This study investigated the relationship between resting state EEG characteristics and general cognitive ability in 79 healthy volunteers. Graph measures of functional connectivity during resting state with eyes closed, including weighted clusterization coefficient and weighted average path length, were calculated for different frequency bands. Higher intelligence was correlated with higher weighted clusterization coefficients in the alpha and beta 2 bands, as well as lower weighted average path lengths in the alpha and beta 2 bands. These results provide support for the neural efficiency hypothesis and suggest that resting state brain networks are relevant to individual differences in intelligence.

1. EEG resting state correlates of intelligence
Introduction
Materials and methods
Results
Conclusions
References
In line with neural efficiency hypothesis (Haier et al., 1992), functional connectivity
in the brain during task performance has been associated with the intelligence level.
Recent studies of brain connectivity during intelligence-related cognitive tasks have
demonstrated that brain functional connectivity of people with higher intelligence is
more effective than those of less intelligent ones (Salvador et al., 2005; Basset &
Bullmore, 2016).
There are no robust results regarding whether the spontaneous brain activity at rest
is relevant to the individual differences in intelligence. In the present study we
analysed the relationship between scalp EEG resting state characteristics and general
cognitive ability
We found that individuals with higher intelligence
demonstrate high interconnectedness of
connectivity graphs in alpha and beta frequency
band. Our data is in agreement with the neural
efficiency hypothesis.
Alpha_range
Beta_range
Left: participant
w i t h h i g h t o t a l
Raven’s scores
Right: participant
w i t h l o w t o t a l
Raven’s scores
5% of the strongest
s y n c h ro n i s a t i o n
sites (highest PLV)
is displayed
Random participant. The width and
brightness of the lines represent the
strength of synchronisation between
electrode sites (sPLV)
Future directions
• Closed eyes Vs. Open eyes
• Small World Index
• Source space
• Different IQ measures
Ilya Zakharov1, Anna Tabueva2, Nikita Yakovlev3
1Psychological Institute of Russian Academy of Education, Russia
2Higher School of Economics, Russia
3Tomsk State University, Russia
e-mail to: iliazaharov@gmail.com
Participants: 79 healthy volunteers participated in the study
(17 to 34 years, mean=21.74; SD=3.56, 49 females).
Paradigm:
Resting state - 10 minutes. 2 minutes intervals with either closed
eyes (CE) or open eyes (OE) in the following order: CE-EO-CE-EO-
CE. Only CE data was used for the present analysis,
General cognitive ability - Raven’s Standard Progressive Matrices
(Raven, )
EEG recording: 64 active electrodes, BrainProducts ActiChamp
Amp., 0.1-40 Hz range, 500 Hz discretisation rate, 2 ms epochs.
All connections between
electrode sites.
EEG measures:
single phase locking value (sPLV, Lachaux et al., 2000) -
synchronisation between electrodes
Graph measures:
Weighted Clusterization coefficient
(Cw, Hardmeier etal., 2014): for one node - how many of
its neighbours are neighbours to each other, then average
all nodes
Weighted Average Path length (Lw) -
the sum of edges between a node,
averaged for all nodes
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2. Lachaux J.P. Measuring phase synchrony in brain signals / J. P. Lachaux, E. Rodriguez, J. Martinerie, F. J. Varela // Hum Brain Mapp – 1999. – Т. 8 – № 4– 194–208с.
3. Haier R.J. Intelligence and changes in regional cerebral glucose metabolic rate following learning / R. J. Haier, B. Siegel, C. Tang, L. Abel, M. S. Buchsbaum //
Intelligence – 1992. – Т. 16 – № 3–4– 415–426с.
4. Hardmeier M. Reproducibility of Functional Connectivity and Graph Measures Based on the Phase Lag Index (PLI) and Weighted Phase Lag Index (wPLI) Derived from
High Resolution EEG / M. Hardmeier, F. Hatz, H. Bousleiman, C. Schindler, C. J. Stam, P. Fuhr // PLOS ONE – 2014. – Т. 9 – № 10– e108648с.
5. Salvador R. Undirected graphs of frequency-dependent functional connectivity in whole brain networks / R. Salvador, J. Suckling, C. Schwarzbauer, E. Bullmore //
Philosophical Transactions of the Royal Society of London B: Biological Sciences – 2005. – Т. 360 – № 1457– 937–946с.
Table 1 Correlations between graph measures and general cognitive ability
EEG_range Cw*Raven's Score Lw*Raven's Score
Alpha 0.32** 95 % CI [0.1; 0.5] -0.34** 95 % CI [-0.52; -0.12]
Beta_1 0.18 -0.11
Beta_2 0.28* 95 % CI [0.05; 0.46] -0.33** 95 % CI [-0.51; -0.11]
Theta 0.12 -0.07
FDR corrected. Significant at p<0.05 level: *p<.05. **p<.01