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Linking Macroscale Graph Analytical Organization to Microscale Neuroarchitectonics in the Macaque Connectome
Open accessAbstract
Macroscale connectivity of the mammalian brain has been shown to display several characteristics of an efficient communication network architecture. In parallel, at the microscopic scale, histological studies have extensively revealed large interregional variation in cortical neural architectonics. However, how these two “scales” of cerebrum organization are linked remains an open question. Collating and combining data across multiple studies on the cortical cytoarchitecture of the macaque cortex with information on macroscale anatomical wiring derived from tract tracing studies, this study focuses on examining the interplay between macroscale organization of the macaque connectome and microscale cortical neuronal architecture. Our findings show that both macroscale degree as well as the topological role in the overall network are related to the level of neuronal complexity of cortical regions at the microscale, showing (among several effects) a positive overall association between macroscale degree and metrics of microscale pyramidal complexity. Macroscale hub regions, together forming a densely interconnected “rich club,” are noted to display a high level of neuronal complexity, findings supportive of a high level of integrative neuronal processes to occur in these regions. Together, we report on cross-scale observations that jointly suggest that a region's microscale neuronal architecture is tuned to its role in the global brain network.
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“macroscale FC is known to be modulated by interareal correlated gene expression, areal densities of excitatory and inhibitory receptors, and other features of microscale architecture that vary across brain structures (Richiardi et al., 2015; Scholtens et al., 2014; Turk et al., 2016; van den Heuvel et al., 2016b)”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Scholtens et al., 2014
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Cites
Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820
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
The control of the false discovery rate in multiple testing under dependency
Benjamini, Y., & Yekutieli, D. (2001) · The Annals of Statistics · doi:10.1214/aos/1013699998 · Cited by 10,490
Distributed hierarchical processing in the primate cerebral cortex
Felleman, D. J., & Van Essen, D. C. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1 · Cited by 7,783
Objective Criteria for the Evaluation of Clustering Methods
Rand, W. M. (1971) · Journal of the American Statistical Association · doi:10.1080/01621459.1971.10482356 · Cited by 6,493
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
Preface: Cerebral Cortex Has Come of Age
Goldman, P. S., & RAKIC, P. T. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1-a · Cited by 3,537
Specificity and Stability in Topology of Protein Networks
Maslov, S., & Sneppen, K. (2002) · Science · doi:10.1126/science.1065103 · Cited by 3,051
Dynamic functional connectivity: Promise, issues, and interpretations
Hutchison, R. M., Womelsdorf, T., Allen, E. A., Bandettini, P. A., Calhoun, V. D., Corbetta, M., Della Penna, S., Duyn, J. H., Glover, G. H., Gonzalez-Castillo, J., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.079 · Cited by 2,962
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
Research works printed but not linked: 1
- Mantel, 1967, The detection of disease clustering and a generalized regression approach, Cancer Res, 27, 209
Cited by
Sorted by citations
The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture
Fan, L., Li, H., Zhuo, J., Zhang, Y., Wang, J., Chen, L., Yang, Z., Chu, C., Xie, S., Laird, A. R., et al. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw157 · Cited by 3,083
Sydnor, V. J., Larsen, B., Bassett, D. S., Alexander‐Bloch, A. F., Fair, D. A., Liston, C. M., Mackey, A. P., Milham, M. P., Pines, A. R., Roalf, D. R., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.06.016 · Cited by 750
A Large-Scale Circuit Mechanism for Hierarchical Dynamical Processing in the Primate Cortex
Chaudhuri, R., Knoblauch, K., Gariel, M.-A., Kennedy, H., & Wang, X. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.008 · Cited by 618
An active inference theory of allostasis and interoception in depression
Barrett, L. F., Quigley, K. S., & Hamilton, P. (2016) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2016.0011 · Cited by 554
The modular and integrative functional architecture of the human brain
Bertolero, M. A., Yeo, B. T. T., & D’Esposito, M. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1510619112 · Cited by 572
A cross-disorder connectome landscape of brain dysconnectivity
van den Heuvel, M. P., & Sporns, O. (2019) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0177-6 · Cited by 462
Environmental influences on the pace of brain development
Tooley, U. A., Bassett, D. S., & Mackey, A. P. (2021) · Nature reviews. Neuroscience · doi:10.1038/s41583-021-00457-5 · Cited by 488
Grayson, D. S., & Fair, D. A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.079 · Cited by 411
“More to the point, macroscale FC is known to be modulated by interareal correlated gene expression, areal densities of excitatory and inhibitory receptors, and other features of microscale architecture that vary across brain structures (Richiardi et al., 2015; Scholtens et al., 2014; Turk et al., 2016; van den Heuvel…”
BackgroundThe physics of brain network structure, function and control
Lynn, C. W., & Bassett, D. S. (2019) · Nature Reviews Physics · doi:10.1038/s42254-019-0040-8 · Cited by 452
Small-world human brain networks: Perspectives and challenges
Liao, X., Vasilakos, A. V., & Yong He, A. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.03.018 · Cited by 443
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
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
Hierarchical dynamics as a macroscopic organizing principle of the human brain
Raut, R. V., Snyder, A. Z., & Raichle, M. E. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2003383117 · Cited by 307
Macroscopic gradients of synaptic excitation and inhibition in the neocortex
Wang, X. (2020) · Nature reviews. Neuroscience · doi:10.1038/s41583-020-0262-x · Cited by 289
Huntenburg, J. M., Bazin, P., Goulas, A., Tardif, C. L., Villringer, A., & Margulies, D. S. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx030 · Cited by 261
The development of brain network hubs
Oldham, S., & Fornito, A. (2018) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2018.12.005 · Cited by 257
‘Hierarchy’ in the organization of brain networks
Hilgetag, C. C., & Goulas, A. (2020) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2019.0319 · Cited by 239
Virtual Cortical Resection Reveals Push-Pull Network Control Preceding Seizure Evolution
Khambhati, A. N., Davis, K. A., Lucas, T. H., Litt, B., & Bassett, D. S. (2016) · Neuron · doi:10.1016/j.neuron.2016.07.039 · Cited by 212
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
Wagstyl, K., Larocque, S., Cucurull, G., Lepage, C., Cohen, J. P., Bludau, S., Palomero-Gallagher, N., Lewis, L. B., Funck, T., Spitzer, H., et al. (2020) · PLOS Biology · doi:10.1371/journal.pbio.3000678 · Cited by 218
Null models in network neuroscience
Váša, F., & Mišić, B. (2022) · Nature reviews. Neuroscience · doi:10.1038/s41583-022-00601-9 · Cited by 226
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
Dwelling quietly in the rich club: brain network determinants of slow cortical fluctuations
Gollo, L. L., Zalesky, A., Hutchison, R. M., van den Heuvel, M. P., & Breakspear, M. J. (2015) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2014.0165 · Cited by 189
A mechanistic model of connector hubs, modularity and cognition
Bertolero, M. A., Yeo, B. T. T., Bassett, D. S., & D’Esposito, M. (2018) · Nature Human Behaviour · doi:10.1038/s41562-018-0420-6 · Cited by 213
Genetic mapping and evolutionary analysis of human-expanded cognitive networks
Wei, Y., de Lange, S. C., Scholtens, L. H., Watanabe, K., Ardesch, D. J., Jansen, P. R., Savage, J. E., Li, L., Preuss, T. M., Rilling, J. K., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12764-8 · Cited by 179
Self-citationFunctional Connectome of the Fetal Brain
Turk, E., van den Heuvel, M. I., Benders, M. J., de Heus, R., Franx, A., Manning, J. H., Hect, J. L., Hernandez-Andrade, E., Hassan, S. S., Romero, R., et al. (2019) · The Journal of Neuroscience · doi:10.1523/jneurosci.2891-18.2019 · Cited by 176
A mechanistic model of connector hubs, modularity and cognition
Bertolero, M. A., Yeo, B. T. T., Bassett, D. S., & D’Esposito, M. (2018) · RePEc: Research Papers in Economics · Cited by 181
The emergence of functional architecture during early brain development
Keunen, K., Counsell, S. J., & Benders, M. J. N. L. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.047 · Cited by 189
Cortical thickness gradients in structural hierarchies
Wagstyl, K., Ronan, L., Goodyer, I. M., & Fletcher, P. C. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.02.036 · Cited by 169
Cellular correlates of cortical thinning throughout the lifespan
Vidal-Pineiro, D., Parker, N., Shin, J., French, L., Grydeland, H., Jackowski, A. P., Mowinckel, A. M., Patel, Y., Pausova, Z., Salum, G., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-78471-3 · Cited by 154
Developmental mechanisms channeling cortical evolution
Finlay, B. L., & Uchiyama, R. (2015) · Trends in Neurosciences · doi:10.1016/j.tins.2014.11.004 · Cited by 163
Large and fast human pyramidal neurons associate with intelligence
Goriounova, N. A., Heyer, D. B., Wilbers, R., Verhoog, M. B., Giugliano, M., Verbist, C., Obermayer, J., Kerkhofs, A., Smeding, H. M. M., Verberne, M., et al. (2018) · eLife · doi:10.7554/elife.41714 · Cited by 165
Topographic gradients of intrinsic dynamics across neocortex
Shafiei, G., Markello, R. D., de Wael, R. V., Bernhardt, B. C., Fulcher, B., & Mišić, B. (2020) · eLife · doi:10.7554/elife.62116 · Cited by 154
Bertolero, M. A., Yeo, B. T. T., & D’Esposito, M. (2017) · Nature Communications · doi:10.1038/s41467-017-01189-w · Cited by 164
Collin, G., Scholtens, L. H., Kahn, R. S., Hillegers, M. H. J., & van den Heuvel, M. P. (2017) · Biological Psychiatry · doi:10.1016/j.biopsych.2017.06.013 · Cited by 155
Self-citationSchröter, M. S., Charlesworth, P., Kitzbichler, M. G., Paulsen, O., & Bullmore, E. T. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.4259-14.2015 · Cited by 149
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
What We Know About the Brain Structure–Function Relationship
García-Ramó, K. B., & Fernández, C. I. (2018) · Behavioral Sciences · doi:10.3390/bs8040039 · Cited by 152
van den Heuvel, M. P., de Reus, M. A., Feldman Barrett, L., Scholtens, L. H., Coopmans, F. M. T., Schmidt, R., Preuss, T. M., Rilling, J. K., & Li, L. (2015) · Human Brain Mapping · doi:10.1002/hbm.22828 · Cited by 131
Self-citationGenes, Cells and Brain Areas of Intelligence
Goriounova, N. A., & Mansvelder, H. D. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00044 · Cited by 148
Bridging Cytoarchitectonics and Connectomics in Human Cerebral Cortex
van den Heuvel, M. P., Scholtens, L. H., Barrett, L. F., Hilgetag, C. C., & de Reus, M. A. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.2630-15.2015 · Cited by 129
Self-citationFunctional Clusters, Hubs, and Communities in the Cortical Microconnectome
Shimono, M., & Beggs, J. M. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhu252 · Cited by 125
A Predictive Structural Model of the Primate Connectome
Beul, S. F., Barbas, H., & Hilgetag, C. C. (2017) · Scientific Reports · doi:10.1038/srep43176 · Cited by 122
Functional hierarchy underlies preferential connectivity disturbances in schizophrenia
Yang, G. J., Murray, J. D., Wang, X.-J., Glahn, D. C., Pearlson, G. D., Repovs, G., Krystal, J. H., & Anticevic, A. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1508436113 · Cited by 153
The primate connectome in context: Principles of connections of the cortical visual system
Hilgetag, C. C., Medalla, M., Beul, S. F., & Barbas, H. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.04.017 · Cited by 109
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
Shared and Distinct Functional Architectures of Brain Networks Across Psychiatric Disorders
Xia, M., Womer, F. Y., Chang, M., Zhu, Y., Zhou, Q., Edmiston, E. K., Jiang, X., Wei, S., Duan, J., Xu, K., et al. (2018) · Schizophrenia Bulletin · doi:10.1093/schbul/sby046 · Cited by 103
Spatial Embedding Imposes Constraints on Neuronal Network Architectures
Stiso, J., & Bassett, D. S. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.09.007 · Cited by 98
Gradients of neurotransmitter receptor expression in the macaque cortex
Froudist‐Walsh, S., Xu, T., Niu, M., Rapan, L., Zhao, L., Margulies, D. S., Zilles, K., Wang, X., & Palomero‐Gallagher, N. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01351-2 · Cited by 103
Rich-club neurocircuitry: function, evolution, and vulnerability
Griffa, A., & van den Heuvel, M. P. (2018) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2018.20.2/agriffa · Cited by 94
Multiscale examination of cytoarchitectonic similarity and human brain connectivity
Wei, Y., Scholtens, L. H., Turk, E., & van den Heuvel, M. P. (2018) · Network Neuroscience · doi:10.1162/netn_a_00057 · Cited by 88
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
Kuramoto model simulation of neural hubs and dynamic synchrony in the human cerebral connectome
Schmidt, R., LaFleur, K. J. R., de Reus, M. A., van den Berg, L. H., & van den Heuvel, M. P. (2015) · BMC Neuroscience · doi:10.1186/s12868-015-0193-z · Cited by 84
Self-citationSchmidt, R., de Reus, M. A., Scholtens, L. H., van den Berg, L. H., & van den Heuvel, M. P. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.04.005 · Cited by 87
Self-citationStructural Attributes and Principles of the Neocortical Connectome in the Marmoset Monkey
Theodoni, P., Majka, P., Reser, D. H., Wojcik, D. K., Rosa, M. G. P., & Wang, X. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab191 · Cited by 83
Multiscale Neuroscience of Psychiatric Disorders
van den Heuvel, M. P., Scholtens, L. H., & Kahn, R. S. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.05.015 · Cited by 79
Self-citationA Connectomic Hypothesis for the Hominization of the Brain
Changeux, J., Goulas, A., & Hilgetag, C. C. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa365 · Cited by 80
Rich-club in the brain’s macrostructure: Insights from graph theoretical analysis
Kim, D.-J., & Min, B.-K. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.06.039 · Cited by 73
Associated Microscale Spine Density and Macroscale Connectivity Disruptions in Schizophrenia
van den Heuvel, M. P., Scholtens, L. H., de Reus, M. A., & Kahn, R. S. (2015) · Biological Psychiatry · doi:10.1016/j.biopsych.2015.10.005 · Cited by 70
Self-citationThe metabolic costs of cognition
Jamadar, S. D., Behler, A., Deery, H. A., & Breakspear, M. J. (2025) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.11.010 · Cited by 81
Biophysical Modeling of Large-Scale Brain Dynamics and Applications for Computational Psychiatry
Murray, J. D., Demirtaş, M., & Anticevic, A. (2018) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2018.07.004 · Cited by 67
A network engineering perspective on probing and perturbing cognition with neurofeedback
Bassett, D. S., & Khambhati, A. N. (2017) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.13338 · Cited by 64
Neuron density fundamentally relates to architecture and connectivity of the primate cerebral cortex
Beul, S. F., & Hilgetag, C. C. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.01.010 · Cited by 59
Topological organization of connectivity strength in the rat connectome
van den Heuvel, M. P., Scholtens, L. H., & de Reus, M. A. (2015) · Brain Structure and Function · doi:10.1007/s00429-015-0999-6 · Cited by 58
Self-citationHilgetag, C. C., Beul, S. F., van Albada, S. J., & Goulas, A. (2019) · Network Neuroscience · doi:10.1162/netn_a_00100 · Cited by 53
Rich Club Organization and Cognitive Performance in Healthy Older Participants
Baggio, H. C., Segura, B., Junqué, C., de Reus, M. A., Sala‐Llonch, R., & van den Heuvel, M. P. (2015) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00821 · Cited by 54
Meta-connectomics: human brain network and connectivity meta-analyses
Crossley, N. A., Fox, P. T., & Bullmore, E. T. (2016) · Psychological Medicine · doi:10.1017/s0033291715002895 · Cited by 51
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
Connectomics in Schizophrenia: From Early Pioneers to Recent Brain Network Findings
Collin, G., Turk, E., & van den Heuvel, M. P. (2016) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2016.01.002 · Cited by 45
High-Expanding Regions in Primate Cortical Brain Evolution Support Supramodal Cognitive Flexibility
Sneve, M. H., Grydeland, H., Rosa, M. G. P., Paus, T., Chaplin, T. A., Walhovd, K. B., & Fjell, A. M. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy268 · Cited by 41
Schizophrenia Exhibits Bi-directional Brain-Wide Alterations in Cortico-Striato-Cerebellar Circuits
Ji, J. L., Diehl, C., Schleifer, C. H., Tamminga, C. A., Keshavan, M. S., Sweeney, J. A., Clementz, B. A., Hill, S. K., Pearlson, G. D., Yang, G., et al. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy306 · Cited by 44
Multimodal 3D atlas of the macaque monkey motor and premotor cortex
Rapan, L., Froudist‐Walsh, S., Niu, M., Xu, T., Funck, T., Zilles, K., & Palomero‐Gallagher, N. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117574 · Cited by 40
Genetic Mechanisms Underlying the Evolution of Connectivity in the Human Cortex
Schmidt, E. R. E., & Polleux, F. (2022) · Frontiers in Neural Circuits · doi:10.3389/fncir.2021.787164 · Cited by 49
Braga, R. M., & Leech, R. (2015) · The Neuroscientist · doi:10.1177/1073858415585730 · Cited by 42
van den Heuvel, M. P., Scholtens, L. H., Turk, E., Mantini, D., Vanduffel, W., & Barrett, L. F. (2016) · Human Brain Mapping · doi:10.1002/hbm.23229 · Cited by 40
Self-citationEfficient coding in the economics of human brain connectomics
Zhou, D., Lynn, C. W., Cui, Z., Ćirić, R., Baum, G. L., Moore, T. M., Roalf, D. R., Detre, J. A., Gur, R. C., Gur, R. E., et al. (2021) · Network Neuroscience · doi:10.1162/netn_a_00223 · Cited by 39
Sprooten, E., O’Halloran, R., Dinse, J., Lee, W. H., Moser, D. A., Doucet, G. E., Goodman, M. C., Krinsky, H., Paulino, A. J., Rasgon, A., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.10.023 · Cited by 40
Collin, G., de Nijs, J., Pol, H. E. H., Cahn, W., & van den Heuvel, M. P. (2015) · Schizophrenia Research · doi:10.1016/j.schres.2015.03.012 · Cited by 42
Stability of spontaneous, correlated activity in mouse auditory cortex
Betzel, R. F., Wood, K. C., Angeloni, C. F., Geffen, M. N., & Bassett, D. S. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007360 · Cited by 36
Estimating the impact of structural directionality: How reliable are undirected connectomes?
Kale, P., Zalesky, A., & Gollo, L. L. (2018) · Network Neuroscience · doi:10.1162/netn_a_00040 · Cited by 37
Decomposing complex links between the childhood environment and brain structure in school-aged youth
Hong, S., Sisk, L. M., Caballero, C., Mekhanik, A., Roy, A. K., Milham, M. P., & Gee, D. G. (2021) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2021.100919 · Cited by 42
Ypma, R. J. F., & Bullmore, E. T. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005104 · Cited by 35
Functional Connectivity Hubs and Networks in the Awake Marmoset Brain
Belcher, A. M., Yen, C. C., Notardonato, L. D., Ross, T. J., Volkow, N. D., Yang, Y., Stein, E. A., Silva, A. C., & Tomasi, D. G. (2016) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2016.00009 · Cited by 35
Epicentral Disruption of Structural Connectivity in Alzheimer's Disease
Mallio, C. A., Schmidt, R., de Reus, M. A., Vernieri, F., Quintiliani, L., Curcio, G., Beomonte Zobel, B., Quattrocchi, C. C., & van den Heuvel, M. P. (2015) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.12397 · Cited by 39
Self-citationBeul, S. F., Goulas, A., & Hilgetag, C. C. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006550 · Cited by 31
Ball, G., Beare, R. J., & Seal, M. L. (2019) · Human Brain Mapping · doi:10.1002/hbm.24726 · Cited by 27
Early and late development of hub connectivity in the human brain
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Self-citation
Metadata
- Authors
- Scholtens, L. H., Schmidt, R., de Reus, M. A., & van den Heuvel, M. P.
- Affiliations
- University Medical Center Utrecht (NL)
- ORCID
- Scholtens, Lianne H.
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2014-09-03
- Volume
- 34
- Issue
- 36
- Pages
- 12192-12205
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC6608464
- OpenAlex
- W2162763711
- Cited by
- 160
- References
- 86
- Citations by year
- 2026: 2 · 2025: 10 · 2024: 14 · 2023: 10 · 2022: 9 · 2021: 13 · 2020: 10 · 2019: 14 · 2018: 27 · 2017: 14 · 2016: 16 · 2015: 19 · 2014: 2
- FWCI
- 9.48
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (publishedVersion, other-oa)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (#451-12-001)
- 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
- cortical cytoarchitecture, neuronal complexity, pyramidal neurons, network hubs, rich club, brain network topology, neural tracing, cortical connectivity
- MeSH terms
- Animals, Brain, Macaca, Models, Neurological, Connectome
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