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Cited by 5,818

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Measuring the thickness of the human cerebral cortex from magnetic resonance images

Open access
Fischl, Bruce, Dale, Anders M.·2000·Proceedings of the National Academy of Sciences
DOI 10.1073/pnas.200033797
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Abstract

Accurate and automated methods for measuring the thickness of human cerebral cortex could provide powerful tools for diagnosing and studying a variety of neurodegenerative and psychiatric disorders. Manual methods for estimating cortical thickness from neuroimaging data are labor intensive, requiring several days of effort by a trained anatomist. Furthermore, the highly folded nature of the cortex is problematic for manual techniques, frequently resulting in measurement errors in regions in which the cortical surface is not perpendicular to any of the cardinal axes. As a consequence, it has been impractical to obtain accurate thickness estimates for the entire cortex in individual subjects, or group statistics for patient or control populations. Here, we present an automated method for accurately measuring the thickness of the cerebral cortex across the entire brain and for generating cross-subject statistics in a coordinate system based on cortical anatomy. The intersubject standard deviation of the thickness measures is shown to be less than 0.5 mm, implying the ability to detect focal atrophy in small populations or even individual subjects. The reliability and accuracy of this new method are assessed by within-subject test–retest studies, as well as by comparison of cross-subject regional thickness measures with published values.

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Show all 5 references

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Cited by

Metadata

Authors
Fischl, B., & Dale, A. M.
Venue
Proceedings of the National Academy of Sciences
Publisher
Proceedings of the National Academy of Sciences
Published
2000-09-12
Volume
97
Issue
20
Pages
11050-11055
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
DOI
10.1073/pnas.200033797
OpenAlex
W2110208125
Cited by
5,818
References
5
FWCI
18.85
Type
journal article
Language
en
Open access
green

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.