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Related Concept Videos

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Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
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Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
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Task-evoked brain network dynamics underlying cognitive control using source-localized EEG and machine learning.

Alfia Parvez1, Shreya Verma1, Jonathan Cerna2

  • 1Department of Health and Kinesiology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.

International Journal of Psychophysiology : Official Journal of the International Organization of Psychophysiology
|February 28, 2026
PubMed
Summary
This summary is machine-generated.

This study reveals how brain network connectivity changes during cognitive tasks. Age and BMI influence these changes, impacting cognitive control performance.

Keywords:
Brain networksCognitive controlEEG source localizationFunctional connectivityObesity and brain functionTask-related brain activation

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Area of Science:

  • Neuroscience
  • Cognitive Neuroscience
  • Brain Connectivity Research

Background:

  • Functional connectivity (FC) is crucial for cognitive tasks.
  • The impact of demographics and health on dynamic FC during tasks is not well understood.
  • Seven core brain networks were investigated: Default Mode (DMN), Ventral Attention (VAN), Somatomotor (SMN), Dorsal Attention (DAN), Frontoparietal (FPN), Visual (VIN), and Limbic (LIN).

Purpose of the Study:

  • To identify network configurations related to cognitive control demands during a modified Eriksen flanker task.
  • To assess how demographic (age, sex, income) and health (BMI) factors influence task-related dynamic FC.
  • To understand the relationship between dynamic FC patterns and reaction time (RT) in high-performing individuals.

Main Methods:

  • Secondary analysis of source-localized EEG data from 255 adults.
  • Hidden Markov Model (HMM) for extracting spatiotemporal dynamics and quantifying network correlations.
  • Extreme Gradient Boosting (XGBoost) and recursive feature elimination to identify predictive FC features for RT.
  • Regression models to evaluate the effects of age, sex, BMI, and income on task-related FC.

Main Results:

  • Machine learning models accurately predicted RT for both congruent (AUC=0.81) and incongruent (AUC=0.86) trials.
  • Specific FC patterns (e.g., VAN-LIN, VIN-VAN, SMN-DAN) were key for congruent trials, while others (e.g., SMN-DMN, LIN-FPN, VIN-DAN) were crucial for incongruent trials.
  • Increased age correlated with reduced FC; higher BMI showed modest positive associations with FC in specific networks (LIN-FPN, VIN-DAN). Sex and income were not significant predictors.

Conclusions:

  • Dynamic interactions within and between brain networks are vital for cognitive control.
  • Age and BMI are significant demographic and health factors influencing brain connectivity during cognitive tasks.
  • Future research should consider these factors when examining task-based functional connectivity.