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Relating Structure and Function in the Human Brain: Relative Contributions of Anatomy, Stationary Dynamics, and Non-stationarities
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Investigating the relationship between brain structure and function is a central endeavor for neuroscience research. Yet, the mechanisms shaping this relationship largely remain to be elucidated and are highly debated. In particular, the existence and relative contributions of anatomical constraints and dynamical physiological mechanisms of different types remain to be established. We addressed this issue by systematically comparing functional connectivity (FC) from resting-state functional magnetic resonance imaging data with simulations from increasingly complex computational models, and by manipulating anatomical connectivity obtained from fiber tractography based on diffusion-weighted imaging. We hypothesized that FC reflects the interplay of at least three types of components: (i) a backbone of anatomical connectivity, (ii) a stationary dynamical regime directly driven by the underlying anatomy, and (iii) other stationary and non-stationary dynamics not directly related to the anatomy. We showed that anatomical connectivity alone accounts for up to 15% of FC variance; that there is a stationary regime accounting for up to an additional 20% of variance and that this regime can be associated to a stationary FC; that a simple stationary model of FC better explains FC than more complex models; and that there is a large remaining variance (around 65%), which must contain the non-stationarities of FC evidenced in the literature. We also show that homotopic connections across cerebral hemispheres, which are typically improperly estimated, play a strong role in shaping all aspects of FC, notably indirect connections and the topographic organization of brain networks.
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“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 Messe et al., 2014
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“Models can involve highly parameterized simulations of regional activity (Hansen et al., 2015; Messe et al., 2014; Sanz-Leon et al., 2015), or sparser analytic predictions of FC using graph metrics of mutual communication (Goni et al., 2014; Grayson et al., 2016) (although, both approaches appear to be comparably effective; see Figure 1D).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Messe et al., 2014
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“Even the best-performing SC-FC models generally account for less than 50% of the variance in FC across the brain in both humans and non-human primates (Grayson et al., 2016; Messe et al., 2014; 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 Messe 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
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
Hubert, L., & Arabie, P. (1985) · Journal of Classification · doi:10.1007/bf01908075 · Cited by 8,219
Dissociable intrinsic connectivity networks for salience processing and executive control
Seeley, W. W., Menon, V., Schatzberg, A. F., Keller, J., Glover, G. H., Kenna, H., Reiss, A. L., & Greicius, M. D. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.5587-06.2007 · Cited by 7,426
Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging
Fox, M. D., & Raichle, M. E. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2201 · Cited by 6,700
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
Correspondence of the brain's functional architecture during activation and rest
Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., Filippini, N., Watkins, K. E., Toro, R., Laird, A. R., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0905267106 · Cited by 5,150
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
Friston, K. J., Harrison, L., & Penny, W. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00202-7 · Cited by 4,583
Consistent resting-state networks across healthy subjects
Damoiseaux, J. S., Rombouts, S. A. R. B., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., & Beckmann, C. F. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601417103 · Cited by 4,326
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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
Functional connectivity dynamics: modeling the switching behavior of the resting state
Hansen, E. C. A., Battaglia, D., Spiegler, A., Deco, G., & Jirsa, V. K. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.11.001 · Cited by 528
Gradients of structure–function tethering across neocortex
Vázquez-Rodríguez, B., Suárez, L. E., Markello, R. D., Shafiei, G., Paquola, C., Hagmann, P., van den Heuvel, M. P., Bernhardt, B. C., Spreng, R. N., & Mišić, B. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1903403116 · Cited by 472
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…”
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Deco, G., Ponce‐Alvarez, A., Hagmann, P., Romani, G. L., Mantini, D., & Corbetta, M. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.5068-13.2014 · Cited by 393
Cabral, J., Kringelbach, M. L., & Deco, G. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.045 · Cited by 364
Trait and state anxiety are mapped differently in the human brain
Saviola, F., Pappaianni, E., Monti, A., Grecucci, A., Jovicich, J., & Pisapia, N. D. (2020) · Scientific Reports · doi:10.1038/s41598-020-68008-z · Cited by 395
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
Dynamic coupling of whole-brain neuronal and neurotransmitter systems
Kringelbach, M. L., Cruzat, J., Cabral, J., Knudsen, G. M., Carhart-Harris, R. L., Whybrow, P. C., Logothetis, N. K., & Deco, G. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1921475117 · Cited by 284
Structural disconnections explain brain network dysfunction after stroke
Griffis, J. C., Metcalf, N. V., Corbetta, M., & Shulman, G. L. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.07.100 · Cited by 213
Deco, G., Cruzat, J., Cabral, J., Knudsen, G. M., Carhart-Harris, R. L., Whybrow, P. C., Logothetis, N. K., & Kringelbach, M. L. (2018) · Current Biology · doi:10.1016/j.cub.2018.07.083 · Cited by 211
Hellyer, P. J., Scott, G., Shanahan, M., Sharp, D. J., & Leech, R. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.4648-14.2015 · Cited by 187
Hasson, U., Egidi, G., Marelli, M., & Willems, R. M. (2018) · Cognition · doi:10.1016/j.cognition.2018.06.018 · Cited by 157
Grayson, 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
Network 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
Gilson, M., Moreno‐Bote, R., Ponce‐Alvarez, A., Ritter, P., & Deco, G. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004762 · Cited by 163
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Meier, J. M., Tewarie, P. K. B., Hillebrand, A., Douw, L., van Dijk, B. W., Stufflebeam, S. M., & Mieghem, P. V. (2016) · Brain Connectivity · doi:10.1089/brain.2015.0408 · Cited by 142
Stiso, J., Khambhati, A. N., Menara, T., Kahn, A. E., Stein, J. M., Das, S. R., Gorniak, R., Tracy, J. I., Litt, B., Davis, K. A., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2019.08.008 · Cited by 136
Complex Brain Network Analysis and Its Applications to Brain Disorders: A Survey
Liu, J., Li, M., Pan, Y. F., Lan, W., Zheng, R., Wu, F., & Wang, J. (2017) · Complexity · doi:10.1155/2017/8362741 · Cited by 147
Chen, B., Xu, T., Zhou, C., Wang, L., Yang, N., Wang, Z., Dong, H.-M., Yang, Z., Zang, Y., Zuo, X., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0144963 · Cited by 136
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
Structure-function coupling in the human connectome: A machine learning approach
Sarwar, T., Tian, Y., Yeo, B. T. T., Ramamohanarao, K., & Zalesky, A. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117609 · Cited by 143
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
A novel brain partition highlights the modular skeleton shared by structure and function
Diez, I. P., Bonifazi, P., Escudero, I., Goñi, B. M., Muñoz, M. A., Stramaglia, S., & Cortés, J. M. (2015) · Scientific Reports · doi:10.1038/srep10532 · Cited by 131
Effects of lesions on synchrony and metastability in cortical networks
Váša, F., Shanahan, M., Hellyer, P. J., Scott, G., Cabral, J., & Leech, R. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.05.042 · Cited by 115
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
Seguin, C., Tian, Y., & Zalesky, A. (2020) · Network Neuroscience · doi:10.1162/netn_a_00161 · Cited by 120
Jobst, B., Hindriks, R., Laufs, H., Tagliazucchi, E., Hahn, G., Ponce‐Alvarez, A., Stevner, A. B. A., Kringelbach, M. L., & Deco, G. (2017) · Scientific Reports · doi:10.1038/s41598-017-04522-x · Cited by 113
Controversies and progress on standardization of large-scale brain network nomenclature
Uddin, L. Q., Betzel, R. F., Cohen, J. R., Damoiseaux, J. S., Brigard, F. D., Eickhoff, S. B., Fornito, A., Gratton, C., Gordon, E. M., Laird, A. R., et al. (2023) · Network Neuroscience · doi:10.1162/netn_a_00323 · Cited by 125
Finger, H., Bönstrup, M., Cheng, B., Messé, A., Hilgetag, C. C., Thomalla, G., Gerloff, C. P., & Peter König, B. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005025 · Cited by 109
Self-citationMessé, A., Rudrauf, D., Giron, A., & Marrelec, G. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.02.001 · Cited by 106
Self-citationA brain-wide functional map of the serotonergic responses to acute stress and fluoxetine
Grandjean, J., Corcoba, A., Kahn, M. C., Upton, A. L., Deneris, E. S., Seifritz, E., Helmchen, F., Mansuy, I. M., Mann, E. O., Rudin, M., et al. (2019) · Nature Communications · doi:10.1038/s41467-018-08256-w · Cited by 101
Hellyer, P. J., Jachs, B., Claudia Clopath, A., & Leech, R. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.08.069 · Cited by 95
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
Keilholz, S. D., Caballero‐Gaudes, C., Bandettini, P. A., Deco, G., & Calhoun, V. D. (2017) · Brain Connectivity · doi:10.1089/brain.2017.0543 · Cited by 92
Chase, H. W., Clos, M., Dibble, S. M., Fox, P. M., Grace, A. A., Phillips, M. L., & Eickhoff, S. B. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.02.069 · Cited by 89
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Langer, N., Benjamin, C. F. A., Becker, B. L. C., & Gaab, N. (2019) · Human Brain Mapping · doi:10.1002/hbm.24552 · Cited by 99
Gibb, B. E., McGeary, J. E., & Beevers, C. G. (2015) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32383 · Cited by 97
Individual structural features constrain the mouse functional connectome
Melozzi, F., Bergmann, E., Harris, J. A., Kahn, I., Jirsa, V., & Bernard, C. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1906694116 · Cited by 89
Optimal control based seizure abatement using patient derived connectivity
Taylor, P. N., Thomas, J., Sinha, N., Dauwels, J., Kaiser, M., Thesen, T., & Ruths, J. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00202 · Cited by 88
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Bansal, K., Nakuci, J., & Muldoon, S. F. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.04.014 · Cited by 81
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Huang, H., & Ding, M. (2015) · Brain Connectivity · doi:10.1089/brain.2015.0382 · Cited by 93
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Golos, M., Jirsa, V., & Daucé, E. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004644 · Cited by 80
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Adams, B., & Petruccione, F. (2020) · AVS Quantum Science · doi:10.1116/1.5135170 · Cited by 80
Park, B., de Wael, R. V., Paquola, C., Larivière, S., Benkarim, O. M., Royer, J., Tavakol, S., Rodríguez‐Cruces, R., Li, Q., Valk, S. L., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117429 · Cited by 67
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Liégeois, R., Ziegler, E., Phillips, C. L. M., Geurts, P., Gómez, F. A., Bahri, M. A., Yeo, B. T. T., Soddu, A., Vanhaudenhuyse, A., Laureys, S., et al. (2015) · Brain Structure and Function · doi:10.1007/s00429-015-1083-y · Cited by 74
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Deligianni, F., Carmichael, D. W., Zhang, H. G., Clark, C. A., & Clayden, J. D. (2016) · PLoS ONE · doi:10.1371/journal.pone.0153404 · Cited by 73
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Matsui, T., Murakami, T., & Ohki, K. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy045 · Cited by 79
Rudrauf, D. (2014) · Advances in Neuroscience · doi:10.1155/2014/462765 · Cited by 74
Self-citationLim, S. I., Radicchi, F., van den Heuvel, M. P., & Sporns, O. (2019) · Scientific Reports · doi:10.1038/s41598-019-39243-w · Cited by 73
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Perl, Y. S., Bocaccio, H., Pallavicini, C., Ipiña, I. P., Laureys, S., Laufs, H., Kringelbach, M. L., Deco, G., & Tagliazucchi, E. (2021) · Physical review. E · doi:10.1103/physreve.104.014411 · Cited by 80
Gilson, M., Deco, G., Friston, K. J., Hagmann, P., Mantini, D., Betti, V., Romani, G. L., & Corbetta, M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.09.061 · Cited by 72
Khalili‐Mahani, N., Rombouts, S. A. R. B., van Osch, M. J. P., Duff, E. P., Carbonell, F., Nickerson, L. D. H., Becerra, L. R., Dahan, A., Evans, A. C., Soucy, J., et al. (2017) · Human Brain Mapping · doi:10.1002/hbm.23516 · Cited by 63
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Ipiña, I. P., Kehoe, P. D., Kringelbach, M. L., Laufs, H., Ibáñez, A. M., Deco, G., Perl, Y. S., & Tagliazucchi, E. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116833 · Cited by 67
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Warnick, R., Guindani, M., Erhardt, E. B., Allen, E. A., Calhoun, V. D., & Vannucci, M. (2017) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1379404 · Cited by 64
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Popp, J. L., Thiele, J. A., Faskowitz, J., Seguin, C., Sporns, O., & Hilger, K. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120563 · Cited by 65
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Wein, S., Malloni, W. M., Tomé, A. M., Frank, S., Henze, G., Wüst, S., Greenlee, M. W., & Lang, E. W. (2021) · Scientific Reports · doi:10.1038/s41598-021-87411-8 · Cited by 66
Messé, A., Hütt, M. ‐., Peter König, B., & Hilgetag, C. C. (2015) · Scientific Reports · doi:10.1038/srep07870 · Cited by 62
Self-citationBettinardi, R. G., Deco, G., Karlaftis, V. M., van Hartevelt, T. J., Fernandes, H. M., Kourtzi, Z., Kringelbach, M. L., & Zamora‐López, G. (2017) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.4980099 · Cited by 63
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Tagliazucchi, E., Crossley, N. A., Bullmore, E. T., & Laufs, H. (2015) · Brain Structure and Function · doi:10.1007/s00429-015-1162-0 · Cited by 54
Kuceyeski, A. F., Jamison, K. W., Owen, J. P., Raj, A., & Mukherjee, P. (2019) · Human Brain Mapping · doi:10.1002/hbm.24713 · Cited by 49
Wu, H., Yan, H., Yang, Y., Xu, M., Shi, Y., Zeng, W., Li, J., Zhang, J., Chang, C., & Wang, N. (2020) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.00215 · Cited by 59
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Lee, W. H., & Frangou, S. (2017) · Scientific Reports · doi:10.1038/s41598-017-16789-1 · Cited by 58
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Vohryzek, J., Cabral, J., Vuust, P., Deco, G., & Kringelbach, M. L. (2022) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2021.0247 · Cited by 56
Deco, G., van Hartevelt, T. J., Fernandes, H. M., Stevner, A. B. A., & Kringelbach, M. L. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.10.047 · Cited by 47
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Minati, L. (2015) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.4936791 · Cited by 49
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Juneja, S., Dhiman, G., Kautish, S., Viriyasitavat, W., & Yadav, K. (2021) · Journal of Healthcare Engineering · doi:10.1155/2021/6712424 · Cited by 49
Panda, R., López‐González, A., Gilson, M., Gosseries, O., Thibaut, A., Frasso, G., Cecconi, B., Escrichs, A., Coma Science Group Collaborators, Deco, G., et al. (2023) · Human Brain Mapping · doi:10.1002/hbm.26386 · Cited by 54
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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
Pallarés, V., Insabato, A., Sanjuán, A., Kühn, S., Mantini, D., Deco, G., & Gilson, M. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.04.070 · Cited by 42
Alderson, T. H., Bokde, A. L. W., Kelso, J. A. S., Maguire, L., & Coyle, D. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.033 · Cited by 46
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Rolls, E. T., Cheng, W., Gilson, M., Gong, W., Deco, G., Lo, C. Z., Yang, A. C., Tsai, S., Liu, M., Lin, C., et al. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz161 · Cited by 48
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
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Saggio, M. L., Ritter, P., & Jirsa, V. (2016) · PLoS ONE · doi:10.1371/journal.pone.0157292 · Cited by 43
Wada, A., Shizukuishi, T., Kikuta, J., Yamada, H., Watanabe, Y., Imamura, Y., Shinozaki, T., Dezawa, K., Haradome, H., & Abe, O. (2017) · Neuroradiology · doi:10.1007/s00234-017-1830-2 · Cited by 43
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Glomb, K., Ponce‐Alvarez, A., Gilson, M., Ritter, P., & Deco, G. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.065 · Cited by 42
Nagaratnam, J. M., Sharmin, S., Diker, A., Lim, W. K., & Maier, A. B. (2020) · Clinical Gerontologist · doi:10.1080/07317115.2020.1756021 · Cited by 46
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Metadata
- Authors
- Messé, A., Rudrauf, D., Benali, H., & Marrelec, G.
- Affiliations
- Inserm (FR)
- Corresponding author
- Messé, Arnaud
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-03-20
- Volume
- 10
- Issue
- 3
- Pages
- e1003530
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC3961181
- OpenAlex
- W2022159725
- Cited by
- 215
- References
- 57
- Citations by year
- 2026: 4 · 2025: 12 · 2024: 11 · 2023: 12 · 2022: 19 · 2021: 23 · 2020: 25 · 2019: 17 · 2018: 24 · 2017: 22 · 2016: 17 · 2015: 22 · 2014: 7
- FWCI
- 12.64
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- Princeton University
- Universitat Pompeu Fabra
- Institut National de la Santé et de la Recherche Médicale
- Université de Lausanne
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- structure-function relationship, functional connectivity, resting-state fMRI, structural connectivity, non-stationary dynamics, fiber tractography, interhemispheric connections, indirect connections, diffusion-weighted imaging
- MeSH terms
- Adult, Brain, Brain Mapping, Computer Simulation, Female, Hemodynamics, Humans, Magnetic Resonance Imaging, Male, Models, Neurological, Nerve Net, Neural Pathways, Reproducibility of Results, Diffusion Magnetic Resonance Imaging, Young Adult
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