The DOI matches its Crossref record; 150 of the 150 references it cites were found in a registry or an index.
Imaging-based parcellations of the human brain
Open accessAbstract
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“This ensured that the regions were representative of the general population, minimizing inter-subject variability without completely removing it.”
From Damage to Broca’s area does not contribute to long-term speech production outcome after stroke (2021) · cites this paper as Eickhoff et al., 2018
Citations
Cites
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
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-citationDistributed 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
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-citationCorrespondence 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
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
Cited by
Sorted by citations
Items 101–200 of 489
Statistical Learning Methods for Neuroimaging Data Analysis with Applications
Hongtu Zhu, B., Li, T., & Zhao, B. (2023) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-020722-100353 · Cited by 46
Improving practices and inferences in developmental cognitive neuroscience
Flournoy, J. C., Vijayakumar, N., Cheng, T. W., Cosme, D., Flannery, J., & Pfeifer, J. H. (2020) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2020.100807 · Cited by 41
Yoon, J. H., Lee, H., Kwon, D., Lee, D., Lee, S., Cho, E., Kim, J., & Kim, D. (2024) · Brain Communications · doi:10.1093/braincomms/fcae265 · Cited by 43
Characterizing the gradients of structural covariance in the human hippocampus
Masouleh, S. K., Plachti, A., Hoffstaedter, F., Eickhoff, S. B., & Genon, S. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116972 · Cited by 37
Self-citationGradients of connectivity as graph Fourier bases of brain activity
Lioi, G., Gripon, V., Brahim, A., Rousseau, F., & Farrugia, N. (2021) · Network Neuroscience · doi:10.1162/netn_a_00183 · Cited by 37
Bagging improves reproducibility of functional parcellation of the human brain
Nikolaidis, A., Heinsfeld, A. S., Xu, T., Bellec, P., Vogelstein, J. T., & Milham, M. P. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116678 · Cited by 38
Individual variability in functional organization of the neonatal brain
Molloy, M. F., & Saygin, Z. M. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119101 · Cited by 42
Fenske, S. J., Bierer, D. W., Chelimsky, G. G., Conant, L. L., Ustine, C., Yan, K., Chelimsky, T. C., & Kutch, J. J. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102443 · Cited by 38
McGrath, H., Zaveri, H. P., Collins, E., Jafar, T., Chishti, O., Obaïd, S., Ksendzovsky, A., Wu, K., Papademetris, X., & Spencer, D. D. (2022) · Scientific Reports · doi:10.1038/s41598-022-21543-3 · Cited by 42
Kim, H. (2020) · Brain Research · doi:10.1016/j.brainres.2020.147049 · Cited by 35
Whole-brain estimates of directed connectivity for human connectomics
Frässle, S., Manjaly, Z. M., Tri, C., Kasper, L., Pruessmann, K. P., & Stephan, K. E. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117491 · Cited by 35
Functional connectome through the human life span
Sun, L., Zhao, T., Liang, X., Xia, M., Li, Q., Liao, X., Gong, G., Wang, Q., Pang, C., Yu, Q., et al. (2024) · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · doi:10.58530/2024/1294 · Cited by 42
Community structure of the creative brain at rest
Kenett, Y. N., Betzel, R. F., & Beaty, R. E. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116578 · Cited by 39
Domhof, J. W. M., Jung, K., Eickhoff, S. B., & Popovych, O. V. (2021) · Network Neuroscience · doi:10.1162/netn_a_00202 · Cited by 37
Self-citationVisualisation of Brain Statistics with R-packages ggseg and ggseg3dPreprint
Mowinckel, A. M., & Dídac Vidal-piñeiro, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.08200 · Cited by 36
Jackson, R. L., Bajada, C. J., Ralph, M. A. L., & Cloutman, L. L. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz079 · Cited by 34
Lowe, A. J., Paquola, C., de Wael, R. V., Girn, M., Larivière, S., Tavakol, S., Caldairou, B., Royer, J., Schrader, D. V., Bernasconi, A., et al. (2019) · Human Brain Mapping · doi:10.1002/hbm.24767 · Cited by 30
van der Meer, D., & Kaufmann, T. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02193-5 · Cited by 35
Genomic loci influence patterns of structural covariance in the human brain
Wen, J., Nasrallah, I. M., Abdulkadir, A., Satterthwaite, T. D., Yang, Z., Erus, G., Robert‐Fitzgerald, T., Singh, A., Sotiras, A., Boquet-Pujadas, A., et al. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2300842120 · Cited by 33
Recent developments in representations of the connectome
Bijsterbosch, J. D., Valk, S. L., Wang, D., & Glasser, M. F. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118533 · Cited by 34
What Can Computational Models Contribute to Neuroimaging Data Analytics?
Popovych, O. V., Manos, T., Hoffstaedter, F., & Eickhoff, S. B. (2019) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2018.00068 · Cited by 33
Self-citationOsada, T., Ogawa, A., Suda, A., Nakajima, K., Tanaka, M., Oka, S., Kamagata, K., Aoki, S., Oshima, Y., Tanaka, S., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109732 · Cited by 33
Bedini, M., Olivetti, E., Avesani, P., & Baldauf, D. (2023) · Brain Structure and Function · doi:10.1007/s00429-023-02641-y · Cited by 35
Space-independent community and hub structure of functional brain networks
Esfahlani, F. Z., Bertolero, M. A., Bassett, D. S., & Betzel, R. F. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116612 · Cited by 32
Regions and Connections: Complementary Approaches to Characterize Brain Organization and Function
Horien, C. L., Greene, A. S., Constable, R. T., & Scheinost, D. (2019) · The Neuroscientist · doi:10.1177/1073858419860115 · Cited by 29
Data and model considerations for estimating time-varying functional connectivity in fMRI
Ahrends, C., Stevner, A. B. A., Pervaiz, U., Kringelbach, M., Vuust, P., Woolrich, M. W., & Vidaurre, D. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119026 · Cited by 35
Using Tertiary Sulci to Map the “Cognitive Globe” of Prefrontal Cortex
Miller, J. A., D’Esposito, M., & Weiner, K. S. (2021) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01696 · Cited by 31
Casas-Roma, J., Martínez‐Heras, E., Solé‐Ribalta, A., Solana, E., López-Soley, E., Vivó, F., Díaz-Hurtado, M., Albà-Arbalat, S., Sepúlveda, M., Blanco, Y., et al. (2022) · Network Neuroscience · doi:10.1162/netn_a_00258 · Cited by 29
Engineering NIR-II luminescent lanthanide nanoprobes for imaging brain diseases in vivo
Du, Y., Ni, S., Ma, Q., Song, X., & Yang, H. (2023) · Coordination Chemistry Reviews · doi:10.1016/j.ccr.2023.215401 · Cited by 29
The emergence of multiscale connectomics-based approaches in stroke recovery
Latifi, S., & Carmichael, S. T. (2024) · Trends in Neurosciences · doi:10.1016/j.tins.2024.01.003 · Cited by 30
Combining fMRI during resting state and an attention bias task in children
Harrewijn, A., Abend, R., Linke, J. O., Brotman, M. A., Fox, N. A., Leibenluft, E., Winkler, A. M., & Pine, D. S. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116301 · Cited by 29
Inter-subject and inter-parcellation variability of resting-state whole-brain dynamical modeling
Popovych, O. V., Jung, K., Manos, T., Diaz-Pier, S., Hoffstaedter, F., Schreiber, J., Yeo, B. T. T., & Eickhoff, S. B. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118201 · Cited by 26
Self-citationDynamic reconfiguration of functional brain networks during working memory trainingPreprint
Finc, K., Bonna, K., He, X., Lydon‐Staley, D. M., Kühn, S., Duch, W., & Bassett, D. S. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/685487 · Cited by 28
Analyses of microstructural variation in the human striatum using non-negative matrix factorization
Robert, C. M., Patel, R., Blostein, N., Steele, C. C., & Chakravarty, M. M. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118744 · Cited by 23
The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives
Bröhl, T., Rings, T., Pukropski, J., von Wrede, R. D., & Lehnertz, K. (2024) · Frontiers in Network Physiology · doi:10.3389/fnetp.2023.1338864 · Cited by 32
Nishimaki, K., Onda, K., Ikuta, K., Chotiyanonta, J. S., Uchida, Y., Mori, S., Iyatomi, H., Oishi, K., Initiative, A. D. N., & Ageing, A. I. B. A. L. F. S. O. (2024) · Human Brain Mapping · doi:10.1002/hbm.70063 · Cited by 26
Topological learning for brain networks
Songdechakraiwut, T., & Chung, M. K. (2023) · The Annals of Applied Statistics · doi:10.1214/22-aoas1633 · Cited by 32
A Riemannian approach to predicting brain function from the structural connectome
Benkarim, O. M., Paquola, C., Park, B., Royer, J., Rodríguez‐Cruces, R., de Wael, R. V., Mišić, B., Piella, G., & Bernhardt, B. C. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119299 · Cited by 27
Convergence and Divergence of Rare Genetic Disorders on Brain Phenotypes
Raznahan, A., Won, H., Glahn, D. C., & Jacquemont, S. (2022) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2022.1450 · Cited by 29
Zhou, Z., Li, H., Srinivasan, D., Abdulkadir, A., Nasrallah, I. M., Wen, J., Doshi, J. J., Erus, G., Mamourian, E., Bryan, R. N., et al. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.119911 · Cited by 26
Yin, G., Li, T., Jin, S., Wang, N., Li, J., Wu, C., He, H., & Wang, J. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad178 · Cited by 26
Deep learning models of cognitive processes constrained by human brain connectomes
Zhang, Y., Farrugia, N., & Bellec, P. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102507 · Cited by 25
Functional connectivity in behavioral variant frontotemporal dementia
Ferreira, L. K., Lindberg, O. E., Santillo, A. F., & Wahlund, L. (2022) · Brain and Behavior · doi:10.1002/brb3.2790 · Cited by 27
Joint Multi-modal Parcellation of the Human Striatum: Functions and Clinical Relevance
Liu, X., Eickhoff, S. B., Hoffstaedter, F., Genon, S., Caspers, S., Reetz, K., Dogan, I., Eickhoff, C. R., Ji, C., Caspers, J., et al. (2020) · Neuroscience Bulletin · doi:10.1007/s12264-020-00543-1 · Cited by 25
Self-citationOsada, T., Nakajima, K., Shirokoshi, T., Ogawa, A., Oka, S., Kamagata, K., Aoki, S., Oshima, Y., Tanaka, S., & Konishi, S. (2024) · Nature Communications · doi:10.1038/s41467-024-54564-9 · Cited by 26
Brain topography beyond parcellations: Local gradients of functional maps
Dohmatob, E., Richard, H., Pinho, A. L., & Thirion, B. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2020.117706 · Cited by 24
Pegg, E., McKavanagh, A., Bracewell, R. M., Chen, Y., Das, K., Denby, C. E., Kreilkamp, B. A. K., Laiou, P., Marson, A. G., Mohanraj, R., et al. (2021) · Brain Communications · doi:10.1093/braincomms/fcab196 · Cited by 26
Qiu, W., Ma, L., Jiang, T., & Zhang, Y. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.838347 · Cited by 24
Roy, M., Edde, M., Fortier, M., Croteau, É., Castellano, C., St‐Pierre, V., Vandenberghe, C., Rheault, F., Dadar, M., Duchesne, S., et al. (2022) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2022.04.005 · Cited by 25
Zhuang, W., Jia, H., Liu, Y., Cong, J., Chen, K., Yao, D., Kang, X., Xu, P., & Zhang, T. (2023) · Autism Research · doi:10.1002/aur.2974 · Cited by 24
The Future of Disentangling the Heterogeneity of Autism With Neuroimaging Studies
Duan, X., Shan, X., Uddin, L. Q., & Chen, H. (2024) · Biological Psychiatry · doi:10.1016/j.biopsych.2024.08.008 · Cited by 25
Beyond consensus: Embracing heterogeneity in curated neuroimaging meta-analysis
Ngo, G. H., Eickhoff, S. B., Nguyen, M., Sevinc, G., Fox, P. T., Spreng, R. N., & Yeo, B. T. T. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.06.037 · Cited by 20
Self-citationKo, A. L., Tong, A. P., Mossa‐Basha, M., Weaver, K. E., Ojemann, J. G., Miller, J. W., & Hakimian, S. (2021) · Epilepsia · doi:10.1111/epi.17059 · Cited by 23
Nazeri, A., Krsnik, Ž., Kostović, I., Ha, S. M., Kopić, J., Alexopoulos, D. K., Kaplan, S., Meyer, D., Luby, J. L., Warner, B. B., et al. (2022) · Neuron · doi:10.1016/j.neuron.2022.09.020 · Cited by 23
Graph Theory Measures and Their Application to Neurosurgical Eloquence
Tanglay, O., Dadario, N. B., Chong, E. H. N., Tang, S. J., Young, I. M., & Sughrue, M. E. (2023) · Cancers · doi:10.3390/cancers15020556 · Cited by 24
Brain fingerprint is based on the aperiodic, scale-free, neuronal activity
Sorrentino, P., Lopez, E. T., Romano, A., Granata, C., Corsi, M., Sorrentino, G., & Jirsa, V. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120260 · Cited by 23
Macaque Brainnetome Atlas: A multifaceted brain map with parcellation, connection, and histology
Lu, Y., Cui, Y. J., Cao, L., Dong, Z., Cheng, L., Wu, W., Wang, C., Liu, X., Liu, Y., Zhang, B., et al. (2024) · 中国科学通报:英文版 · doi:10.1016/j.scib.2024.03.031 · Cited by 24
Reliability and subject specificity of personalized whole-brain dynamical models
Domhof, J. W. M., Eickhoff, S. B., & Popovych, O. V. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119321 · Cited by 23
Self-citationInterrelating differences in structural and functional connectivity in the older adult's brain
Stumme, J., Krämer, C., Miller, T., Schreiber, J., Caspers, S., & Jockwitz, C. (2022) · Human Brain Mapping · doi:10.1002/hbm.26030 · Cited by 21
Supervised Phenotype Discovery From Multimodal Brain Imaging
Gong, W., Bai, S., Zheng, Y., Smith, S. M., & Beckmann, C. F. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3218720 · Cited by 22
Parcellating the human brain using resting-state dynamic functional connectivity
Peng, L., Luo, Z., Zeng, L., Hou, C., Shen, H., Zhou, Z., & Hu, D. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac293 · Cited by 22
BAI-Net: Individualized Anatomical Cerebral Cartography Using Graph Neural Network
Ma, L., Zhang, Y., Zhang, H., Cheng, L., Yang, Z., Lu, Y., Shi, W., Li, W., Zhuo, J., Wang, J., et al. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3213581 · Cited by 21
Deep Learning Approach for Classification and Interpretation of Autism Spectrum Disorder
Prasad, P. K. C., Khare, Y., Dadi, K., Vinod, P. K. U., & Bapi, R. S. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892350 · Cited by 21
Feilong, M., Nastase, S. A., Jiahui, G., Halchenko, Y. O., Gobbini, M. I., & Haxby, J. V. (2023) · Imaging Neuroscience · doi:10.1162/imag_a_00032 · Cited by 21
Han, L., Chan, M. Y., Agres, P. F., Winter-Nelson, E., Zhang, Z., & Wig, G. S. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhad506 · Cited by 21
Multivariate spatial feature selection in fMRI
Jolly, E., & Chang, L. J. (2021) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsab010 · Cited by 24
Browsing Multiple Subjects When the Atlas Adaptation Cannot Be Achieved via a Warping Strategy
Rivière, D., Leprince, Y., Labra, N., Vindas, N., Foubet, O., Cagna, B., Loh, K. K., Hopkins, W. D., Balzeau, A., Mancip, M., et al. (2022) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2022.803934 · Cited by 20
Atlas-guided parcellation: Individualized functionally-homogenous parcellation in cerebral cortex
Li, Y., Liu, A., Fu, X., McKeown, M. J., Wang, Z. J., & Chen, X. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106078 · Cited by 20
Kopál, J., Pidnebesna, A., Tomeček, D., Tintěra, J., & Hlinka, J. (2020) · Human Brain Mapping · doi:10.1002/hbm.25195 · Cited by 19
Structure and Dynamics of Large‐Scale Cognitive Networks in Huntington's Disease
Aracil‐Bolaños, I., Martínez‐Horta, S., Gónzalez‐de‐Echávarri, J. M., Sampedro, F., Pérez‐Pérez, J., Horta‐Barba, A., Campolongo, A., Izquierdo, C., Gómez‐Ansón, B., Pagonabarraga, J., et al. (2021) · Movement Disorders · doi:10.1002/mds.28839 · Cited by 18
Isallari, M., & Rekik, I. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102084 · Cited by 19
A network correspondence toolbox for quantitative evaluation of novel neuroimaging resultsPreprint
Kong, R., Spreng, R. N., Xue, A., Betzel, R. F., Cohen, J. R., Damoiseaux, J. S., Brigard, F. D., Eickhoff, S. B., Fornito, A., Gratton, C., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.17.599426 · Cited by 19
Self-citationCao, L., Li, H., Liu, J., Jiang, J., Li, B., Li, X., Zhang, S., Gao, Y., Liang, K., Hu, X., et al. (2022) · Communications Biology · doi:10.1038/s42003-022-04115-z · Cited by 18
Connectivity-Based Brain Parcellation for Parkinson's Disease
Li, Y., Liu, A., Li, L., Yun-hu, W., McKeown, M. J., Chen, X., & Wu, F. H. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2022.3222072 · Cited by 18
Hippocampal anterior- posterior shift in childhood and adolescence
Plachti, A., Latzman, R. D., Balajoo, S. M., Hoffstaedter, F., Madsen, K. S., Baaré, W. F. C., Siebner, H. R., Eickhoff, S. B., & Genon, S. (2023) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2023.102447 · Cited by 20
Self-citationMindfulness Meditation and Network Neuroscience: Review, Synthesis, and Future Directions
Prakash, R. S., Shankar, A., Tripathi, V., Yang, W. F. Z., Fisher, M. E., Bauer, C. C. C., Betzel, R. F., & Sacchet, M. D. (2024) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2024.11.005 · Cited by 24
Functional connectivity patterns predict naturalistic viewing versus rest across development
Sanchez–Alonso, S., Rosenberg, M. D., & Aslin, R. Ν. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2020.117630 · Cited by 20
Li, X., Liu, H., Ge, L., Yan, Y., Lo, W. L. A., Li, L., & Wang, C. (2021) · Neural Plasticity · doi:10.1155/2021/6666024 · Cited by 18
TMS brain mapping of the pharyngeal cortical representation in healthy subjects
Li, W., Lin, T., Li, X., Jing, Y., Wu, C., Li, M., Ding, Q., Lan, Y., & Xu, G. (2020) · Brain stimulation · doi:10.1016/j.brs.2020.02.031 · Cited by 17
A Cross-Species Neuroimaging Study of Sex Chromosome Dosage Effects on Human and Mouse Brain Anatomy
Guma, E., Beauchamp, A., Liu, S., Levitis, E., CLASEN, L. S., Torres, E. N., Blumenthal, J. D., Lalonde, F., Qiu, L. R., Hrncir, H., et al. (2023) · Journal of Neuroscience · doi:10.1523/jneurosci.1761-22.2022 · Cited by 17
Levi, P. T., Chopra, S., Pang, J. C. S., Holmes, A., Gajwani, M., Sassenberg, T. A., DeYoung, C. G., & Fornito, A. (2023) · Network Neuroscience · doi:10.1162/netn_a_00329 · Cited by 18
Hendricks, B. K., Scherschinski, L., Jubran, J. H., Dadario, N. B., Karahalios, K., Benner, D., VanBrabant, D., & Lawton, M. T. (2024) · Journal of neurosurgery · doi:10.3171/2023.12.jns232588 · Cited by 17
Tutorial: a guide to diffusion MRI and structural connectomics
Shamir, I., & Assaf, Y. (2024) · Nature Protocols · doi:10.1038/s41596-024-01052-5 · Cited by 19
“Advances in biomarker discovery and diagnostics for alzheimer’s disease”
Bhatia, V., Chandel, A., Minhas, Y., & Kushawaha, S. K. (2025) · Neurological Sciences · doi:10.1007/s10072-025-08023-y · Cited by 20
MRI-based visualization of rTMS-induced cortical plasticity in the primary motor cortex
Tamura, K., Osada, T., Ogawa, A., Tanaka, M., Suda, A., Shimo, Y., Hattori, N., Kamagata, K., Hori, M., Aoki, S., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0224175 · Cited by 17
Striatal Subdivisions Estimated via Deep Embedded Clustering With Application to Parkinson's Disease
Li, Y., Liu, A., Mi, T., Yang, R., Chan, P., McKeown, M. J., Chen, X., & Wu, F. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3083879 · Cited by 17
A network approach to the topological organization of the Brodmann map
Bruner, E. (2022) · The Anatomical Record · doi:10.1002/ar.24941 · Cited by 17
Genetic influences on the intrinsic and extrinsic functional organizations of the cerebral cortexPreprint
Zhao, B., Li, T., Smith, S. M., Fan, Z., Yang, X., Yang, Y., Shu, J., Xiong, D., Wang, X., Yang, Y., et al. (2021) · medRxiv · doi:10.1101/2021.07.27.21261187 · Cited by 13
He, T., Kong, R., Holmes, A. J., Nguyen, M., Sabuncu, M. R., Eickhoff, S. B., Bzdok, D., Feng, J., & Yeo, B. T. T. (2018) · doi:10.1101/473603 · Cited by 16
Self-citationPetrican, R., Miles, S., Rudd, L., Wasiewska, W., Graham, K. S., & Lawrence, A. D. (2021) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2021.101032 · Cited by 17
HumanBrainAtlas: an in vivo MRI dataset for detailed segmentations
Schira, M. M., Isherwood, Z. J., Kassem, M. S., Barth, M., Shaw, T. B., Roberts, M. M., & Paxinos, G. (2023) · Brain Structure and Function · doi:10.1007/s00429-023-02653-8 · Cited by 17
Du, J., DiNicola, L. M., Angeli, P. A., Saadon‐Grosman, N., Sun, W., Kaiser, S., Ladopoulou, J., Xue, A., Yeo, B. T. T., Eldaief, M. C., et al. (2026) · doi:10.21428/8e6ba8ef.7dba6cc4 · Cited by 15
Self-citationNetwork neuroscience and the connectomics revolution
Betzel, R. F. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821861-7.00002-6 · Cited by 18
Bridging the gap between single receptor type activity and whole‐brain dynamics
Jancke, D., Herlitze, S., Kringelbach, M. L., & Deco, G. (2021) · FEBS Journal · doi:10.1111/febs.15855 · Cited by 17
A cortical surface template for human neuroscience
Feilong, M., Jiahui, G., Gobbini, M. I., & Haxby, J. V. (2024) · Nature Methods · doi:10.1038/s41592-024-02346-y · Cited by 21
Multiscale network perspectives on glioma: from tumour biology to symptoms, survival and treatment
Douw, L., Reijneveld, J. C., & Mandal, A. S. (2025) · Nature Reviews Neurology · doi:10.1038/s41582-025-01171-x · Cited by 18
Applications of optimal nonlinear control to a whole-brain network of FitzHugh-Nagumo oscillators
Chouzouris, T., Roth, N., Cakan, C., & Obermayer, K. (2021) · Physical review. E · doi:10.1103/physreve.104.024213 · Cited by 15
Brainnetome atlas of preadolescent children based on anatomical connectivity profiles
Li, W., Fan, L., Shi, W., Lu, Y., Li, J., Luo, N., Wang, H., Chu, C., Ma, L., Song, M., et al. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac415 · Cited by 17
Grey and white matter network disruption is associated with sensory deficits after stroke
Kessner, S. S., Schlemm, E., Gerloff, C. P., Thomalla, G., & Cheng, B. (2021) · NeuroImage Clinical · doi:10.1016/j.nicl.2021.102698 · Cited by 15
Macroscopic modelling of Alzheimer’s disease: difficulties and challenges
Bertsch, M., Franchi, B., Raj, A., & Tesi, M. C. (2021) · Brain Multiphysics · doi:10.1016/j.brain.2021.100040 · Cited by 14
Metadata
- Authors
- Eickhoff, S. B., Yeo, B. T. T., & Genon, S.
- Affiliations
- Forschungszentrum Jülich (DE)
- Harvard University (US)
- Corresponding author
- Eickhoff, Simon B.
- Venue
- Nature reviews. Neuroscience
- Published
- 2018-10-09
- Volume
- 19
- Issue
- 11
- Pages
- 672-686
- ISSN
- 1471-003X (ISSN-L)
- 1471-0048
- 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
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