The DOI matches its Crossref record; 84 of the 84 references it cites were found in a registry or an index.
Information Processing Architecture of Functionally Defined Clusters in the Macaque Cortex
Open accessAbstract
Computational and empirical neuroimaging studies have suggested that the anatomical connections between brain regions primarily constrain their functional interactions. Given that the large-scale organization of functional networks is determined by the temporal relationships between brain regions, the structural limitations may extend to the global characteristics of functional networks. Here, we explored the extent to which the functional network community structure is determined by the underlying anatomical architecture. We directly compared macaque (Macaca fascicularis) functional connectivity (FC) assessed using spontaneous blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI) to directed anatomical connectivity derived from macaque axonal tract tracing studies. Consistent with previous reports, FC increased with increasing strength of anatomical connection, and FC was also present between regions that had no direct anatomical connection. We observed moderate similarity between the FC of each region and its anatomical connectivity. Notably, anatomical connectivity patterns, as described by structural motifs, were different within and across functional modules: partitioning of the functional network was supported by dense bidirectional anatomical connections within clusters and unidirectional connections between clusters. Together, our data directly demonstrate that the FC patterns observed in resting-state BOLD-fMRI are dictated by the underlying neuroanatomical architecture. Importantly, we show how this architecture contributes to the global organizational principles of both functional specialization and integration.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Interregional FC arises from anatomical projection strengths (Messe et al., 2014; Shen et al., 2012), correlated gene expression (Richiardi et al., 2015), and synaptic receptor densities (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 Shen et al., 2012
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500
Fast unfolding of communities in large networks
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/2008/10/p10008 · Cited by 21,424
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1993) · doi:10.1007/978-1-4899-4541-9 · Cited by 21,230
An Integrative Theory of Prefrontal Cortex Function
Miller, E. K., & Cohen, J. D. (2001) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.24.1.167 · Cited by 12,803
The Brain's Default Network: Anatomy, Function, and Relevance to Disease
Buckner, R. L., Andrews‐Hanna, J. R., & Schacter, D. L. (2008) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1440.011 · Cited by 9,761
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
The organization of the human cerebellum estimated by intrinsic functional connectivity
Buckner, R. L., Krienen, F. M., Castellanos, A., Diaz, J. C., & Yeo, B. T. T. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00339.2011 · Cited by 7,304
The brainweb: phase synchronization and large-scale integration
Varela, F., Lachaux, J.-P., Rodriguez, E., & Martinerie, J. (2001) · Nature Reviews Neuroscience · doi:10.1038/35067550 · Cited by 4,761
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
Functional cartography of complex metabolic networks
Guimerà, R., & Nunes Amaral, L. A. (2005) · Nature · doi:10.1038/nature03288 · Cited by 3,983
Cited by
Sorted by citations
Structural and Functional Brain Networks: From Connections to Cognition
Park, H.-J., & Friston, K. (2013) · Science · doi:10.1126/science.1238411 · Cited by 2,106
Structure and function of complex brain networks
Sporns, O. (2013) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2013.15.3/osporns · Cited by 964
Signature of consciousness in the dynamics of resting-state brain activity
Barttfeld, P., Uhrig, L., Sitt, J. D., Sigman, M., Jarraya, B., & Dehaene, S. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1418031112 · Cited by 670
Linking Structure and Function in Macroscale Brain Networks
Suárez, L. E., Markello, R. D., Betzel, R. F., & Mišić, B. (2020) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2020.01.008 · Cited by 769
Contributions and challenges for network models in cognitive neuroscience
Sporns, O. (2014) · Nature Neuroscience · doi:10.1038/nn.3690 · Cited by 767
Development of structure–function coupling in human brain networks during youth
Baum, G. L., Cui, Z., Roalf, D. R., Ciric, R., Betzel, R. F., Larsen, B., Cieslak, M., Cook, P. A., Xia, C. H., Moore, T. M., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912034117 · Cited by 544
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
Grayson, D. S., & Fair, D. A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.079 · Cited by 411
“Interregional FC arises from anatomical projection strengths (Messe et al., 2014; Shen et al., 2012), correlated gene expression (Richiardi et al., 2015), and synaptic receptor densities (Turk et al., 2016; van den Heuvel et al., 2016b) – properties that, in turn, undergo experience-dependent and…”
Backgroundvan 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
Network-Level Structure-Function Relationships in Human Neocortex
Mišić, B., Betzel, R. F., de Reus, M. A., van den Heuvel, M. P., Berman, M. G., McIntosh, A. R., & Sporns, O. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw089 · Cited by 293
Self-citationProgress and challenges in probing the human brain
Poldrack, R. A., & Farah, M. J. (2015) · Nature · doi:10.1038/nature15692 · Cited by 285
Mapping human brain networks with cortico-cortical evoked potentials
Keller, C. J., Honey, C. J., Mégevand, P., Entz, L., Ulbert, I., & Mehta, A. D. (2014) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2013.0528 · Cited by 257
Imaging structural and functional brain networks in temporal lobe epilepsy
Bernhardt, B. C., Hong, S., Bernasconi, A., & Bernasconi, N. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00624 · Cited by 244
Understanding Structural-Functional Relationships in the Human Brain
Wang, Z., Dai, Z., Gong, G., Zhou, C., & Yong He, A. (2014) · The Neuroscientist · doi:10.1177/1073858414537560 · Cited by 232
Heritability and interindividual variability of regional structure-function coupling
Gu, Z., Jamison, K. W., Sabuncu, M. R., & Kuceyeski, A. F. (2021) · Nature Communications · doi:10.1038/s41467-021-25184-4 · Cited by 213
Multilayer motif analysis of brain networks
Battiston, F., Nicosia, V., Chávez, M., & Latora, V. (2017) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.4979282 · Cited by 197
Identification of optimal structural connectivity using functional connectivity and neural modeling
Deco, G., McIntosh, A. R., Shen, K., Hutchison, R. M., Menon, R. S., Everling, S., Hagmann, P., & Jirsa, V. K. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.4423-13.2014 · Cited by 162
Self-citationGrayson, D. S., Bliss-Moreau, E., Machado, C. J., Bennett, J., Shen, K., Grant, K. A., Fair, D. A., & Amaral, D. G. (2016) · Neuron · doi:10.1016/j.neuron.2016.06.005 · Cited by 172
Self-citationNetwork structure shapes spontaneous functional connectivity dynamics
Shen, K., Hutchison, R. M., Bezgin, G., Everling, S., & McIntosh, A. R. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.4903-14.2015 · Cited by 171
Self-citationRich Club Organization and Intermodule Communication in the Cat Connectome
de Reus, M. A., & van den Heuvel, M. P. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.1448-13.2013 · Cited by 163
Schmidt, M., Bakker, R., Shen, K., Bezgin, G., Diesmann, M., & van Albada, S. J. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006359 · Cited by 157
Self-citationMiranda-Dominguez, O., Mills, B. D., Grayson, D., Woodall, A., Grant, K. A., Kroenke, C. D., & Fair, D. A. (2014) · The Journal of Neuroscience · doi:10.1523/jneurosci.4229-13.2014 · Cited by 130
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
Fukushima, M., Betzel, R. F., He, Y., van den Heuvel, M. P., Zuo, X., & Sporns, O. (2017) · Brain Structure and Function · doi:10.1007/s00429-017-1539-3 · Cited by 137
Prefrontal connectomics: From anatomy to human imaging
Haber, S. N., Liu, H., Seidlitz, J., & Bullmore, E. (2022) · Neuropsychopharmacology · doi:10.1038/s41386-021-01156-6 · Cited by 129
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
Functional Clusters, Hubs, and Communities in the Cortical Microconnectome
Shimono, M., & Beggs, J. M. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhu252 · Cited by 125
Brain Connectivity and Visual Attention
Parks, E. L., & Madden, D. J. (2013) · Brain Connectivity · doi:10.1089/brain.2012.0139 · Cited by 138
Stable long-range interhemispheric coordination is supported by direct anatomical projections
Shen, K., Mišić, B., Cipollini, B., Bezgin, G., Buschkuehl, M., Hutchison, R. M., Jaeggi, S. M., Kross, E. F., Peltier, S., Everling, S., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1503436112 · Cited by 114
Self-citationTowards 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
Straathof, M., Sinke, M. R. T., Dijkhuizen, R. M., & Otte, W. M. (2018) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x18809547 · Cited by 109
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
Self-citationHutchison, R. M., Culham, J. C., Everling, S., Flanagan, J. R., & Gallivan, J. P. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.03.068 · Cited by 97
Self-citationFrontoparietal Functional Connectivity in the Common Marmoset
Ghahremani, M., Hutchison, R. M., Menon, R. S., & Everling, S. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw198 · Cited by 91
Self-citationClauss, J. A., Avery, S. N., & Blackford, J. U. (2015) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2015.03.001 · Cited by 93
Functional connectomics from a “big data” perspective
Xia, M., & Yong He, A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.031 · Cited by 85
Functional network alterations and their structural substrate in drug-resistant epilepsy
Caciagli, L., Bernhardt, B. C., Hong, S., Bernasconi, A., & Bernasconi, N. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00411 · Cited by 84
A macaque connectome for large-scale network simulations in TheVirtualBrain
Shen, K., Bezgin, G., Schirner, M., Ritter, P., Everling, S., & McIntosh, A. R. (2019) · Scientific Data · doi:10.1038/s41597-019-0129-z · Cited by 78
Self-citationLinking Functional Connectivity and Structural Connectivity Quantitatively: A Comparison of Methods
Huang, H., & Ding, M. (2015) · Brain Connectivity · doi:10.1089/brain.2015.0382 · Cited by 93
Fine-grained mapping of mouse brain functional connectivity with resting-state fMRI
Mechling, A. E., Hübner, N. S., Lee, H., Hennig, J., von Elverfeldt, D., & Harsan, L. A. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.03.078 · Cited by 82
An open resource combining multi-contrast MRI and microscopy in the macaque brain
Howard, A. F. D., Huszár, I. N., Smart, A., Cottaar, M., Daubney, G., Hanayik, T., Khrapitchev, A. A., Mars, R. B., Mollink, J., Scott, C., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-39916-1 · Cited by 68
O’Neil, E. B., Hutchison, R. M., McLean, D. A., & Köhler, S. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.02.005 · Cited by 64
Self-citationAn Anatomical Interface between Memory and Oculomotor Systems
Shen, K., Bezgin, G., Selvam, R., McIntosh, A. R., & Ryan, J. D. (2016) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01007 · Cited by 58
Self-citationA set of hub neurons and non-local connectivity features support global brain dynamics in C. elegans
Uzel, K., Kato, S., & Zimmer, M. (2022) · Current Biology · doi:10.1016/j.cub.2022.06.039 · Cited by 54
Hutchison, R. M., & Morton, J. B. (2015) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2015.08.006 · Cited by 54
Self-citationFluctuations between high- and low-modularity topology in time-resolved functional connectivity
Fukushima, M., Betzel, R. F., He, Y., de Reus, M. A., van den Heuvel, M. P., Zuo, X., & Sporns, O. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.044 · Cited by 54
Hosseini, S. M. H., & Kesler, S. R. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.032 · Cited by 59
Hutchison, R. M., Culham, J. C., Flanagan, J. R., Everling, S., & Gallivan, J. P. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.04.068 · Cited by 52
Self-citationHori, Y., Schaeffer, D. J., Gilbert, K. M., Hayrynen, L. K., Cléry, J. C., Gati, J. S., Menon, R. S., & Everling, S. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116241 · Cited by 47
Self-citationDelineating the Macroscale Areal Organization of the Macaque Cortex In Vivo
Xu, T., Falchier, A., Sullivan, E. L., Linn, G. S., Ramirez, J. S. B., Ross, D. A., Feczko, E., Opitz, A., Bagley, J. L., Sturgeon, D., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.03.049 · Cited by 46
Arbabyazd, L., Shen, K., Wang, Z., Hofmann‐Apitius, M., Ritter, P., McIntosh, A. R., Battaglia, D. A., & Jirsa, V. (2021) · eNeuro · doi:10.1523/eneuro.0475-20.2021 · Cited by 44
Self-citationStructural connectivity of the developing human amygdala
Saygin, Z. M., Osher, D. E., Koldewyn, K., Martin, R. E., Finn, A., Saxe, R., Gabrieli, J. D. E., & Sheridan, M. (2015) · PLoS ONE · doi:10.1371/journal.pone.0125170 · Cited by 45
Cai, D.-C., Wang, Z., Bo, T., Yan, S., Liu, Z., Liu, Z., Zeljic, K., Chen, X., Zhan, Y., Du, Y., et al. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.2727-19.2020 · Cited by 39
Stephan, K. E. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.03.016 · Cited by 39
Babapoor-Farrokhran, S., Hutchison, R. M., Gati, J. S., Menon, R. S., & Everling, S. (2013) · Journal of Neurophysiology · doi:10.1152/jn.01000.2012 · Cited by 34
Self-citationPace-Schott, E. F., & Picchioni, D. (2016) · Elsevier eBooks · doi:10.1016/b978-0-323-24288-2.00051-9 · Cited by 37
Ye, Q., & Bai, F. (2018) · Stroke and Vascular Neurology · doi:10.1136/svn-2017-000080 · Cited by 33
Commisso, B., Ding, L., Varadi, K., Gorges, M., Bayer, D., Boeckers, T. M., Ludolph, A. C., Kassubek, J., Müller, O. J., & Roselli, F. (2018) · eLife · doi:10.7554/elife.36892 · Cited by 33
Shen, K., Goulas, A., Grayson, D. S., Eusebio, J., Gati, J. S., Menon, R. S., McIntosh, A. R., & Everling, S. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.02.018 · Cited by 36
Self-citationBezgin, G., Solodkin, A., Bakker, R., Ritter, P., & McIntosh, A. R. (2017) · Human Brain Mapping · doi:10.1002/hbm.23506 · Cited by 30
Self-citationBrain network motifs are markers of loss and recovery of consciousness
Duclos, C., Nadin, D., Mahdid, Y., Tarnal, V., Picton, P., Vanini, G., Golmirzaie, G., Janke, E., Avidan, M. S., Kelz, M. B., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-83482-9 · Cited by 33
Chan, S. Y., Ong, Z. Y., Ngoh, Z. M., Chong, Y. S., Zhou, J. H., Fortier, M. V., Daniel, L. M., Qiu, A., Meaney, M. J., & Tan, A. P. (2022) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2022.101107 · Cited by 28
Identifying topological motif patterns of human brain functional networks
Wei, Y., Liao, X., Yan, C., Yong He, A., & Xia, M. (2017) · Human Brain Mapping · doi:10.1002/hbm.23557 · Cited by 28
A Riemannian approach to predicting brain function from the structural connectome
Benkarim, O. M., Paquola, C., Park, B., Royer, J., Rodríguez‐Cruces, R., de Wael, R. V., Mišić, B., Piella, G., & Bernhardt, B. C. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119299 · Cited by 27
Broad intrinsic functional connectivity boundaries of the macaque prefrontal cortex
Hutchison, R. M., & Everling, S. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.11.024 · Cited by 26
Self-citationMotifs for Processes on Networks
Schwarze, A. C., & Porter, M. A. (2021) · SIAM Journal on Applied Dynamical Systems · doi:10.1137/20m1361602 · Cited by 32
Froudist‐Walsh, S., Browning, P. G. F., Young, J., Murphy, K. L., Mars, R. B., Fleysher, L., & Croxson, P. L. (2018) · eLife · doi:10.7554/elife.34354 · Cited by 23
Straathof, M., Sinke, M. R. T., Roelofs, T. J. M., Blezer, E. L. A., Sarabdjitsingh, R. A., van der Toorn, A., Schmitt, O., Otte, W. M., & Dijkhuizen, R. M. (2020) · Scientific Reports · doi:10.1038/s41598-019-56834-9 · Cited by 25
Age of Childhood Onset in Type 1 Diabetes and Functional Brain Connectivity in Midlife
Ryan, J. P., Aizenstein, H. J., Orchard, T. J., Ryan, C. M., Saxton, J. A., Fine, D. F., Nunley, K. A., & Rosano, C. (2015) · Psychosomatic Medicine · doi:10.1097/psy.0000000000000206 · Cited by 23
Sharma, S., Mantini, D., Vanduffel, W., & Nelissen, K. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.02.045 · Cited by 21
Thomas, F., Moulier, V., Valero‐Cabré, A., & Januel, D. (2016) · Revue Neurologique · doi:10.1016/j.neurol.2016.09.001 · Cited by 21
Network topology of the marmoset connectome
Liu, Z.-Q., Zheng, Y., & Mišić, B. (2020) · Network Neuroscience · doi:10.1162/netn_a_00159 · Cited by 19
Resting state functional atlas and cerebral networks in mouse lemur primates at 11.7 Tesla
Garin, C. M., Nadkarni, N. A., Landeau, B., Chételat, G., Picq, J., Bougacha, S., & Dhénain, M. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117589 · Cited by 19
Lee, D., Knight, E. Q., Song, H., Lee, S., Pae, C., Yoo, S., & Park, H. (2021) · PLoS ONE · doi:10.1371/journal.pone.0260295 · Cited by 20
Koval, M. J., Hutchison, R. M., Lomber, S. G., & Everling, S. (2013) · Journal of Neurophysiology · doi:10.1152/jn.00626.2013 · Cited by 21
Self-citationMapping the structure-function relationship along macroscale gradients in the human brain
Collins, E., Chishti, O., Obaïd, S., McGrath, H., King, A., Shen, X., Arora, J., Papademetris, X., Constable, R. T., Spencer, D. D., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-51395-6 · Cited by 17
Kwak, Y. B., Cho, K. I. K., Hwang, W. J., Kim, A., Ha, M., Park, H., Lee, J., Lee, T. Y., Kim, M., & Kwon, J. S. (2021) · Scientific Reports · doi:10.1038/s41598-021-99170-7 · Cited by 16
Grayson, D. S., Bliss‐Moreau, E., Bennett, J., Lavenex, P., & Amaral, D. G. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx080 · Cited by 14
Good, T. J., Schirner, M., Shen, K., Ritter, P., Mukherjee, P., Levine, B., & McIntosh, A. R. (2022) · eNeuro · doi:10.1523/eneuro.0075-21.2022 · Cited by 14
Self-citationArbabyazd, L., Shen, K., Wang, Z., Hofmann-Apitius, M., Ritter, P., Initiative, T. A. D. N., McIntosh, A. R., Battaglia, D. A., & Jirsa, V. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.18.911248 · Cited by 14
Self-citationKang, Y., Chen, R.-T., Ding, H., Li, L., Gao, J., Liu, L., & Zhang, Y. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.915164 · Cited by 13
Cortical connectivity is embedded in resting state at columnar resolution
Card, N. S., & Gharbawie, O. A. (2022) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2022.102263 · Cited by 11
Jia, S., Zuo, R., Zhang, T., Liu, H., & Xu, B. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746157 · Cited by 11
Zhang, W., Braden, B. B., Miranda, G., Shu, K., Wang, S., Liu, H., & Wang, Y. (2021) · Neuroinformatics · doi:10.1007/s12021-021-09523-w · Cited by 9
Neudecker, V., Perez‐Zoghbi, J. F., Miranda-Domínguez, Ó., Schenning, K. J., Ramirez, J. S. B., Mitchell, A. J., Perrone, A. J., Earl, E., Carpenter, S., Martin, L. D., et al. (2023) · British Journal of Anaesthesia · doi:10.1016/j.bja.2023.07.031 · Cited by 10
Multiregional integration in the brain during resting-state fMRI activity
Hay, E., Ritter, P., Lobaugh, N. J., & McIntosh, A. R. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005410 · Cited by 8
Self-citationPronold, J., Morales-Gregorio, A., Rostami, V., & van Albada, S. J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.30.577979 · Cited by 9
Stability of Network Communities as a Function of Task Complexity
Fuertinger, S., & Simonyan, K. (2016) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01026 · Cited by 6
Wu, X., Guo, Y., Xue, J., Dong, Y., Sun, Y., Wang, B., Xiang, J., & Liu, Y. (2023) · Brain Sciences · doi:10.3390/brainsci13091331 · Cited by 6
Neuroimaging: Structural, Functional, Pharmacological, Bioelementology and Nutritionology
Pyatin, V. F., Maslova, O., Romanchuk, N., Volobuev, A. N., Bulgakova, S. V., Romanov, D. V., & Sirotko, I. I. (2021) · Bulletin of Science and Practice · doi:10.33619/2414-2948/71/18 · Cited by 5
Wang, Y., Wei, J., Yan, Y., Wang, M., Fan, H., Du, Y., Yang, X., Ma, X., & Ma, X. (2025) · Neuropsychiatric Disease and Treatment · doi:10.2147/ndt.s515540 · Cited by 4
Bezgin, G., Rybacki, K., van Opstal, A. V., Bakker, R., Shen, K., Vakorin, V. A., Mcintosh, A. R., & Kötter, R. (2014) · Brain and Language · doi:10.1016/j.bandl.2014.05.006 · Cited by 3
Self-citationJia, S., Zhang, T., Zuo, R., & Xu, B. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1132269 · Cited by 3
Construction of a multi-scale spiking model of macaque visual cortexPreprint
Schmidt, M., Bakker, R., Diesmann, M., & van Albada, S. J. (2015) · arXiv (Cornell University) · Cited by 3
Functional vs anatomical cortico-striatal connectivity in the macaque brain
Tang, W., Monko, M. E., Liu, Z. S.-J., Manea, A. M. G., Aguilar-Ortega, F., Hart, D. W., Zhou, J., Zimmermann, J., & Heilbronner, S. R. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03757-x · Cited by 2
A New Hope: A better ICM to understand human cognitive architectural variability
Poirier, P., & Faucher, L. (2020) · Synthese · doi:10.1007/s11229-020-02739-4 · Cited by 2
Sisk, L. M., Keding, T. J., Drew, A., Ma, E., Shafiei, G., Cieslak, M., Satterthwaite, T. D., & Gee, D. G. (2026) · Journal of Neuroscience · doi:10.1523/jneurosci.2327-25.2026 · Cited by 1
Li, B., Chen, X., Yuan, Y., & Wang, Y. (2018) · International Conference on Signal Processing · doi:10.1109/icsp.2018.8652368 · Cited by 1
Hashemi, M. (2015) · Scholarship@Western (Western University) · Cited by 1
Ouyang, M., Fields, C. R., Zhu, T., & Huang, H. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-85280-7.00018-x · Cited by 1
Metadata
- Authors
- Shen, K., Bezgin, G., Hutchison, R. M., Gati, J. S., Menon, R. S., Everling, S., & McIntosh, A. R.
- Affiliations
- Rotman Research Institute (CA)
- Western University (CA)
- University of Toronto (CA)
- ORCID
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2012-11-28
- Volume
- 32
- Issue
- 48
- Pages
- 17465-17476
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC6621872
- OpenAlex
- W2057895125
- Cited by
- 123
- References
- 84
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 5 · 2023: 6 · 2022: 7 · 2021: 13 · 2020: 9 · 2019: 7 · 2018: 7 · 2017: 9 · 2016: 9 · 2015: 16 · 2014: 14 · 2013: 10
- FWCI
- 6.54
- 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)
- Funders
- James S. McDonnell Foundation
- Canadian Institutes of Health Research
- 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
- functional connectivity, structural connectivity, resting-state networks, community structure, macaque cortex, structural motifs, functional modules, brain network organization
- MeSH terms
- Animals, Brain Mapping, Cerebral Cortex, Female, Macaca fascicularis, Magnetic Resonance Imaging, Male, Models, Neurological, Nerve Net, Neurons, Synapses
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