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Resting Potential Decay

Resting Potential Decay

The resting membrane potential of a neuron (-70mV) is sustained due to the selective ion permeability of the membrane. At the resting potential, the membrane is slightly permeable to ions like sodium (Na+) and chloride (Cl−) and highly permeable to potassium ions (K+). Differences in the ions' concentration inside the cell compared to the outside are maintained by membrane transport proteins like channels and pumps.
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Updated: Jan 19, 2026

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

Metastable Resting State Brain Dynamics.

Peter Beim Graben¹, Antonio Jimenez-Marin², Ibai Diez^3,4,5

¹Communication Engineering, Institute of Electrical Engineering and Information Science, Brandenburg University of Technology Cottbus - Senftenberg, Cottbus, Germany.

Frontiers in Computational Neuroscience

|September 26, 2019

Summary

This summary is machine-generated.

This study introduces recurrence structure analysis (RSA) to analyze brain dynamics from fMRI data, identifying 40 metastable brain states. These findings reveal complex, dynamic brain activity patterns beyond static models.

Keywords:

BOLD fMRI brain hierarchical atlas diffusion tensor imaging metastability recurrence structure analysis resting state

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Area of Science:

Neuroscience
Complex Systems
Dynamical Systems Theory

Background:

Metastability describes systems spending prolonged periods in specific states before transitioning.
Recurrence plot (RP) techniques offer a method for segmenting system trajectories into metastable states.
Previous brain atlases (e.g., Brain Hierarchical Atlas) defined time-invariant brain regions.

Purpose of the Study:

To apply recurrence structure analysis (RSA) to resting-state functional magnetic resonance imaging (fMRI) data for the first time.
To segment brain dynamics into metastable states and determine the optimal number of states.
To characterize these metastable brain states functionally and structurally.

Main Methods:

Functional magnetic resonance imaging (fMRI) data from resting-state brain dynamics were analyzed.
Brain regions were defined using the Brain Hierarchical Atlas (BHA).
Recurrence structure analysis (RSA) was employed to segment trajectories into metastable states, with complexity measured by the number of states.

Main Results:

Recurrence structure analysis (RSA) converged to an optimal segmentation of 40 metastable states for normalized BOLD signals, averaged over BHA modules.
A bistable dynamics at the population level was constructed by pooling 30 subjects after Hausdorff clustering.
Metastable states were characterized using resting state network (RSN) templates and the Automated Anatomical Labeling (AAL) atlas.

Conclusions:

The study demonstrates the utility of RSA for analyzing complex brain dynamics and identifying metastable states.
Findings suggest that resting-state brain activity exhibits dynamic, metastable properties, challenging purely static models.
The identified metastable states offer insights into brain complexity and potential modeling frameworks like heteroclinic dynamics.