Getting Spearman off the Skyhook: One More in a Century (Since Thomson, 1916) of Attempts to Vanquish<i>g</i>

Deary, Ian J.; Cox, Simon R.; Ritchie, Stuart J.

doi:10.1080/1047840x.2016.1186525

Cited by 19 publications

(15 citation statements)

References 33 publications

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“…Deary et al recently wrote [97]: 'the effort to understand the psychobiology of intelligence has a resemblance with digging the tunnel between England and France: We hope, with workers on both sides having a good sense of direction, that we can meet and marry brain biology and cognitive differences'. The present study is a step in this direction, offering the to-date most robust investigation specifically focused on predicting intelligence from resting-state fMRI data.…”

Section: Discussionmentioning

confidence: 99%

A distributed brain network predicts general intelligence from resting-state human neuroimaging data

Dubois

Galdi

Paul

et al. 2018

Phil. Trans. R. Soc. B

273

205

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Individual people differ in their ability to reason, solve problems, think abstractly, plan and learn. A reliable measure of this general ability, also known as intelligence, can be derived from scores across a diverse set of cognitive tasks. There is great interest in understanding the neural underpinnings of individual differences in intelligence, because it is the single best predictor of long-term life success. The most replicated neural correlate of human intelligence to date is total brain volume; however, this coarse morphometric correlate says little about function. Here, we ask whether measurements of the activity of the resting brain (resting-state fMRI) might also carry information about intelligence. We used the final release of the Young Adult Human Connectome Project ( = 884 subjects after exclusions), providing a full hour of resting-state fMRI per subject; controlled for gender, age and brain volume; and derived a reliable estimate of general intelligence from scores on multiple cognitive tasks. Using a cross-validated predictive framework, we predicted 20% of the variance in general intelligence in the sampled population from their resting-state connectivity matrices. Interestingly, no single anatomical structure or network was responsible or necessary for this prediction, which instead relied on redundant information distributed across the brain.This article is part of the theme issue 'Causes and consequences of individual differences in cognitive abilities'.

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Section: Discussionmentioning

confidence: 99%

A distributed brain network predicts general intelligence from resting-state human neuroimaging data

Dubois

Galdi

Paul

et al. 2018

Phil. Trans. R. Soc. B

273

205

View full text Add to dashboard Cite

show abstract

“…Deary et al recently wrote [120] : "the effort to understand the psychobiology of intelligence has a resemblance with digging the tunnel between England and France: We hope, with workers on both sides having a good sense of direction, that we can meet and marry brain biology and cognitive differences." The present study is a step in this direction, offering to our knowledge the to-date most robust investigation specifically focused on predicting intelligence from resting-state fMRI data.…”

Section: Discussionmentioning

confidence: 99%

A distributed brain network predicts general intelligence from resting-state human neuroimaging data

Dubois

Galdi

Paul

et al. 2018

Preprint

102

149

View full text Add to dashboard Cite

Individual people differ in their ability to reason, solve problems, think abstractly, plan and learn. A reliable measure of this general ability, also known as intelligence, can be derived from scores across a diverse set of cognitive tasks. There is great interest in understanding the neural underpinnings of individual differences in intelligence, since it is the single best predictor of long-term life success, and since individual differences in a similar broad ability are found across animal species. The most replicated neural correlate of human intelligence to date is total brain volume. However, this coarse morphometric correlate gives no insights into mechanisms; it says little about function. Here we ask whether measurements of the activity of the resting brain (resting-state fMRI) might also carry information about intelligence. We used the final release of the Young Adult Human Connectome Project dataset (N=884 subjects after exclusions), providing a full hour of resting-state fMRI per subject; controlled for gender, age, and brain volume; and derived a reliable estimate of general intelligence from scores on multiple cognitive tasks. Using a cross-validated predictive framework, we predicted 20% of the variance in general intelligence in the sampled population from their resting-state fMRI data. Interestingly, no single anatomical structure or network was responsible or necessary for this prediction, which instead relied on redundant information distributed across the brain.peer-reviewed)

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“…It must be noted however that meta-analysis reveals that the association is weak (ρ=0.24), indicating that the majority of the variance in brain volume is unrelated to variance in GMA (36). Furthermore studies utilizing POLY COG have found that it does not predict variation in brain volume, despite both POLY COG and brain volume making independent contributions to GMA (37). These findings suggest 1 3 that the decline in brain volume during the Holocene may have been a consequence of enhanced brain efficiency stemming from increased corticalization and neuronal connectivity, with more bioenergetically optimized brains simply requiring less mass to achieve greater processing power.…”

Section: G-test; P-valuementioning

confidence: 98%

Holocene selection for variants associated with cognitive ability: Comparing ancient and modern genomes

Younuskunja

Balan²,

Piffer

2017

Preprint

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Human populations living in Eurasia during the Holocene experienced considerable microevolutionary change. It has been predicted that the transition of Holocene populations into agrarianism and urbanization brought about culture-gene coevolution that favoured via directional selection genetic variants associated with higher general cognitive ability (GCA). To examine whether GCA might have risen during the Holocene, we compare a sample of 99 ancient Eurasian genomes (ranging from 4.56 to 1.21 kyr BP) with a sample of 503 modern European genomes, using three different cognitive polygenic scores. Significant differences favouring the modern genomes were found for all three polygenic scores (Odds Ratios=0.92, p=0.037; 0.81, p=0.001 and 0.81, p=0.02). Furthermore, a significant increase in positive allele count over 3.25 kyr was found using a subsample of 66 ancient genomes (r=0.217, p one-tailed =0.04). These observations are consistent with the expectation that GCA rose during the Holocene.peer-reviewed)

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Getting Spearman off the Skyhook: One More in a Century (Since Thomson, 1916) of Attempts to Vanquishg

Cited by 19 publications

References 33 publications

A distributed brain network predicts general intelligence from resting-state human neuroimaging data

A distributed brain network predicts general intelligence from resting-state human neuroimaging data

A distributed brain network predicts general intelligence from resting-state human neuroimaging data

Holocene selection for variants associated with cognitive ability: Comparing ancient and modern genomes

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