The DOI matches its Crossref record, with a small difference in title or year; 45 of the 45 references it cites were found in a registry or an index.
Individual Variability of the System-Level Organization of the Human Brain
Open accessAbstract
Recent functional magnetic resonance imaging-based resting-state functional connectivity analyses of group average data have characterized large-scale systems that represent a high level in the organizational hierarchy of the human brain. These systems are likely to vary spatially across individuals, even after anatomical alignment, but the characteristics of this variance are unknown. Here, we characterized large-scale brain systems across two independent datasets of young adults. In these individuals, we were able to identify brain systems that were similar to those described in the group average, and we observed that individuals had consistent topological arrangement of the system features present in the group average. However, the size of system features varied across individuals in systematic ways, such that expansion of one feature of a given system predicted expansion of other parts of the system. Individual-specific systems also contained unique topological features not present in group average systems; some of these features were consistent across a minority of individuals. These effects were observed even after controlling for data quality and for the accuracy of anatomical registration. The variability characterized here has important implications for cognitive neuroscience investigations, which often assume the functional equivalence of aligned brain regions across individuals.
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.
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?
The cited source says what the claim attributes to it.
In the citing paper
“Additionally, although individuals have remarkably variable functional brain circuitry (32), most clinics apply a “one size fits all” scalp-targeted approach, neglecting to account for individual differences.”
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
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
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
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
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
Self-citationThe 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
Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging
Fox, M. D., & Raichle, M. E. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2201 · Cited by 6,700
Nonparametric permutation tests for functional neuroimaging: A primer with examples
Nichols, T. E., & Holmes, A. P. (2002) · Human Brain Mapping · doi:10.1002/hbm.1058 · Cited by 6,256
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
Maps of random walks on complex networks reveal community structure
Rosvall, M., & Bergstrom, C. T. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0706851105 · Cited by 4,583
Flagged first, then by citations
Cited by
Sorted by citations
Items 101–200 of 219
Bringing Anatomical Information into Neuronal Network Models
van Albada, S. J., Morales-Gregorio, A., Dickscheid, T., Goulas, A., Bakker, R., Bludau, S., Palm, G., Hilgetag, C. C., & Diesmann, M. (2021) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-89439-9_9 · Cited by 29
Hrybouski, S., Das, S. R., Xie, L., Wisse, L. E. M., Kelley, M., Lane, J., Sherin, M., DiCalogero, M., Nasrallah, I. M., Detre, J. A., et al. (2023) · Brain Communications · doi:10.1093/braincomms/fcad245 · Cited by 38
Striatal subdivisions that coherently interact with multiple cerebrocortical networks
Ogawa, A., Osada, T., Tanaka, M., Hori, M., Aoki, S., Nikolaidis, A., Milham, M. P., & Konishi, S. (2018) · Human Brain Mapping · doi:10.1002/hbm.24275 · Cited by 24
DeYoung, C. G., Sassenberg, T. A., Abend, R., Allen, T. A., Beaty, R. E., Bellgrove, M. A., Blain, S. D., Bzdok, D., Chavez, R. S., Engel, S. A., et al. (2022) · doi:10.31234/osf.io/sfnmk · Cited by 25
Robinson, J. L., Yanes, J. A., Reid, M. A., Murphy, J. E., Busler, J. N., Mumford, P. W., Young, K. C., Pietrzkowski, Z. J., Nemzer, B. V., Hunter, J. M., et al. (2021) · Antioxidants · doi:10.3390/antiox10020144 · Cited by 26
Network targets for therapeutic brain stimulation: towards personalized therapy for pain
Motzkin, J. C., Kanungo, I., D’Esposito, M., & Shirvalkar, P. R. (2023) · Frontiers in Pain Research · doi:10.3389/fpain.2023.1156108 · Cited by 25
Variability and stability of large-scale cortical oscillation patterns
Cox, R., Schapiro, A. C., & Stickgold, R. (2018) · Network Neuroscience · doi:10.1162/netn_a_00046 · Cited by 27
Hemispheric Asymmetries of Individual Differences in Functional Connectivity
Perez, D. C., Dworetsky, A., Braga, R. M., Beeman, M. J., & Gratton, C. (2022) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01945 · Cited by 25
Detecting stable individual differences in the functional organization of the human basal ganglia
García-García, M., Nikolaidis, A., Bellec, P., Craddock, R. C., Cheung, B., Castellanos, F. X., & Milham, M. P. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.029 · Cited by 22
Gupta, S., Rajapakse, J. C., & Welsch, R. E. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102186 · Cited by 21
Ma, L., Tian, L., Hu, T., Jiang, T., & Zuo, N. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab059 · Cited by 23
Wang, X., Li, Q., Zhao, Y. M., He, Y., Ma, B., Fu, Z., & Li, S. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118252 · Cited by 21
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
Hierarchical modelling of functional brain networks in population and individuals from big fMRI data
Farahibozorg, S.-R., Bijsterbosch, J. D., Gong, W., Jbabdi, S., Smith, S. M., Harrison, S. J., & Woolrich, M. W. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118513 · Cited by 21
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
Hierarchical organization of spontaneous co-fluctuations in densely sampled individuals using fMRI
Betzel, R. F., Cutts, S. A., Tanner, J., Greenwell, S. A., Varley, T. F., Faskowitz, J., & Sporns, O. (2023) · Network Neuroscience · doi:10.1162/netn_a_00321 · Cited by 20
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
Caeyenberghs, K., Imms, P. E., Andrei Irimia, A., Monti, M. M., Esopenko, C., de Souza, N. L., D, J. F. D., Newsome, M. R., Dobryakova, E., Cwiek, A. P., et al. (2024) · NeuroImage Clinical · doi:10.1016/j.nicl.2024.103585 · Cited by 19
Human striatal association megaclusters
Kosakowski, H. L., Saadon‐Grosman, N., Du, J., Eldaief, M. C., & Buckner, R. L. (2024) · Journal of Neurophysiology · doi:10.1152/jn.00387.2023 · Cited by 23
Action-mode subnetworks for decision-making, action control, and feedback
D’Andrea, C. B., Laumann, T. O., Newbold, D. J., Lynch, C. J., Hadji, M., Nelson, S. M., Nielsen, A. N., Chauvin, R. J., Krimmel, S. R., Snyder, A. Z., et al. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2502021122 · Cited by 23
Self-citationDeep Learning‐based Classification of Resting‐state fMRI Independent‐component Analysis
Nozais, V., Boutinaud, P., Verrecchia, V., Gueye, M.-F., Hervé, P., Tzourio, C., Mazoyer, B. M., & Joliot, M. (2021) · Neuroinformatics · doi:10.1007/s12021-021-09514-x · Cited by 19
Iraji, A., Fu, Z., Faghiri, A., Duda, M., Chen, J., Rachakonda, S., DeRamus, T. P., Kochunov, P. V., Adhikari, B. M., Belger, A., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.03.506487 · Cited by 20
Controversies and progress on standardization of large-scale brain network nomenclaturePreprint
Uddin, L. Q., Betzel, R. F., Cohen, J. R., Damoiseaux, J. S., Brigard, F. D., Eickhoff, S. B., Fornito, A., Gratton, C., Gordon, E. M., Laird, A., et al. (2022) · doi:10.31219/osf.io/25za6 · Cited by 18
Self-citationGroup-to-Individual Generalizability and Individual-Level Inferences in Cognitive Neuroscience
Mattoni, M., Fisher, A. J., Gates, K. M., Chein, J. M., & Olino, T. M. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2025.106024 · Cited by 22
Gallucci, J., Tan, T., Schifani, C., Dickie, E. W., Voineskos, A. N., & Hawco, C. S. (2022) · Schizophrenia Research · doi:10.1016/j.schres.2022.07.012 · Cited by 17
Towards personalized precision functional mapping in infancy
Moore, L. A., Hermosillo, R. J. M., Feczko, E., Moser, J., Koirala, S., Allen, M. C., Buß, C., Conan, G., Juliano, A., Marr, M. C., et al. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00165 · Cited by 18
Li, M., Dahmani, L., Hubbard, C. S., Hu, Y., Wang, M., Wang, D., & Liu, H. (2022) · Neuropsychopharmacology · doi:10.1038/s41386-022-01500-4 · Cited by 18
Unraveling reproducible dynamic states of individual brain functional parcellation
Boukhdhir, A., Zhang, Y., Mignotte, M., & Bellec, P. (2020) · Network Neuroscience · doi:10.1162/netn_a_00168 · Cited by 15
Precision Functional Mapping to Advance Developmental Psychiatry Research
Labonte, A. K., Camacho, M. C., Moser, J., Koirala, S., Laumann, T. O., Marek, S. A., Fair, D. A., & Sylvester, C. M. (2024) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2024.100370 · Cited by 18
Self-citationDecoding task specific and task general functional architectures of the brain
Gupta, S., Lim, M. J. W., & Rajapakse, J. C. (2022) · Human Brain Mapping · doi:10.1002/hbm.25817 · Cited by 12
Zhu, H., Jin, W., Zhou, J., Tong, S., Xu, X.-K., & Sun, J. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab144 · Cited by 12
The Biological Bridge Between Behavioral Inhibition and Psychopathology
Sylvester, C. M., & Pine, D. S. (2018) · Springer eBooks · doi:10.1007/978-3-319-98077-5_14 · Cited by 12
Customizing TMS Applications in Traumatic Brain Injury Using Neuroimaging
Herrold, A. A., Siddiqi, S., Livengood, S. L., Pape, T., Higgins, J. P., Adamson, M. M., Leung, A., & Raij, T. (2020) · Journal of Head Trauma Rehabilitation · doi:10.1097/htr.0000000000000627 · Cited by 12
Lapate, R. C., Heckner, M. K., Phan, A., Tambini, A., & D’Esposito, M. (2024) · Nature Communications · doi:10.1038/s41467-024-48015-8 · Cited by 11
Aging and Alzheimer’s Disease Have Dissociable Effects on Medial Temporal Lobe ConnectivityPreprint
Hrybouski, S., Das, S. R., Xie, L., Wisse, L. E. M., Kelley, M., Lane, J., Sherin, M., DiCalogero, M., Nasrallah, I. M., Detre, J. A., et al. (2023) · medRxiv · doi:10.1101/2023.01.18.23284749 · Cited by 11
Gordon, E. M., May, G. J., & Nelson, S. M. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.06.026 · Cited by 13
Self-citationLi, J., Wang, Q., Li, K., Li, Y., & Guo, X. (2023) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.28984 · Cited by 9
Tepper, Á., Núñez, J. V., Ramírez-Mahaluf, J. P., Aguirre, J. M., Barbagelata, D., Maldonado, E. M., Dellarossa, C. D., Nachar, R., González‐Valderrama, A., Undurraga, J. P., et al. (2023) · NeuroImage Clinical · doi:10.1016/j.nicl.2023.103391 · Cited by 9
Hua, L., Gao, F., Xia, X., Guo, Q., Zhao, Y., Huang, S., & Yuan, Z. (2023) · Communications Biology · doi:10.1038/s42003-023-04952-6 · Cited by 9
Gallucci, J., Pomarol‐Clotet, E., Voineskos, A. N., Guerrero‐Pedraza, A., Alonso-Lana, S., Vieta, E., Salvador, R. M., & Hawco, C. S. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.103269 · Cited by 9
A Precision Functional Atlas of Network Probabilities and Individual-Specific Network TopographyPreprint
Hermosillo, R. J. M., Moore, L. A., Feczko, E., Dworetsky, A., Pines, A. R., Conan, G., Mooney, M. A., Randolph, A., Adeyemo, B., Earl, E., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.12.475422 · Cited by 9
Self-citationLee, J., & Lee, J. (2023) · Human Brain Mapping · doi:10.1002/hbm.26561 · Cited by 8
A subset of cortical areas exhibit adult-like functional network patterns in early childhood
Tu, J. C., Wang, Y., Wang, X., Dierker, D. L., Sobolewski, C. M., Day, T. K. M., Kardan, O., Miranda-Domínguez, Ó., Moore, L. A., Feczko, E., et al. (2025) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2025.101551 · Cited by 10
Self-citationNeuromodulation of the Cingulate Cortex for Pain
Strohman, A., & Legon, W. (2025) · The Neuroscientist · doi:10.1177/10738584251337652 · Cited by 8
Falco, D., Chowdury, A., Rosenberg, D. R., Bressler, S. L., & Diwadkar, V. A. (2020) · Brain Structure and Function · doi:10.1007/s00429-020-02061-2 · Cited by 9
Parcellation of individual brains: From group level atlas to precise mapping.
Li, C., Yu, S., & Cui, Y. (2025) · Neuroscience and Biobehavioral Reviews · doi:10.1016/j.neubiorev.2025.106172 · Cited by 11
Brain network dynamics fingerprints are resilient to data heterogeneity
Menara, T., Lisi, G., Pasqualetti, F., & Cortese, A. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/abd684 · Cited by 8
Human Fronto-Striatal Connectivity is Organized into Discrete Functional SubnetworksPreprint
Gordon, E. M., Laumann, T. O., Marek, S. A., Newbold, D. J., Hampton, J. M., Seider, N. A., Montez, D. F., Nielsen, A. M., Van, A. N., Zheng, A., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.12.439415 · Cited by 8
Self-citationHeterogenous brain activations across individuals localize to a common network
Peng, S., Cui, Z., Zhong, S., Zhang, Y., Cohen, A. L., Fox, M., & Gong, G. (2024) · Communications Biology · doi:10.1038/s42003-024-06969-x · Cited by 8
Personality traits vary in their association with brain activity across situations
Hardikar, S., Mckeown, B., Turnbull, A. G., Xu, T., Valk, S. L., Bernhardt, B. C., Margulies, D. S., Milham, M. P., Jefferies, E., Leech, R., et al. (2024) · Communications Biology · doi:10.1038/s42003-024-07061-0 · Cited by 8
Reshaping Cortical Connectivity Gradient by Long-Term Cognitive Training During Development
Xu, T., Wu, Y., Zhang, Y., Zuo, X., Chen, F., & Zhou, C. (2022) · Neuroscience Bulletin · doi:10.1007/s12264-023-01108-8 · Cited by 6
Application of deep learning in fMRI-based human brain parcellation: a review
Li, Y., Chen, X., Ling, Q., He, Z., & Liu, A. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/ad14e2 · Cited by 6
Bagging Improves Reproducibility of Functional Parcellation of the Human BrainPreprint
Nikolaidis, A., Heinsfeld, A. S., Xu, T., Bellec, P., Vogelstein, J. T., & Milham, M. P. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/343392 · Cited by 6
Li, C., Lu, Y., Yu, S. P., & Cui, Y. J. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103297 · Cited by 5
Li, L., Xu, Z., Chen, L., Suo, X., Fu, S., Wang, S., Lui, S., Huang, X., Li, L., Li, S., et al. (2021) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2021.06.010 · Cited by 8
Cook, K. M., Asis‐Cruz, J. D., Basu, S. K., Andescavage, N. N., Murnick, J. G., Spoehr, E., du Plessis, A. J., & Limperopoulos, C. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1214080 · Cited by 5
Bijsterbosch, J. D., Farahibozorg, S.-R., Glasser, M. F., Essen, D. C. V., Snyder, L. H., Woolrich, M. W., & Smith, S. M. (2023) · Imaging Neuroscience · doi:10.1162/imag_a_00046 · Cited by 5
Deschwanden, P. F., Piñeiro, A. L., Hotz, I., Malagurski, B., Mérillat, S., & Jäncke, L. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00127 · Cited by 4
Kurkela, K. A., & Ritchey, M. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00169 · Cited by 10
Chandra, N. K., Sitek, K. R., Chandrasekaran, B., & Sarkar, A. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00258 · Cited by 6
Functional Connectivity Biomarkers in Schizophrenia
Howell, A. M., & Anticevic, A. (2024) · Advances in neurobiology · doi:10.1007/978-3-031-69491-2_10 · Cited by 5
Dissociating individual connectome traits using low-rank learning
Qin, J., Shen, H., Zeng, L., Gao, K., Luo, Z., & Hu, D. (2019) · Brain Research · doi:10.1016/j.brainres.2019.146348 · Cited by 5
Sato, J. R., White, T. P., & Biazoli, C. E. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00085 · Cited by 5
The organization of individually mapped structural and functional semantic networks in aging adults
Ketchabaw, W. T., DeMarco, A. T., Paul, S., Dvorak, E., van der Stelt, C. M., & Turkeltaub, P. E. (2022) · Brain Structure and Function · doi:10.1007/s00429-022-02544-4 · Cited by 7
Relationship of neurite architecture to brain activity during task-based fMRI
Schifani, C., Hawco, C. S., Nazeri, A., & Voineskos, A. N. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119575 · Cited by 5
Dissociable contributions of the medial parietal cortex to recognition memory
Koslov, S. R., Kable, J. W., & Foster, B. L. (2024) · Journal of Neuroscience · doi:10.1523/jneurosci.2220-23.2024 · Cited by 8
Brain functional gradients are related to cortical folding gradient
He, Z., Zhang, T., Wang, Q., Zhang, S., Cao, G., Liu, T., Zhao, S., Jiang, X., Guo, L., Yuan, Y., et al. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae453 · Cited by 4
Inferring Individual-Level Variations in the Functional Parcellation of the Cerebral Cortex
Nie, L., Matthews, P. M., & Guo, Y. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2571221 · Cited by 4
Webler, R. D., Carrasco, C. M., Cooper, S. E., Chen, M., Hunt, C. O., Hennessy, S., Cao, L., Lam, C., Chiu, A., Differding, C., et al. (2024) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2024.100309 · Cited by 4
Cultural Evolutionary Neuroscience
Uchiyama, R., & Muthukrishna, M. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780190057695.013.3 · Cited by 5
Assessing Variations in Areal Organization for the Intrinsic Brain: From Fingerprints to ReliabilityPreprint
Xu, T., Opitz, A., Craddock, R. C., Wright, M. J., Zuo, X., & Milham, M. P. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/035790 · Cited by 4
Commonality and variability in functional networks in children under 5 years old
Tu, J. C., Lu, C., Day, T. K. M., Hermosillo, R. J. M., Moore, L. A., Wang, A., Wang, X., Dierker, D. L., Latham, A. R., Kenley, J. K., et al. (2026) · Communications Biology · doi:10.1038/s42003-026-10599-w · Cited by 4
Self-citationHoang, N., Sardaripour, N., Ramey, G. D., Schilling, K., Liao, E., Chen, Y.-T., Park, J. H., Bledsoe, X., Landman, B. A., Gamazon, E. R., et al. (2024) · PLoS Biology · doi:10.1371/journal.pbio.3002782 · Cited by 3
Mullin, H. A., Carpenter, C. M., Cwiek, A. P., Lan, G., Chase, S. O., Carter, E. E., Vervoordt, S. M., Rabinowitz, A. R., Venkatesan, U. M., & Hillary, F. G. (2025) · Network Neuroscience · doi:10.1162/netn_a_00459 · Cited by 3
Efficacy of functional connectome fingerprinting using tangent-space brain networks
Curic, D., Krishna, S., & Davidsen, J. (2025) · Network Neuroscience · doi:10.1162/netn_a_00445 · Cited by 3
Siddiqi, S., Trapp, N. T., Shahim, P., Hacker, C. D., Laumann, T. O., Kandala, S., Carter, A. R., & Brody, D. L. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/151696 · Cited by 3
Self-citationDevelopment of areal-level individualized homologous functional parcellations in youth
Li, J., Zhang, Y., Xin, W., Xue, M., Wang, Z., Lv, S., Yang, R., Zhu, W., Li, X., Yan, T., et al. (2025) · Communications Biology · doi:10.1038/s42003-025-08509-7 · Cited by 2
Individual‐specific resting‐state networks predict language dominance in drug‐resistant epilepsy
Lim, M. J. R., Zhang, S., Pande, S., Xue, A., Kong, R., Zaghloul, K. A., Inati, S., & Yeo, B. T. T. (2026) · Epilepsia · doi:10.1002/epi.70323 · Cited by 2
Mamah, D. T., Chen, S., Gordon, E. M., Kandala, S., Barch, D. M., & Harms, M. P. (2024) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2024.100386 · Cited by 2
Self-citationTell me why: A scoping review on the fundamental building blocks of fMRI-based network analysis
van der Pal, Z., Douw, L., Genis, A., van den Bergh, D., Marsman, M., Schrantee, A., & Blanken, T. F. (2025) · NeuroImage Clinical · doi:10.1016/j.nicl.2025.103785 · Cited by 3
Kelly, R. E., Hoptman, M. J., Alexopoulos, G. S., Gunning, F. M., & McKeown, M. J. (2019) · Human Brain Mapping · doi:10.1002/hbm.24692 · Cited by 1
On Statistical Analysis of Brain VariabilityPreprint
Chén, O. Y., Phan, H., Nagels, G., & Vos, M. D. (2020) · Preprints.org · doi:10.20944/preprints202008.0428.v1 · Cited by 2
Individual variability in sensorimotor learning reflects trait-like neurobehavioral subject factorsPreprint
Areshenkoff, C. N., de Brouwer, A. J., Standage, D., Nashed, J. Y., Gale, D. J., Flanagan, J. R., & Gallivan, J. P. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.11.589135 · Cited by 2
Cyr, C., Holubecki, A. M., Shi, L., Lakshman, M., Salvo, J. J., Anderson, N. L., Kragel, J. E., Lurie, S. M., Voss, J. L., Kokkinos, V., et al. (2026) · Journal of Neurophysiology · doi:10.1152/jn.00589.2025 · Cited by 1
Compensating cortical thickness for cortical folding-related variation
Demirci, N., Coalson, T. S., Holland, M. A., Essen, D. C. V., & Glasser, M. F. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1106 · Cited by 2
The Statistical Analysis of the Varying Brain
Chén, O. Y., Vũ, D. T., Greub, G., Cao, H., He, X., Müller, Y. D., Petrovas, C., Shou, H., Nguyen, V.-D., Zhi, B., et al. (2023) · IEEE Statistical Signal Processing Workshop (SSP) · doi:10.1109/ssp53291.2023.10208029 · Cited by 1
Deep learning-based embedding of functional connectivity profiles for precision functional mapping
Tu, J. C., Kim, J., Lu, C., Luckett, P. H., Adeyemo, B., Shimony, J. S., Elison, J. T., Eggebrecht, A. T., & Wheelock, M. D. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.129 · Cited by 5
Self-citationVariability and stability of large-scale cortical oscillation patternsPreprint
Cox, R., Schapiro, A. C., & Stickgold, R. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/093005 · Cited by 1
Data driven methods for characterizing individual differences in brain MRI
Kumar, K. (2018) · Espace ÉTS (ETS) · doi:10.13140/rg.2.2.12193.71525 · Cited by 1
Smith, D. M., Perez, D. C., Porter, A., Dworetsky, A., & Gratton, C. (2020) · Current Opinion in Behavioral Sciences · doi:10.31234/osf.io/9e28n · Cited by 1
Hua, L., Gao, F., Xia, X., Guo, Q., Zhao, Y., & Yuan, Z. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.14.516364 · Cited by 1
Betzel, R. F. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.16.660604 · Cited by 1
Precision functional imaging in infants using multi-echo fMRI at 7TPreprint
Moser, J., Sadeghi‐Tarakameh, A., Ramirez, J. S. B., Madison, T. J., Hantzsch, L., Weldon, K. B., Pham, H. H. N., Lundquist, J. T., Sung, S., Ukpong, I., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.09.687453 · Cited by 1
Oberman, L. M., Penafiel, A. I., Dieterich, R., Phan, C. T., Chou, Y.-Y., Pham, D. L., Adamson, M. M., Hines, C. E., Rezaee, Z., Deng, Z.-D., et al. (2025) · Frontiers in Neurology · doi:10.3389/fneur.2025.1605157
Dissociating Group and Individual Profile of Functional Connectivity Using Low Rank Matrix Recovery
Qin, J., Shen, H., Zeng, L., Gao, K., & Hu, D. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-02698-1_56
Low-Rank Learning of Functional Connectivity Reveals Neural Traits of Individual Differences
Hu, D., & Zeng, L. (2019) · Springer eBooks · doi:10.1007/978-981-32-9523-0_10
Brain regions and networks in meditation
Tang, Y., & Tang, R. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-818266-6.00007-1
Bedside Diffuse Optical Tomography of Disrupted Brain Connectivity During Acute StrokePreprint
Burke, B. A., Bergonzi, K. M., Sherafati, A., Fishell, A. K., Burns‐Yocum, T. M., Ferradal, S. L., Palanca, B. J. A., Dhar, R., Kumar, G., Lee, J., et al. (2020) · Research Square · doi:10.21203/rs.3.rs-107369/v1
Dietary Polyphenols and Neuroprotection
Silva, R. F. M., & Pogačnik, L. (2022) · MDPI eBooks · doi:10.3390/books978-3-0365-2882-3
Grady, C. L., & Rieck, J. R. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.733065202.793555009
Metadata
- Authors
- Gordon, E. M., Laumann, T. O., Adeyemo, B., & Petersen, S. E.
- Affiliations
- VA Heart of Texas Health Care Network (US)
- Washington University in St. Louis (US)
- Corresponding author
- Gordon, Evan M.
- Venue
- Cerebral Cortex
- Publisher
- Oxford University Press (OUP)
- Published
- 2015-10-13
- Volume
- 27
- Issue
- 1
- Pages
- bhv239
- ISSN
- 1047-3211 (ISSN-L)
- 1460-2199
- PMC
- PMC5939195
- OpenAlex
- W2403664975
- Cited by
- 219
- References
- 45
- Citations by year
- 2026: 9 · 2025: 18 · 2024: 28 · 2023: 29 · 2022: 26 · 2021: 24 · 2020: 23 · 2019: 19 · 2018: 21 · 2017: 11 · 2016: 11
- FWCI
- 8.61
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Cerebral Cortex (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (NS061144, MH100872, MH091657)
- McDonnell Foundation
- Simons Foundation (Award, 95177)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- interindividual variability, resting-state functional connectivity, brain network topology, anatomical alignment, individual differences
- MeSH terms
- Biological Variation, Individual, Brain, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Young Adult
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