1. Developmental Biology
  2. Neuroscience

Using multi-modal neuroimaging to characterise social brain specialisation in infants

  1. Maheen Siddiqui  Is a corresponding author
  2. Paola Pinti
  3. Sabrina Brigadoi
  4. Sarah Lloyd-Fox
  5. Clare E Elwell
  6. Mark H Johnson
  7. Ilias Tachtsidis
  8. Emily JH Jones
  1. Birkbeck, University of London, United Kingdom
  2. University of Padova, Italy
  3. University of Cambridge, United Kingdom
  4. University College London, United Kingdom
https://doi.org/10.7554/eLife.84122
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Cite this article
  1. Maheen Siddiqui
  2. Paola Pinti
  3. Sabrina Brigadoi
  4. Sarah Lloyd-Fox
  5. Clare E Elwell
  6. Mark H Johnson
  7. Ilias Tachtsidis
  8. Emily JH Jones
(2023)
Using multi-modal neuroimaging to characterise social brain specialisation in infants
eLife 12:e84122.
https://doi.org/10.7554/eLife.84122
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Abstract

The specialised regional functionality of the mature human cortex partly emerges through experience-dependent specialisation during early development. Our existing understanding of functional specialisation in the infant brain is based on evidence from unitary imaging modalities and has thus focused on isolated estimates of spatial or temporal selectivity of neural or haemodynamic activation, giving an incomplete picture. We speculate that functional specialisation will be underpinned by better coordinated haemodynamic and metabolic changes in a broadly orchestrated physiological response. To enable researchers to track this process through development, we develop new tools that allow the simultaneous measurement of coordinated neural activity (EEG), metabolic rate and oxygenated blood supply (broadband near-infrared spectroscopy) in the awake infant. In 4-to-7-month-old infants, we use these new tools to show that social processing is accompanied by spatially and temporally specific increases in coupled activation in the temporal-parietal junction, a core hub region of the adult social brain. During non-social processing coupled activation decreased in the same region, indicating specificity to social processing. Coupling was strongest with high frequency brain activity (beta and gamma), consistent with the greater energetic requirements and more localised action of high frequency brain activity. The development of simultaneous multi-modal neural measures will enable future researchers to open new vistas in understanding functional specialisation of the brain.

Data availability

The data contains human subject data from minors and guardians provided informed consent to having data shared only with researchers involved in the project, in anonymised form. A Patient and Public Involvement (PPI) initiative at the Centre for Brain and Cognitive Development aimed to actively work in partnership with parents and guardians participating in research studies to help design and manage future research. A comprehensive public survey was conducted as part of this initiative which aimed to evaluate parent attitudes to data sharing in developmental science. This survey revealed that majority of parents do not want their data to be shared openly but are open to the data being shared with other researchers related to the project. Therefore, in order to adhere to participant preference/choice, a curated data sharing approach must be followed wherein the data can only be made available upon reasonable request through a formal data sharing and project affiliation agreement. The researcher will have to contact MFS and complete a project affiliation form providing their study aims, a detailed study proposal, plan for the analysis protocol, ethics, and plans for data storage and protection. Successful proposals will have aims aligned with the aims of the original study. Raw NIRS data, EEG data and integrated NIRS-EEG data can be made available in anonymised form. ID numbers linking the NIRS and EEG data, however, cannot be provided as parents/guardians have consented only to data being shared in anonymised form. All code used to analyse the NIRS data and the integration of the NIRS and EEG data is available on GitHub (https://github.com/maheensiddiqui91/NIRS-EEG). EEG data was processed using EEGlab which is a publicly available toolbox.

Article and author information

Author details

  1. Maheen Siddiqui

    Centre for Brain and Cognitive Development, Birkbeck, University of London, London, United Kingdom
    For correspondence
    m.siddiqui@bbk.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2037-6964

  2. Paola Pinti

    Centre for Brain and Cognitive Development, Birkbeck, University of London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

  3. Sabrina Brigadoi

    Department of Development and Social Psychology, University of Padova, Padova, Italy
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3032-7381

  4. Sarah Lloyd-Fox

    Department of Psychology, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

  5. Clare E Elwell

    Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

  6. Mark H Johnson

    Department of Psychology, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

  7. Ilias Tachtsidis

    Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

  8. Emily JH Jones

    Centre for Brain and Cognitive Development, Birkbeck, University of London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5747-9540

Funding

Biotechnology and Biological Sciences Research Council ([BB/J014567/1])

  • Maheen Siddiqui

Economic and Social Research Council (ES/V012436/1)

  • Maheen Siddiqui

Economic and Social Research Council (ES/R009368/1)

  • Maheen Siddiqui

Horizon 2020 Framework Programme (777394)

  • Maheen Siddiqui
  • Mark H Johnson
  • Emily JH Jones

Wellcome Trust (104580/Z/14/Z)

  • Ilias Tachtsidis

UK Research and Innovation (MR/S018425/1)

  • Sarah Lloyd-Fox

Bill and Melinda Gates Foundation (OPP1127625)

  • Sarah Lloyd-Fox
  • Clare E Elwell

Medical Research Council (MR/K021389/1,MR/T003057/1)

  • Mark H Johnson
  • Emily JH Jones

University of Padova (C96C18001930005)

  • Sabrina Brigadoi

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: The study protocol was approved by the Birkbeck Ethics Committee, ethics approval number 161747. Participants were forty-two 4-to-7-month-old infants (mean age: 179{plus minus} 16 days; 22 males and 20 females); parents provided written informed consent to participate in the study, for the publication of the research and additionally for the publication and use of any photographs taken during the study of the infant wearing the NIRS-EEG headgear.

Copyright

© 2023, Siddiqui et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Maheen Siddiqui
  2. Paola Pinti
  3. Sabrina Brigadoi
  4. Sarah Lloyd-Fox
  5. Clare E Elwell
  6. Mark H Johnson
  7. Ilias Tachtsidis
  8. Emily JH Jones
(2023)
Using multi-modal neuroimaging to characterise social brain specialisation in infants
eLife 12:e84122.
https://doi.org/10.7554/eLife.84122

Share this article

https://doi.org/10.7554/eLife.84122

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