In-scanner head motion and structural covariance networks

Heath R. Pardoe, Samantha P. Martin

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)

Abstract

In-scanner head motion systematically reduces estimated regional gray matter volumes obtained from structural brain MRI. Here, we investigate how head motion affects structural covariance networks that are derived from regional gray matter volumetric estimates. We acquired motion-affected and low-motion whole brain T1-weighted MRI in 29 healthy adult subjects and estimated relative regional gray matter volumes using a voxel-based morphometry approach. Structural covariance network analyses were undertaken while systematically increasing the number of included motion-affected scans. We demonstrate that the standard deviation in regional gray matter estimates increases as the number of motion-affected scans increases. This increases pairwise correlations between regions, a key determinant for construction of structural covariance networks. We further demonstrate that head motion systematically alters graph theoretic metrics derived from these networks. Finally, we present evidence that weighting correlations using image quality metrics can mitigate the effects of head motion. Our findings suggest that in-scanner head motion is a source of error that violates the assumption that structural covariance networks reflect neuroanatomical connectivity between brain regions. Results of structural covariance studies should be interpreted with caution, particularly when subject groups are likely to move their heads in the scanner.

Original languageEnglish
Pages (from-to)4335-4346
Number of pages12
JournalHuman Brain Mapping
Volume43
Issue number14
DOIs
Publication statusPublished - 1 Oct 2022
Externally publishedYes

Keywords

  • graph theory
  • morphometrics
  • quality control
  • volumetry

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