The DOI matches its Crossref record; 81 of the 81 references it cites were found in a registry or an index.
The contribution of geometry to the human connectome
Open accessAbstract
The human connectome is a topologically complex, spatially embedded network. While its topological properties have been richly characterized, the constraints imposed by its spatial embedding are poorly understood. By applying a novel resampling method to tractography data, we show that the brain's spatial embedding makes a major, but not definitive, contribution to the topology of the human connectome. We first identify where the brain's structural hubs would likely be located if geometry was the sole determinant of brain topology. Empirical networks show a widespread shift away from this geometric center toward more peripheral interconnected skeletons in each hemisphere, with discrete clusters around the anterior insula, and the anterior and posterior midline regions of the cortex. A relatively small number of strong inter-hemispheric connections assimilate these intra-hemispheric structures into a rich club, whose connections are locally more clustered but globally longer than predicted by geometry. We also quantify the extent to which the segregation, integration, and modularity of the human brain are passively inherited from its geometry. These analyses reveal novel insights into the influence of spatial geometry on the human connectome, highlighting specific topological features that likely confer functional advantages but carry an additional metabolic cost.
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
“For example, Roberts et al. (2016) demonstrated that empirical human tractography graphs exhibit a more peripheral hub network structure than predicted by their corresponding geometric surrogate graphs, similar to our tracer results in Fig. 6 and in contrast to what we observed with tractography networks.”
From The role of spatial embedding in mouse brain networks constructed from diffusion tractography and tracer injections (2021) · cites this paper as Roberts et al. (2016)
In the citing paper
“true falloff of both structural and functional connectivity with distance has been shown with high-resolution invasive imaging as well as tractography in a number of mammalian species (Horvát et al., 2016; Markov et al., 2013; Perinelli et al., 2019; Roberts et al., 2016; Rubinov et al., 2015; Supekar et al., 2009).”
From The role of spatial embedding in mouse brain networks constructed from diffusion tractography and tracer injections (2021) · cites this paper as Roberts et al., 2016
In the citing paper
“the brain is itself a spatially embedded network with properties partially inherited from geometry: there is a metabolic wiring cost in the establishment of long-range connections”
From The role of spatial embedding in mouse brain networks constructed from diffusion tractography and tracer injections (2021) · cites this paper as Roberts et al., 2016
In the citing paper
“Previous work with human tractography datasets (Roberts et al., 2016) has revealed that the human brain places its strongest nodes further into geometrically peripheral regions than would be expected by weight-distance effects alone.”
From The role of spatial embedding in mouse brain networks constructed from diffusion tractography and tracer injections (2021) · cites this paper as Roberts et al., 2016
In the citing paper
“The number of streamlines (NOS) is one widely used measure to quantify the connectivity strength (Hagmann et al., 2008b; Ingalhalikar et al., 2014; Roberts et al., 2016; Sporns et al., 2005; van Dellen et al., 2018).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Roberts et al., 2016
In the citing paper
“The most popular scalar value for connectivity matrix analysis is the NOS (Hagmann et al., 2008b; Ingalhalikar et al., 2014; Roberts et al., 2016; Sporns et al., 2005; van Dellen et al., 2018), where higher values are considered to indicate stronger connectivity between pairs of gray matter regions.”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Roberts et al., 2016
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
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
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
Complex network measures of brain connectivity: Uses and interpretations
Rubinov, M., & Sporns, O. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.003 · Cited by 11,522
Strogatz, S. H. (2001) · Nature · doi:10.1038/35065725 · Cited by 8,236
A nonparametric method for automatic correction of intensity nonuniformity in MRI data
Sled, J. G., Zijdenbos, A. P., & Evans, A. C. (1998) · IEEE Transactions on Medical Imaging · doi:10.1109/42.668698 · Cited by 4,731
Mapping the structural core of human cerebral cortex
Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., & Sporns, O. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060159 · Cited by 4,173
Testing for nonlinearity in time series: the method of surrogate data
Theiler, J. P., Eubank, S., Longtin, A., Galdrikian, B., & Farmer, J. D. (1992) · Physica D Nonlinear Phenomena · doi:10.1016/0167-2789(92)90102-s · Cited by 3,759
Neural systems supporting interoceptive awareness
Critchley, H. D., Wiens, S., Rotshtein, P., Öhman, A., & Dolan, R. J. (2004) · Nature Neuroscience · doi:10.1038/nn1176 · Cited by 3,406 · cited 1×
“For the insula, this is likely facilitated through its characteristic deep infolding andmay be required to keep it in relative proximity to brainstem autonomic centers for which it is the relevant sensory (interoceptive) cortex (Critchley et al., 2004).”
BackgroundThe economy of brain network organization
Bullmore, E., & Sporns, O. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3214 · Cited by 3,284
Cited by
Sorted by citations
Dynamic models of large-scale brain activity
Breakspear, M. (2017) · Nature Neuroscience · doi:10.1038/nn.4497 · Cited by 1,239
Self-citationLinking 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
Standardizing workflows in imaging transcriptomics with the abagen toolbox
Markello, R. D., Arnatkevičiūtė, A., Poline, J., Fulcher, B. D., Fornito, A., & Mišić, B. (2021) · eLife · doi:10.7554/elife.72129 · Cited by 569
Criticality in the brain: A synthesis of neurobiology, models and cognition
Cocchi, L., Gollo, L. L., Zalesky, A., & Breakspear, M. J. (2017) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2017.07.002 · Cited by 555
Self-citationGradients of structure–function tethering across neocortex
Vázquez-Rodríguez, B., Suárez, L. E., Markello, R. D., Shafiei, G., Paquola, C., Hagmann, P., van den Heuvel, M. P., Bernhardt, B. C., Spreng, R. N., & Mišić, B. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1903403116 · Cited by 472
van den Heuvel, M. P., Bullmore, E. T., & Sporns, O. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.03.001 · Cited by 328
Decoupling of brain function from structure reveals regional behavioral specialization in humans
Preti, M. G., & Ville, D. V. D. (2019) · Nature Communications · doi:10.1038/s41467-019-12765-7 · Cited by 337
Brain network communication: concepts, models and applications
Seguin, C., Sporns, O., & Zalesky, A. (2023) · Nature reviews. Neuroscience · doi:10.1038/s41583-023-00718-5 · Cited by 329
Generative models of the human connectome
Betzel, R. F., Avena-Koenigsberger, A., Goñi, J., He, Y., de Reus, M. A., Griffa, A., Vértes, P. E., Mišic, B., Thiran, J.-P., Hagmann, P., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.09.041 · Cited by 303
Zhang, F., Daducci, A., He, Y., Schiavi, S., Seguin, C., Smith, R. E., Yeh, C.-H., Zhao, T., & O’Donnell, L. J. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2021.118870 · Cited by 302
“[112, 429, 211, 362, 182], where higher values are considered”
Self-citationBuilding connectomes using diffusion MRI: why, how and but
Sotiropoulos, S. N., & Zalesky, A. (2019) · NMR in Biomedicine · doi:10.1002/nbm.3752 · Cited by 290
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
Connectome sensitivity or specificity: which is more important?
Zalesky, A., Fornito, A., Cocchi, L., Gollo, L. L., van den Heuvel, M. P., & Breakspear, M. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.06.035 · Cited by 275
Self-citationRoberts, J. A., Gollo, L. L., Abeysuriya, R. G., Roberts, G., Mitchell, P. B., Woolrich, M. W., & Breakspear, M. (2019) · Nature Communications · doi:10.1038/s41467-019-08999-0 · Cited by 264
Self-citationOptimally controlling the human connectome: the role of network topology
Betzel, R. F., Gu, S., Medaglia, J. D., Pasqualetti, F., & Bassett, D. S. (2016) · Scientific Reports · doi:10.1038/srep30770 · Cited by 247
Boguñá, M., Bonamassa, I., Domenico, M. D., Havlin, S., Krioukov, D., & Serrano, M. Á. (2021) · Nature Reviews Physics · doi:10.1038/s42254-020-00264-4 · Cited by 232
Null models in network neuroscience
Váša, F., & Mišić, B. (2022) · Nature reviews. Neuroscience · doi:10.1038/s41583-022-00601-9 · Cited by 226
Specificity and robustness of long-distance connections in weighted, interareal connectomes
Betzel, R. F., & Bassett, D. S. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1720186115 · Cited by 223
Horvát, S., Gămănuț, R., Ercsey-Ravasz, M., Magrou, L., Gămănuț, B., Van Essen, D. C., Burkhalter, A., Knoblauch, K., Toroczkai, Z., & Kennedy, H. (2016) · PLoS Biology · doi:10.1371/journal.pbio.1002512 · Cited by 198
Consistency-based thresholding of the human connectome
Roberts, J. A., Perry, A., Roberts, G., Mitchell, P. B., & Breakspear, M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.09.053 · Cited by 189
Self-citationGenetic influences on hub connectivity of the human connectome
Arnatkevičiūtė, A., Fulcher, B., Oldham, S., Tiego, J., Paquola, C., Gerring, Z. F., Aquino, K. M., Hawi, Z., Johnson, B. P., Ball, G., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24306-2 · Cited by 187
From maps to multi-dimensional network mechanisms of mental disorders
Braun, U., Schaefer, A., Betzel, R. F., Tost, H., Meyer-Lindenberg, A., & Bassett, D. S. (2018) · Neuron · doi:10.1016/j.neuron.2017.11.007 · Cited by 177
Fulcher, B. D., Arnatkeviciute, A., & Fornito, A. (2021) · Nature Communications · doi:10.1038/s41467-021-22862-1 · Cited by 164
Seguin, C., van den Heuvel, M. P., & Zalesky, A. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1801351115 · Cited by 173
Kerkman, J. N., Daffertshofer, A., Gollo, L. L., Breakspear, M. J., & Boonstra, T. W. (2018) · Science Advances · doi:10.1126/sciadv.aat0497 · Cited by 171
Self-citationBetzel, R. F., Griffa, A., Hagmann, P., & Mišić, B. (2018) · Network Neuroscience · doi:10.1162/netn_a_00075 · Cited by 172
Spatial Patterning of Tissue Volume Loss in Schizophrenia Reflects Brain Network Architecture
Shafiei, G., Markello, R. D., Makowski, C., Talpalaru, A., Kirschner, M., Devenyi, G. A., Guma, E., Hagmann, P., Cashman, N. R., Lepage, M., et al. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.09.031 · Cited by 152
The modular organization of human anatomical brain networks: Accounting for the cost of wiring
Betzel, R. F., Medaglia, J. D., Papadopoulos, L., Baum, G. L., Gur, R. C., Gur, R. E., Roalf, D. R., Satterthwaite, T. D., & Bassett, D. S. (2017) · Network Neuroscience · doi:10.1162/netn_a_00002 · Cited by 148
Mapping how local perturbations influence systems-level brain dynamics
Gollo, L. L., Roberts, J. A., & Cocchi, L. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.057 · Cited by 144
Self-citationStiso, J., Khambhati, A. N., Menara, T., Kahn, A. E., Stein, J. M., Das, S. R., Gorniak, R., Tracy, J. I., Litt, B., Davis, K. A., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2019.08.008 · Cited by 136
Learning function from structure in neuromorphic networks
Suárez, L. E., Richards, B. A., Lajoie, G., & Mišić, B. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00376-1 · Cited by 137
Local molecular and global connectomic contributions to cross-disorder cortical abnormalities
Hansen, J. Y., Shafiei, G., Vogel, J. W., Smart, K. A., Bearden, C. E., Hoogman, M., Franke, B., van Rooij, D., Buitelaar, J. K., McDonald, C. R., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32420-y · Cited by 130
Karrer, T. M., Kim, J. Z., Stiso, J., Kahn, A. E., Pasqualetti, F., Habel, U., & Bassett, D. S. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab6e8b · Cited by 124
Toward Best Practices for Imaging Transcriptomics of the Human Brain
Arnatkevičiūtė, A., Markello, R. D., Fulcher, B., Mišić, B., & Fornito, A. (2022) · Biological Psychiatry · doi:10.1016/j.biopsych.2022.10.016 · Cited by 119
Dimitriadis, S. I., Salis, C. I., Tarnanas, I., & Linden, D. E. J. (2017) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2017.00028 · Cited by 117
Local vulnerability and global connectivity jointly shape neurodegenerative disease propagation
Zheng, Y., Zhang, Y., Yau, Y. H. C., Zeighami, Y., Larcher, K. M.-H., Mišić, B., & Dagher, A. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000495 · Cited by 119
Seguin, C., Tian, Y., & Zalesky, A. (2020) · Network Neuroscience · doi:10.1162/netn_a_00161 · Cited by 120
Imaging transcriptomics of brain disorders
Arnatkeviciute, A., Fulcher, B. D., Bellgrove, M. A., & Fornito, A. (2022) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2021.10.002 · Cited by 113
Functional brain networks reflect spatial and temporal autocorrelation
Shinn, M., Hu, A., Turner, L., Noble, S., Preller, K. H., Ji, J. L., Moujaes, F., Achard, S., Scheinost, D., Constable, R. T., et al. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01299-3 · Cited by 115
Towards a biologically annotated brain connectome
Bazinet, V., Hansen, J. Y., & Mišić, B. (2023) · Nature reviews. Neuroscience · doi:10.1038/s41583-023-00752-3 · Cited by 118
The nonhuman primate neuroimaging and neuroanatomy project
Hayashi, T., Hou, Y., Glasser, M. F., Autio, J. A., Knoblauch, K., Inoue‐Murayama, M., Coalson, T., Yacoub, E. S., Smith, S. M., Kennedy, H., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117726 · Cited by 110
Graph theoretical modeling of baby brain networks
Zhao, T., Xu, Y., & Yong He, A. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.06.038 · Cited by 108
Fragility and volatility of structural hubs in the human connectome
Gollo, L. L., Roberts, J. A., Cropley, V., Biase, M. A. D., Pantelis, C., Zalesky, A., & Breakspear, M. J. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0188-z · Cited by 105
Self-citationTime-resolved structure-function coupling in brain networks
Liu, Z.-Q., Vázquez-Rodríguez, B., Spreng, R. N., Bernhardt, B. C., Betzel, R. F., & Mišić, B. (2022) · Communications Biology · doi:10.1038/s42003-022-03466-x · Cited by 103
Edges in brain networks: Contributions to models of structure and function
Faskowitz, J., Betzel, R. F., & Sporns, O. (2021) · Network Neuroscience · doi:10.1162/netn_a_00204 · Cited by 110
Cocchi, L., Sale, M. V., Gollo, L. L., Bell, P. T., Nguyen, V. T., Zalesky, A., Breakspear, M. J., & Mattingley, J. B. (2016) · eLife · doi:10.7554/elife.15252 · Cited by 100
Self-citationMechanisms of Connectome Development
Kaiser, M. (2017) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.05.010 · Cited by 99
Zimmermann, J., Perry, A., Breakspear, M. J., Schirner, M., Sachdev, P. S., Wen, W., Kochan, N. A., Mapstone, M., Ritter, P., McIntosh, A. R., et al. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.04.017 · Cited by 102
Self-citationTokariev, A., Roberts, J. A., Zalesky, A., Zhao, X., Vanhatalo, S., Breakspear, M. J., & Cocchi, L. (2019) · Nature Communications · doi:10.1038/s41467-019-10467-8 · Cited by 99
Self-citationRare long-range cortical connections enhance human information processing
Deco, G., Perl, Y. S., Vuust, P., Tagliazucchi, E., Kennedy, H., & Kringelbach, M. L. (2021) · Current Biology · doi:10.1016/j.cub.2021.07.064 · Cited by 95
Brain rhythms define distinct interaction networks with differential dependence on anatomy
Vezoli, J., Vinck, M. A., Bosman, C. A., Bastos, A. M., Lewis, C., Kennedy, H., & Fries, P. (2021) · Neuron · doi:10.1016/j.neuron.2021.09.052 · Cited by 93
Zimmermann, J., Ritter, P., Shen, K., Rothmeier, S., Schirner, M., & McIntosh, A. R. (2016) · Human Brain Mapping · doi:10.1002/hbm.23200 · Cited by 99
Geometric renormalization unravels self-similarity of the multiscale human connectome
Zheng, M., Allard, A., Hagmann, P., Alemán‐Gómez, Y., & Serrano, M. Á. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1922248117 · Cited by 89
Hansen, J. Y., Shafiei, G., Markello, R. D., Smart, K. A., Cox, S. M. L., Nørgaard, M., Beliveau, V., Wu, Y., Gallezot, J., Aumont, É., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.28.466336 · Cited by 82
A Whole-Cortex Probabilistic Diffusion Tractography Connectome
Rosen, B. Q., & Halgren, E. (2021) · eNeuro · doi:10.1523/eneuro.0416-20.2020 · Cited by 82
Hub connectivity, neuronal diversity, and gene expression in the Caenorhabditis elegans connectome
Arnatkevičiūtė, A., Fulcher, B., Pocock, R., & Fornito, A. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005989 · Cited by 79
Towards a fourth spatial dimension of brain activity
Tozzi, A., & Peters, J. F. (2016) · Cognitive Neurodynamics · doi:10.1007/s11571-016-9379-z · Cited by 74
Hansen, J. Y., Shafiei, G., Voigt, K., Liang, E. X., Cox, S. M. L., Leyton, M., Jamadar, S. D., & Mišić, B. (2023) · PLoS Biology · doi:10.1371/journal.pbio.3002314 · Cited by 70
Gradients of connectivity distance are anchored in primary cortex
Oligschläger, S., Huntenburg, J. M., Golchert, J., Lauckner, M. E., Bonnen, T., & Margulies, D. S. (2016) · Brain Structure and Function · doi:10.1007/s00429-016-1333-7 · Cited by 67
Constraints and spandrels of interareal connectomes
Rubinov, M. (2016) · Nature Communications · doi:10.1038/ncomms13812 · Cited by 69
Luppi, A. I., Hansen, J. Y., Adapa, R., Carhart-Harris, R. L., Roseman, L., Timmermann, C., Golkowski, D., Ranft, A., Ilg, R., Jordan, D., et al. (2023) · Science Advances · doi:10.1126/sciadv.adf8332 · Cited by 68
Signal propagation via cortical hierarchies
Vázquez-Rodríguez, B., Liu, Z.-Q., Hagmann, P., & Mišić, B. (2020) · Network Neuroscience · doi:10.1162/netn_a_00153 · Cited by 65
Lawn, T., Howard, M. A., Turkheimer, F., Mišić, B., Deco, G., Martins, D., & Dipasquale, O. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105193 · Cited by 64
Roberts, G. M. P., Perry, A., Lord, A., Frankland, A., Leung, V., Holmes-Preston, E., Lévy, F., Lenroot, R., Mitchell, P. B., & Breakspear, M. J. (2016) · Molecular Psychiatry · doi:10.1038/mp.2016.216 · Cited by 63
Self-citationChen, Y., Wang, S.-J., Hilgetag, C. C., & Zhou, C. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005776 · Cited by 60
Oldham, S., Fulcher, B., Aquino, K. M., Arnatkevičiūtė, A., Paquola, C., Shishegar, R., & Fornito, A. (2022) · Science Advances · doi:10.1126/sciadv.abm6127 · Cited by 62
Wang, M. B., Owen, J. P., Mukherjee, P., & Raj, A. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005550 · Cited by 61
Wirsich, J., Ridley, B., Besson, P., Jirsa, V., Bénar, C., Ranjeva, J., & Guye, M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.055 · Cited by 62
Luppi, A. I., Singleton, S. P., Hansen, J. Y., Jamison, K. W., Bzdok, D., Kuceyeski, A. F., Betzel, R. F., & Mišić, B. (2024) · Nature Biomedical Engineering · doi:10.1038/s41551-024-01242-2 · Cited by 61
Where the genome meets the connectome: Understanding how genes shape human brain connectivity
Arnatkevičiūtė, A., Fulcher, B., Bellgrove, M. A., & Fornito, A. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118570 · Cited by 60
Delettre, C., Messé, A., Dell, L.-A., Foubet, O., Heuer, K., Larrat, B., Meriaux, S., Mangin, J.-F., Reillo, I., de Juan Romero, C., et al. (2019) · Network Neuroscience · doi:10.1162/netn_a_00098 · Cited by 55
Hilgetag, C. C., Beul, S. F., van Albada, S. J., & Goulas, A. (2019) · Network Neuroscience · doi:10.1162/netn_a_00100 · Cited by 53
Wang, Y., Royer, J., Park, B., de Wael, R. V., Larivière, S., Tavakol, S., Rodríguez‐Cruces, R., Paquola, C., Hong, S., Margulies, D. S., et al. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac172 · Cited by 52
Benchmarking methods for mapping functional connectivity in the brain
Liu, Z.-Q., Luppi, A. I., Hansen, J. Y., Tian, Y., Zalesky, A., Yeo, B. T. T., Fulcher, B., & Mišić, B. (2025) · Nature Methods · doi:10.1038/s41592-025-02704-4 · Cited by 55
Network neuroscience for optimizing brain–computer interfaces
Fallani, F. D. V., & Bassett, D. S. (2019) · Physics of Life Reviews · doi:10.1016/j.plrev.2018.10.001 · Cited by 47
Gradients of connectivity distance in the cerebral cortex of the macaque monkey
Oligschläger, S., Xu, T., Bączkowski, B. M., Falkiewicz, M., Falchier, A., Linn, G. S., & Margulies, D. S. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1811-1 · Cited by 45
Beyond modularity: Fine-scale mechanisms and rules for brain network reconfiguration
Khambhati, A. N., Mattar, M. G., Wymbs, N. F., Grafton, S. T., & Bassett, D. S. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.11.015 · Cited by 46
Modularity maximization as a flexible and generic framework for brain network exploratory analysis
Esfahlani, F. Z., Jo, Y., Puxeddu, M. G., Merritt, H., Tanner, J., Greenwell, S. A., Patel, R., Faskowitz, J., & Betzel, R. F. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118607 · Cited by 47
Stochastic synchronization of dynamics on the human connectome
Pang, J. C. S., Gollo, L. L., & Roberts, J. A. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117738 · Cited by 44
Self-citationNormalizing the brain connectome for communication through synchronization
Petkoski, S., & Jirsa, V. (2022) · Network Neuroscience · doi:10.1162/netn_a_00231 · Cited by 44
Does the brain behave like a (complex) network? I. Dynamics
Papo, D., & Buldú, J. M. (2023) · Physics of Life Reviews · doi:10.1016/j.plrev.2023.12.006 · Cited by 44
Assortative mixing in micro-architecturally annotated brain connectomes
Bazinet, V., Hansen, J. Y., de Wael, R. V., Bernhardt, B. C., van den Heuvel, M. P., & Mišić, B. (2023) · Nature Communications · doi:10.1038/s41467-023-38585-4 · Cited by 41
Magrou, L., Barone, P., Markov, N. T., Killackey, H. P., Giroud, P., Berland, M., Knoblauch, K., Dehay, C., & Kennedy, H. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy125 · Cited by 41
Robinson, P. A. (2019) · Physical review. E · doi:10.1103/physreve.99.012421 · Cited by 36
Benchmarking Measures of Network Controllability on Canonical Graph Models
Wu-Yan, E., Betzel, R. F., Tang, E., Gu, S., Pasqualetti, F., & Bassett, D. S. (2018) · Journal of Nonlinear Science · doi:10.1007/s00332-018-9448-z · Cited by 34
Asymmetric signaling across the hierarchy of cytoarchitecture within the human connectome
Parkes, L., Kim, J. Z., Stiso, J., Calkins, M. E., Cieslak, M., Gur, R. E., Gur, R. C., Moore, T. M., Ouellet, M., Roalf, D. R., et al. (2022) · Science Advances · doi:10.1126/sciadv.add2185 · Cited by 34
Gu, Y., Qi, Y., & Gong, P. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006902 · Cited by 33
Fukushima, M., & Sporns, O. (2020) · Communications Biology · doi:10.1038/s42003-020-01331-3 · Cited by 34
The human connectome from an evolutionary perspective
Ardesch, D. J., Scholtens, L. H., & van den Heuvel, M. P. (2019) · Progress in brain research · doi:10.1016/bs.pbr.2019.05.004 · Cited by 33
Xie, K., Royer, J., Rodríguez‐Cruces, R., Horwood, L., Ngo, A., Arafat, T., Auer, H., Sahlas, E., Chen, J., Zhou, Y., et al. (2025) · Advanced Science · doi:10.1002/advs.202406835 · Cited by 33
Characterising brain network topologies: A dynamic analysis approach using heat kernels
Chung, A. W., Schirmer, M. D., Krishnan, M. L., Ball, G., Aljabar, P., Edwards, A. D., & Montana, G. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.07.006 · Cited by 32
Liu, Z.-Q., Shafiei, G., Baillet, S., & Mišić, B. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120276 · Cited by 32
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
Hansen, J. Y., Shafiei, G., Markello, R. D., Smart, K. A., Cox, S. M. L., Wu, Y., Gallezot, J., Aumont, É., Servaes, S., Scala, S. G., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-1040925/v1 · Cited by 30
Hub disruption in patients with chronic neck pain: a graph analytical approach
Pauw, R. D., Aerts, H., Šiugždaitė, R., Meeus, M., Coppieters, I., Caeyenberghs, K., & Cagnie, B. J. (2019) · Pain · doi:10.1097/j.pain.0000000000001762 · Cited by 30
Liang, J., Wang, S.-J., & Zhou, C. (2021) · National Science Review · doi:10.1093/nsr/nwab102 · Cited by 29
Ashourvan, A., Shah, P., Pines, A. R., Gu, S., Lynn, C. W., Bassett, D. S., Davis, K. A., & Litt, B. (2021) · Communications Biology · doi:10.1038/s42003-021-01700-6 · Cited by 30
Hierarchical Complexity of the Macro-Scale Neonatal Brain
Blesa, M., Galdi, P., Cox, S. R., Sullivan, G., Stoye, D. Q., Lamb, G. J., Quigley, A. J., Thrippleton, M. J., Escudero, J., Bastin, M. E., et al. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa345 · Cited by 28
Roberts, G. M. P., Perry, A., Ridgway, K., Leung, V., Campbell, M. E. J., Lenroot, R., Mitchell, P. B., & Breakspear, M. J. (2022) · American Journal of Psychiatry · doi:10.1176/appi.ajp.21010047 · Cited by 26
Self-citationSato, N. (2022) · Scientific Reports · doi:10.1038/s41598-021-04169-9 · Cited by 24
Metadata
- Authors
- Roberts, J. A., Perry, A., Lord, A. R., Roberts, G., Mitchell, P. B., Smith, R. E., Calamante, F., & Breakspear, M.
- Affiliations
- QIMR Berghofer Medical Research Institute (AU)
- Black Dog Institute (AU)
- Florey Institute of Neuroscience and Mental Health (AU)
- The University of Melbourne (AU)
- Corresponding author
- Roberts, James A.
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2016-01
- Volume
- 124
- Issue
- Pt A
- Pages
- 379-393
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W1449393047
- Cited by
- 213
- References
- 81
- Citations by year
- 2026: 13 · 2025: 24 · 2024: 21 · 2023: 22 · 2022: 24 · 2021: 23 · 2020: 16 · 2019: 18 · 2018: 20 · 2017: 14 · 2016: 16
- FWCI
- 10.43
- Type
- journal article
- Language
- en
- Open access
- green
- handle.unsw.edu.au
- Licence
- cc-by-nc-nd
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- UNSWorks (UNSW Sydney) (submittedVersion, cc-by-nc-nd)
- UNSWorks (University of New South Wales, Sydney, Australia) (submittedVersion, cc-by-nc-nd)
- Funders
- National Health and Medical Research Council (1037196, 628952)
- Australian Research Council (CE140100007, FT110100726)
- Victorian Government's Operational Infrastructure Support Grant
- 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 › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- human connectome, brain network topology, spatial embedding, brain geometry, rich club, integration and segregation, network modularity, anterior insula, metabolic cost
- MeSH terms
- Adolescent, Adult, Algorithms, Brain, Data Interpretation, Statistical, Female, Humans, Image Processing, Computer-Assisted, Male, Models, Neurological, Neural Pathways, Diffusion Magnetic Resonance Imaging, Mathematical Concepts, Young Adult, Connectome
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
