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Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke
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Brain Network Dynamics Adhere to a Power Law.

Dardo G Tomasi1, Ehsan Shokri-Kojori1, Nora D Volkow2

  • 1National Institute on Alcohol Abuse and Alcoholism Bethesda, MD, USA.

Frontiers in Neuroscience
|March 7, 2017
PubMed
Summary

Brain network activity follows a power law, similar to other complex systems. This finding reveals how external factors influence brain dynamics and may help identify neurological conditions.

Keywords:
ALFFFCDMTaylor's lawbrain networksfunctional connectivity (FC)graph theory analysislFCDnumerical simulations

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Area of Science:

  • Neuroscience
  • Complex Systems Analysis
  • Network Science

Background:

  • Complex networks, including the Internet, exhibit power scaling in their temporal dynamics.
  • Understanding brain network temporal dynamics is crucial for assessing external influences and randomness.

Purpose of the Study:

  • To test if brain network temporal dynamics follow a power law similar to other complex networks.
  • To investigate the relationship between temporal mean and dispersion of brain signals.
  • To explore the potential of this power law as a biomarker for neuropathology.

Main Methods:

  • Simulated data using a random diffusion model to predict power-law scaling.
  • Analysis of resting-state functional magnetic resonance imaging (fMRI) data from 66 healthy subjects.
  • Computation and analysis of Amplitude of Low-Frequency Fluctuations (ALFF) and local Functional Connectivity Density (lFCD) metrics.

Main Results:

  • A robust power law was observed in the temporal dynamics of ALFF and lFCD metrics.
  • The scaling exponents were insensitive to the computational methods used.
  • Scaling exponents decreased with age and varied across brain regions, with multimodal cortical areas showing lower exponents than limbic and subcortical regions.

Conclusions:

  • Brain network dynamics are governed by a power law, influenced by external inputs and internal randomness.
  • Regional variations in scaling exponents suggest differential influence of external versus internal factors across brain areas.
  • This power-law scaling metric shows potential as a biomarker for characterizing neuropathology.