General, crystallized and fluid intelligence are not associated with functional global network efficiency: A replication study with the human connectome project 1200 data set

Kruschwitz, Johann; Waller, Lea; Daedelow, Laura S.; Walter, Henrik; Veer, Ilya M.

doi:10.1016/j.neuroimage.2018.01.018

Cited by 66 publications

(59 citation statements)

References 32 publications

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“…Although previous research indicates that integrated and segregated information processing are both essential for human cognition (Cohen & D'Esposito, 2016), neither the general level of network integration (indexed by global efficiency; Hilger et al, 2017a;Kruschwitz et al, 2018) nor the general level of network segregation (indexed by global modularity; Hilger et al, 2017b, and present results) seem to differentiate between high versus low general intelligencewhen investigated in static, time-invariant networks. Rather, we observed here that higher intelligence is associated with more stable (i.e., less variable) levels of network segregation over time.…”

Section: Higher Network Stability Associated With Intelligencecontrasting

confidence: 80%

“…Irrespective of the specific task content, the brain seems to decrease its general level of network segregation when switching from rest to task (Shine, Bissett, et al, 2016)-with lower levels of network segregation associated with higher cognitive performance (Cohen & D'Esposito, 2016;Shine, Bissett, et al, 2016). Based on recent evidence demonstrating that, during rest, intelligence is not per se associated with the level of segregation or integration (Hilger et al, 2017a(Hilger et al, , 2017bKruschwitz et al, 2018;Pamplona et al, 2015), one can plausibly assume that more intelligent people may invest more effort into reconfiguring their network when switching from rest to task in order to reach better-suitable network configurations that facilitate high cognitive performance (Cohen & D'Esposito, 2016;Shine, Bissett, et al, 2016). The results of a recent study, however, point into exactly the opposite direction.…”

Section: Higher Network Stability Associated With Intelligencementioning

confidence: 99%

“…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, ). In the field of intelligence research, early graph‐theoretical work proposed that global properties of brain networks such as higher global network efficiency are associated with higher intelligence (van den Heuvel et al, ), a finding not replicated in more recent studies (Kruschwitz, Waller, Daedelow, Walter, & Veer, ; Pamplona, Santos Neto, Rosset, Rogers, & Salmon, ). In contrast, other studies have suggested that intelligence is related to efficiency in the interconnections of specific brain regions (Hilger et al, ).…”

Section: Introductionmentioning

confidence: 97%

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Temporal stability of functional brain modules associated with human intelligence

Hilger

Fukushima

Sporns

et al. 2019

Human Brain Mapping

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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 modularitywhich 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.

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Section: Higher Network Stability Associated With Intelligencecontrasting

confidence: 80%

Section: Higher Network Stability Associated With Intelligencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

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Temporal stability of functional brain modules associated with human intelligence

Hilger

Fukushima

Sporns

et al. 2019

Human Brain Mapping

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“…The original study by van den Heuvel et al (van den Heuvel, Stam, Kahn, & Hulshoff Pol, ) reported an association between global network efficiency and intelligence. However, more recent investigation in a larger sample size showed that global efficiency was not associated with intelligence (Kruschwitz, Waller, Daedelow, Walter, & Veer, ). Instead, connectivity profiles of the frontoparietal network have been identified to be related to human intelligence (Finn et al, ; Hearne et al, ).…”

Section: Discussionmentioning

confidence: 97%

“…Previous brain network studies have demonstrated that organization principles are robust across spatial scales, but quantitative measures of graph metrics, especially for individual regions, vary substantially (de Reus & van den Heuvel, ; Hayasaka & Laurienti, ; Wang et al, ). In particular, the association between global network efficiency and intelligence has not been successfully replicated across different datasets, even with a wide range of network densities and spatial scales (Kruschwitz et al, ; van den Heuvel et al, ). Therefore, caution should be noted when interpreting differences in quantitative measures across different preprocessing strategies, network densities, brain parcellations, and datasets, although organization principles of brain network are robust.…”

Section: Discussionmentioning

confidence: 99%

A graph representation of functional diversity of brain regions

et al. 2019

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Introduction Modern network science techniques are popularly used to characterize the functional organization of the brain. A major challenge in network neuroscience is to understand how functional characteristics and topological architecture are related in the brain. Previous task‐based functional neuroimaging studies have uncovered a core set of brain regions (e.g., frontal and parietal) supporting diverse cognitive tasks. However, the graph representation of functional diversity of brain regions remains to be understood. Methods Here, we present a novel graph measure, the neighbor dispersion index, to test the hypothesis that the functional diversity of a brain region is embodied by the topological dissimilarity of its immediate neighbors in the large‐scale functional brain network. Results We consistently identified in two independent and publicly accessible resting‐state functional magnetic resonance imaging datasets that brain regions in the frontoparietal and salience networks showed higher neighbor dispersion index, whereas those in the visual, auditory, and sensorimotor networks showed lower neighbor dispersion index. Moreover, we observed that human fluid intelligence was associated with the neighbor dispersion index of dorsolateral prefrontal cortex, while no such association for the other metrics commonly used for characterizing network hubs was noticed even with an uncorrected p < .05. Conclusions This newly developed graph theoretical method offers fresh insight into the topological organization of functional brain networks and also sheds light on individual differences in human intelligence.

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Structural architecture and brain network efficiency link polygenic scores to intelligence

Genç

Metzen

Fraenz

et al. 2023

Human Brain Mapping

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Intelligence is highly heritable. Genome‐wide association studies (GWAS) have shown that thousands of alleles contribute to variation in intelligence with small effect sizes. Polygenic scores (PGS), which combine these effects into one genetic summary measure, are increasingly used to investigate polygenic effects in independent samples. Whereas PGS explain a considerable amount of variance in intelligence, it is largely unknown how brain structure and function mediate this relationship. Here, we show that individuals with higher PGS for educational attainment and intelligence had higher scores on cognitive tests, larger surface area, and more efficient fiber connectivity derived by graph theory. Fiber network efficiency as well as the surface of brain areas partly located in parieto‐frontal regions were found to mediate the relationship between PGS and cognitive performance. These findings are a crucial step forward in decoding the neurogenetic underpinnings of intelligence, as they identify specific regional networks that link polygenic predisposition to intelligence.

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General, crystallized and fluid intelligence are not associated with functional global network efficiency: A replication study with the human connectome project 1200 data set

Cited by 66 publications

References 32 publications

Temporal stability of functional brain modules associated with human intelligence

Temporal stability of functional brain modules associated with human intelligence

A graph representation of functional diversity of brain regions

Structural architecture and brain network efficiency link polygenic scores to intelligence

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