IDEAS home Printed from https://ideas.repec.org/a/eee/intell/v76y2019ics0160289619300789.html
   My bibliography  Save this article

Structural brain imaging correlates of general intelligence in UK Biobank

Author

Listed:
  • Cox, S.R.
  • Ritchie, S.J.
  • Fawns-Ritchie, C.
  • Tucker-Drob, E.M.
  • Deary, I.J.

Abstract

The associations between indices of brain structure and measured intelligence are unclear. This is partly because the evidence to-date comes from mostly small and heterogeneous studies. Here, we report brain structure-intelligence associations on a large sample from the UK Biobank study. The overall N = 29,004, with N = 18,426 participants providing both brain MRI and at least one cognitive test, and a complete four-test battery with MRI data available in a minimum N = 7201, depending upon the MRI measure. Participants' age range was 44–81 years (M = 63.13, SD = 7.48). A general factor of intelligence (g) was derived from four varied cognitive tests, accounting for one third of the variance in the cognitive test scores. The association between (age- and sex- corrected) total brain volume and a latent factor of general intelligence is r = 0.276, 95% C.I. = [0.252, 0.300]. A model that incorporated multiple global measures of grey and white matter macro- and microstructure accounted for more than double the g variance in older participants compared to those in middle-age (13.6% and 5. 4%, respectively). There were no sex differences in the magnitude of associations between g and total brain volume or other global aspects of brain structure. The largest brain regional correlates of g were volumes of the insula, frontal, anterior/superior and medial temporal, posterior and paracingulate, lateral occipital cortices, thalamic volume, and the white matter microstructure of thalamic and association fibres, and of the forceps minor. Many of these regions exhibited unique contributions to intelligence, and showed highly stable out of sample prediction.

Suggested Citation

  • Cox, S.R. & Ritchie, S.J. & Fawns-Ritchie, C. & Tucker-Drob, E.M. & Deary, I.J., 2019. "Structural brain imaging correlates of general intelligence in UK Biobank," Intelligence, Elsevier, vol. 76(C).
  • Handle: RePEc:eee:intell:v:76:y:2019:i:c:s0160289619300789
    DOI: 10.1016/j.intell.2019.101376
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0160289619300789
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.intell.2019.101376?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. de Souza, Erick Almeida & Silva, Stéphanie Andrade & Vieira, Bruno Hebling & Salmon, Carlos Ernesto Garrido, 2023. "fMRI functional connectivity is a better predictor of general intelligence than cortical morphometric features and ICA parcellation order affects predictive performance," Intelligence, Elsevier, vol. 97(C).
    2. Bruton, Oliver J., 2021. "Is there a “g-neuron”? Establishing a systematic link between general intelligence (g) and the von Economo neuron," Intelligence, Elsevier, vol. 86(C).
    3. Hilger, Kirsten & Spinath, Frank M. & Troche, Stefan & Schubert, Anna-Lena, 2022. "The biological basis of intelligence: Benchmark findings," Intelligence, Elsevier, vol. 93(C).
    4. Vieira, Bruno Hebling & Pamplona, Gustavo Santo Pedro & Fachinello, Karim & Silva, Alice Kamensek & Foss, Maria Paula & Salmon, Carlos Ernesto Garrido, 2022. "On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting," Intelligence, Elsevier, vol. 93(C).
    5. Danni A. Gadd & Robert F. Hillary & Daniel L. McCartney & Liu Shi & Aleks Stolicyn & Neil A. Robertson & Rosie M. Walker & Robert I. McGeachan & Archie Campbell & Shen Xueyi & Miruna C. Barbu & Claire, 2022. "Integrated methylome and phenome study of the circulating proteome reveals markers pertinent to brain health," Nature Communications, Nature, vol. 13(1), pages 1-14, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:intell:v:76:y:2019:i:c:s0160289619300789. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/intelligence .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.