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Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature
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Precise localization of sulco-gyral structures of the human cerebral cortex is important for the interpretation of morpho-functional data, but requires anatomical expertise and is time consuming because of the brain's geometric complexity. Software developed to automatically identify sulco-gyral structures has improved substantially as a result of techniques providing topologically correct reconstructions permitting inflated views of the human brain. Here we describe a complete parcellation of the cortical surface using standard internationally accepted nomenclature and criteria. This parcellation is available in the FreeSurfer package. First, a computer-assisted hand parcellation classified each vertex as sulcal or gyral, and these were then subparcellated into 74 labels per hemisphere. Twelve datasets were used to develop rules and algorithms (reported here) that produced labels consistent with anatomical rules as well as automated computational parcellation. The final parcellation was used to build an atlas for automatically labeling the whole cerebral cortex. This atlas was used to label an additional 12 datasets, which were found to have good concordance with manual labels. This paper presents a precisely defined method for automatically labeling the cortical surface in standard terminology.
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“The most popularly used cortical parcellation is the Freesurfer Desikan-Killiany cortical parcellation (Desikan et al., 2006; Fischl, 2012), while many other cortical parcellation schemes have also been widely used (Destrieux et al., 2010; Shattuck et al., 2008; Tzourio-Mazoyer et al., 2002).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Destrieux et al., 2010
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Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., Buckner, R. L., Dale, A. M., Maguire, R. P., Hyman, B. T., et al. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.01.021 · Cited by 13,324
Self-citationCortical Surface-Based Analysis
Dale, A. M., Fischl, B., & Sereno, M. I. (1999) · NeuroImage · doi:10.1006/nimg.1998.0395 · Cited by 10,910
Self-citationFischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., et al. (2002) · Neuron · doi:10.1016/s0896-6273(02)00569-x · Cited by 8,610
Self-citationCortical Surface-Based Analysis
Fischl, B., Sereno, M. I., & Dale, A. M. (1999) · NeuroImage · doi:10.1006/nimg.1998.0396 · Cited by 6,147
Self-citationTalairach, J., Tournoux, P., & Rayport, M. (1988) · Thieme eBooks · Cited by 5,058
Automatically Parcellating the Human Cerebral Cortex
Fischl, B. R. (2003) · Cerebral Cortex · doi:10.1093/cercor/bhg087 · Cited by 4,208
Self-citationA hybrid approach to the skull stripping problem in MRI
Ségonne, F., Dale, A. M., Busa, E., Glessner, M., Salat, D. H., Hahn, H., & Fischl, B. R. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.03.032 · Cited by 2,227
Self-citationDale, A. M., & Sereno, M. I. (1993) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1993.5.2.162 · Cited by 2,003
Self-citationConstruction of a 3D probabilistic atlas of human cortical structures
Shattuck, D. W., Mirza, M., Adisetiyo, V., Hojatkashani, C., Salamon, G., Narr, K. L., Poldrack, R. A., Bilder, R. M., & Toga, A. W. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.09.031 · Cited by 1,208
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- Destrieux, 1998, Variability of the human brain studied on the flattened cortical surface, Soc. Neurosci., 1164
- Zilles, 1997, Quantitative analysis of sulci in the human cerebral cortex: development, regional heterogeneity, gender difference, asymetry, intersubject variability and cortical architecture, Hum. Brain Mapp., 5, 218, 10.1002/(SICI)1097-0193(1997)5:4<218::AID-HBM2>3.0.CO;2-6 · DOI
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Michalareas, G., Vezoli, J., van Pelt, S., Schoffelen, J.-M., Kennedy, H., & Fries, P. (2016) · Neuron · doi:10.1016/j.neuron.2015.12.018 · Cited by 696
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Savalia, N. K., Agres, P. F., Chan, M. Y., Feczko, E., Kennedy, K. M., & Wig, G. S. (2016) · Human Brain Mapping · doi:10.1002/hbm.23397 · Cited by 166
Pilgramm, S., de Haas, B., Helm, F., Zentgraf, K., Stark, R. M., Munzert, J., & Krüger, B. (2015) · Human Brain Mapping · doi:10.1002/hbm.23015 · Cited by 168
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Xu, T., Yang, Z., Jiang, L., Xing, X.-X., & Zuo, X. (2015) · 中国科学通报：英文版 · doi:10.1007/s11434-014-0698-3 · Cited by 173
İşcan, Z., Jin, T. B., Kendrick, A., Szeglin, B. C., Lu, H., Trivedi, M., D, M. F. M., McGrath, P. J., Weissman, M. M., Kurian, B. T., et al. (2015) · Human Brain Mapping · doi:10.1002/hbm.22856 · Cited by 166
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Osher, D. E., Saxe, R. R., Koldewyn, K., Gabrieli, J. D. E., Kanwisher, N., & Saygin, Z. M. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhu303 · Cited by 165
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Kutch, J. J., Ichesco, E., Hampson, J. P., Labus, J. S., Farmer, M. A., Martucci, K. T., Ness, T. J., DEUTSCH, G., Apkarian, A. V., Mackey, S., et al. (2017) · Pain · doi:10.1097/j.pain.0000000000001001 · Cited by 156
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Metadata
- Authors
- Destrieux, C., Fischl, B., Dale, A., & Halgren, E.
- Affiliations
- Université de Tours (FR)
- Harvard University (US)
- University of California San Diego (US)
- Corresponding author
- Destrieux, Christophe
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2010-10
- Volume
- 53
- Issue
- 1
- Pages
- 1-15
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC2937159
- OpenAlex
- W2007581301
- Cited by
- 2,956
- References
- 41
- Citations by year
- 2026: 272 · 2025: 328 · 2024: 287 · 2023: 290 · 2022: 291 · 2021: 272 · 2020: 241 · 2019: 188 · 2018: 189 · 2017: 163 · 2016: 130 · 2015: 98 · 2014: 75 · 2013: 62 · 2012: 44
- FWCI
- 48.65
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- HAL (submittedVersion)
- Funders
- Centre Hospitalier Régional et Universitaire de Tours
- National Institute for Neurological Disorders and Stroke (R01 NS18741, R01 NS052585)
- National Center for Research Resources (P41-RR14075, BIRN002, U24 RR021382)
- National Institute for Biomedical Imaging and Bioengineering (R01 EB001550, R01 EB006758, R01 EB009282)
- National Institute on Aging (R01 AG02238)
- Mental Illness and Neuroscience Discovery (MIND) Institute
- National Institutes of Health (U54 EB005149)
- Ellison Medical Foundation
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Physical Sciences › Engineering › Biomedical Engineering › Anatomy and Medical Technology
- Keywords
- cortical parcellation, FreeSurfer, anatomical terminology, brain surface reconstruction
- MeSH terms
- Adolescent, Adult, Algorithms, Brain, Computer Simulation, Female, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Models, Anatomic, Terminology as Topic, Pattern Recognition, Automated, Sensitivity and Specificity, Reproducibility of Results, Imaging, Three-Dimensional, Young Adult
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