Cortical Network Complexity under Different Levels of Excitability Controlled by Electric Fields

Almudena Barbero-CastilloSpain, Julia Weinert, Alessandra Camassa, Lorena Perez-Mendez, Sara Caldas-Martinez, Maurizio Mattia, Maria V. Sanchez-Vives

Research output: Contribution to journalArticle

Abstract

Consciousness has been proposed to depend on the brain capability to support complex activity patterns. An accurate measure of such complexity is challenging. One of the proposed measures is the Perturbational Complexity Index (PCI), which estimates brain complexity based on the response to a perturbation. PCI allows discrimination between consciousness levels in humans and between different neuromodulatory conditions in the in vitro cerebral cortex (slice PCI, sPCI). Entropy has also been used to measure brain complexity by inspecting the richness of spontaneous activity. The aim of this study was to determine whether the complexity of the cortical activity can be modulated by the network excitability, which we regulated by means of electric fields. We recorded the local field potential from cortical slices with a 16-channel multi-electrode array at different electric field intensities while measuring sPCI and the entropy of wavefronts' activation (EWA). We conclude that the complexity of cortical activity increases with network excitability. However, while perturbational methods require a critical change in network dynamics to detect significant variations in the complexity of the response to perturbation, the EAW offers a higher degree of sensitivity in identifying complexity changes.
Original languageEnglish
Pages (from-to)97-99
Number of pages3
JournalBrain Stimulation
Volume12
Issue number2
Publication statusPublished - 2019

Fingerprint

Entropy
Consciousness
Brain
Cerebral Cortex
Electrodes
Discrimination (Psychology)
In Vitro Techniques

Keywords

  • Computational Physics
  • Simulation
  • complex systems

Cite this

Barbero-CastilloSpain, A., Weinert, J., Camassa, A., Perez-Mendez, L., Caldas-Martinez, S., Mattia, M., & Sanchez-Vives, M. V. (2019). Cortical Network Complexity under Different Levels of Excitability Controlled by Electric Fields. Brain Stimulation, 12(2), 97-99.

Cortical Network Complexity under Different Levels of Excitability Controlled by Electric Fields. / Barbero-CastilloSpain, Almudena; Weinert, Julia; Camassa, Alessandra; Perez-Mendez, Lorena; Caldas-Martinez, Sara; Mattia, Maurizio; Sanchez-Vives, Maria V.

In: Brain Stimulation, Vol. 12, No. 2, 2019, p. 97-99.

Research output: Contribution to journalArticle

Barbero-CastilloSpain, A, Weinert, J, Camassa, A, Perez-Mendez, L, Caldas-Martinez, S, Mattia, M & Sanchez-Vives, MV 2019, 'Cortical Network Complexity under Different Levels of Excitability Controlled by Electric Fields', Brain Stimulation, vol. 12, no. 2, pp. 97-99.
Barbero-CastilloSpain A, Weinert J, Camassa A, Perez-Mendez L, Caldas-Martinez S, Mattia M et al. Cortical Network Complexity under Different Levels of Excitability Controlled by Electric Fields. Brain Stimulation. 2019;12(2):97-99.
Barbero-CastilloSpain, Almudena ; Weinert, Julia ; Camassa, Alessandra ; Perez-Mendez, Lorena ; Caldas-Martinez, Sara ; Mattia, Maurizio ; Sanchez-Vives, Maria V. / Cortical Network Complexity under Different Levels of Excitability Controlled by Electric Fields. In: Brain Stimulation. 2019 ; Vol. 12, No. 2. pp. 97-99.
@article{450a4d2e451d4d06bf3de1fda48f85e9,
title = "Cortical Network Complexity under Different Levels of Excitability Controlled by Electric Fields",
abstract = "Consciousness has been proposed to depend on the brain capability to support complex activity patterns. An accurate measure of such complexity is challenging. One of the proposed measures is the Perturbational Complexity Index (PCI), which estimates brain complexity based on the response to a perturbation. PCI allows discrimination between consciousness levels in humans and between different neuromodulatory conditions in the in vitro cerebral cortex (slice PCI, sPCI). Entropy has also been used to measure brain complexity by inspecting the richness of spontaneous activity. The aim of this study was to determine whether the complexity of the cortical activity can be modulated by the network excitability, which we regulated by means of electric fields. We recorded the local field potential from cortical slices with a 16-channel multi-electrode array at different electric field intensities while measuring sPCI and the entropy of wavefronts' activation (EWA). We conclude that the complexity of cortical activity increases with network excitability. However, while perturbational methods require a critical change in network dynamics to detect significant variations in the complexity of the response to perturbation, the EAW offers a higher degree of sensitivity in identifying complexity changes.",
keywords = "Computational Physics, Simulation, complex systems",
author = "Almudena Barbero-CastilloSpain and Julia Weinert and Alessandra Camassa and Lorena Perez-Mendez and Sara Caldas-Martinez and Maurizio Mattia and Sanchez-Vives, {Maria V.}",
year = "2019",
language = "English",
volume = "12",
pages = "97--99",
journal = "Brain Stimulation",
issn = "1935-861X",
publisher = "Elsevier Inc.",
number = "2",

}

TY - JOUR

T1 - Cortical Network Complexity under Different Levels of Excitability Controlled by Electric Fields

AU - Barbero-CastilloSpain, Almudena

AU - Weinert, Julia

AU - Camassa, Alessandra

AU - Perez-Mendez, Lorena

AU - Caldas-Martinez, Sara

AU - Mattia, Maurizio

AU - Sanchez-Vives, Maria V.

PY - 2019

Y1 - 2019

N2 - Consciousness has been proposed to depend on the brain capability to support complex activity patterns. An accurate measure of such complexity is challenging. One of the proposed measures is the Perturbational Complexity Index (PCI), which estimates brain complexity based on the response to a perturbation. PCI allows discrimination between consciousness levels in humans and between different neuromodulatory conditions in the in vitro cerebral cortex (slice PCI, sPCI). Entropy has also been used to measure brain complexity by inspecting the richness of spontaneous activity. The aim of this study was to determine whether the complexity of the cortical activity can be modulated by the network excitability, which we regulated by means of electric fields. We recorded the local field potential from cortical slices with a 16-channel multi-electrode array at different electric field intensities while measuring sPCI and the entropy of wavefronts' activation (EWA). We conclude that the complexity of cortical activity increases with network excitability. However, while perturbational methods require a critical change in network dynamics to detect significant variations in the complexity of the response to perturbation, the EAW offers a higher degree of sensitivity in identifying complexity changes.

AB - Consciousness has been proposed to depend on the brain capability to support complex activity patterns. An accurate measure of such complexity is challenging. One of the proposed measures is the Perturbational Complexity Index (PCI), which estimates brain complexity based on the response to a perturbation. PCI allows discrimination between consciousness levels in humans and between different neuromodulatory conditions in the in vitro cerebral cortex (slice PCI, sPCI). Entropy has also been used to measure brain complexity by inspecting the richness of spontaneous activity. The aim of this study was to determine whether the complexity of the cortical activity can be modulated by the network excitability, which we regulated by means of electric fields. We recorded the local field potential from cortical slices with a 16-channel multi-electrode array at different electric field intensities while measuring sPCI and the entropy of wavefronts' activation (EWA). We conclude that the complexity of cortical activity increases with network excitability. However, while perturbational methods require a critical change in network dynamics to detect significant variations in the complexity of the response to perturbation, the EAW offers a higher degree of sensitivity in identifying complexity changes.

KW - Computational Physics

KW - Simulation

KW - complex systems

M3 - Article

VL - 12

SP - 97

EP - 99

JO - Brain Stimulation

JF - Brain Stimulation

SN - 1935-861X

IS - 2

ER -