A quantified comparison of cortical atlases on the basis of trait morphometricity

Anna E. Fürtjes*, James H. Cole, Baptiste Couvy-Duchesne, Stuart J. Ritchie

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Background: Many different brain atlases exist that subdivide the human cortex into dozens or hundreds of regions-of-interest (ROIs). Inconsistency across studies using one or another cortical atlas may contribute to the replication crisis across the neurosciences.
Methods: Here, we provide a quantitative comparison between seven popular cortical atlases (Yeo, Desikan-Killiany, Destrieux, Jülich-Brain, Gordon, Glasser, Schaefer) and vertex-wise measures (thickness, surface area, and volume), to determine which parcellation retains the most information in the analysis of behavioural traits (incl. age, sex, body mass index, and cognitive ability) in the UK Biobank sample (N∼40,000). We use linear mixed models to compare whole-brain morphometricity; the proportion of trait variance accounted for when using a given atlas.
Results: Commonly-used atlases resulted in a considerable loss of information compared to vertex-wise representations of cortical structure. Morphometricity increased linearly as a function of the log-number of ROIs included in an atlas, indicating atlas-based analyses miss many true associations and yield limited prediction accuracy. Likelihood ratio tests revealed that low-dimensional atlases accounted for unique trait variance rather than variance common between atlases, suggesting that previous studies likely returned atlas-specific findings. Finally, we found that the commonly-used atlases yielded brain-behaviour associations on par with those obtained with random parcellations, where specific region boundaries were randomly generated.
Discussion: Our findings motivate future structural neuroimaging studies to favour vertex-wise cortical representations over coarser atlases, or to consider repeating analyses across multiple atlases, should the use of low-dimensional atlases be necessary. The insights uncovered here imply that cortical atlas choices likely contribute to the lack of reproducibility in ROI-based studies.
Original languageEnglish
Pages (from-to)110-126
Number of pages17
JournalCortex
Volume158
Early online date26 Nov 2022
DOIs
Publication statusPublished - Jan 2023

Keywords / Materials (for Non-textual outputs)

  • morphometricity
  • explained variance by brain morphometry
  • brain structure
  • structural neuroimaging
  • linear mixed models
  • cortical atlases
  • random atlases
  • cognitive abilities
  • sex
  • alcohol consumption
  • age
  • cigarette smoking

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