Spatiotemporal complexity patterns of resting‐state bioelectrical activity explain fluid intelligence: Sex matters. Issue 17 (18th August 2020)

• Record Type: Journal Article
• Authors: Dreszer, Joanna; Grochowski, Marek; Lewandowska, Monika; Nikadon, Jan; Gorgol, Joanna; Bałaj, Bibianna; Finc, Karolina; Duch, Włodzisław; Kałamała, Patrycja; Chuderski, Adam; Piotrowski, Tomasz
• Is Part Of: Human brain mapping. Volume 41:Issue 17(2020)
• Page Start: 4846
• Page End: 4865
• Publication Date: 2020-08-18
• Subjects: fluid intelligence -- frontoparietal network -- multivariate multiscale sample entropy (mMSE) -- rsEEG -- sex -- spatiotemporal complexity patterns; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.25162
• Languages: English
• ISSNs: 1065-9471

Abstract

Abstract: Neural complexity is thought to be associated with efficient information processing but the exact nature of this relation remains unclear. Here, the relationship of fluid intelligence ( gf ) with the resting‐state EEG (rsEEG) complexity over different timescales and different electrodes was investigated. A 6‐min rsEEG blocks of eyes open were analyzed. The results of 119 subjects (57 men, mean age = 22.85 ± 2.84 years) were examined using multivariate multiscale sample entropy (mMSE) that quantifies changes in information richness of rsEEG in multiple data channels at fine and coarse timescales. gf factor was extracted from six intelligence tests. Partial least square regression analysis revealed that mainly predictors of the rsEEG complexity at coarse timescales in the frontoparietal network (FPN) and the temporo‐parietal complexities at fine timescales were relevant to higher gf. Sex differently affected the relationship between fluid intelligence and EEG complexity at rest. In men, gf was mainly positively related to the complexity at coarse timescales in the FPN. Furthermore, at fine and coarse timescales positive relations in the parietal region were revealed. In women, positive relations with gf were mostly observed for the overall and the coarse complexity in the FPN, whereas negative associations with gf were found for the complexity at fine timescales in the parietal and centro‐temporal region. These outcomes indicate that two separate time pathways (corresponding to fine and coarse timescales) used to characterize rsEEG complexity (expressed by mMSE features) are beneficial for effective information processing. Abstract : Neural complexity is thought to be associated with efficient information processing but the exact nature of this relation remains unclear. Here, the relationship of fluid intelligence ( gf ) with the resting‐state EEG (rsEEG) complexity over different timescales and different electrodes was investigated. Partial least square regression analysis revealed that mainly predictors of the rsEEG complexity at coarse timescales in the fronto‐parietal network and the temporo‐parietal complexities at fine timescales were relevant to higher gf .