Alpha Band Resting-State EEG Connectivity Is Associated With Non-verbal Intelligence

Abstract

The aim of the present study was to investigate whether EEG resting state connectivity correlates with intelligence. One-hundred and sixty five participants took part in the study. Six minutes of eyes closed EEG resting state was recorded for each participant. Graph theoretical connectivity metrics were calculated separately for two well-established synchronization measures [weighted Phase Lag Index (wPLI) and Imaginary Coherence (iMCOH)] and for sensor-and source EEG space. Non-verbal intelligence was measured with Raven's Progressive Matrices. In line with the Neural Efficiency Hypothesis, path lengths characteristics of the brain networks (Average and Characteristic Path lengths, Diameter and Closeness Centrality) within alpha band range were significantly correlated with non-verbal intelligence for sensor space but no for source space. According to our results, variance in non-verbal intelligence measure can be mainly explained by the graph metrics built from the networks that include both weak and strong connections between the nodes.

Assessing the Relationship between Verbal and Nonverbal Cognitive Abilities Using Resting-State EEG Functional Connectivity

“…The negative association between segregation and cognition is contrary to the previous results by Langer et al [ 31 ] who showed positive relationships between the degree of segregation of the global brain network and nonverbal cognitive abilities. However, these results are in line with the previous results by Zakharov et al [ 32 ], who showed that the alpha band characteristic path length of the brain graphs are robustly associated with non-verbal intelligence across different connectivity calculation routines. The present results are also consistent with structural brain connectivity data [ 62 ], which showed that high density and low modularity of white matter fibers are associated with higher fluid intelligence.…”

“…However, recent studies have failed to find an association between resting-state fMRI activity and various intelligence measures [ 30 ], while other studies showed that resting-state EEG network integration characteristics were positively correlated to individual differences in non-verbal intelligence [ 31 , 32 ]. It has been hypothesized that this lack of association between fMRI-resting states may be related to its poor temporal resolution (2–3 s [ 33 ]).…”

The thresholding problem and variability in the EEG graph network parameters

“…According to the recent anatomical studies with tracing the connections within the cortical, the brain indeed has a lot of weak connections that has a substantial impact on maintaining the intermodular connections between functional brain modules. Weak connections may also play a significant role in the individual differences in intelligence (Santarnecchi et al, 2014; Zakharov et al, 2020) and its abnormality may be related to the symptomatology of schizophrenic patients (Bassett et al, 2012).…”

Oscillatory activity and EEG phase synchrony of concurrent word segmentation and meaning-mapping in 9-year-old children

“…First, volume conduction may introduce spurious correlations in the phase synchronization analysis when performed in the sensor space ( Bastos and Schoffelen, 2016 ). However, as done in recent MEEG studies ( Chaturvedi et al, 2019 ; Imperatori et al, 2020 ; Zakharov et al, 2020 ; Reiterer et al, 2011 ), we used the wPLI, which is known to decrease the impact of these confounding factors ( Vinck et al, 2011 ). Second, as opposed to effective connectivity measures, the PLI used here provides an index of coordinated activity only and does not imply causal or directional influence as Granger causality does ( Hesse et al, 2003 ; Brovelli et al, 2004 ).…”