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

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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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The Laplacian pyramid as a compact image code
Burt, P., & Adelson, E. (1983) · IEEE Transactions on Communications · doi:10.1109/tcom.1983.1095851 · Cited by 6,077
Medial temporal atrophy on MRI in normal aging and very mild Alzheimer's disease
Jack, C. R., Petersen, R. C., Xu, Y. C., Waring, S. C., O'Brien, P. C., Tangalos, E. G., Smith, G. E., Ivnik, R. J., & Kokmen, E. (1997) · Neurology · doi:10.1212/wnl.49.3.786 · Cited by 1,128
Frequency of hippocampal formation atrophy in normal aging and Alzheimer's disease
De Leon, M. J., George, A. E., Golomb, J., Tarshish, C., Convit, A., Kluger, A., De Santi, S., Mc Rae, T., Ferris, S. H., Reisberg, B., et al. (1997) · Neurobiology of Aging · doi:10.1016/s0197-4580(96)00213-8 · Cited by 429
Pathologic heterogeneity in clinically diagnosed corticobasal degeneration
Boeve, B. F., Maraganore, D. M., Parisi, J. E., Ahlskog, J. E., Graff-Radford, N., Caselli, R. J., Dickson, D. W., Kokmen, E., & Petersen, R. C. (1999) · Neurology · doi:10.1212/wnl.53.4.795 · Cited by 417
Volume loss of the hippocampus and temporal lobe in healthy elderly persons destined to develop dementia
Kaye, J. A., Swihart, T., Howieson, D., Dame, A., Moore, M. M., Karnos, T., Camicioli, R., Ball, M., Oken, B., & Sexton, G. (1997) · Neurology · doi:10.1212/wnl.48.5.1297 · Cited by 399

Show all 5 references

Research works printed but not linked: 35

10.1016/B978-0-12-547625-6.50027-1 · DOI
C von Economo The Cytoarchitectonics of the Human Cerebral Cortex (Oxford Univ. Press, London, 1929).
K Brodmann Vergleichende Lokalisationslehre der Großhirnrinde in ihren Prinzipien dargestellt auf Grund des Zellenbaues (Barth, Leipzig, Germany, 1909).
10.1073/pnas.93.24.13547 · DOI
G B Frisoni, A Beltramello, C Weiss, C Geroldi, A Bianchetti, M Trabucchi Am J Neuroradiol 17, 913–923 (1996).
10.1007/s004010050816 · DOI
10.1016/0197-4580(96)00005-X · DOI
10.1148/radiology.178.1.1984287 · DOI
10.1097/00005072-199805000-00001 · DOI · Paper page
10.1006/exnr.1998.6919 · DOI · Paper page
10.1093/brain/117.4.747 · DOI
10.1001/archpsyc.1992.01820030027004 · DOI · Paper page
10.1001/archpsyc.55.6.540 · DOI
A Pfefferbaum, L Marsh Clin Neurosci 3, 105–111 (1995).
10.1001/archpsyc.56.2.142 · DOI
J R Meyer, S Roychowdhury, E J Russell, C Callahan, D Gitelman, M M Mesulam Am J Neuroradiol 17, 1699–1706 (1996).
10.1162/jocn.1993.5.2.162 · DOI · Paper page
10.1006/nimg.1998.0395 · DOI · Paper page
10.1006/nimg.1998.0396 · DOI · Paper page
Fischl B. Liu A. & Dale A. M. (1999) IEEE Trans. Med. Imaging in press.
10.1002/(SICI)1097-0193(1999)8:4<272::AID-HBM10>3.0.CO;2-4 · DOI · Paper page
10.1007/BF01908877 · DOI · Paper page
D MacDonald Ph.D. thesis (McGill University, Montreal, 1998).
10.1109/42.544496 · DOI
10.1007/BF00133570 · DOI · Paper page
10.1162/jocn.1991.3.1.59 · DOI · Paper page
10.1109/83.536892 · DOI · Paper page
10.1109/42.511745 · DOI · Paper page
10.1016/S1361-8415(96)80007-7 · DOI · Paper page
J A Sethian Level Set Methods. Evolving Interfaces in Geometry, Fluid Mechanics, Computer Vision, and Materials Science (Cambridge Univ. Press, Cambridge, U.K., 1996).
10.4310/jdg/1214444092 · DOI · Paper page
W H Press, S A Teukolsky, W T Vetterling, B P Flannery Numerical Recipes inc (Cambridge Univ. Press, 2nd. Ed., Cambridge, U.K., 1994).
10.1080/10867651.1997.10487472 · DOI · Paper page
D A Sholl The Organization of the Cerebral Cortex (Wiley, New York, 1956).
J Talairach, P Tournoux Co-Planar Stereotaxic Atlas of the Human Brain (Thieme, New York, 1988).

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Mareckova, K., Marecek, R., Andryskova, L., Brazdil, M., & Nikolova, Y. S. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa014 · Cited by 32
Dissociations in cortical thickness and surface area in non-comorbid never-treated patients with social anxiety disorder
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Prefrontal and hippocampal brain volume deficits: role of low physical activity on brain plasticity in first-episode schizophrenia patients.
McEwen, S. C., Hardy, A., Ellingson, B. M., Jarrahi, B., Sandhu, N., Subotnik, K. L., Ventura, J., & Nuechterlein, K. H. (2015) · Journal of the International Neuropsychological Society · doi:10.1017/s1355617715000983 · Cited by 29
Structural Brain Changes and Associated Symptoms of ADHD Subtypes in Children.
Mu, S., Wu, H., Zhang, J., & Chang, C. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhab276 · Cited by 29
Beta amyloid deposition maps onto hippocampal and subiculum atrophy in dementia with Lewy bodies
Mak, E., Donaghy, P. C., McKiernan, E., Firbank, M. J., Lloyd, J., Petrides, G. S., Thomas, A. J., & O'Brien, J. T. (2019) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2018.09.004 · Cited by 29
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Reduced hippocampal N-acetyl-aspartate (NAA) as a biomarker for overweight
Coplan, J. D., Fathy, H. M., Abdallah, C. G., Ragab, S. A., Kral, J. G., Mao, X., Shungu, D. C., & Mathew, S. J. (2014) · NeuroImage: Clinical · doi:10.1016/j.nicl.2013.12.014 · Cited by 28
Normal variation in behavioral adjustment relates to regional differences in cortical thickness in children
Walhovd, K. B., Tamnes, C. K., Østby, Y., Due-Tønnessen, P., & Fjell, A. M. (2012) · European Child & Adolescent Psychiatry · doi:10.1007/s00787-012-0241-5 · Cited by 26
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Reus, L. M., Vijverberg, E. G., Tijms, B. M., Kate, M. T., Gossink, F., Krudop, W. A., Campo, M. D., Teunissen, C. E., Barkhof, F., van der Flier, W. M., et al. (2018) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2018.07.014 · Cited by 26
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Yu, D., Yuan, K., Cheng, J., Guan, Y., Li, Y., Bi, Y., Zhai, J., Luo, L., Liu, B., Xue, T., et al. (2018) · Nicotine & Tobacco Research · doi:10.1093/ntr/ntx146 · Cited by 26
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Stein, M. B., Yuh, E., Jain, S., Okonkwo, D. O., Mac Donald, C. L., Levin, H., Giacino, J. T., Dikmen, S., Vassar, M. J., Diaz-Arrastia, R., et al. (2021) · Biological Psychiatry: Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2020.10.008 · Cited by 24
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“Further details about recon-all pipeline can be found at [14,15,16].”
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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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Measuring the thickness of the human cerebral cortex from magnetic resonance images

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