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The research of constructing dynamic cognition model based on brain network.

Fang Chunying1, Li Haifeng2, Ma Lin2

  • 1School of Computer Science and Technology, Harbin Institute of Technology, 150001 Harbin, China; School of Computer and Information Engineering, Heilongjiang University of Science and Technology, 150027 Harbin, China.

Saudi Journal of Biological Sciences
|April 8, 2017
PubMed
Summary

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This study introduces a novel dynamic programming model using electroencephalography (EEG) to map brain connectivity and cognitive processing. Findings reveal how brain network dynamics evolve over time, offering insights into the human connectome.

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Computational Biology

Background:

  • Understanding the human connectome relies on mapping functional brain interactions.
  • Few studies explore dynamic brain network evolution in cognitive processing using electroencephalography (EEG).

Purpose of the Study:

  • To investigate brain functional connectivity using EEG.
  • To construct a dynamic programming model for cognitive processing.
  • To correlate brain network topology with cognitive evolution.

Main Methods:

  • Functional connectivity was defined as statistical dependence between EEG signals, calculated using wavelet coherence.
  • An accelerated dynamic programming algorithm constructed a dynamic cognitive model.
  • Brain networks were built using wavelet coherence and minimum spanning tree analysis.
Keywords:
Brain functional connectivityCognition processDynamic evolution modelWavelet coherence

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Main Results:

  • The dynamic programming model revealed temporary network states and information transfer characteristics.
  • Brain dynamics influenced complex network properties over time following auditory stimulation.
  • Quantitative analysis showed increased correlation after auditory stimulation.

Conclusions:

  • The developed EEG functional connectivity dynamic evolution model is feasible for studying cognitive processing.
  • This approach provides a new method for understanding whole-brain network dynamics.
  • Findings highlight the link between network topology, dynamic evolution, and cognitive function.