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Related Experiment Video

Updated: Nov 7, 2025

Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy
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Functional Connectivity in Dorsolateral Frontal Cortex: Intracranial Electroencephalogram Study.

Giridhar P Kalamangalam1,2, Mircea I Chelaru2

  • 1Department of Neurology, University of Florida, Gainesville, Florida, USA.

Brain Connectivity
|April 30, 2021
PubMed
Summary
This summary is machine-generated.

Brain network connectivity influences electroencephalogram (EEG) patterns. This study found that local EEG dynamics, measured by sample entropy, inversely correlate with functional connectivity, distinguishing brain regions like primary sensorimotor cortex from association cortex.

Keywords:
eloquent cortexepilepsymotor systemnetworkssample entropy

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Brain-Computer Interfaces

Background:

  • Spatial variations in electroencephalogram (EEG) patterns across the human scalp are not fully understood.
  • The relationship between cortical neurobiology and observable EEG spatial differences requires further characterization.
  • Hypothesized link between EEG spatial variation and underlying cortical connectivity patterns.

Purpose of the Study:

  • To investigate the relationship between resting-state functional connectivity and local EEG dynamic variability.
  • To determine if primary and association brain areas in the lateral frontal lobe exhibit distinct EEG characteristics due to differing connectivity.
  • To explore if functional connectivity patterns can predict local EEG dynamics.

Main Methods:

  • Utilized intracranial EEG (iEEG) data from seven epilepsy patients.
  • Calculated electrode connectivity (EC) by averaging functional connectivity between electrode pairs.
  • Computed mean electrode connectivity (mEC) and mean electrode sample entropy (mESE) to quantify connectivity and local dynamics, respectively.

Main Results:

  • Found an inverse relationship between mean electrode connectivity (mEC) and mean electrode sample entropy (mESE).
  • Observed a rostrocaudal gradient in mEC and mESE across the lateral frontal lobe in all patients.
  • Identified distinct EEG signatures for primary sensorimotor cortex versus association cortex, independent of the mu rhythm.

Conclusions:

  • Brain network architecture (functional connectivity) significantly influences local brain dynamics (EEG entropy).
  • The observed rostrocaudal topography of mEC and mESE aligns with known frontal lobe functional parcellation.
  • Demonstrated that EEG sample entropy can differentiate between primary and association cortical areas based on their connectivity profiles.