Identifying predictors of within-person variance in MRI-based brain volume estimates

Research output: Contribution to journalArticle

Abstract

Adequate reliability of measurement is a precondition for investigating individual differences and age-related changes in brain structure. One approach to improve reliability is to identify and control for variables that are predictive of within-person variance. To this end, we applied both classical statistical methods and machine-learning-inspired approaches to structural magnetic resonance imaging (sMRI) data of six participants aged 24-31 years gathered at 40-50 occasions distributed over 6-8 months from the Day2day study. We explored the within-person associations between 21 variables covering physiological, affective, social, and environmental factors and global measures of brain volume estimated by VBM8 and FreeSurfer. Time since the first scan was reliably associated with Freesurfer estimates of grey matter volume and total cortex volume, in line with a rate of annual brain volume shrinkage of about 1 percent. For the same two structural measures, time of day also emerged as a reliable predictor with an estimated diurnal volume decrease of, again, about 1 percent. Furthermore, we found weak predictive evidence for the number of steps taken on the previous day and testosterone levels. The results suggest a need to control for time-of-day effects in sMRI research. In particular, we recommend that researchers interested in assessing longitudinal change in the context of intervention studies or longitudinal panels make sure that, at each measurement occasion, (a) a given participant is measured at the same time of day; (b) participants overall are measured at about the same time of day. Furthermore, the potential effects of physical activity, including moderate amounts of aerobic exercise, and testosterone levels on MRI-based measures of brain structure deserve further investigation.

Details

Authors
  • Julian D Karch
  • Elisa Filevich
  • Elisabeth Wenger
  • Nina Lisofsky
  • Maxi Becker
  • Oisin Butler
  • Johan Mårtensson
  • Ulman Lindenberger
  • Andreas M Brandmaier
  • Simone Kühn
Organisations
External organisations
  • Max Planck Institute for Human Development
  • University Medical Center Hamburg-Eppendorf
  • Leiden University
  • Bernstein Center for Computational Neuroscience Berlin
  • Humboldt University of Berlin
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Applied Psychology
  • Other Medical Sciences not elsewhere specified
Original languageEnglish
Pages (from-to)575-589
JournalNeuroImage
Volume200
Publication statusPublished - 2019 Oct 15
Publication categoryResearch
Peer-reviewedYes

Bibliographic note

Copyright © 2019. Published by Elsevier Inc.