a
pathway to explore connections between neural oscillations and specific neuronal circuits,
potentially yielding fundamental insights into the functional properties of brain waves. Information
theory frameworks, such as Integrated Information Decomposition (Φ-ID), relate
dynamical regimes with informational properties, providing deeper insights into neuronal
dynamic functions. Here, we investigate wave emergence in an excitatory/inhibitory (E/I)
balanced network of integrate and fire neurons with short-term synaptic plasticity. This
model produces a diverse range of EEG-like rhythms, from low δ waves to high-frequency
oscillations. Through Φ-ID, we analyze the network’s information dynamics and its relation
with different emergent rhythms, elucidating the system’s suitability for functions such as
robust information transfer, storage, and parallel operation. Furthermore, our study helps to
identify regimes that may resemble pathological states due to poor informational properties
and high randomness. We found, e.g., that in silico β and δ waves are associated with maximum
information transfer in inhibitory and excitatory neuron populations, respectively, and
that the coexistence of excitatory θ, α, and β waves is associated to information storage.
Additionally, we observed that high-frequency oscillations can exhibit either high or poor
informational properties, potentially shedding light on ongoing discussions regarding physiological
versus pathological high-frequency oscillations. In summary, our study demonstrates
that dynamical regimes with similar oscillations may exhibit vastly different information
dynamics. Characterizing information dynamics within these regimes serves as a potent
tool for gaining insights into the functions of complex neuronal networks. Finally, our findings
suggest that the use of information dynamics in both model and experimental data analysis,
could help discriminate between oscillations associated with cognitive functions and those
linked to neuronal disorders.