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Related Concept Videos

Propagation of Action Potentials01:23

Propagation of Action Potentials

The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
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Neurons communicate by firing action potentials—the electrochemical signal that is propagated along the axon. The signal results in the release of neurotransmitters at axon terminals, thereby transmitting information to the nervous system. An action potential is a specific "all-or-none" change in membrane potential that results in a rapid spike in voltage.
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A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
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Dynamic causal modelling of evoked potentials: a reproducibility study.

Marta I Garrido1, James M Kilner, Stefan J Kiebel

  • 1The Wellcome Dept. of Imaging Neuroscience, University College London, Queen Square, London, WC1N 3BG, UK. m.garrido@fil.ion.ucl.ac.uk

Neuroimage
|May 5, 2007
PubMed
Summary

Dynamic Causal Modelling (DCM) validates its use for EEG/MEG event-related potentials (ERPs). The forward model (F-model) consistently outperformed the backward model (B-model), with the full model (FB-model) showing superior group-level results.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Biophysics

Background:

  • Dynamic Causal Modelling (DCM) is a technique used to analyze event-related potentials (ERPs) from EEG/MEG data.
  • DCM models interacting cortical sources and explains waveform differences through changes in coupling among these sources.

Purpose of the Study:

  • To assess the reproducibility and validity of DCM for analyzing EEG/MEG data across subjects.
  • To investigate the underlying neural mechanisms of mismatch responses using an oddball paradigm.

Main Methods:

  • Used an oddball paradigm to elicit mismatch responses measured by EEG/MEG.
  • Modeled cortical activity sources as equivalent current dipoles with a biophysical forward model.
  • Employed Bayesian inversion to estimate coupling changes and model likelihoods.
  • Compared three connectivity hypotheses: forward (F-model), backward (B-model), and both (FB-model).

Main Results:

  • DCM results were highly consistent across subjects.
  • The F-model was superior to the B-model in most subjects, despite equal parameter complexity.
  • The FB-model provided a significantly better fit than both F and B models in 7 out of 11 subjects.
  • At the group level, the FB-model was the most likely explanation for the observed ERPs.

Conclusions:

  • DCM is a valid and reproducible method for characterizing EEG/MEG data.
  • DCM can mechanistically model ERPs, providing insights into neural connectivity.
  • The findings support the utility of DCM in understanding brain network dynamics.