Day2day: Investigating daily variability of magnetic resonance imaging measures over half a year

Research output: Contribution to journalArticle

Abstract

Background: Most studies of brain structure and function, and their relationships to cognitive ability, have relied on inter-individual variability in magnetic resonance (MR) images. Intra-individual variability is often ignored or implicitly assumed to be equivalent to the former. Testing this assumption empirically by collecting enough data on single individuals is cumbersome and costly. We collected a dataset of multiple MR sequences and behavioural covariates to quantify and characterize intra-individual variability in MR images for multiple individuals. Methods and design: Eight participants volunteered to undergo brain scanning 40-50 times over the course of 6 months. Six participants completed the full set of sessions. T1-weighted, T2*-weighted during rest, T2-weighted high-resolution hippocampus, diffusion-tensor imaging (DTI), and proton magnetic resonance spectroscopy sequences were collected, along with a rich set of stable and time-varying physical, behavioural and physiological variables. Participants did not change their lifestyle or participated in any training programs during the period of data collection. Conclusion: This imaging dataset provides a large number of MRI scans in different modalities for six participants. It enables the analysis of the time course and correlates of intra-individual variability in structural, chemical, and functional aspects of the human brain.

Details

Authors
  • Elisa Filevich
  • Nina Lisofsky
  • Maxi Becker
  • Oisin Butler
  • Martyna Lochstet
  • Johan Martensson
  • Elisabeth Wenger
  • Ulman Lindenberger
  • Simone Kühn
Organisations
External organisations
  • Max Planck Institute for Human Development
  • University Medical Center Hamburg-Eppendorf
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Psychology

Keywords

  • Ergodicity, Longitudinal design, MRI, Reliability, Resting state, Structural imaging, Variability
Original languageEnglish
Article number65
JournalBMC Neuroscience
Volume18
Issue number1
StatePublished - 2017 Aug 24
Publication categoryResearch
Peer-reviewedYes