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A watershed model of individual differences in fluid intelligence

Rogier A. Kievit, Simon W. Davis, John Griffiths, Marta Correia, Cam-CAN, Richard N. Henson
doi: https://doi.org/10.1101/041368
Rogier A. Kievit
1MRC Cognition and Brain Sciences Unit, 15 Chaucer Rd, Cambridge, CB2 7EF, United Kingdom
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  • For correspondence: Rogier.Kievit@mrc-cbu.cam.ac.uk
Simon W. Davis
2Department of Psychology, University of Cambridge, Downing Street, Cambridge, CB2 3EB, United Kingdom
3Center for Cognitive Neuroscience, Duke University, Durham, North Carolina 27708
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John Griffiths
2Department of Psychology, University of Cambridge, Downing Street, Cambridge, CB2 3EB, United Kingdom
4Rotman Research Institute, Baycrest, Toronto, Ontario M6A 2E1, Canada
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Marta Correia
1MRC Cognition and Brain Sciences Unit, 15 Chaucer Rd, Cambridge, CB2 7EF, United Kingdom
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Cam-CAN
5Cambridge Centre for Ageing and Neuroscience (Cam-CAN), University of Cambridge and MRC Cognition and Brain Sciences Unit, Cambridge, UK,
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Richard N. Henson
1MRC Cognition and Brain Sciences Unit, 15 Chaucer Rd, Cambridge, CB2 7EF, United Kingdom
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Abstract

Fluid intelligence is a crucial cognitive ability that predicts key life outcomes across the lifespan. Strong empirical links exist between fluid intelligence and processing speed on the one hand, and white matter integrity and processing speed on the other. We propose a watershed model that integrates these three explanatory levels in a principled manner in a single statistical model, with processing speed and white matter figuring as intermediate endophenotypes. We fit this model in a large (N=555) adult lifespan cohort from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) using multiple measures of processing speed, white matter health and fluid intelligence. The model fit the data well, outperforming competing models and providing evidence for a many-to-one mapping between white matter integrity, processing speed and fluid intelligence. The model can be naturally extended to integrate other cognitive domains, endophenotypes and genotypes.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted June 23, 2016.
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A watershed model of individual differences in fluid intelligence
Rogier A. Kievit, Simon W. Davis, John Griffiths, Marta Correia, Cam-CAN, Richard N. Henson
bioRxiv 041368; doi: https://doi.org/10.1101/041368
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A watershed model of individual differences in fluid intelligence
Rogier A. Kievit, Simon W. Davis, John Griffiths, Marta Correia, Cam-CAN, Richard N. Henson
bioRxiv 041368; doi: https://doi.org/10.1101/041368

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