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.
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Empirical human tractography graphs exhibit a more peripheral hub network structure than predicted by their corresponding geometric surrogate graphs.
“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)
There is a true falloff of both structural and functional connectivity with distance in a number of mammalian species.
“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
The brain is a spatially embedded network with properties partially inherited from geometry.
“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
The human brain places its strongest nodes further into geometrically peripheral regions than would be expected by weight-distance effects alone.
“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
The number of streamlines (NOS) is a widely used measure to quantify connectivity strength.
“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
The most popular scalar value for connectivity matrix analysis is the NOS, where higher values are considered to indicate stronger connectivity between pairs of gray matter regions.
“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
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Strogatz, S. H. (2001) · Nature · doi:10.1038/35065725 · Cited by 8,236 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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× · see citing works
“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 · see citing works
Specificity and Stability in Topology of Protein Networks
Maslov, S., & Sneppen, K. (2002) · Science · doi:10.1126/science.1065103 · Cited by 3,051 · see citing works
A mesoscale connectome of the mouse brain
Oh, S. W., Harris, J. A., Ng, L., Winslow, B., Cain, N., Mihalas, S., Wang, Q., Lau, C., Kuan, L., Henry, A. M., et al. (2014) · Nature · doi:10.1038/nature13186 · Cited by 2,612 · see citing works
Newman, M. E. J. (2004) · Physical Review E · doi:10.1103/physreve.70.056131 · Cited by 2,483 · see citing works
Barthélemy, M. (2011) · Physics Reports · doi:10.1016/j.physrep.2010.11.002 · Cited by 2,469 · see citing works
Achard, S., Salvador, R., Whitcher, B., Suckling, J., & Bullmore, E. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.3874-05.2006 · Cited by 2,393 · see citing works
Rich-club organization of the human connectome
van den Heuvel, M. P., & Sporns, O. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.3539-11.2011 · Cited by 2,354 · see citing works
Tournier, J., Calamante, F., & Connelly, A. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.02.016 · Cited by 2,191 · see citing works
Self-citationNetworks of the Brain: Quantitative Analysis and Modeling
Sporns, O. (2010) · The MIT Press eBooks · doi:10.7551/mitpress/8476.001.0001 · Cited by 1,750 · see citing works
The connectomics of brain disorders
Fornito, A., Zalesky, A., & Breakspear, M. (2015) · Nature Reviews Neuroscience · doi:10.1038/nrn3901 · Cited by 1,734 · see citing works
Self-citationSchreiber, T., & Schmitz, A. (2000) · Physica D Nonlinear Phenomena · doi:10.1016/s0167-2789(00)00043-9 · Cited by 1,551 · see citing works
Jeurissen, B., Tournier, J.-D., Dhollander, T., Connelly, A., & Sijbers, J. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.07.061 · Cited by 1,484 · see citing works
MRtrix: Diffusion tractography in crossing fiber regions
Tournier, J., Calamante, F., & Connelly, A. (2012) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22005 · Cited by 1,370 · see citing works
Self-citationThe Small World of the Cerebral Cortex
Sporns, O., & Zwi, J. D. (2004) · Neuroinformatics · doi:10.1385/ni:2:2:145 · Cited by 1,356 · see citing works
Modern network science of neurological disorders
Stam, C. J. (2014) · Nature reviews. Neuroscience · doi:10.1038/nrn3801 · Cited by 1,284 · see citing works
Schreiber, T., & Schmitz, A. (1999) · Cited by 1,276 · see citing works
The B‐matrix must be rotated when correcting for subject motion in DTI data
Leemans, A., & Jones, D. K. (2009) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21890 · Cited by 1,266 · see citing works
Smith, R. E., Tournier, J.-D., Calamante, F., & Connelly, A. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.005 · Cited by 1,216 · see citing works
Self-citationWhole-brain anatomical networks: does the choice of nodes matter?
Zalesky, A., Fornito, A., Harding, I. H., Cocchi, L., Yücel, M., Pantelis, C., & Bullmore, E. T. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.027 · Cited by 1,169 · see citing works
Jeurissen, B., Leemans, A., Tournier, J., Jones, D. K., & Sijbers, J. (2013) · Human Brain Mapping · doi:10.1002/hbm.22099 · Cited by 1,088 · see citing works
Detecting rich-club ordering in complex networks
Colizza, V., Flammini, A., Serrano, M. A., & Vespignani, A. (2006) · Nature Physics · doi:10.1038/nphys209 · Cited by 1,001 · see citing works
On the strength of connectedness of a random graph
Erdös, P., & Rényi, A. (1964) · Acta Mathematica Academiae Scientiarum Hungaricae · doi:10.1007/bf02066689 · Cited by 794 · see citing works
High-cost, high-capacity backbone for global brain communication
van den Heuvel, M. P., Kahn, R. S., Goñi, J., & Sporns, O. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1203593109 · Cited by 781 · see citing works
Smith, R. E., Tournier, J., Calamante, F., & Connelly, A. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.06.092 · Cited by 758 · see citing works
Self-citationSIFT: Spherical-deconvolution informed filtering of tractograms
Smith, R. E., Tournier, J.-D., Calamante, F., & Connelly, A. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.11.049 · Cited by 752 · see citing works
Self-citationGraph analysis of the human connectome: Promise, progress, and pitfalls
Fornito, A., Zalesky, A., & Breakspear, M. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.087 · Cited by 743 · see citing works
Self-citationEvidence for hubs in human functional brain networks
Power, J. D., Schlaggar, B. L., Lessov-Schlaggar, C. N., & Petersen, S. E. (2013) · Neuron · doi:10.1016/j.neuron.2013.07.035 · Cited by 716 · see citing works
Raffelt, D., Tournier, J.-D., Rose, S., Ridgway, G., Henderson, R., Crozier, S., Salvado, O., & Connelly, A. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.045 · Cited by 616 · see citing works
Tournier, J., Yeh, C., Calamante, F., Cho, K.-H., Connelly, A., & Lin, C. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.05.002 · Cited by 591 · see citing works
Self-citationEnergetic cost of brain functional connectivity
Tomasi, D. G., Wang, G., & Volkow, N. D. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1303346110 · Cited by 569 · see citing works
Pushing the limits of in vivo diffusion MRI for the Human Connectome Project
Setsompop, K., Kimmlingen, R., Eberlein, E., Witzel, T., Cohen-Adad, J., McNab, J. A., Keil, B., Tisdall, M. D., Hoecht, P., Dietz, P., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.078 · Cited by 545 · see citing works
Field Theory of Electromagnetic Brain Activity
Jirsa, V., & Haken, H. (1996) · Physical Review Letters · doi:10.1103/physrevlett.77.960 · Cited by 487 · see citing works
A Predictive Network Model of Cerebral Cortical Connectivity Based on a Distance Rule
Ercsey-Ravasz, M., Markov, N. T., Lamy, C., Van Essen, D. C., Knoblauch, K., Toroczkai, Z., & Kennedy, H. (2013) · Neuron · doi:10.1016/j.neuron.2013.07.036 · Cited by 487 · see citing works
Propagation and stability of waves of electrical activity in the cerebral cortex
Robinson, P. A., RENNIE, C. J., & Wright, J. J. (1997) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.56.826 · Cited by 486 · see citing works
Cognitive fitness of cost-efficient brain functional networks
Bassett, D. S., Bullmore, E. T., Meyer‐Lindenberg, A., Apud, J. A., Weinberger, D. R., & Coppola, R. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0903641106 · Cited by 429 · see citing works
Robust detection of dynamic community structure in networks
Bassett, D. S., Porter, M. A., Wymbs, N. F., Grafton, S. T., Carlson, J. M., & Mucha, P. J. (2013) · Chaos: An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.4790830 · Cited by 425 · see citing works
Robust detection of dynamic community structure in networks
Bassett, D. S., Porter, M. A., Wymbs, N. F., Grafton, S. T., Carlson, J. M., & Mucha, P. J. (2013) · Oxford University Research Archive (ORA) (University of Oxford) · Cited by 411 · see citing works
Bassett, D. S., Greenfield, D. L., Meyer‐Lindenberg, A., Weinberger, D. R., Moore, S. W., & Bullmore, E. T. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000748 · Cited by 401 · see citing works
Prominence and Control: The Weighted Rich-Club Effect
Opsahl, T., Vittoria Colizza, A., Panzarasa, P., & Ramasco, J. J. (2008) · Physical Review Letters · doi:10.1103/physrevlett.101.168702 · Cited by 395 · see citing works
Langton, C. G. (1990) · Artificial Life · Cited by 373 · see citing works
Simple models of human brain functional networks
Vértes, P. E., Alexander‐Bloch, A. F., Gogtay, N., Giedd, J. N., Rapoport, J. L., & Bullmore, E. T. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1111738109 · Cited by 359 · see citing works
Improving diffusion MRI using simultaneous multi-slice echo planar imaging
Setsompop, K., Cohen‐Adad, J., Gagoski, B. A., Raij, T., Yendiki, A., Keil, B. R., Wedeen, V. J., & Wald, L. L. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.033 · Cited by 357 · see citing works
The Human Connectome Project and beyond: Initial applications of 300mT/m gradients
McNab, J. A., Edlow, B. L., Witzel, T., Huang, S. Y., Bhat, H., Heberlein, K., Feiweier, T., Liu, K., Keil, B. R., Cohen‐Adad, J., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.074 · Cited by 345 · see citing works
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 · see citing works
Genetic Influences on Cost-Efficient Organization of Human Cortical Functional Networks
Fornito, A., Zalesky, A., Bassett, D. S., David Meunier, A., Ellison-Wright, I., Yücel, M., Wood, S. J., Shaw, K., O'Connor, J., Nertney, D. A., et al. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.4858-10.2011 · Cited by 280 · see citing works
Computational analysis of functional connectivity between areas of primate cerebral cortex
Stephan, K. E., Hilgetag, C. C., Burns, G., O’Neill, M. A., Young, M. P., & Kötter, R. (2000) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2000.0552 · Cited by 269 · see citing works
The effects of SIFT on the reproducibility and biological accuracy of the structural connectome
Smith, R. E., Tournier, J.-D., Calamante, F., & Connelly, A. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.10.004 · Cited by 266 · see citing works
Self-citationCollin, G., Sporns, O., Mandl, R. C. W., & van den Heuvel, M. P. (2013) · Cerebral Cortex · doi:10.1093/cercor/bht064 · Cited by 255 · see citing works
A Bayesian framework for global tractography
Jbabdi, S., Woolrich, M. W., Andersson, J., & Behrens, T. E. J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.04.039 · Cited by 251 · see citing works
Wiring cost and topological participation of the mouse brain connectome
Rubinov, M., Ypma, R. J. F., Watson, C., & Bullmore, E. T. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1420315112 · Cited by 219 · see citing works
Perception and self-organized instability
Friston, K. J., Breakspear, M. J., & Deco, G. (2012) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2012.00044 · Cited by 181 · see citing works
Self-citationConstrained Randomization of Time Series Data
Schreiber, T. (1998) · Physical Review Letters · doi:10.1103/physrevlett.80.2105 · Cited by 174 · see citing works
Test your surrogate data before you test for nonlinearity
Kugiumtzis, D. (1999) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.60.2808 · Cited by 133 · see citing works
The Human Cortical White Matter
Braitenberg, V., & Sch√ºz, A. (2002) · Conceptual advances in brain research · doi:10.1201/9780203299296.ch16 · Cited by 112 · see citing works
Coombes, S., Venkov, N. A., Shiau, L., Bojak, I., Liley, D. T. J., & Laing, C. R. (2007) · Physical Review E · doi:10.1103/physreve.76.051901 · Cited by 112 · see citing works
Kaiser, M., Hilgetag, C. C., & Arjen Van Ooyen, B. (2009) · Cerebral Cortex · doi:10.1093/cercor/bhp071 · Cited by 106 · see citing works
Spatiotemporal wavelet resampling for functional neuroimaging data
Breakspear, M. J., Brammer, M. J., Bullmore, E. T., Das, P., & Williams, L. M. (2004) · Human Brain Mapping · doi:10.1002/hbm.20045 · Cited by 104 · see citing works
Self-citationSpatial embedding of structural similarity in the cerebral cortex
Song, H. F., Kennedy, H., & Wang, X. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1414153111 · Cited by 101 · see citing works
Roberts, G. M. P., Green, M. J., Breakspear, M. J., McCormack, C. A., Frankland, A., Wright, A., Lévy, F., Lenroot, R., Chan, H. N., & Mitchell, P. B. (2012) · Biological Psychiatry · doi:10.1016/j.biopsych.2012.11.004 · Cited by 100 · see citing works
Self-citationThe frustrated brain: from dynamics on motifs to communities and networks
Gollo, L. L., & Breakspear, M. J. (2014) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2013.0532 · Cited by 92 · see citing works
Self-citationResolving Structural Variability in Network Models and the Brain
Klimm, F., Bassett, D. S., Carlson, J. M., & Mucha, P. J. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003491 · Cited by 92 · see citing works
Influence of Wiring Cost on the Large-Scale Architecture of Human Cortical Connectivity
Samu, D., Seth, A. K., & Nowotny, T. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003557 · Cited by 92 · see citing works
Geometric Effects on Complex Network Structure in the Cortex
Henderson, J. A., & Robinson, P. A. (2011) · Physical Review Letters · doi:10.1103/physrevlett.107.018102 · Cited by 90 · see citing works
A Unifying Framework for Measuring Weighted Rich Clubs
Alstott, J., Panzarasa, P., Rubinov, M., Bullmore, E. T., & Vértes, P. E. (2014) · Scientific Reports · doi:10.1038/srep07258 · Cited by 79 · see citing works
The organisation of the elderly connectome
Perry, A., Wen, W. X., Lord, A., Thalamuthu, A., Roberts, G. M. P., Mitchell, P. B., Sachdev, P. S., & Breakspear, M. J. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.04.009 · Cited by 59 · see citing works
Self-citationRelations Between the Geometry of Cortical Gyrification and White-Matter Network Architecture
Henderson, J. A., & Robinson, P. A. (2014) · Brain Connectivity · doi:10.1089/brain.2013.0183 · Cited by 52 · see citing works
Henderson, J. A., & Robinson, P. A. (2013) · Brain Connectivity · doi:10.1089/brain.2013.0151 · Cited by 42 · see citing works
Modeling distributed axonal delays in mean-field brain dynamics
Roberts, J. A., & Robinson, P. A. (2008) · Physical Review E · doi:10.1103/physreve.78.051901 · Cited by 39 · see citing works
Self-citationA Dendritic Mechanism for Decoding Traveling Waves: Principles and Applications to Motor Cortex
Heitmann, S., Boonstra, T. W., & Breakspear, M. J. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003260 · Cited by 33 · see citing works
Self-citationGenetic white matter fiber tractography with global optimization
Wu, X., Xu, Q., Xu, L., Zhou, J., Anderson, A. W., & Ding, Z. (2009) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2009.07.032 · Cited by 20 · see citing works
Stochastic geometric network models for groups of functional and structural connectomes
Friedman, E. J., Landsberg, A. S., Owen, J. P., Li, Y., & Mukherjee, P. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.07.039 · Cited by 16 · see citing works
A Unifying Framework for Measuring Weighted Rich Clubs by Integrating Randomized Controls.Preprint
Alstott, J., Panzarasa, P., Rubinov, M., et al. (2014) · arXiv (Cornell University) · Cited by 3 · see citing works
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Dynamic models of large-scale brain activity
Breakspear, M. (2017) · Nature Neuroscience · doi:10.1038/nn.4497 · Cited by 1,239 · see citing works
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 745 · see citing works
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 541 · see citing works
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 511 · see citing works
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 458 · see citing works
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 · see citing works
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 324 · see citing works
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 305 · see citing works
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 · see citing works
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 · see citing works
“[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 · see citing works
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 283 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 218 · see citing works
Null models in network neuroscience
Váša, F., & Mišić, B. (2022) · Nature reviews. Neuroscience · doi:10.1038/s41583-022-00601-9 · Cited by 216 · see citing works
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 214 · see citing works
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 · see citing works
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 · see citing works
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 184 · see citing works
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 · see citing works
Fulcher, B., Arnatkevičiūtė, A., & Fornito, A. (2021) · Nature Communications · doi:10.1038/s41467-021-22862-1 · Cited by 174 · see citing works
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 · see citing works
Kerkman, J. N., Daffertshofer, A., Gollo, L. L., Breakspear, M. J., & Boonstra, T. W. (2018) · Science Advances · doi:10.1126/sciadv.aat0497 · Cited by 169 · see citing works
Self-citationBetzel, R. F., Griffa, A., Hagmann, P., & Mišić, B. (2018) · Network Neuroscience · doi:10.1162/netn_a_00075 · Cited by 165 · see citing works
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 149 · see citing works
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 144 · see citing works
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 143 · see citing works
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 133 · see citing works
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 132 · see citing works
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 125 · see citing works
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 121 · see citing works
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 118 · see citing works
Dimitriadis, S. I., Salis, C. I., Tarnanas, I., & Linden, D. E. J. (2017) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2017.00028 · Cited by 116 · see citing works
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 116 · see citing works
Seguin, C., Tian, Y., & Zalesky, A. (2020) · Network Neuroscience · doi:10.1162/netn_a_00161 · Cited by 114 · see citing works
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 · see citing works
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 109 · see citing works
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 108 · see citing works
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 106 · see citing works
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 104 · see citing works
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 103 · see citing works
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 · see citing works
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 102 · see citing works
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 99 · see citing works
Self-citationMechanisms of Connectome Development
Kaiser, M. (2017) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.05.010 · Cited by 97 · see citing works
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 97 · see citing works
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 96 · see citing works
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 93 · see citing works
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 · see citing works
Zimmermann, J., Ritter, P., Shen, K., Rothmeier, S., Schirner, M., & McIntosh, A. R. (2016) · Human Brain Mapping · doi:10.1002/hbm.23200 · Cited by 92 · see citing works
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 88 · see citing works
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 81 · see citing works
A Whole-Cortex Probabilistic Diffusion Tractography Connectome
Rosen, B. Q., & Halgren, E. (2021) · eNeuro · doi:10.1523/eneuro.0416-20.2020 · Cited by 78 · see citing works
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 75 · see citing works
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 73 · see citing works
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 68 · see citing works
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 66 · see citing works
Constraints and spandrels of interareal connectomes
Rubinov, M. (2016) · Nature Communications · doi:10.1038/ncomms13812 · Cited by 66 · see citing works
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 67 · see citing works
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 64 · see citing works
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 63 · see citing works
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 62 · see citing works
Self-citationChen, Y., Wang, S.-J., Hilgetag, C. C., & Zhou, C. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005776 · Cited by 60 · see citing works
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 59 · see citing works
Wang, M. B., Owen, J. P., Mukherjee, P., & Raj, A. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005550 · Cited by 58 · see citing works
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 58 · see citing works
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 56 · see citing works
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 55 · see citing works
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 · see citing works
Hilgetag, C. C., Beul, S. F., van Albada, S. J., & Goulas, A. (2019) · Network Neuroscience · doi:10.1162/netn_a_00100 · Cited by 52 · see citing works
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 51 · see citing works
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 50 · see citing works
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 · see citing works
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 · see citing works
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 45 · see citing works
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 44 · see citing works
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 43 · see citing works
Self-citationNormalizing the brain connectome for communication through synchronization
Petkoski, S., & Jirsa, V. (2022) · Network Neuroscience · doi:10.1162/netn_a_00231 · Cited by 42 · see citing works
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 42 · see citing works
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 · see citing works
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 39 · see citing works
Robinson, P. A. (2019) · Physical review. E · doi:10.1103/physreve.99.012421 · Cited by 35 · see citing works
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 · see citing works
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 · see citing works
Gu, Y., Qi, Y., & Gong, P. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006902 · Cited by 33 · see citing works
Fukushima, M., & Sporns, O. (2020) · Communications Biology · doi:10.1038/s42003-020-01331-3 · Cited by 33 · see citing works
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 32 · see citing works
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 31 · see citing works
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 31 · see citing works
Liu, Z.-Q., Shafiei, G., Baillet, S., & Mišić, B. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120276 · Cited by 30 · see citing works
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 30 · see citing works
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 · see citing works
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 29 · see citing works
Liang, J., Wang, S.-J., & Zhou, C. (2021) · National Science Review · doi:10.1093/nsr/nwab102 · Cited by 29 · see citing works
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 29 · see citing works
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 26 · see citing works
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 · see citing works
Self-citationSato, N. (2022) · Scientific Reports · doi:10.1038/s41598-021-04169-9 · Cited by 24 · see citing works
- 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 · 2015: 2
- 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
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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 ›
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