Spatiotemporal complexity patterns of resting‐state bioelectrical activity explain fluid intelligence: Sex matters

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.

Multimodal Brain Signal Complexity Predicts Human Intelligence

“…Sample entropy of frontocentral channels demonstrated non-significant negative associations with intelligence at finer timescales, while sample entropy of fronto-parietal channels at coarser time scales demonstrated non-significant tendencies towards positive associations. Together, the finding of lower entropy at finer time scales and tendencies towards higher entropy at coarser time scales to be associated with higher intelligence is in line with the findings of Dreszer et al (2020) suggesting differences in local (linked to entropy at finer time scales) versus global (as reflected by entropy at coarser time scales) aspects (Vakorin et al, 2011;McIntosh et al, 2014;Courtiol et al, 2016) of intelligence-related information processes. Interestingly, it has been shown that entropy at finer time scales increases while entropy at coarser time scales decreases with increasing age (McIntosh et al, 2014), which is in regard to our observations, plausible as fluid intelligence decreases with increasing age (Schaie, 1994;Salthouse, 2010;Ghisletta et al, 2012).…”

“…Microstate A has been related to reduced activity of the temporal network (Michel and Koenig, 2018) and especially to areas implicated in phonological processing (Britz et al, 2010), which is consistent with the observation that Microstate A is more present during visualization tasks expectedly implying inhibition of left-hemispheric language areas (Milz et al, 2016). However, implications of microstate patterns on cognition are far away from being completely understood and more research is needed to clarify whether and to which extend a higher presence of microstates A may possibly reflect intrinsic dispositions for verbal processing (Dreszer et al, 2020) or visualization (Milz et al, 2016).…”

“…In analogy to the most popular prediction frameworks in neuroimaging (connectome-based predictive modeling, CPM, Finn et al, 2015; Shen et al, 2017), the input features of this model were the means of z-normalized complexity measures positively X + and negatively X - correlated with intelligence ( p < .05): with β 0 = 0 and β 1 = 1 and the predicted intelligence score . To account for multicollinearity and the high proportion of MSE measures, MSE measures were averaged within spatial and temporal clusters (Dreszer et al, 2020). Specifically, we combined electrodes into seven spatial clusters: frontopolar (Fp1, Fp2), frontocentral (FC1, FC2, FC5, FC6), frontal (F3, F4, F7, F8), temporal (T7, T8), centroparietal (CP1, CP2, CP5, CP6), parietal (P3, P4, P7, P8, Pz), occipital (O1, O2, Oz) and aggregated the MSE values of these clusters over five time scales.…”

“…Higher intelligence has been associated with higher complexity (Jaušovec and Jaušovec, 2003; Thatcher et al, 2005; Stankova and Myshkin, 2016), Dreszner et al (2020) found both positive and negative associations, and other studies could not find any significant relation (Anokhin et al, 1999; Ueno et al, 2015). Sample characteristics (Dreszer et al, 2020), varying complexity measures (Goldberger et al, 2002; Ferenets et al, 2006), and differences in the considered time scale capturing, e.g., local vs. global processing (Vakorin et al, 2011; Courtiol et al, 2016; Dreszer et al, 2020) were proposed as contributing to this heterogeneity. Consequently, results of the existing studies are difficult to compare and highlight the need for a holistic framework simultaneously investigating the relation between intelligence and brain signal complexity with various measures, on multiple spatial and temporal scales and in different study samples.…”

“…Sufficient complexity of neural signals was hypothesized to constitute the basis for efficient neural communication (de Pasquale et al, 2016) and higher complexity was related to increased information processing capacity (Heisz and McIntosh, 2013). Further, individual variations in brain signal complexity have been associated with differences in creativity (Kaur et al, 2021), the genetic risk for psychological diseases (Li et al, 2008), and also with differences in intelligence (Dreszer et al, 2020; Lutzenberger et al, 1992). However, across-study findings on relations between individual differences in intelligence and intrinsic brain signal complexity are heterogeneous.…”