Lower resting-state EEG mean eigenvector centrality is associated with higher math anxiety.

Area of Science:

• Neuroscience
• Cognitive Psychology
• Educational Psychology

Background:

• Math anxiety is a prevalent issue affecting academic performance.
• Understanding the neural underpinnings of math anxiety is crucial for developing interventions.
• Resting-state functional connectivity offers insights into brain network dynamics.

Purpose of the Study:

• To investigate the resting-state neural correlates of math anxiety in adolescents.
• To examine the relationship between brain network integration and math anxiety levels.
• To utilize a graph-theoretical approach on electroencephalography (EEG) data.

Main Methods:

• Recruited 60 tenth-grade students (mean age 16.15 years).
• Acquired resting-state EEG data using a 32-channel wireless system.
• Assessed math anxiety with the Abbreviated Math Anxiety Scale and analyzed network integration using eigenvector centrality.

Main Results:

• Significant negative associations found between mean eigenvector centrality and math anxiety.
• Associations observed in the alpha band (eyes-closed), theta band (eyes-open), and beta band (eyes-closed).
• Correlation coefficients ranged from r = -0.28 to -0.34, with no significant sex differences.

Conclusions:

• Lower functional network integration in the brain is associated with higher math anxiety.
• These findings support previous research linking reduced network integration to math anxiety.
• Suggests potential neural targets for interventions aimed at mitigating math anxiety.