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Brain entropy and human intelligence: A resting-state fMRI study

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  • Glenn N Saxe
  • Daniel Calderone
  • Leah J Morales

Abstract

Human intelligence comprises comprehension of and reasoning about an infinitely variable external environment. A brain capable of large variability in neural configurations, or states, will more easily understand and predict variable external events. Entropy measures the variety of configurations possible within a system, and recently the concept of brain entropy has been defined as the number of neural states a given brain can access. This study investigates the relationship between human intelligence and brain entropy, to determine whether neural variability as reflected in neuroimaging signals carries information about intellectual ability. We hypothesize that intelligence will be positively associated with entropy in a sample of 892 healthy adults, using resting-state fMRI. Intelligence is measured with the Shipley Vocabulary and WASI Matrix Reasoning tests. Brain entropy was positively associated with intelligence. This relation was most strongly observed in the prefrontal cortex, inferior temporal lobes, and cerebellum. This relationship between high brain entropy and high intelligence indicates an essential role for entropy in brain functioning. It demonstrates that access to variable neural states predicts complex behavioral performance, and specifically shows that entropy derived from neuroimaging signals at rest carries information about intellectual capacity. Future work in this area may elucidate the links between brain entropy in both resting and active states and various forms of intelligence. This insight has the potential to provide predictive information about adaptive behavior and to delineate the subdivisions and nature of intelligence based on entropic patterns.

Suggested Citation

  • Glenn N Saxe & Daniel Calderone & Leah J Morales, 2018. "Brain entropy and human intelligence: A resting-state fMRI study," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-21, February.
  • Handle: RePEc:plo:pone00:0191582
    DOI: 10.1371/journal.pone.0191582
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    References listed on IDEAS

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    1. D. B. Chklovskii & B. W. Mel & K. Svoboda, 2004. "Cortical rewiring and information storage," Nature, Nature, vol. 431(7010), pages 782-788, October.
    2. Ze Wang & Yin Li & Anna Rose Childress & John A Detre, 2014. "Brain Entropy Mapping Using fMRI," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-8, March.
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    Cited by:

    1. Zeinali, Narges & Pourdarvish, Ahmad, 2022. "An entropy-based estimator of the Hurst exponent in fractional Brownian motion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    2. Mazen El-Baba & Daniel J Lewis & Zhuo Fang & Adrian M Owen & Stuart M Fogel & J Bruce Morton, 2019. "Functional connectivity dynamics slow with descent from wakefulness to sleep," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-13, December.
    3. Varriale, Vincenzo & De Pascalis, Vilfredo & van der Molen, Maurits W., 2021. "Post-error slowing is associated with intelligence," Intelligence, Elsevier, vol. 89(C).

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