Parcellations and Hemispheric Asymmetries of Human Cerebral Cortex Analyzed on Surface-Based Atlases
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We report on surface-based analyses that enhance our understanding of human cortical organization, including its convolutions and its parcellation into many distinct areas. The surface area of human neocortex averages 973 cm(2) per hemisphere, based on cortical midthickness surfaces of 2 cohorts of subjects. We implemented a method to register individual subjects to a hybrid version of the FreeSurfer "fsaverage" atlas whose left and right hemispheres are in precise geographic correspondence. Cortical folding patterns in the resultant population-average "fs_LR" midthickness surfaces are remarkably similar in the left and right hemispheres, even in regions showing significant asymmetry in 3D position. Both hemispheres are equal in average surface area, but hotspots of surface area asymmetry are present in the Sylvian Fissure and elsewhere, together with a broad pattern of asymmetries that are significant though small in magnitude. Multiple cortical parcellation schemes registered to the human atlas provide valuable reference data sets for comparisons with other studies. Identified cortical areas vary in size by more than 2 orders of magnitude. The total number of human neocortical areas is estimated to be ∼150 to 200 areas per hemisphere, which is modestly larger than a recent estimate for the macaque.
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If inter-subject alignment of rfMRI data is only driven by information from high resolution structural images, alignment of cortical functional areas across subjects will be imperfect in many regions, given the variable relationship between cortical areas and structural features such as tissue intensities and cortical folding patterns.
“if inter-subject alignment of rfMRI data is only driven by information from high resolution structural images, it is expected that alignment of cortical functional areas across subjects will be imperfect in many regions, given the variable relationship between cortical areas and structural features such as tissue intensities and cortical folding patterns [69].”
From Functional connectomics from resting-state fMRI (2013) · cites this paper as [69]
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Flagged first, then by citations
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Self-citationA multi-modal parcellation of human cerebral cortex
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Conserved cell types with divergent features in human versus mouse cortex
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- Authors
- Van Essen, D. C., Glasser, M. F., Dierker, D. L., Harwell, J., & Coalson, T.
- Affiliations
- Washington University in St. Louis (US)
- Venue
- Cerebral Cortex
- Publisher
- Oxford University Press (OUP)
- Published
- 2011-11-02
- Volume
- 22
- Issue
- 10
- Pages
- 2241-2262
- ISSN
- 1047-3211 (ISSN-L)
- 1460-2199
- PMC
- PMC3432236
- OpenAlex
- W2162261751
- Cited by
- 616
- References
- 96
- Citations by year
- 2026: 37 · 2025: 39 · 2024: 59 · 2023: 50 · 2022: 43 · 2021: 64 · 2020: 45 · 2019: 44 · 2018: 49 · 2017: 29 · 2016: 36 · 2015: 35 · 2014: 29 · 2013: 39 · 2012: 13
- FWCI
- 13.97
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- other-oa
- Locations
- Cerebral Cortex (publishedVersion, other-oa)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (1u54mh091657-01, 1U54‐MH‐091657)
- McDonnell Center for Systems Neuroscience (1U54MH091657)
- National Institute of Mental Health (R01 MH60974, R01MH060974, 1U54-MH091657-01, 1U54 MH091657)
- NIH Blueprint for Neuroscience Research (1U54-MH-091657)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- human cerebral cortex, cortical parcellation, hemispheric lateralization, cortical folding, cortical surface area, FreeSurfer, Sylvian fissure
- MeSH terms
- Cerebral Cortex, Humans, Models, Anatomic, Models, Neurological, Connectome
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