The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Dynamical consequences of lesions in cortical networks
Open accessAbstract
To understand the effects of a cortical lesion it is necessary to consider not only the loss of local neural function, but also the lesion‐induced changes in the larger network of endogenous oscillatory interactions in the brain. To investigate how network embedding influences a region's functional role, and the consequences of its being damaged, we implement two models of oscillatory cortical interactions, both of which inherit their coupling architecture from the available anatomical connection data for macaque cerebral cortex. In the first model, node dynamics are governed by Kuramoto phase oscillator equations, and we investigate the sequence in which areas entrain one another in the transition to global synchrony. In the second model, node dynamics are governed by a more realistic neural mass model, and we assess long‐run inter‐regional interactions using a measure of directed information flow. Highly connected parietal and frontal areas are found to synchronize most rapidly, more so than equally highly connected visual and somatosensory areas, and this difference can be explained in terms of the network's clustered architecture. For both models, lesion effects extend beyond the immediate neighbors of the lesioned site, and the amplitude and dispersal of nonlocal effects are again influenced by cluster patterns in the network. Although the consequences of in vivo lesions will always depend on circuitry local to the damaged site, we conclude that lesions of parietal regions (especially areas 5 and 7a) and frontal regions (especially areas 46 and FEF) have the greatest potential to disrupt the integrative aspects of neocortical function. Hum Brain Mapp, 2008. © 2008 Wiley‐Liss, Inc.
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?
In the citing paper
“simulation experiments using structure-function models have predicted that even purely focal and transient perturbations in activity should result in diffuse network reorganization that affects distant brain systems (Alstott et al., 2009; Honey and Sporns, 2008; Misic et al., 2015).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Honey and Sporns, 2008
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
Control of goal-directed and stimulus-driven attention in the brain
Corbetta, M., & Shulman, G. L. (2002) · Nature Reviews Neuroscience · doi:10.1038/nrn755 · Cited by 12,881
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
Neuronal Oscillations in Cortical Networks
Buzsáki, G., & Draguhn, A. (2004) · Science · doi:10.1126/science.1099745 · Cited by 6,576
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
Measuring Information Transfer
Schreiber, T. (2000) · Physical Review Letters · doi:10.1103/physrevlett.85.461 · Cited by 4,571
Dynamic predictions: Oscillations and synchrony in top–down processing
Engel, A. K., Fries, P., & Singer, W. (2001) · Nature Reviews Neuroscience · doi:10.1038/35094565 · Cited by 3,454
Voltage oscillations in the barnacle giant muscle fiber
Morris, C., & Lecar, H. (1981) · Biophysical Journal · doi:10.1016/s0006-3495(81)84782-0 · Cited by 2,302
Network structure of cerebral cortex shapes functional connectivity on multiple time scales
Honey, C. J., Kötter, R., Breakspear, M. J., & Sporns, O. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0701519104 · Cited by 1,727
Self-citationMapping complex tissue architecture with diffusion spectrum magnetic resonance imaging
Wedeen, V. J., Hagmann, P., Tseng, W. I., Reese, T. G., & Weisskoff, R. M. (2005) · Magnetic Resonance in Medicine · doi:10.1002/mrm.20642 · Cited by 1,339
Normal and pathological oscillatory communication in the brain
Schnitzler, A., & Groß, J. (2005) · Nature reviews. Neuroscience · doi:10.1038/nrn1650 · Cited by 1,283
Flagged first, then by citations
Research works printed but not linked: 1
- Kuramoto Y, 1975, Lecture Notes in Physics No. 30, 420
Cited by
Sorted by citations
Items 101–200 of 354
An optimal strategy for epilepsy surgery: Disruption of the rich-club?
Lopes, M. A., Richardson, M. P., Abela, E., Rummel, C., Schindler, K. A., Goodfellow, M., & Terry, J. R. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005637 · Cited by 88
Dissociable Network Properties of Anesthetic State Transitions
Lee, U., Müller, M., Noh, G., Choi, B., & Mashour, G. A. (2011) · Anesthesiology · doi:10.1097/aln.0b013e31821102c9 · Cited by 90
Occipital White Matter Tracts in Human and Macaque
Takemura, H., Pestilli, F., Weiner, K. S., Keliris, G. A., Landi, S. M., Sliwa, J., Ye, F. Q., Barnett, M., Leopold, D. A., Freiwald, W. A., et al. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx070 · Cited by 87
The human endogenous attentional control network includes a ventro-temporal cortical node
Sani, I., Stemmann, H., Caron, B. A., Bullock, D. N., Stemmler, T., Fahle, M. W., Pestilli, F., & Freiwald, W. A. (2021) · Nature Communications · doi:10.1038/s41467-020-20583-5 · Cited by 93
Kraft, R. H., Mckee, P. J., Dagro, A. M., & Grafton, S. T. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002619 · Cited by 88
Ruff, R. M., & Jamora, C. W. (2011) · Wiley eBooks · doi:10.1002/9781444395150.ch5 · Cited by 87
Cerebral Energy Metabolism and the Brain’s Functional Network Architecture: An Integrative Review
Lord, L.-D., Expert, P., Huckins, J. F., & Turkheimer, F. (2013) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1038/jcbfm.2013.94 · Cited by 86
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
Characterizing the role of the structural connectome in seizure dynamics
Shah, P., Ashourvan, A., Mikhail, F., Pines, A. R., Kini, L. G., Oechsel, K. F., Das, S. R., Stein, J. M., Shinohara, R. T., Bassett, D. S., et al. (2019) · Brain · doi:10.1093/brain/awz125 · Cited by 94
Nakagawa, T., Jirsa, V., Spiegler, A., McIntosh, A. R., & Deco, G. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.055 · Cited by 87
Petkoski, S., Palva, J. M., & Jirsa, V. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006160 · Cited by 88
Role of Network Science in the Study of Anesthetic State Transitions
Lee, U., & Mashour, G. A. (2018) · Anesthesiology · doi:10.1097/aln.0000000000002228 · Cited by 83
Human hippocampal CA3 damage disrupts both recent and remote episodic memories
Miller, T. D., Chong, T. T. ‐., Davies, A. M. A., Johnson, M. R., Irani, S. R., Husain, M., Ng, T. W. C., Jacob, S., Maddison, P., Kennard, C., et al. (2020) · eLife · doi:10.7554/elife.41836 · Cited by 85
de Haan, W., van Straaten, E. C. W., Gouw, A. A., & Stam, C. J. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005707 · Cited by 78
Longitudinal Effects of Lesions on Functional Networks after Stroke
Ovadia‐Caro, S., Villringer, K., Fiebach, J. B., Jungehulsing, G. J., van der Meer, E. E., Margulies, D. S., & Villringer, A. (2013) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1038/jcbfm.2013.80 · Cited by 83
van Dellen, E., Hillebrand, A., Douw, L., Heimans, J. J., Reijneveld, J. C., & Stam, C. J. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.06.009 · Cited by 76
Fellrath, J., Mottaz, A., Schnider, A., Guggisberg, A. G., & Ptak, R. (2016) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2016.07.012 · Cited by 77
Weng, L., Xie, Q., Zhao, L., Zhang, R., Ma, Q., Wang, J., Jiang, W., Yan-bin, H., Chen, Y., Li, C., et al. (2017) · Cortex · doi:10.1016/j.cortex.2017.02.011 · Cited by 79
Recovery of neural dynamics criticality in personalized whole-brain models of stroke
Rocha, R. P., Koçillari, L., Suweis, S. S., Grazia, M. D. F. D., de Schotten, M. T., Zorzi, M., & Corbetta, M. (2022) · Nature Communications · doi:10.1038/s41467-022-30892-6 · Cited by 79
Opportunities and Challenges for Psychiatry in the Connectomic Era
Fornito, A., Bullmore, E. T., & Zalesky, A. (2017) · Biological Psychiatry: Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2016.08.003 · Cited by 74
Zink, N., Lenartowicz, A., & Markett, S. A. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.02.011 · Cited by 87
Reber, J., Hwang, K., Bowren, M. D., Bruss, J., Mukherjee, P., Tranel, D., & Boes, A. D. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2018784118 · Cited by 77
Cocchi, L., Sale, M. V., Lord, A., Zalesky, A., Breakspear, M. J., & Mattingley, J. B. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00850.2014 · Cited by 74
Associative white matter connecting the dorsal and ventral posterior human cortex
Bullock, D. N., Takemura, H., Caiafa, C. F., Kitchell, L. M., McPherson, B., Caron, B. A., & Pestilli, F. (2019) · Brain Structure and Function · doi:10.1007/s00429-019-01907-8 · Cited by 72
The impact of ischemic stroke on connectivity gradients
Bayrak, Ş., Khalil, A. A., Villringer, K., Fiebach, J. B., Villringer, A., Margulies, D. S., & Ovadia‐Caro, S. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101947 · Cited by 73
Luppi, A. I., Mediano, P. A. M., Rosas, F. E., Allanson, J. E., Pickard, J. D., Williams, G. B., Craig, M. M., Finoia, P., Peattie, A. R. D., Coppola, P., et al. (2022) · Communications Biology · doi:10.1038/s42003-022-03330-y · Cited by 79
Chen, Y., Wang, S.-J., Hilgetag, C. C., & Zhou, C. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005776 · Cited by 60
Yang, C., Xiao, K., Ao, Y., Cui, Q., Jing, X., & Wang, Y. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.103295 · Cited by 62
Multi-Dimensional Dynamics of Human Electromagnetic Brain Activity
Kida, T., Tanaka, E., & Kakigi, R. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00713 · Cited by 67
Huo, C., Xu, G., Li, Z., Lv, Z., Liu, Q., Li, W., Ma, H., Wang, D., & Fan, Y. (2019) · Scientific Reports · doi:10.1038/s41598-019-42674-0 · Cited by 59
The Value of Resting-State Functional Magnetic Resonance Imaging in Stroke
Ovadia‐Caro, S., Margulies, D. S., & Villringer, A. (2014) · Stroke · doi:10.1161/strokeaha.114.003689 · Cited by 63
Larivière, S., de Wael, R. V., Paquola, C., Hong, S., Mišić, B., Bernasconi, N., Bernasconi, A., Bonilha, L., & Bernhardt, B. C. (2018) · Brain Connectivity · doi:10.1089/brain.2018.0587 · Cited by 51
The Diversity and Multiplexity of Edge Communities within and between Brain Systems
Jo, Y., Esfahlani, F. Z., Faskowitz, J., Chumin, E. J., Sporns, O., & Betzel, R. F. (2020) · Cell Reports · doi:10.1016/j.celrep.2021.110032 · Cited by 59
Self-citationDynamic Mechanisms of Neocortical Focal Seizure Onset
Wang, Y., Goodfellow, M., Taylor, P. N., & Baier, G. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003787 · Cited by 58
Topographic Hub Maps of the Human Structural Neocortical Network
Nijhuis, E. H. J., van Walsum, A. V. C., & Norris, D. G. (2013) · PLoS ONE · doi:10.1371/journal.pone.0065511 · Cited by 54
Neuroanatomical correlates of stroke-associated infection and stroke-induced immunodepression
Urra, X., Laredo, C., Zhao, Y., Amaro, S., Rudilosso, S., Renú, A., Prats‐Galino, A., Planas, A. M., Oleaga, L., & Chamorro, Á. (2016) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2016.10.004 · Cited by 53
Sani, I., McPherson, B., Stemmann, H., Pestilli, F., & Freiwald, W. A. (2019) · eLife · doi:10.7554/elife.40520 · Cited by 52
neuroVIISAS: Approaching Multiscale Simulation of the Rat Connectome
Schmitt, O., & Eipert, P. (2012) · Neuroinformatics · doi:10.1007/s12021-012-9141-6 · Cited by 51
Systematic network lesioning reveals the core white matter scaffold of the human brain
Andrei Irimia, A., & Horn, J. D. V. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00051 · Cited by 51
The dynamical renaissance in neuroscience
Favela, L. H. (2020) · Synthese · doi:10.1007/s11229-020-02874-y · Cited by 55
Towards a large-scale model of patient-specific epileptic spike-wave discharges
Taylor, P. N., Goodfellow, M., Wang, Y., & Baier, G. (2012) · Biological Cybernetics · doi:10.1007/s00422-012-0534-2 · Cited by 49
Learning, Memory, and the Role of Neural Network Architecture
Hermundstad, A. M., Brown, K., Bassett, D. S., & Carlson, J. M. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002063 · Cited by 52
EEG topographies provide subject-specific correlates of motor control
Pirondini, E., Coscia, M., Minguillón, J., Millán, J. D. R., Ville, D. V. D., & Micera, S. (2017) · Scientific Reports · doi:10.1038/s41598-017-13482-1 · Cited by 49
Cacciola, A., Naro, A., Milardi, D., Bramanti, A., Malatacca, L., Spitaleri, M., Leo, A., Muscoloni, A., Cannistraci, C. V., Bramanti, P., et al. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8030306 · Cited by 55
Lee, J., Park, E., Lee, A., Chang, W. H., Kim, D., & Kim, Y. (2018) · Brain Topography · doi:10.1007/s10548-018-0644-9 · Cited by 48
Homeostatic plasticity and emergence of functional networks in a whole-brain model at criticality
Rocha, R. P., Koçillari, L., Suweis, S., Corbetta, M., & Maritan, A. (2018) · Scientific Reports · doi:10.1038/s41598-018-33923-9 · Cited by 50
Cortical Resonance Frequencies Emerge from Network Size and Connectivity
Lea‐Carnall, C., Montemurro, M. A., Trujillo‐Barreto, N. J., Parkes, L. M., & El‐Deredy, W. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004740 · Cited by 49
Predicting the Response to Non-invasive Brain Stimulation in Stroke
Ovadia‐Caro, S., Khalil, A. A., Sehm, B., Villringer, A., Nikulin, V. V., & Nazarova, M. A. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.00302 · Cited by 49
Breakdown of specific functional brain networks in clinical variants of Alzheimer’s disease
Pini, L., Wennberg, A. M. V., Salvalaggio, A., Vallesi, A., Pievani, M., & Corbetta, M. (2021) · Ageing Research Reviews · doi:10.1016/j.arr.2021.101482 · Cited by 48
Investigating the concept of representation in the neural and psychological sciences
Favela, L. H., & Machery, É. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1165622 · Cited by 59
Kim, M., Kim, S., Mashour, G. A., & Lee, U. (2017) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2017.00055 · Cited by 49
Altered dynamics of brain segregation and integration in poststroke aphasia
Guo, J., Biswal, B. B., Han, S., Li, J., Yang, S., Yang, M., & Chen, H. (2019) · Human Brain Mapping · doi:10.1002/hbm.24605 · Cited by 45
Lynch, C. J., Breeden, A. L., Gordon, E. M., Cherry, J. B. C., Turkeltaub, P. E., & Vaidya, C. J. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy270 · Cited by 47
The road ahead in clinical network neuroscience
Douw, L., van Dellen, E., Gouw, A. A., Griffa, A., de Haan, W., van den Heuvel, M. P., Hillebrand, A., Mieghem, P. V., Nissen, I. A., Otte, W. M., et al. (2019) · Network Neuroscience · doi:10.1162/netn_a_00103 · Cited by 48
Kuceyeski, A. F., Shah, S. A., Dyke, J. P., Bickel, S., Abdelnour, F., Schiff, N. D., Voss, H. U., & Raj, A. (2016) · NeuroImage Clinical · doi:10.1016/j.nicl.2016.04.006 · Cited by 45
Immediate and Longitudinal Alterations of Functional Networks after Thalamotomy in Essential Tremor
Jang, C., Park, H., Chang, W. S., Pae, C., & Chang, J. W. (2016) · Frontiers in Neurology · doi:10.3389/fneur.2016.00184 · Cited by 48
Kringelbach, M. L., Perl, Y. S., & Deco, G. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.03.009 · Cited by 46
Lord, L.-D., Allen, P., Expert, P., Howes, O. D., Broome, M. R., Lambiotte, R., Fusar‐Poli, P., Valli, I., McGuire, P. K., & Turkheimer, F. (2012) · NeuroImage Clinical · doi:10.1016/j.nicl.2012.09.008 · Cited by 48
Tao, Y., Ficek, B., Rapp, B. C., & Tsapkini, K. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.09.007 · Cited by 46
Sporns, O. (2009) · Elsevier eBooks · doi:10.1016/b978-0-12-374709-9.00014-6 · Cited by 46
Self-citationMapping correlated neurological deficits after stroke to distributed brain networks
Siegel, J. S., Shulman, G. L., & Corbetta, M. (2022) · Brain Structure and Function · doi:10.1007/s00429-022-02525-7 · Cited by 44
Charidimou, A., Kasselimis, D. S., Varkanitsa, M., Selai, C. E., Potagas, C. M., & Evdokimidis, I. (2014) · Journal of Clinical Neurology · doi:10.3988/jcn.2014.10.2.75 · Cited by 45
Multidimensional encoding of brain connectomes
Caiafa, C. F., & Pestilli, F. (2017) · Scientific Reports · doi:10.1038/s41598-017-09250-w · Cited by 46
Kang, J., Pae, C., & Park, H. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.075 · Cited by 43
Toppi, J., Laura Astolfi, A., Risetti, M., Anzolin, A., Kober, S. E., Wood, G. M. D. O., & Donatella Mattia, A. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00637 · Cited by 39
Tan, X., Zhou, Z., Gao, J. J., Meng, F., Yu, Y., Zhang, J., He, F., Wei, R., Wang, J., Peng, G., et al. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101702 · Cited by 40
Alteration and reorganization of functional networks: a new perspective in brain injury study
Castellanos, N. P. (2011) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2011.00090 · Cited by 38
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
Highlighting the Structure-Function Relationship of the Brain with the Ising Model and Graph Theory
Das, T. K., Abeyasinghe, P. M., Crone, J. S., Sosnowski, A., Laureys, S., Owen, A. M., & Soddu, A. (2014) · BioMed Research International · doi:10.1155/2014/237898 · Cited by 38
Chen, S., Kang, J., Xing, Y., & Wang, G. (2015) · Human Brain Mapping · doi:10.1002/hbm.23007 · Cited by 38
Robinson, P. A. (2019) · Physical review. E · doi:10.1103/physreve.99.012421 · Cited by 36
dos Santos, F. P., & Verschure, P. F. M. J. (2022) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2021.806544 · Cited by 35
Predicting Concussion Outcome by Integrating Finite Element Modeling and Network Analysis
Anderson, E. D., Giudice, J. S., Wu, T., Panzer, M. B., & Meaney, D. F. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.00309 · Cited by 35
Magnetoencephalography in Stroke Recovery and Rehabilitation
Paggiaro, A., Birbaumer, N., Cavinato, M., Turco, C., Formaggio, E., Felice, A. D., Masiero, S., & Piccione, F. (2016) · Frontiers in Neurology · doi:10.3389/fneur.2016.00035 · Cited by 38
Quantitative evaluation of simulated functional brain networks in graph theoretical analysis
Lee, W. H., Bullmore, E. T., & Frangou, S. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.08.050 · Cited by 35
Kang, J., Pae, C., & Park, H. (2019) · PLoS ONE · doi:10.1371/journal.pone.0222161 · Cited by 33
Dissociated multimodal hubs and seizures in temporal lobe epilepsy
Douw, L., Desalvo, M. N., Tanaka, N., Cole, A. J., Liu, H., Reinsberger, C., & Stufflebeam, S. M. (2015) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.173 · Cited by 34
Network analysis of time-lapse microscopy recordings
Smedler, E., Malmersjö, S., & Uhlén, P. (2014) · Frontiers in Neural Circuits · doi:10.3389/fncir.2014.00111 · Cited by 33
Neural Network Modeling of EEG Patterns in Encephalopathy
Ponten, S. C., Tewarie, P. K. B., Slooter, A. J. C., Stam, C. J., & van Dellen, E. (2013) · Journal of Clinical Neurophysiology · doi:10.1097/wnp.0b013e3182a73e16 · Cited by 30
Bourdillon, P., Apra, C., Guénot, M., & Duffau, H. (2017) · Epilepsia · doi:10.1111/epi.13935 · Cited by 32
Yang, Y., Sun, H.-Z., Ruan, L., Wang, Y., Chen, S.-C., Zhao, T., Huang, Q., Hu, Z., Xia, N., Liu, J., et al. (2017) · Neural Regeneration Research · doi:10.4103/1673-5374.208575 · Cited by 32
Endogenous oscillations and networks in functional magnetic resonance imaging
Bandettini, P. A., & Bullmore, E. T. (2008) · Human Brain Mapping · doi:10.1002/hbm.20607 · Cited by 32
The Lesioned Brain: Still a Small-World?
Douw, L., van Dellen, E., Baayen, J. C., Klein, M., Velis, D. N., Alpherts, W. C. J., Heimans, J. J., Reijneveld, J. C., & Stam, C. J. (2010) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2010.00174 · Cited by 32
Tao, Y., & Rapp, B. C. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101865 · Cited by 32
Brain Connectivity Modulation After Exoskeleton-Assisted Gait in Chronic Hemiplegic Stroke Survivors
Molteni, F., Formaggio, E., Bosco, A., Guanziroli, E., Piccione, F., Masiero, S., & Felice, A. D. (2020) · American Journal of Physical Medicine & Rehabilitation · doi:10.1097/phm.0000000000001395 · Cited by 31
Naro, A., Maggio, M. G., Leo, A., & Calabrò, R. S. (2020) · International Journal of Neural Systems · doi:10.1142/s0129065720500525 · Cited by 31
A novel stroke lesion network mapping approach: improved accuracy yet still low deficit prediction
Pini, L., Salvalaggio, A., Grazia, M. D. F. D., Zorzi, M., de Schotten, M. T., & Corbetta, M. (2021) · Brain Communications · doi:10.1093/braincomms/fcab259 · Cited by 30
Moon, J.-Y., Kim, J., Ko, T.-W., Kim, M., Iturria‐Medina, Y., Choi, J. H., Lee, J., Mashour, G. A., & Lee, U. (2017) · Scientific Reports · doi:10.1038/srep46606 · Cited by 30
Minati, L., Ito, H., Perinelli, A., Ricci, L., Faes, L., Yoshimura, N., Koike, Y., & Frasca, M. (2019) · IEEE Access · doi:10.1109/access.2019.2957014 · Cited by 30
The Intrinsic Connectome of the Rat Amygdala
Schmitt, O., Eipert, P., Philipp, K., Kettlitz, R., Fuellen, G., & Wree, A. (2012) · Frontiers in Neural Circuits · doi:10.3389/fncir.2012.00081 · Cited by 31
zhong, J., Chen, D. Q., Nantes, J. C., Holmes, S. A., Hodaie, M., & Koski, L. M. (2016) · Brain Imaging and Behavior · doi:10.1007/s11682-016-9551-4 · Cited by 30
Dick, A. S., Beharelle, A. R., Solodkin, A., & Small, S. L. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.2851-12.2013 · Cited by 30
Douw, L., Baayen, J. C., Klein, M., Velis, D., Alpherts, W. C. J., Bot, J., Heimans, J. J., Reijneveld, J. C., & Stam, C. J. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.02.034 · Cited by 29
Butz, M., Steenbuck, I. D., & van Ooyen, A. (2014) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2014.00115 · Cited by 31
Schoo, L. A., van Zandvoort, M. J. E., Biessels, G. J., Kappelle, L. J., Postma, A., & de Haan, E. H. F. (2010) · Journal of Neuropsychology · doi:10.1348/174866410x504059 · Cited by 28
Remodeling Functional Connectivity in Multiple Sclerosis: A Challenging Therapeutic Approach
Bassi, M. S., Gilio, L., Buttari, F., Maffei, P., Marfia, G. A., Restivo, D. A., Centonze, D., & Iezzi, E. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00710 · Cited by 27
Diverse pathophysiological processes converge on network disruption in mania
Lee, I., Nielsen, K., Nawaz, U., Hall, M., Öngür, D., Keshavan, M. S., & Brady, R. O. (2018) · Journal of Affective Disorders · doi:10.1016/j.jad.2018.10.087 · Cited by 27
Revealing epilepsy type using a computational analysis of interictal EEG
Lopes, M. A., Perani, S., Yaakub, S. N., Richardson, M. P., Goodfellow, M., & Terry, J. R. (2019) · Scientific Reports · doi:10.1038/s41598-019-46633-7 · Cited by 27
The influence of filtering and downsampling on the estimation of transfer entropy
Weber, I., Florin, E., von Papen, M., & Timmermann, L. (2017) · PLoS ONE · doi:10.1371/journal.pone.0188210 · Cited by 26
Hildesheim, F. E., Silver, A. N., Dominguez-Vargas, A.-U., Andrushko, J. W., Edwards, J., Dancause, N., & Thiel, A. (2022) · Frontiers in Rehabilitation Sciences · doi:10.3389/fresc.2022.795335 · Cited by 28
Metadata
- Authors
- Honey, C. J., & Sporns, O.
- Affiliations
- Indiana University Bloomington (US)
- Venue
- Human Brain Mapping
- Publisher
- Wiley
- Published
- 2008-04-25
- Volume
- 29
- Issue
- 7
- Pages
- 802-809
- ISSN
- 1065-9471 (ISSN-L)
- 1097-0193
- PMC
- PMC6870962
- OpenAlex
- W2105504969
- Cited by
- 353
- References
- 39
- Citations by year
- 2026: 7 · 2025: 8 · 2024: 13 · 2023: 18 · 2022: 23 · 2021: 18 · 2020: 14 · 2019: 25 · 2018: 15 · 2017: 33 · 2016: 32 · 2015: 18 · 2014: 26 · 2013: 26 · 2012: 22
- FWCI
- 9.47
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Human Brain Mapping (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- James S. McDonnell Foundation
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Nonlinear Dynamics and Pattern Formation
- Keywords
- cortical lesions, cortical oscillations, neural mass model, directed information flow, network synchronization, structural connectivity, network clustering, parietal cortex, frontal cortex
- MeSH terms
- Animals, Cerebral Cortex, Macaca, Models, Neurological, Nerve Net, Neural Pathways
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
