Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Developmental cognitive neuroscience using Latent Change Score models: A tutorial and applications

View ORCID ProfileRogier A. Kievit, Andreas M. Brandmaier, Gabriel Ziegler, Anne-Laura van Harmelen, Susanne de Mooij, Michael Moutoussis, Ian Goodyer, Ed Bullmore, Peter Jones, Peter Fonagy, the NSPN Consortium, Ulman Lindenberger, Raymond J. Dolan
doi: https://doi.org/10.1101/110429
Rogier A. Kievit
aMax Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London WC1B 5EH, UK
bMRC Cognition and Brain Sciences Unit, Cambridge, 15 Chaucer Rd, Cambridge CB2 7EF
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Rogier A. Kievit
  • For correspondence: rogier.kievit@mrc-cbu.cam.ac.uk
Andreas M. Brandmaier
cMax Planck Institute for Human Development, Berlin, Germany
dMax Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Gabriel Ziegler
lInstitute of Cognitive Neurology and Dementia Research, Otto-von-Guericke-University Magdeburg, Germany
mGerman Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Anne-Laura van Harmelen
eDepartment of Psychiatry, University of Cambridge, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Susanne de Mooij
gWellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Michael Moutoussis
aMax Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London WC1B 5EH, UK
gWellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ian Goodyer
eDepartment of Psychiatry, University of Cambridge, UK
hCambridgeshire and Peterborough National Health Service Foundation Trust, Cambridge, CB21 5EF, United Kingdom
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ed Bullmore
eDepartment of Psychiatry, University of Cambridge, UK
hCambridgeshire and Peterborough National Health Service Foundation Trust, Cambridge, CB21 5EF, United Kingdom
iImmunoPsychiatry, GlaxoSmithKline Research and Development, Stevenage SG1 2NY, United Kingdom
jMedical Research Council/Wellcome Trust Behavioural and Clinical Neuroscience Institute, University of Cambridge
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Peter Jones
eDepartment of Psychiatry, University of Cambridge, UK
hCambridgeshire and Peterborough National Health Service Foundation Trust, Cambridge, CB21 5EF, United Kingdom
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Peter Fonagy
kResearch Department of Clinical, Educational and Health Psychology, University College London
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
jMedical Research Council/Wellcome Trust Behavioural and Clinical Neuroscience Institute, University of Cambridge
Ulman Lindenberger
aMax Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London WC1B 5EH, UK
cMax Planck Institute for Human Development, Berlin, Germany
dMax Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Raymond J. Dolan
aMax Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London WC1B 5EH, UK
gWellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Assessing and analysing individual differences in change over time is of central scientific importance to developmental neuroscience. However, the extant literature is based largely on cross-sectional comparisons, which reflect a variety of influences and cannot directly represent change. We advocate using latent change score (LCS) models in longitudinal samples as a statistical framework to tease apart the complex processes underlying lifespan development in brain and behaviour using longitudinal data. LCS models provide a flexible framework that naturally accommodates key developmental questions as model parameters and can even be used, with some limitations, in cases with only two measurement occasions. We illustrate the use of LCS models with two empirical examples. In a lifespan cognitive training study (COGITO, N = 204, two waves) we observe correlated change in brain and behaviour in the context of a high-intensity training intervention. In an adolescent development cohort (NSPN, N = 176, two waves) we find greater variability in cortical thinning in males than in females. To facilitate the adoption of LCS by the developmental community, we provide analysis code that can be adapted by other researchers and basic primers in two freely available SEM software packages (lavaan and Ωnyx).

Highlights (maximum 85 characters) -We describe Latent change score modelling as a powerful statistical tool

-Key developmental questions can be readily formalized using LCS models

-We provide accessible open source code and software examples to fit LCS models

-White matter structural change is negatively correlated with processing speed gains

-Frontal lobe thinning in adolescence is more variable in males than females

Footnotes

  • j See supplementary information for a full list of contributors

  • 1 It is worth noting that such hypotheses of temporal precedence in measurable properties do not imply a dualist perspective on mental and physical processes (cf.Kievit et al., 2011). They do suggest scientifically relevant distinctions can be made with implications for interpretation, the likely consequences of interventions and early detection of non-typical development.

  • 2 https://osf.io/4bpmq/?view_only=5b07ead0ef5147b4af2261cb864eca32

  • 3 https://osf.io/4bpmq/?view_only=5b07ead0ef5147b4af2261cb864eca32

  • 4 Note: some firewalls block the app. A zipped folder that contains all scripts and can be run locally is available on http://brandmaier.de/shiny/sample-apps/SimLCS_app/

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 4.0 International license.
Back to top
PreviousNext
Posted February 22, 2017.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Developmental cognitive neuroscience using Latent Change Score models: A tutorial and applications
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Developmental cognitive neuroscience using Latent Change Score models: A tutorial and applications
Rogier A. Kievit, Andreas M. Brandmaier, Gabriel Ziegler, Anne-Laura van Harmelen, Susanne de Mooij, Michael Moutoussis, Ian Goodyer, Ed Bullmore, Peter Jones, Peter Fonagy, the NSPN Consortium, Ulman Lindenberger, Raymond J. Dolan
bioRxiv 110429; doi: https://doi.org/10.1101/110429
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Developmental cognitive neuroscience using Latent Change Score models: A tutorial and applications
Rogier A. Kievit, Andreas M. Brandmaier, Gabriel Ziegler, Anne-Laura van Harmelen, Susanne de Mooij, Michael Moutoussis, Ian Goodyer, Ed Bullmore, Peter Jones, Peter Fonagy, the NSPN Consortium, Ulman Lindenberger, Raymond J. Dolan
bioRxiv 110429; doi: https://doi.org/10.1101/110429

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (4087)
  • Biochemistry (8762)
  • Bioengineering (6479)
  • Bioinformatics (23341)
  • Biophysics (11750)
  • Cancer Biology (9149)
  • Cell Biology (13247)
  • Clinical Trials (138)
  • Developmental Biology (7416)
  • Ecology (11369)
  • Epidemiology (2066)
  • Evolutionary Biology (15087)
  • Genetics (10399)
  • Genomics (14009)
  • Immunology (9121)
  • Microbiology (22040)
  • Molecular Biology (8779)
  • Neuroscience (47367)
  • Paleontology (350)
  • Pathology (1420)
  • Pharmacology and Toxicology (2482)
  • Physiology (3704)
  • Plant Biology (8050)
  • Scientific Communication and Education (1431)
  • Synthetic Biology (2208)
  • Systems Biology (6016)
  • Zoology (1249)