Influence of Processing Pipeline on Cortical Thickness Measurement

Shahrzad Kharabian Masouleh, Simon B. Eickhoff, Yashar Zeighami, Lindsay B. Lewis, Robert Dahnke, Christian Gaser, Francois Chouinard-Decorte, Claude Lepage, Lianne H. Scholtens, Felix Hoffstaedter, David C. Glahn, John Blangero, Alan C. Evans, Sarah Genon, Sofie L. Valk

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

In recent years, replicability of neuroscientific findings, specifically those concerning correlates of morphological properties of gray matter (GM), have been subject of major scrutiny. Use of different processing pipelines and differences in their estimates of the macroscale GM may play an important role in this context. To address this issue, here, we investigated the cortical thickness estimates of three widely used pipelines. Based on analyses in two independent large-scale cohorts, we report high levels of within-pipeline reliability of the absolute cortical thickness-estimates and comparable spatial patterns of cortical thickness-estimates across all pipelines. Within each individual, absolute regional thickness differed between pipelines, indicating that in-vivo thickness measurements are only a proxy of actual thickness of the cortex, which shall only be compared within the same software package and thickness estimation technique. However, at group level, cortical thickness-estimates correlated strongly between pipelines, in most brain regions. The smallest between-pipeline correlations were observed in para-limbic areas and insula. These regions also demonstrated the highest interindividual variability and the lowest reliability of cortical thickness-estimates within each pipeline, suggesting that structural variations within these regions should be interpreted with caution.

Original languageEnglish
Pages (from-to)5014-5027
Number of pages14
JournalCerebral cortex (New York, N.Y. : 1991)
Volume30
Issue number9
Early online date7 May 2020
DOIs
Publication statusPublished - Sept 2020

Funding

The authors thank the Compute Canada (https://www.compute canada.ca) and the Jülich Supercomputing Centre (https://www. fz-juelich.de/ias/jsc/) for the usage of the computing facilities in the development of this work. Data were provided [in part] by the HCP, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. The Deutsche Forschungsgemeinschaft (DFG, GE 2835/1-1 and EI 816/4-1), the Helmholtz Portfolio Theme “Supercomputing and Modelling for the Human Brain,” and the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 720270 (HBP SGA1) and Grant Agreement No. 785907 (HBP SGA2).

FundersFunder number
Helmholtz Portfolio Theme
National Institutes of Health
National Institute of Mental HealthU54MH091657
NIH Blueprint for Neuroscience Research
McDonnell Center for Systems Neuroscience
Horizon 2020 Framework Programme785907, 720270
Deutsche ForschungsgemeinschaftEI 816/4-1, GE 2835/1-1

    Keywords

    • in-vivo cortical thickness
    • interindividual variability
    • reliability
    • replicability
    • software comparison

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