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Predicting cognitive abilities across individuals using sparse EEG connectivity

Nicole Hakim, Edward Awh, Edward K Vogel, View ORCID ProfileMonica D Rosenberg
doi: https://doi.org/10.1101/2020.07.22.216705
Nicole Hakim
1Department of Psychology, University of Chicago, Chicago, Il
2Institute for Mind and Biology, University of Chicago, Chicago, Il
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  • For correspondence: nhakim@uchicago.edu awh@uchicago.edu edvogel@uchicago.edu mdrosenberg@uchicago.edu
Edward Awh
1Department of Psychology, University of Chicago, Chicago, Il
2Institute for Mind and Biology, University of Chicago, Chicago, Il
3Grossman Institute for Neuroscience, Quantitative Biology, and Human Behavior, University of Chicago, Chicago, I
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  • For correspondence: nhakim@uchicago.edu awh@uchicago.edu edvogel@uchicago.edu mdrosenberg@uchicago.edu
Edward K Vogel
1Department of Psychology, University of Chicago, Chicago, Il
2Institute for Mind and Biology, University of Chicago, Chicago, Il
3Grossman Institute for Neuroscience, Quantitative Biology, and Human Behavior, University of Chicago, Chicago, I
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  • For correspondence: nhakim@uchicago.edu awh@uchicago.edu edvogel@uchicago.edu mdrosenberg@uchicago.edu
Monica D Rosenberg
1Department of Psychology, University of Chicago, Chicago, Il
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  • ORCID record for Monica D Rosenberg
  • For correspondence: nhakim@uchicago.edu awh@uchicago.edu edvogel@uchicago.edu mdrosenberg@uchicago.edu
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ABSTRACT

Human brains share a broadly similar functional organization with consequential individual variation. This duality in brain function has primarily been observed when using techniques that consider the spatial organization of the brain, such as MRI. Here, we ask whether these common and unique signals of cognition are also present in temporally sensitive, but spatially insensitive, neural signals. To address this question, we compiled EEG data from individuals performing multiple working memory tasks at two different data-collection sites (ns = 171 and 165). Results revealed that EEG connectivity patterns were stable within individuals and unique across individuals. Furthermore, models based on these connectivity patterns generalized across datasets to predict participants’ working memory capacity and general fluid intelligence. Thus, EEG connectivity provides a signature of working memory and fluid intelligence in humans and a new framework for characterizing individual differences in cognitive abilities.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
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-ND 4.0 International license.
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Posted July 24, 2020.
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Predicting cognitive abilities across individuals using sparse EEG connectivity
Nicole Hakim, Edward Awh, Edward K Vogel, Monica D Rosenberg
bioRxiv 2020.07.22.216705; doi: https://doi.org/10.1101/2020.07.22.216705
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Predicting cognitive abilities across individuals using sparse EEG connectivity
Nicole Hakim, Edward Awh, Edward K Vogel, Monica D Rosenberg
bioRxiv 2020.07.22.216705; doi: https://doi.org/10.1101/2020.07.22.216705

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