The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Functional and structural mapping of human cerebral cortex: Solutions are in the surfaces
Open accessAbstract
The human cerebral cortex is notorious for the depth and irregularity of its convolutions and for its variability from one individual to the next. These complexities of cortical geography have been a chronic impediment to studies of functional specialization in the cortex. In this report, we discuss ways to compensate for the convolutions by using a combination of strategies whose common denominator involves explicit reconstructions of the cortical surface. Surface-based visualization involves reconstructing cortical surfaces and displaying them, along with associated experimental data, in various complementary formats (including three-dimensional native configurations, two-dimensional slices, extensively smoothed surfaces, ellipsoidal representations, and cortical flat maps). Generating these representations for the cortex of the Visible Man leads to a surface-based atlas that has important advantages over conventional stereotaxic atlases as a substrate for displaying and analyzing large amounts of experimental data. We illustrate this by showing the relationship between functionally specialized regions and topographically organized areas in human visual cortex. Surface-based warping allows data to be mapped from individual hemispheres to a surface-based atlas while respecting surface topology, improving registration of identifiable landmarks, and minimizing unwanted distortions. Surface-based warping also can aid in comparisons between species, which we illustrate by warping a macaque flat map to match the shape of a human flat map. Collectively, these approaches will allow more refined analyses of commonalities as well as individual differences in the functional organization of primate cerebral cortex.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Distributed hierarchical processing in the primate cerebral cortex
Felleman, D. J., & Van Essen, D. C. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1 · Cited by 7,783
Architectonic subdivision of the orbital and medial prefrontal cortex in the macaque monkey
Carmichael, S. T., & Price, J. L. (1994) · Journal of Comparative Neurology · doi:10.1002/cne.903460305 · Cited by 656
Cited by
Cortical Surface-Based Analysis
Dale, A. M., Fischl, B., & Sereno, M. I. (1999) · NeuroImage · doi:10.1006/nimg.1998.0395 · Cited by 10,910
Cortical Surface-Based Analysis
Fischl, B., Sereno, M. I., & Dale, A. M. (1999) · NeuroImage · doi:10.1006/nimg.1998.0396 · Cited by 6,147
The Human Connectome: A Structural Description of the Human Brain
Sporns, O., Tononi, G., & Kötter, R. (2005) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0010042 · Cited by 3,260
Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature
Destrieux, C., Fischl, B., Dale, A., & Halgren, E. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.010 · Cited by 2,956
Information-based functional brain mapping
Kriegeskorte, N., Goebel, R., & Bandettini, P. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0600244103 · Cited by 2,113
Winkler, A. M., Kochunov, P., Blangero, J., Almasy, L., Zilles, K., Fox, P. T., Duggirala, R., & Glahn, D. C. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.028 · Cited by 1,172
Parcellations and Hemispheric Asymmetries of Human Cerebral Cortex Analyzed on Surface-Based Atlases
Van Essen, D. C., Glasser, M. F., Dierker, D. L., Harwell, J., & Coalson, T. (2012) · Cerebral Cortex · doi:10.1093/cercor/bhr291 · Cited by 616
Human brain activity time-locked to perceptual event boundaries
Zacks, J. M., Braver, T. S., Sheridan, M. A., Donaldson, D. I., Snyder, A. Z., Ollinger, J. M., Buckner, R. L., & Raichle, M. E. (2001) · Nature Neuroscience · doi:10.1038/88486 · Cited by 572
Multiple Realizability Revisited: Linking Cognitive and Neural States
Bechtel, W., & Mundale, J. (1999) · Philosophy of Science · doi:10.1086/392683 · Cited by 483
Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex
Arslan, S., Ktena, S. I., Makropoulos, A., Robinson, E. C., Rueckert, D., & Parisot, S. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.04.014 · Cited by 374
A new method for improving functional-to-structural MRI alignment using local Pearson correlation
Saad, Z. S., Glen, D. R., Chen, G., Beauchamp, M. S., Desai, R., & Cox, R. W. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.09.037 · Cited by 405
A Comparison of Visual and Auditory Motion Processing in Human Cerebral Cortex
Lewis, J. W. (2000) · Cerebral Cortex · doi:10.1093/cercor/10.9.873 · Cited by 381
Golland, Y., Bentin, S., Gelbard, H., Benjamini, Y., Heller, R., Nir, Y., Hasson, U., & Malach, R. (2007) · Cerebral Cortex · doi:10.1093/cercor/bhk030 · Cited by 337
Visual cortex activity in early and late blind people
Burton, H. (2003) · The Journal of Neuroscience · doi:10.1523/jneurosci.23-10-04005.2003 · Cited by 270
Simplified intersubject averaging on the cortical surface using SUMA
Argall, B. D., Saad, Z. S., & Beauchamp, M. S. (2006) · Human Brain Mapping · doi:10.1002/hbm.20158 · Cited by 200
Structural and Maturational Covariance in Early Childhood Brain Development
Geng, X., Li, G., Lu, Z., Gao, W., Wang, L., Shen, D., Zhu, H., & Gilmore, J. H. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw022 · Cited by 175
Imaging the Human Medial Temporal Lobe with High-Resolution fMRI
Carr, V. A., Rissman, J., & Wagner, A. D. (2010) · Neuron · doi:10.1016/j.neuron.2009.12.022 · Cited by 174
Detection of fMRI activation using Cortical Surface Mapping
Andrade, A., Kherif, F., Mangin, J.-F., Worsley, K. J., Paradis, A., Simon, O., Dehaene, S., Bihan, D. L., & Poline, J. (2001) · Human Brain Mapping · doi:10.1002/1097-0193(200102)12:2<79::aid-hbm1005>3.0.co;2-i · Cited by 150
Dickson, J., Drury, H., & Van Essen, D. C. (2001) · Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences · doi:10.1098/rstb.2001.0913 · Cited by 54
Self-citationCreze, M., Versheure, L., Besson, P., Sauvage, C., Leclerc, X., & Jissendi‐Tchofo, P. (2014) · Human Brain Mapping · doi:10.1002/hbm.22369 · Cited by 14
Metadata
- Authors
- Van Essen, D. C., Drury, H. A., Joshi, S., & Miller, M. I.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 1998-02-03
- Volume
- 95
- Issue
- 3
- Pages
- 788-795
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2139629216
- Cited by
- 374
- References
- 2
- FWCI
- 9.91
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.