Resting-State EEG Graph Analysis Reveals Enhanced Alpha-Band Integration in High-IQ Individuals: The MENSA-Connect Project

Background: To assess whether resting-state electroencephalography (EEG) connectivity differs between high-intelligence individuals (MENSA members) and controls using graph-theory metrics: clustering coefficient, characteristic path length, and small-worldness. Methods: Resting-state 64-channel EEG data were recorded in 29 MENSA participants and 20 controls. Functional connectivity was estimated using Lagged Linear Coherence across delta, theta, alpha, and beta bands. Graph-theory measures were computed using sLORETA and the Brain Connectivity Toolbox. Between-group comparisons were performed using the Mann–Whitney U test; p-values were corrected for multiple comparisons using the Benjamini–Hochberg false discovery rate (FDR) procedure, and effect sizes with 95% confidence intervals were calculated. Results: Compared with controls, MENSA participants showed significantly lower characteristic path length in the alpha band (Benjamini–Hochberg (BH) critical value = 0.004), indicating greater global network integration. In the beta band, they showed a higher clustering coefficient (BH critical value = 0.008), reflecting greater local specialization; together with a higher characteristic path length (BH critical value = 0.013), suggesting reduced global communication. No significant group differences were observed in small-worldness across frequency bands. Conclusions: High-Intelligence Quotient (IQ) individuals show a frequency-dependent resting-state cortical organization, characterized by enhanced alpha-band integration and increased beta-band local specialization. These findings support a nuanced neural-efficiency model of intelligence without changes in small-world architecture. Given the cross-sectional and observational design, these findings should be regarded as associative rather than causal and require replication in larger, independent samples. These findings indicate frequency-specific differences in functional network organization, although their cognitive implications remain to be established.