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

Proceedings of the National Academy of Sciences · 2000 · Vol. 97(20) · pp. 11050–11055
Bruce FischlAnders M. Dale

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.

Functional Brain Connectivity StudiesAdvanced MRI Techniques and ApplicationsAdvanced Neuroimaging Techniques and ApplicationsCortex (anatomy)Cerebral cortexNeuroimagingHuman brainMagnetic resonance imagingStandard deviationReliability (semiconductor)AtrophyComputer scienceNeuroscience

MeSH terms

Cerebral CortexHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingRadiography

Funding

  • National Institute of Mental Health
  • National Cancer Institute
  • National Institute of Neurological Disorders and Stroke
  • National Center for Research Resources
Citations
5,969
FWCI
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47
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98%
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References
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Journal of Computational Physics · 2001 · 2,244 citations
Deformable templates using large deformation kinematics
IEEE Transactions on Image Processing · 1996 · 1,130 citations
The Organization of the Cerebral Cortex
The American Journal of the Medical Sciences · 1957 · 763 citations
Snakes: Active contour models
International Journal of Computer Vision · 1988 · 16,976 citations
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