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Interdatabase Variability in Cortical Thickness Measurements.

M Ethan MacDonald1,2,3,4, Rebecca J Williams1,2,3,4, Nils D Forkert1,2,3,4

  • 1Departments of Radiology, University of Calgary, Calgary, Alberta, Canada.

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|August 24, 2018
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This summary is machine-generated.

Cortical thinning rates vary significantly across different brain imaging databases due to acquisition differences. These variations impact age prediction models, highlighting the need to account for database heterogeneity in neuroimaging research.

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Area of Science:

  • Neuroimaging
  • Aging Research
  • Computational Neuroscience

Background:

  • Cortical thinning with age is a known phenomenon.
  • Observed rates of thinning vary across studies.
  • Differences may stem from imaging techniques and hardware variations.

Purpose of the Study:

  • To investigate variations in cerebral cortex thinning rates across four distinct neuroimaging databases.
  • To assess the impact of database differences on age prediction modeling.
  • To explore inter-regional correlations of cortical thinning within databases.

Main Methods:

  • Analysis of cortical thinning rates versus age in 1382 subjects across four databases.
  • Examination of regional correlation matrices and regression bootstrapping.
  • Age prediction modeling using leave-one-out cross-validation and cross-database calibration.

Main Results:

  • Significant differences in cortical thinning rates were found between databases across all 68 parcellated regions (ANCOVA, P < 0.001).
  • Subtle variations were noted in correlation matrices and bootstrapping convergence.
  • Age prediction model performance varied (R2: 0.64–0.82) between databases, decreasing when models were applied across datasets.

Conclusions:

  • Substantial differences exist in measured cortical thinning rates between large-scale neuroimaging databases.
  • Database heterogeneity can significantly affect the reliability and generalizability of neuroimaging findings, particularly in age prediction.
  • Careful consideration of database origin and potential biases is crucial for robust neuroimaging research.