Abstract
Individual differences in general cognitive ability (i.e., intelligence) have been linked to individual variations in the modular organization of functional brain networks. However, these analyses have been limited to static (time‐averaged) connectivity, and have not yet addressed whether dynamic changes in the configuration of brain networks relate to general intelligence. Here, we used multiband functional MRI resting‐state data (N = 281) and estimated subject‐specific time‐varying functional connectivity networks. Modularity optimization was applied to determine individual time‐variant module partitions and to assess fluctuations in modularity across time. We show that higher intelligence, indexed by an established composite measure, the Wechsler Abbreviated Scale of Intelligence (WASI), is associated with higher temporal stability (lower temporal variability) of brain network modularity. Post‐hoc analyses reveal that subjects with higher intelligence scores engage in fewer periods of extremely high modularity — which are characterized by greater disconnection of task‐positive from task‐negative networks. Further, we show that brain regions of the dorsal attention network contribute most to the observed effect. In sum, our study suggests that investigating the temporal dynamics of functional brain network topology contributes to our understanding of the neural bases of general cognitive abilities.
Highlights
Intelligence describes our ability to reason, to understand complex ideas, to learn from experiences, and to adapt effectively to the environment (Neisser et al, 1996)
To explore whether the frequency of those states relates to general intelligence, we identified, per subject, states of high or low modularity and tested across subjects whether the count of occurrences of these states correlated with intelligence
We have shown that human intelligence is associated with the dynamic reconfiguration in functional brain networks as indexed by temporal fluctuations in global modularity
Summary
Intelligence describes our ability to reason, to understand complex ideas, to learn from experiences, and to adapt effectively to the environment (Neisser et al, 1996). Understanding the biological bases of human intelligence is an important scientific aim, and neuroscientific research has begun to contribute insights about how individual differences in brain function (Duncan, 2005; Sripada, Angstadt, & Rutherford, 2018), brain structure (Gregory et al, 2016; Haier, Jung, Yeo, Head, & Alkire, 2004), and intrinsic brain connectivity (Hilger, Ekman, Fiebach, & Basten, 2017a; Van den Heuvel, Stam, Kahn, & Hulshoff Pol, 2009) relate to general intelligence (for review see Basten, Hilger, & Fiebach, 2015; Jung & Haier, 2007). Resting-state conditions; Biswal, Yetkin, Haughton, & Hyde, 1995) The topology of these networks determines how information is transferred between brain regions, and graph theory provides a set of tools to study these topological characteristics (Rubinov & Sporns, 2010). Region-specific modularity was recently shown to covary significantly with individual differences in general intelligence (Hilger, Ekman, Fiebach, & Basten, 2017b)
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