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Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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Volume entropy for modeling information flow in a brain graph.

Hyekyoung Lee1,2, Eunkyung Kim3,4, Seunggyun Ha5

  • 1Biomedical Research Institute, Seoul National University Hospital, Seoul, South Korea. hklee.brain@gmail.com.

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|January 24, 2019
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Summary
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This study introduces novel brain network measures, volume entropy and capacity, to quantify information flow. These measures reveal how aging impacts brain network efficiency and specific node/edge contributions.

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Area of Science:

  • Neuroscience
  • Network Science
  • Information Theory

Background:

  • Brain networks efficiently transmit information via neuronal connections.
  • Understanding information propagation dynamics is crucial for neuroscience.

Purpose of the Study:

  • To model information propagation in brain networks using a generalized Markov system.
  • To derive novel global and local network measures: volume entropy and capacity.

Main Methods:

  • Developed a new edge-transition matrix for information flow modeling.
  • Applied derived measures to functional MRI (fMRI) and FDG PET data.
  • Analyzed resting-state fMRI data from healthy subjects.

Main Results:

  • Introduced volume entropy (global measure) and node/edge capacity (local measure).
  • Volume entropy quantifies the exponential growth rate of network paths.
  • Node and edge capacity represent the stationary distribution of information flow.

Conclusions:

  • Volume entropy negatively correlates with aging in healthy subjects.
  • Specific brain nodes and edges show age-related changes in capacity.
  • Identified key brain regions contributing to aging-related network alterations.