Cited by 489Functional Brain Connectivity StudiesAdvanced Neuroimaging Techniques and ApplicationsMedical Image Segmentation Techniques

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Imaging-based parcellations of the human brain

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Eickhoff, Simon B., Yeo, B. T. Thomas, Genon, Sarah·2018·Nature reviews. Neuroscience

DOI 10.1038/s41583-018-0071-7

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Abstract

A defining aspect of brain organization is its spatial heterogeneity, which gives rise to multiple topographies at different scales. Brain parcellation - defining distinct partitions in the brain, be they areas or networks that comprise multiple discontinuous but closely interacting regions - is thus fundamental for understanding brain organization and function. The past decade has seen an explosion of in vivo MRI-based approaches to identify and parcellate the brain on the basis of a wealth of different features, ranging from local properties of brain tissue to long-range connectivity patterns, in addition to structural and functional markers. Given the high diversity of these various approaches, assessing the convergence and divergence among these ensuing maps is a challenge. Inter-individual variability adds to this challenge but also provides new opportunities when coupled with cross-species and developmental parcellation studies.

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Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single subject brain
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002) · NeuroImage · doi:10.1006/nimg.2001.0978 · Cited by 16,319
An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest
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
Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820
Data clustering: 50 years beyond K-means
Jain, A. K. (2009) · Pattern Recognition Letters · doi:10.1016/j.patrec.2009.09.011 · Cited by 10,021
Functional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812
The organization of the human cerebral cortex estimated by intrinsic functional connectivity
Thomas Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., et al. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00338.2011 · Cited by 9,182
Self-citation
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
Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.018 · Cited by 7,429
The organization of the human cerebellum estimated by intrinsic functional connectivity
Buckner, R. L., Krienen, F. M., Castellanos, A., Diaz, J. C., & Yeo, B. T. T. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00339.2011 · Cited by 7,304
Self-citation
Correspondence of the brain's functional architecture during activation and rest
Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., Filippini, N., Watkins, K. E., Toro, R., Laird, A. R., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0905267106 · Cited by 5,150

Show all 150 references

Research works printed but not linked: 2

Maier-Hein, K. H. et al. The challenge of mapping the human connectome based on diffusion tractography. Nat. Commun. 8, 1349 (2017).
Huth, A. G., Griffiths, T. L., Theunissen, F. E. & Gallant, J. L. PrAGMATiC: a probabilistic and generative model of areas tiling the cortex. Preprint at http://arxiv.org/abs/1504.03622 (2015). · arXiv

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Self-citation
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Park, H. (2024) · Biostatistics · doi:10.1093/biostatistics/kxae023 · Cited by 6
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Authors: Eickhoff, S. B., Yeo, B. T. T., & Genon, S.
Affiliations: Forschungszentrum Jülich (DE)
Harvard University (US)
Corresponding author: Eickhoff, Simon B.
ORCID: Eickhoff, Simon B.
Yeo, B. T. Thomas
Genon, Sarah
Venue: Nature reviews. Neuroscience
Publisher: Springer Science and Business Media LLC
Published: 2018-10-09
Volume: 19
Issue: 11
Pages: 672-686
ISSN: 1471-003X (ISSN-L)
1471-0048
DOI: 10.1038/s41583-018-0071-7
OpenAlex: W2896587207
Cited by: 484
References: 150
Citations by year: 2026: 50 · 2025: 66 · 2024: 66 · 2023: 63 · 2022: 69 · 2021: 84 · 2020: 52 · 2019: 28 · 2018: 5
FWCI: 5.66
Type: journal article
Language: en
Open access: green
orbi.uliege.be
Licence: other-oa
www.springer.com/tdm (tdm)
Locations: Nature reviews. Neuroscience (publishedVersion)
PubMed (publishedVersion)
National University of Singapore (submittedVersion)
ORBi (University of Liège) (submittedVersion, other-oa)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Keywords: brain parcellation, brain networks, structural connectivity, functional connectivity, cytoarchitecture, interindividual variability
MeSH terms: Brain, Brain Mapping, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Neural Pathways, Reproducibility of Results

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