Brain Structure and Function

, Volume 224, Issue 9, pp 3373–3385 | Cite as

White-matter microstructural properties of the corpus callosum: test–retest and repositioning effects in two parcellation schemes

  • Chaitali Anand
  • Andreas M. Brandmaier
  • Muzamil Arshad
  • Jonathan Lynn
  • Jeffrey A. StanleyEmail author
  • Naftali RazEmail author
Original Article


We investigated test–retest reliability of two MRI-derived indices of white-matter microstructural properties in the human corpus callosum (CC): myelin water fraction (MWF) and geometric mean T2 relaxation time of intra/extracellular water (geomT2IEW), using a 3D gradient and multi spin-echo sequence in 20 healthy adults (aged 24–69 years, 10 men). For each person, we acquired two back-to-back acquisitions in a single session, and the third after a break and repositioning the participant in the scanner. We assessed the contribution of session-related variance to reliability, using intra-class effect decomposition (ICED) while comparing two CC parcellation schemes that divided the CC into five and ten regions. We found high construct-level reliability of MWF and geomT2IEW in all regions of both schemes, except the posterior body—a slender region with a smaller number of large myelinated fibers. Only in that region, we observed significant session-specific variance in the MWF, interpreted as an effect of repositioning in the scanner. The geomT2IEW demonstrated higher reliability than MWF across both parcellation schemes and all CC regions. Thus, in both CC parcellation approaches, MWF and geomT2IEW have good test–retest reliability and are, therefore, suitable for longitudinal investigations in healthy adults. However, the five-region scheme appears more appropriate for MWF, whereas both schemes are suitable for geomT2IEW studies. Given the lower reliability in the posterior body, which may reflect sensitivity to the repositioning of the participant in the scanner, caution should be exercised in interpreting differential findings in that region.


Myelin T2 spin-spin relaxation time MRI Reliability Commissural fibers Multi-echo imaging 



This work was supported by the National Institutes of Health grants F31-AG058420-01 (CA), R01-AG011230 (NR) and R21-AG059160 (JAS and NR), and by the Lycaki-Young Funds from the State of Michigan. We thank Dalal Khatib, Caroline Zajac-Benitez, Cheryl Dahle, and Matthew Hilton for technical support and assistance in data collection.

Author contributions

NR, AB, JS, and CA conceptualized the study. CA, NR, and JS drafted the manuscript. CA analyzed the data and designed the figures. AB developed the statistical model and supervised the statistical analyses. MA collected and processed the MRI data. JS, JL, and CA processed the MRI data. NR secured the funding. All authors contributed to editing multiple drafts of the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This study contains an extended analysis of the MRI data that were previously collected by our group. Non-overlapping analyses have been published in Arshad et al. (2017) and Brandmaier et al. (2018). The data collection was conducted following the protocol approved by the Wayne State University Institutional Review Board, in accordance with the Declaration of Helsinki (1964).

Informed consent

All participants provided informed consent.

Supplementary material

429_2019_1981_MOESM1_ESM.pdf (547 kb)
Supplementary material 1 (PDF 546 kb)
429_2019_1981_MOESM2_ESM.docx (29 kb)
Supplementary material 2 (DOCX 29 kb)


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Psychiatry and Behavioral NeurosciencesWayne State UniversityDetroitUSA
  2. 2.Institute of GerontologyWayne State UniversityDetroitUSA
  3. 3.Center for Lifespan PsychologyMax Planck Institute for Human DevelopmentBerlinGermany
  4. 4.Max Planck, UCL Centre for Computational Psychiatry and Ageing ResearchBerlinGermany
  5. 5.Max Planck, UCL Centre for Computational Psychiatry and Ageing ResearchLondonUK
  6. 6.Department of PsychologyWayne State UniversityDetroitUSA

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