The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.
Parcellating cortical functional networks in individuals
Open accessAbstract
The capacity to identify the unique functional architecture of an individual's brain is a crucial step toward personalized medicine and understanding the neural basis of variation in human cognition and behavior. Here we developed a cortical parcellation approach to accurately map functional organization at the individual level using resting-state functional magnetic resonance imaging (fMRI). A population-based functional atlas and a map of inter-individual variability were employed to guide the iterative search for functional networks in individual subjects. Functional networks mapped by this approach were highly reproducible within subjects and effectively captured the variability across subjects, including individual differences in brain lateralization. The algorithm performed well across different subject populations and data types, including task fMRI data. The approach was then validated by invasive cortical stimulation mapping in surgical patients, suggesting potential for use in clinical applications.
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
“In recent years, it has become possible to construct a resting-state functional magnetic resonance imaging (rsfMRI)-based connectome map with high test–retest reliability in an individual subject (36, 37).”
In the citing paper
“Precision brain-mapping, which involves the collection of several hours of imaging data from individual patients, has shown individual differences in the organization of functional networks across the cerebral cortex,104–107 as well as in subcortical structures such as the amygdala.108”
From Precision clinical trials: a framework for getting to precision medicine for neurobehavioural disorders (2021) · cites this paper as [105]
In the citing paper
“Studies with these long scan times have revealed that both task-based and resting-state brain activity measures can be obtained with high reliability and precision, allowing researchers to focus on individuals rather than on groups.”
From Precision clinical trials: a framework for getting to precision medicine for neurobehavioural disorders (2021) · cites this paper as [105]
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
The assessment and analysis of handedness: The Edinburgh inventory
Oldfield, R. C. (1971) · Neuropsychologia · doi:10.1016/0028-3932(71)90067-4 · Cited by 35,900
Advances in functional and structural MR image analysis and implementation as FSL
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., et al. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.051 · Cited by 13,630
Control of goal-directed and stimulus-driven attention in the brain
Corbetta, M., & Shulman, G. L. (2002) · Nature Reviews Neuroscience · doi:10.1038/nrn755 · Cited by 12,881
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002) · NeuroImage · doi:10.1006/nimg.2002.1132 · Cited by 11,888
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-citationThe human brain is intrinsically organized into dynamic, anticorrelated functional networks
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0504136102 · Cited by 8,553
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
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-citationSpontaneous 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
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- Brodmann, K. Brodmann’s: Localisation in the Cerebral Cortex 3rd edn. (Springer, 2006).
Cited by
Sorted by citations
Situating the default-mode network along a principal gradient of macroscale cortical organization
Margulies, D. S., Ghosh, S. S., Goulas, A., Falkiewicz, M., Huntenburg, J. M., Langs, G., Bezgin, G., Eickhoff, S. B., Castellanos, F. X., Petrides, M., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1608282113 · Cited by 2,321
Self-citationPrecision Functional Mapping of Individual Human Brains
Gordon, E. M., Laumann, T. O., Gilmore, A. W., Newbold, D. J., Greene, D. J., Berg, J. J., Ortega, M., Hoyt-Drazen, C., Gratton, C., Sun, H., et al. (2017) · Neuron · doi:10.1016/j.neuron.2017.07.011 · Cited by 1,335
The Human Connectome Project's neuroimaging approach
Glasser, M. F., Smith, S. M., Marcus, D. S., Andersson, J. L. R., Auerbach, E. J., Behrens, T. E. J., Coalson, T. S., Harms, M. P., Jenkinson, M., Moeller, S., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4361 · Cited by 1,090
Gratton, C., Laumann, T. O., Nielsen, A. N., Greene, D. J., Gordon, E. M., Gilmore, A. W., Nelson, S. M., Coalson, R. S., Snyder, A. Z., Schlaggar, B. L., et al. (2018) · Neuron · doi:10.1016/j.neuron.2018.03.035 · Cited by 846
Linking Structure and Function in Macroscale Brain Networks
Suárez, L. E., Markello, R. D., Betzel, R. F., & Mišić, B. (2020) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2020.01.008 · Cited by 769
Towards a universal taxonomy of macro-scale functional human brain networks
Uddin, L. Q., Yeo, B. T. T., & Spreng, R. N. (2019) · Brain Topography · doi:10.1007/s10548-019-00744-6 · Cited by 683
Building a Science of Individual Differences from fMRI
Dubois, J., & Adolphs, R. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.03.014 · Cited by 644
Kong, R., Li, J., Orbán, C., Sabuncu, M. R., Liu, H., Schaefer, A., Sun, N., Zuo, X., Holmes, A. J., Eickhoff, S. B., et al. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy123 · Cited by 615
Self-citationMapping the human brain's cortical-subcortical functional network organization
Ji, J. L., Spronk, M., Kulkarni, K. R., Repovš, G., Anticevic, A., & Cole, M. W. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.10.006 · Cited by 604
Weigand, A., Horn, A., Caballero, R., Cooke, D. L., Stern, A. P., Taylor, S. F., Press, D. Z., Pascual‐Leone, Á., & Fox, M. (2017) · Biological Psychiatry · doi:10.1016/j.biopsych.2017.10.028 · Cited by 492
Self-citationImaging-based parcellations of the human brain
Eickhoff, S. B., Yeo, B. T. T., & Genon, S. (2018) · Nature reviews. Neuroscience · doi:10.1038/s41583-018-0071-7 · Cited by 484
Linked dimensions of psychopathology and connectivity in functional brain networks
Xia, C. H., Ma, Z., Ciric, R., Gu, S., Betzel, R. F., Kaczkurkin, A. N., Calkins, M. E., Cook, P. A., García de la Garza, A., Vandekar, S. N., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-05317-y · Cited by 437
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
On the nature and use of models in network neuroscience
Bassett, D. S., Zurn, P., & Gold, J. I. (2018) · Nature reviews. Neuroscience · doi:10.1038/s41583-018-0038-8 · Cited by 392
Causal mapping of human brain function
Siddiqi, S., Kording, K. P., Parvizi, J., & Fox, M. (2022) · Nature reviews. Neuroscience · doi:10.1038/s41583-022-00583-8 · Cited by 368
Self-citationMultimodal surface matching with higher-order smoothness constraints
Robinson, E. C., Garcia, K., Glasser, M. F., Chen, Z., Coalson, T. S., Makropoulos, A., Božek, J., Wright, R., Schuh, A., Webster, M., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.10.037 · Cited by 326
The impact of traditional neuroimaging methods on the spatial localization of cortical areas
Coalson, T. S., Essen, D. C. V., & Glasser, M. F. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1801582115 · Cited by 311
Frontostriatal salience network expansion in individuals in depression
Lynch, C. J., Elbau, I., Ng, T. H. B., Ayaz, A., Zhu, S., Wolk, D., Manfredi, N., Johnson, M. A., Chang, M., Chou, J., et al. (2024) · Nature · doi:10.1038/s41586-024-07805-2 · Cited by 353
The normative modeling framework for computational psychiatry
Rutherford, S. E., Kia, S. M., Wolfers, T., Fraza, C., Zabihi, M., Dinga, R., Berthet, P., Worker, A., Verdi, S., Ruhé, H. G., et al. (2022) · Nature Protocols · doi:10.1038/s41596-022-00696-5 · Cited by 296
Individual Variation in Functional Topography of Association Networks in Youth
Cui, Z., Li, H., Xia, C. H., Larsen, B., Adebimpe, A., Baum, G. L., Cieslak, M., Gur, R. E., Gur, R. C., Moore, T. M., et al. (2020) · Neuron · doi:10.1016/j.neuron.2020.01.029 · Cited by 274
Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior
Kong, R., Yang, Q., Gordon, E. M., Xue, A., Yan, X., Orbán, C., Zuo, X., Spreng, R. N., Ge, T., Holmes, A. J., et al. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab101 · Cited by 261
Self-citationGroppe, D. M., Bickel, S., Dykstra, A. R., Wang, X. H., Mégevand, P., Mercier, M. R., Lado, F. A., Mehta, A. D., & Honey, C. J. (2017) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2017.01.022 · Cited by 247
Self-citationIndividual differences in functional connectivity during naturalistic viewing conditions
Vanderwal, T., Eilbott, J. A., Finn, E. S., Craddock, R. C., Turnbull, A. G., & Castellanos, F. X. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.06.027 · Cited by 246
Dixon, M. L., Andrews‐Hanna, J. R., Spreng, R. N., Irving, Z. C., Mills, C., Girn, M., & Kalina Christoff, A. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.12.073 · Cited by 239
Sezer, I., Pizzagalli, D. A., & Sacchet, M. D. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104583 · Cited by 214
Trait-like variants in human functional brain networks
Seitzman, B. A., Gratton, C., Laumann, T. O., Gordon, E. M., Adeyemo, B., Dworetsky, A., Kraus, B. T., Gilmore, A. W., Berg, J. J., Ortega, M., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1902932116 · Cited by 219
The relationship between spatial configuration and functional connectivity of brain regions
Bijsterbosch, J. D., Woolrich, M. W., Glasser, M. F., Robinson, E. C., Beckmann, C. F., Essen, D. C. V., Harrison, S. J., & Smith, S. M. (2018) · eLife · doi:10.7554/elife.32992 · Cited by 237
Network localization of cervical dystonia based on causal brain lesions
Joutsa, J., Darby, R. R., Delnooz, C. C. S., van de Warrenburg, B. P. C., Cooke, D., Prudente, C. N., Ren, J., Reich, M. M., Batla, A., Bhatia, K. P., et al. (2019) · Brain · doi:10.1093/brain/awz112 · Cited by 217
Self-citationIndividual-specific features of brain systems identified with resting state functional correlations
Gordon, E. M., Laumann, T. O., Adeyemo, B., Gilmore, A. W., Nelson, S. M., Dosenbach, N. U. F., & Petersen, S. E. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.08.032 · Cited by 222
Modern views of machine learning for precision psychiatry
Chen, Z. S., Kulkarni, P., Galatzer‐Levy, I. R., Bigio, B., Nasca, C., & Zhang, Y. (2022) · Patterns · doi:10.1016/j.patter.2022.100602 · Cited by 247
Salehi, M., Greene, A. S., Karbasi, A., Shen, X., Scheinost, D., & Constable, R. T. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116366 · Cited by 203
Defining Individual-Specific Functional Neuroanatomy for Precision Psychiatry
Gratton, C., Kraus, B. T., Greene, D. J., Gordon, E. M., Laumann, T. O., Nelson, S. M., Dosenbach, N. U. F., & Petersen, S. E. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.10.026 · Cited by 202
The individual functional connectome is unique and stable over months to years
Horien, C. L., Shen, X., Scheinost, D., & Constable, R. T. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.02.002 · Cited by 189
The Developing Human Connectome Project: typical and disrupted perinatal functional connectivity
Eyre, M., Fitzgibbon, S. P., Ciarrusta, J., Cordero‐Grande, L., Price, A. N., Poppe, T., Schuh, A., Hughes, E., O’Keeffe, C., Brandon, J., et al. (2021) · Brain · doi:10.1093/brain/awab118 · Cited by 201
Opportunities of connectomic neuromodulation
Horn, A., & Fox, M. D. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117180 · Cited by 187
Self-citationDefault-mode network streams for coupling to language and control systems
Gordon, E. M., Laumann, T. O., Marek, S. A., Raut, R. V., Gratton, C., Newbold, D. J., Greene, D. J., Coalson, R. S., Snyder, A. Z., Schlaggar, B. L., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2005238117 · Cited by 178
Geerligs, L., Tsvetanov, K. A., Cam‐CAN, & Henson, R. N. (2017) · Human Brain Mapping · doi:10.1002/hbm.23653 · Cited by 176
The brain’s action-mode network
Dosenbach, N. U. F., Raichle, M. E., & Gordon, E. M. (2025) · Nature reviews. Neuroscience · doi:10.1038/s41583-024-00895-x · Cited by 224
Ciftify: A framework for surface-based analysis of legacy MR acquisitions
Dickie, E. W., Anticevic, A., Smith, D. E., Coalson, T. S., Manogaran, M., Calarco, N., Viviano, J. D., Glasser, M. F., Essen, D. C. V., & Voineskos, A. N. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.04.078 · Cited by 152
The cerebral basis of Parkinsonian tremor: A network perspective
Helmich, R. C. G. (2017) · Movement Disorders · doi:10.1002/mds.27224 · Cited by 164
Braga, R. M., Van Dijk, K. R. A., Polimeni, J. R., Eldaief, M. C., & Buckner, R. L. (2019) · Journal of Neurophysiology · doi:10.1152/jn.00808.2018 · Cited by 162
Self-citationFunctional connectomics of affective and psychotic pathology
Baker, J. T., Dillon, D. G., Patrick, L. M., Roffman, J. L., Brady, R. O., Pizzagalli, D. A., Öngür, D., & Holmes, A. J. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1820780116 · Cited by 156
Self-citationPositive affect, surprise, and fatigue are correlates of network flexibility
Betzel, R. F., Satterthwaite, T. D., Gold, J. I., & Bassett, D. S. (2017) · Scientific Reports · doi:10.1038/s41598-017-00425-z · Cited by 178
Ozdemir, R. A., Tadayon, E., Boucher, P. O., Momi, D., Karakhanyan, K., Fox, M., Halko, M. A., Pascual‐Leone, Á., Shafi, M. M., & Santarnecchi, E. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1911240117 · Cited by 145
Self-citationGene expression links functional networks across cortex and striatum
Anderson, K. M., Krienen, F. M., Choi, E. Y., Reinen, J. M., Yeo, B. T. T., & Holmes, A. J. (2018) · Nature Communications · doi:10.1038/s41467-018-03811-x · Cited by 138
Self-citationUnsupervised Classification During Time-Series Model Building
Gates, K. M., Lane, S., Varangis, E. M. L., Giovanello, K. S., & Guiskewicz, K. (2016) · Multivariate Behavioral Research · doi:10.1080/00273171.2016.1256187 · Cited by 145
Individual-specific functional connectivity of the amygdala: A substrate for precision psychiatry
Sylvester, C. M., Yu, Q., Srivastava, A. B., Marek, S., Zheng, A., Alexopoulos, D., Smyser, C. D., Shimony, J. S., Ortega, M., Dierker, D. L., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1910842117 · Cited by 139
Regional and network properties of white matter function in Parkinson's disease
Ji, G., Ren, C., Li, Y., Sun, J., Liu, T., Gao, Y., Xue, D., Shen, L., Cheng, W., Zhu, C., et al. (2018) · Human Brain Mapping · doi:10.1002/hbm.24444 · Cited by 141
Self-citationChen, B., Xu, T., Zhou, C., Wang, L., Yang, N., Wang, Z., Dong, H.-M., Yang, Z., Zang, Y., Zuo, X., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0144963 · Cited by 136
Just a thought: How mind-wandering is represented in dynamic brain connectivity
Kucyi, A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.001 · Cited by 142
Wang, D., Li, M., Wang, M., Schoeppe, F., Ren, J., Chen, H., Öngür, D., Brady, R. O., Baker, J. T., & Liu, H. (2018) · Molecular Psychiatry · doi:10.1038/s41380-018-0276-1 · Cited by 133
Self-citationAging, frailty and complex networks
Mitnitski, A. B., Rutenberg, A. D., Farrell, S., & Rockwood, K. (2017) · Biogerontology · doi:10.1007/s10522-017-9684-x · Cited by 118
Transdiagnostic Associations Between Functional Brain Network Integrity and Cognition
Sheffield, J. M., Kandala, S., Tamminga, C. A., Pearlson, G. D., Keshavan, M. S., Sweeney, J. A., Clementz, B. A., Lerman-Sinkoff, D. B., Hill, S. K., & Barch, D. M. (2017) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2017.0669 · Cited by 129
Individual differences and time-varying features of modular brain architecture
Liao, X., Cao, M., Xia, M., & Yong He, A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.066 · Cited by 113
Human lifespan changes in the brain’s functional connectome
Sun, L., Zhao, T., Liang, X., Xia, M., Li, Q., Liao, X., Gong, G., Wang, Q., Pang, C., Yu, Q., et al. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-01907-4 · Cited by 132
Self-citationLebois, L. A. M., Li, M., Baker, J. T., Wolff, J. D., Wang, D., Lambros, A. M., Grinspoon, E., Winternitz, S., Ren, J., Gönenç, A., et al. (2021) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2020.19060647 · Cited by 115
Self-citationLi, M., Wang, D., Ren, J., Langs, G., Stoecklein, S., Brennan, B. P., Lu, J., Chen, H., & Liu, H. (2019) · PLoS Biology · doi:10.1371/journal.pbio.2007032 · Cited by 116
Self-citationAutomated optimization of TMS coil placement for personalized functional network engagement
Lynch, C. J., Elbau, I., Ng, T. H. B., Wolk, D., Zhu, S., Ayaz, A., Power, J. D., Zebley, B., Gunning, F. M., & Liston, C. M. (2022) · Neuron · doi:10.1016/j.neuron.2022.08.012 · Cited by 113
Controversies and progress on standardization of large-scale brain network nomenclature
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. R., et al. (2023) · Network Neuroscience · doi:10.1162/netn_a_00323 · Cited by 125
Stoeckel, L. E., Arvanitakis, Z., Gandy, S., Small, D. M., Kahn, C. R., Pascual‐Leone, Á., Pawlyk, A. C., Sherwin, R., & Smith, P. (2016) · F1000Research · doi:10.12688/f1000research.8300.2 · Cited by 113
The Roles of Subdivisions of Human Insula in Emotion Perception and Auditory Processing
Zhang, Y., Zhou, W., Wang, S., Zhou, Q., Wang, H., Zhang, B., Huang, J., Hong, B., & Wang, X. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhx334 · Cited by 112
Self-citationIndividual parcellation of resting fMRI with a group functional connectivity prior
Chong, M., Bhushan, C., Joshi, A. A., Choi, S., Haldar, J. P., Shattuck, D. W., Spreng, R. N., & Leahy, R. M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.04.054 · Cited by 109
Wu, X., Ji, G.-J., Geng, Z., Wang, L., Yan, Y., Wu, Y., Xiao, G., Gao, L., Wei, Q., Zhou, S., et al. (2022) · Brain Stimulation · doi:10.1016/j.brs.2021.11.007 · Cited by 109
Self-citationSiddiqi, S. H., Trapp, N. T., Hacker, C. D., Laumann, T. O., Kandala, S., Hong, X., Trillo, L., Shahim, P., Leuthardt, E. C., Carter, A. R., et al. (2019) · Journal of Neurotrauma · doi:10.1089/neu.2018.5889 · Cited by 106
Using fMRI to study reward processing in humans: past, present, and future
Wang, K. S., Smith, D. V., & Delgado, M. R. (2016) · Journal of Neurophysiology · doi:10.1152/jn.00333.2015 · Cited by 105
Self-citationGriffa, A., Amico, E., Liégeois, R., Ville, D. V. D., & Preti, M. G. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118970 · Cited by 98
Intracranial Electrophysiology of the Human Default Network
Fox, K. C. R., Foster, B. L., Kucyi, A., Daitch, A. L., & Parvizi, J. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.02.002 · Cited by 96
Yu, Q., Du, Y., Chen, J., Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., Adalı, T., Pearlson, G. D., & Calhoun, V. D. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2018.2825200 · Cited by 96
Spontaneous cognitive processes and the behavioral validation of time-varying brain connectivity
Kucyi, A., Tambini, A., Sadaghiani, S., Keilholz, S. D., & Cohen, J. R. (2018) · Network Neuroscience · doi:10.1162/netn_a_00037 · Cited by 95
Adaptive cortical parcellations for source reconstructed EEG/MEG connectomes
Farahibozorg, S.-R., Henson, R. N., & Hauk, O. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.09.009 · Cited by 96
Nielsen, A. N., Greene, D. J., Gratton, C., Dosenbach, N. U. F., Petersen, S. E., & Schlaggar, B. L. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy117 · Cited by 95
Principles for models of neural information processing
Kay, K., & Weiner, K. S. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.016 · Cited by 90
Fedorenko, E. G. (2021) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2021.02.023 · Cited by 84
The Myth of Optimality in Clinical Neuroscience
Holmes, A. J., & Patrick, L. M. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.12.006 · Cited by 89
Self-citationAge differences in the functional architecture of the human brain
Setton, R., Mwilambwe-Tshilobo, L., Girn, M., Lockrow, A. W., Baracchini, G., Hughes, C. S., Lowe, A. J., Cassidy, B. N., Li, J., Luh, W., et al. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac056 · Cited by 94
Reinen, J., Chén, O. Y., Hutchison, R. M., Yeo, B. T. T., Anderson, K. M., Sabuncu, M. R., Öngür, D., Roffman, J. L., Smoller, J. W., Baker, J. T., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03462-y · Cited by 84
Self-citationFunctional connectomics from a “big data” perspective
Xia, M., & Yong He, A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.031 · Cited by 85
Parallel hippocampal-parietal circuits for self- and goal-oriented processing
Zheng, A., Montez, D. F., Marek, S. A., Gilmore, A. W., Newbold, D. J., Laumann, T. O., Kay, B. P., Seider, N. A., Van, A. N., Hampton, J. M., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2101743118 · Cited by 87
Clinical Concepts Emerging from fMRI Functional Connectomics
Matthews, P. M., & Hampshire, A. (2016) · Neuron · doi:10.1016/j.neuron.2016.07.031 · Cited by 83
Nakajima, K., Osada, T., Ogawa, A., Tanaka, M., Oka, S., Kamagata, K., Aoki, S., Oshima, Y., Tanaka, S., & Konishi, S. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111197 · Cited by 80
Virtual brain grafting: Enabling whole brain parcellation in the presence of large lesions
Radwan, A., Emsell, L., Blommaert, J., Zhylka, A., Kovacs, S., Theys, T., Sollmann, N., Dupont, P. J., & Sunaert, S. G. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117731 · Cited by 81
Heritability of individualized cortical network topography
Anderson, K. M., Ge, T., Kong, R., Patrick, L. M., Spreng, R. N., Sabuncu, M. R., Yeo, B. T. T., & Holmes, A. J. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2016271118 · Cited by 90
Self-citationLarge-scale sparse functional networks from resting state fMRI
Li, H., Satterthwaite, T. D., & Yong Fan, A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.05.004 · Cited by 77
Organization of Propagated Intrinsic Brain Activity in Individual Humans
Raut, R. V., Mitra, A., Marek, S. A., Ortega, M., Snyder, A. Z., Tanenbaum, A., Laumann, T. O., Dosenbach, N. U. F., & Raichle, M. E. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz198 · Cited by 78
White matter connections of the inferior parietal lobule: A study of surgical anatomy
Burks, J. D., Boettcher, L. B., Conner, A. K., Glenn, C. A., Bonney, P. A., Baker, C. M., Briggs, R. G., Pittman, N. A., OʼDonoghue, D. L., Wu, D. H., et al. (2017) · Brain and Behavior · doi:10.1002/brb3.640 · Cited by 79
A precision functional atlas of personalized network topography and probabilities
Hermosillo, R. J. M., Moore, L. A., Feczko, E., Miranda-Domínguez, Ó., Pines, A. R., Dworetsky, A., Conan, G. M., Mooney, M. A., Randolph, A., Graham, A. M., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01596-5 · Cited by 100
Recent advances in psychoradiology
Luo, L., You, W., DelBello, M. P., Gong, Q., & Li, F. Y. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac9d1e · Cited by 77
Body Topography Parcellates Human Sensory and Motor Cortex
Kuehn, E., Dinse, J., Jakobsen, E., Long, X., Andreas Schäfer, A., Bazin, P., Villringer, A., Sereno, M. I., & Margulies, D. S. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx026 · Cited by 74
Voineskos, A. N., Jacobs, G. R., & Ameis, S. H. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.09.004 · Cited by 75
Ning, L., Makris, N., Camprodon, J. A., & Yogesh Rathi, A. (2018) · Brain stimulation · doi:10.1016/j.brs.2018.10.004 · Cited by 71
Liu, F., Xu, J., Guo, L., Qin, W., Liang, M., Schumann, G., & Yu, C. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01669-6 · Cited by 72
Keller, A. S., Pines, A. R., Shanmugan, S., Sydnor, V. J., Cui, Z., Bertolero, M. A., Barzilay, R., Alexander‐Bloch, A. F., Byington, N., Chen, A. A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-44087-0 · Cited by 77
Voineskos, A. N., Hawco, C. S., Neufeld, N. H., Turner, J. A., Ameis, S. H., Anticevic, A., Buchanan, R. W., Cadenhead, K. S., Dazzan, P., Dickie, E. W., et al. (2024) · World Psychiatry · doi:10.1002/wps.21159 · Cited by 81
Cochereau, J., Deverdun, J., Herbet, G., Charroud, C., Boyer, A., Moritz‐Gasser, S., Bars, E. L., Molino, F., Bonafé, A., de Champfleur, N. M., et al. (2016) · Human Brain Mapping · doi:10.1002/hbm.23270 · Cited by 71
Sex differences in the functional topography of association networks in youth
Shanmugan, S., Seidlitz, J., Cui, Z., Adebimpe, A., Bassett, D. S., Bertolero, M. A., Davatzikos, C. A., Fair, D. A., Gur, R. E., Gur, R. C., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2110416119 · Cited by 69
Individualized functional networks reconfigure with cognitive state
Salehi, M., Karbasi, A., Barron, D. S., Scheinost, D., & Constable, R. T. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116233 · Cited by 68
Dickie, E. W., Ameis, S. H., Shahab, S., Calarco, N., Smith, D. E., Miranda, D., Viviano, J. D., & Voineskos, A. N. (2018) · Biological Psychiatry · doi:10.1016/j.biopsych.2018.02.1174 · Cited by 64
Stoecklein, S., Hilgendorff, A., Li, M., Förster, K. M., Flemmer, A. W., Galiè, F., Wunderlich, S., Wang, D., Stein, S., Ehrhardt, H., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1907892117 · Cited by 69
Self-citationZhang, W., Sweeney, J. A., Bishop, J. R., Gong, Q., & Lui, S. (2023) · Nature Mental Health · doi:10.1038/s44220-023-00019-x · Cited by 67
Iraji, A., Fu, Z., Faghiri, A., Duda, M., Chen, J., Rachakonda, S., DeRamus, T. P., Kochunov, P., Adhikari, B. M., Belger, A., et al. (2023) · Human Brain Mapping · doi:10.1002/hbm.26472 · Cited by 79
Metadata
- Authors
- Wang, D., Buckner, R. L., Fox, M. D., Holt, D. J., Holmes, A. J., Stoecklein, S., Langs, G., Pan, R., Qian, T., Li, K., Baker, J. T., Stufflebeam, S. M., Wang, K., Wang, X., Hong, B., & Liu, H.
- Affiliations
- Harvard University (US)
- Beth Israel Deaconess Medical Center (US)
- Massachusetts Institute of Technology (US)
- Capital Medical University (CN)
- First Affiliated Hospital of Anhui Medical University (CN)
- Beijing Institute for Brain Disorders (CN)
- Tsinghua University (CN)
- Corresponding author
- Hong, Bo
- Liu, Hesheng
- ORCID
- Venue
- Nature Neuroscience
- Published
- 2015-11-09
- Volume
- 18
- Issue
- 12
- Pages
- 1853-1860
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC4661084
- OpenAlex
- W2212967118
- Cited by
- 527
- References
- 67
- Citations by year
- 2026: 42 · 2025: 47 · 2024: 54 · 2023: 48 · 2022: 52 · 2021: 65 · 2020: 44 · 2019: 44 · 2018: 46 · 2017: 51 · 2016: 33 · 2015: 1
- FWCI
- 26.24
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Funders
- National Institutes of Health (5u54mh091657-05, 5k23mh104515-05)
- National Institute of Mental Health (1U54 MH091657, U54-MH091657, K23-MH104515, K01MH099232, P50MH106435)
- National Institute of Neurological Disorders and Stroke (K23NS083741, K25NS069805, R01NS091604, R01NS069696)
- National Institute of Biomedical Imaging and Bioengineering (P41EB015902)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD067312)
- 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
- cortical parcellation, resting-state networks, interindividual variability, hemispheric lateralization, personalized medicine, task-based fMRI, reproducibility
- MeSH terms
- Adolescent, Adult, Brain Mapping, Cerebral Cortex, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Nerve Net, 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
