Despite substantial recent progress, our understanding of the principles and mechanisms underlying complex brain function and cognition remains incomplete. Network neuroscience proposes to tackle these enduring challenges. Approaching brain structure and function from an explicitly integrative perspective, network neuroscience pursues new ways to map, record, analyze and model the elements and interactions of neurobiological systems. Two parallel trends drive the approach: the availability of new empirical tools to create comprehensive maps and record dynamic patterns among molecules, neurons, brain areas and social systems; and the theoretical framework and computational tools of modern network science. The convergence of empirical and computational advances opens new frontiers of scientific inquiry, including network dynamics, manipulation and control of brain networks, and integration of network processes across spatiotemporal domains. We review emerging trends in network neuroscience and attempt to chart a path toward a better understanding of the brain as a multiscale networked system.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Nature Communications Open Access 12 November 2022
Alteration of a brain network with stable and strong functional connections in subjects with schizophrenia
Schizophrenia Open Access 04 November 2022
Genome-wide association study of the human brain functional connectome reveals strong vascular component underlying global network efficiency
Scientific Reports Open Access 02 September 2022
Subscribe to Nature+
Get immediate online access to Nature and 55 other Nature journal
Subscribe to Journal
Get full journal access for 1 year
only $6.58 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
Sejnowski, T.J., Churchland, P.S. & Movshon, J.A. Putting big data to good use in neuroscience. Nat. Neurosci. 17, 1440–1441 (2014).
Jorgenson, L.A. et al. The BRAIN Initiative: developing technology to catalyse neuroscience discovery. Phil. Trans. R. Soc. B 370, 20140164 (2015).
Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186–198 (2009).
Sporns, O. Contributions and challenges for network models in cognitive neuroscience. Nat. Neurosci. 17, 652–660 (2014).
Medaglia, J.D., Lynall, M.E. & Bassett, D.S. Cognitive network neuroscience. J. Cogn. Neurosci. 27, 1471–1491 (2015).
Sporns, O. Networks of the Brain (MIT Press, 2010).
Cunningham, J.P. & Yu, B.M. Dimensionality reduction for large-scale neural recordings. Nat. Neurosci. 17, 1500–1509 (2014).
Freeman, J. et al. Mapping brain activity at scale with cluster computing. Nat. Methods 11, 941–950 (2014).
Poldrack, R.A. & Gorgolewski, K.J. Making big data open: data sharing in neuroimaging. Nat. Neurosci. 17, 1510–1517 (2014).
White, J.G., Southgate, E., Thomson, J.N. & Brenner, S. The structure of the nervous system of the nematode Caenorhabditis elegans. Philos Trans R Soc Lond B Biol Sci 314, 1–340 (1986).
Bentley, B. et al. The multilayer connectome of Caenorhabditis elegans. PLoS Comput. Biol. 12, e1005283 (2016).
Jarrell, T.A. et al. The connectome of a decision-making neural network. Science 337, 437–444 (2012).
Takemura, S.Y. et al. A visual motion detection circuit suggested by Drosophila connectomics. Nature 500, 175–181 (2013).
Lichtman, J.W. & Denk, W. The big and the small: challenges of imaging the brain's circuits. Science 334, 618–623 (2011).
Helmstaedter, M. et al. Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature 500, 168–174 (2013).
Kebschull, J.M. et al. High-throughput mapping of single-neuron projections by sequencing of barcoded RNA. Neuron 91, 975–987 (2016).
Shih, C.T. et al. Connectomics-based analysis of information flow in the Drosophila brain. Curr. Biol. 25, 1249–1258 (2015).
Oh, S.W. et al. A mesoscale connectome of the mouse brain. Nature 508, 207–214 (2014).
Bota, M., Sporns, O. & Swanson, L.W. Architecture of the cerebral cortical association connectome underlying cognition. Proc. Natl. Acad. Sci. USA 112, E2093–E2101 (2015).
Stephan, K.E. et al. Advanced database methodology for the Collation of Connectivity data on the Macaque brain (CoCoMac). Phil. Trans. R. Soc. B 356, 1159–1186 (2001).
Markov, N.T. et al. A weighted and directed interareal connectivity matrix for macaque cerebral cortex. Cereb. Cortex 24, 17–36 (2014).
Jbabdi, S., Sotiropoulos, S.N., Haber, S.N., Van Essen, D.C. & Behrens, T.E. Measuring macroscopic brain connections in vivo. Nat. Neurosci. 18, 1546–1555 (2015).
Thomas, C. et al. Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited. Proc. Natl. Acad. Sci. USA 111, 16574–16579 (2014).
Jones, D.K., Knösche, T.R. & Turner, R. White matter integrity, fiber count, and other fallacies: the do's and don'ts of diffusion MRI. Neuroimage 73, 239–254 (2013).
Donahue, C.J. et al. Using diffusion tractography to predict cortical connection strength and distance: a quantitative comparison with tracers in the monkey. J. Neurosci. 36, 6758–6770 (2016).
Hamel, E.J., Grewe, B.F., Parker, J.G. & Schnitzer, M.J. Cellular level brain imaging in behaving mammals: an engineering approach. Neuron 86, 140–159 (2015).
Keller, P.J. & Ahrens, M.B. Visualizing whole-brain activity and development at the single-cell level using light-sheet microscopy. Neuron 85, 462–483 (2015).
Power, J.D. et al. Functional network organization of the human brain. Neuron 72, 665–678 (2011).
Cole, M.W., Bassett, D.S., Power, J.D., Braver, T.S. & Petersen, S.E. Intrinsic and task-evoked network architectures of the human brain. Neuron 83, 238–251 (2014).
Rosenthal, G., Sporns, O. & Avidan, G. Stimulus dependent dynamic reorganization of the human face processing network. Cereb. Cortex http://dx.doi.org/10.1093/cercor/bhw279 (2016).
Mišic´, B. & Sporns, O. From regions to connections and networks: new bridges between brain and behavior. Curr. Opin. Neurobiol. 40, 1–7 (2016).
Vogelstein, J.T. et al. Discovery of brainwide neural-behavioral maps via multiscale unsupervised structure learning. Science 344, 386–392 (2014).
Crossley, N.A. et al. Cognitive relevance of the community structure of the human brain functional coactivation network. Proc. Natl. Acad. Sci. USA 110, 11583–11588 (2013).
Izquierdo, E.J. & Beer, R.D. Connecting a connectome to behavior: an ensemble of neuroanatomical models of C. elegans klinotaxis. PLoS Comput. Biol. 9, e1002890 (2013).
Fornito, A. & Bullmore, E.T. Connectomic intermediate phenotypes for psychiatric disorders. Front. Psychiatry 3, 32 (2012).
Ideker, T., Galitski, T. & Hood, L. A new approach to decoding life: systems biology. Annu. Rev. Genomics Hum. Genet. 2, 343–372 (2001).
Barabási, A.L. & Oltvai, Z.N. Network biology: understanding the cell's functional organization. Nat. Rev. Genet. 5, 101–113 (2004).
Vidal, M., Cusick, M.E. & Barabási, A.L. Interactome networks and human disease. Cell 144, 986–998 (2011).
Geschwind, D.H. & Flint, J. Genetics and genomics of psychiatric disease. Science 349, 1489–1494 (2015).
Barabási, A.L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011).
Fornito, A., Zalesky, A., Pantelis, C. & Bullmore, E.T. Schizophrenia, neuroimaging and connectomics. Neuroimage 62, 2296–2314 (2012).
de la Torre-Ubieta, L., Won, H., Stein, J.L. & Geschwind, D.H. Advancing the understanding of autism disease mechanisms through genetics. Nat. Med. 22, 345–361 (2016).
Lazer, D. et al. Life in the network: the coming age of computational social science. Science 323, 721–723 (2009).
Onnela, J.P. & Rauch, S.L. Harnessing smartphone-based digital phenotyping to enhance behavioral and mental health. Neuropsychopharmacology 41, 1691–1696 (2016).
Newman, M. Networks: An Introduction (Oxford University Press, 2010).
Fornito, A., Zalesky, A. & Bullmore, E. Fundamentals of Brain Network Analysis (Academic Press, 2016).
Bassett, D.S. & Bullmore, E.T. Small-world brain networks revisited. The Neuroscientist http://dx.doi.org/10.1177%2F1073858416667720 (2016).
van den Heuvel, M.P. & Sporns, O. Network hubs in the human brain. Trends Cogn. Sci. 17, 683–696 (2013).
Zamora-López, G., Zhou, C. & Kurths, J. Cortical hubs form a module for multisensory integration on top of the hierarchy of cortical networks. Front. Neuroinform. 4, 1 (2010).
van den Heuvel, M.P. & Sporns, O. Rich-club organization of the human connectome. J. Neurosci. 31, 15775–15786 (2011).
Markov, N.T. et al. Cortical high-density counterstream architectures. Science 342, 1238406 (2013).
Chaudhuri, R., Knoblauch, K., Gariel, M.A., Kennedy, H. & Wang, X.J. A large-scale circuit mechanism for hierarchical dynamical processing in the primate cortex. Neuron 88, 419–431 (2015).
Horvát, S. et al. Spatial embedding and wiring cost constrain the functional layout of the cortical network of rodents and primates. PLoS Biol. 14, e1002512 (2016).
Betzel, R.F. et al. The modular organization of human anatomical brain networks: accounting for the cost of wiring. Network Neurosci. http://doi.org/10.1162/NETN_a_00002 (2017).
Fornito, A., Zalesky, A. & Breakspear, M. Graph analysis of the human connectome: promise, progress, and pitfalls. Neuroimage 80, 426–444 (2013).
Sporns, O. & Betzel, R.F. Modular brain networks. Annu. Rev. Psychol. 67, 613–640 (2016).
Hinne, M., Heskes, T., Beckmann, C.F. & van Gerven, M.A. Bayesian inference of structural brain networks. Neuroimage 66, 543–552 (2013).
Zalesky, A., Fornito, A. & Bullmore, E.T. Network-based statistic: identifying differences in brain networks. Neuroimage 53, 1197–1207 (2010).
Mucha, P.J., Richardson, T., Macon, K., Porter, M.A. & Onnela, J.P. Community structure in time-dependent, multiscale, and multiplex networks. Science 328, 876–878 (2010).
Rosvall, M. & Bergstrom, C.T. An information-theoretic framework for resolving community structure in complex networks. Proc. Natl. Acad. Sci. USA 104, 7327–7331 (2007).
Betzel, R.F. et al. Generative models of the human connectome. Neuroimage 124 Pt A, 1054–1064 (2016).
Avena-Koenigsberger, A., Goñi, J., Solé, R. & Sporns, O. Network morphospace. J. R. Soc. Interface 12, 20140881 (2015).
Giusti, C., Ghrist, R. & Bassett, D.S. Two's company, three (or more) is a simplex: Algebraic-topological tools for understanding higher-order structure in neural data. Preprint at https://arxiv.org/abs/1601.01704 (2016).
Courtney, O.T. & Bianconi, G. Generalized network structures: The configuration model and the canonical ensemble of simplicial complexes. Preprint at https://arxiv.org/abs/1602.04110 (2016).
Sizemore, A., Giusti, C. & Bassett, D. Classification of weighted networks through mesoscale homological features. J Complex Netw http://dx.doi.org/10.1093/comnet/cnw013 (2015).
Bassett, D.S., Wymbs, N.F., Porter, M.A., Mucha, P.J. & Grafton, S.T. Cross-linked structure of network evolution. Chaos 24, 013112 (2014).
Dotko, P. et al. Topological analysis of the connectome of digital reconstructions of neural microcircuits. Preprint at https://arxiv.org/abs/1601.01580 (2016).
Sizemore, A., Giusti, C., Betzel, R.F. & Bassett, D.S. Closures and Cavities in the Human Connectome. Preprint at https://arxiv.org/abs/1608.03520 (2016).
Zalesky, A., Fornito, A. & Bullmore, E. On the use of correlation as a measure of network connectivity. Neuroimage 60, 2096–2106 (2012).
Nigam, S. et al. Rich-club organization in effective connectivity among cortical neurons. J. Neurosci. 36, 670–684 (2016).
Friston, K.J., Li, B., Daunizeau, J. & Stephan, K.E. Network discovery with DCM. Neuroimage 56, 1202–1221 (2011).
Jirsa, V.K., Sporns, O., Breakspear, M., Deco, G. & McIntosh, A.R. Towards the virtual brain: network modeling of the intact and the damaged brain. Arch. Ital. Biol. 148, 189–205 (2010).
Hines, M.L. & Carnevale, N.T. The NEURON simulation environment. Neural Comput. 9, 1179–1209 (1997).
Szigeti, B. et al. OpenWorm: an open-science approach to modeling Caenorhabditis elegans. Front. Comput. Neurosci. 8, 137 (2014).
Stephan, K.E., Iglesias, S., Heinzle, J. & Diaconescu, A.O. Translational perspectives for computational neuroimaging. Neuron 87, 716–732 (2015).
Karlebach, G. & Shamir, R. Modelling and analysis of gene regulatory networks. Nat. Rev. Mol. Cell Biol. 9, 770–780 (2008).
Karr, J.R. et al. A whole-cell computational model predicts phenotype from genotype. Cell 150, 389–401 (2012).
Castellano, C., Fortunato, S. & Loreto, V. Statistical physics of social dynamics. Rev. Mod. Phys. 81, 591 (2009).
Holme, P. Temporal networks. Phys. Rep. 519, 97–125 (2012).
Honey, C.J., Kötter, R., Breakspear, M. & Sporns, O. Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc. Natl. Acad. Sci. USA 104, 10240–10245 (2007).
Goñi, J. et al. Resting-brain functional connectivity predicted by analytic measures of network communication. Proc. Natl. Acad. Sci. USA 111, 833–838 (2014).
Deco, G., Jirsa, V.K., Robinson, P.A., Breakspear, M. & Friston, K. The dynamic brain: from spiking neurons to neural masses and cortical fields. PLoS Comput. Biol. 4, e1000092 (2008).
Raj, A., Kuceyeski, A. & Weiner, M. A network diffusion model of disease progression in dementia. Neuron 73, 1204–1215 (2012).
Mišic´, B., Sporns, O. & McIntosh, A.R. Communication efficiency and congestion of signal traffic in large-scale brain networks. PLoS Comput. Biol. 10, e1003427 (2014).
Hutchison, R.M. et al. Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage 80, 360–378 (2013).
Conaco, C. et al. Functionalization of a protosynaptic gene expression network. Proc. Natl. Acad. Sci. USA 109 (Suppl. 1), 10612–10618 (2012).
Beagan, J.A. et al. Local genome topology can exhibit an incompletely rewired 3D-folding state during somatic cell reprogramming. Cell Stem Cell 18, 611–624 (2016).
Kivelä, M. et al. Multilayer networks. J. Compl. Netw. 2, 203–271 (2014).
Betzel, R.F. et al. Functional brain modules reconfigure at multiple scales across the human lifespan. Preprint at https://arxiv.org/abs/1510.08045 (2015).
Bassett, D.S., Yang, M., Wymbs, N.F. & Grafton, S.T. Learning-induced autonomy of sensorimotor systems. Nat. Neurosci. 18, 744–751 (2015).
De Domenico, M., Sasai, S. & Arenas, A. Mapping multiplex hubs in human functional brain networks. Front. Neurosci. 10, 326 (2016).
Calhoun, V.D., Miller, R., Pearlson, G. & Adali, T. The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron 84, 262–274 (2014).
Chai, L. et al. Evolution of brain network dynamics in neurodevelopment. Netw. Neurosci. http://doi.org/10.1162/NETN_a_00001 (2017).
Kopell, N.J., Gritton, H.J., Whittington, M.A. & Kramer, M.A. Beyond the connectome: the dynome. Neuron 83, 1319–1328 (2014).
O'Donnell, M.B. & Falk, E.B. Big data under the microscope and brains in social context integrating methods from computational social science and neuroscience. Ann. Am. Acad. Pol. Soc. Sci. 659, 274–289 (2015).
Hasson, U. & Frith, C.D. Mirroring and beyond: coupled dynamics as a generalized framework for modelling social interactions. Phil. Trans. R. Soc. B 371, 20150366 (2016).
Proulx, S.R., Promislow, D.E. & Phillips, P.C. Network thinking in ecology and evolution. Trends Ecol. Evol. 20, 345–353 (2005).
Sacchet, M.D., Prasad, G., Foland-Ross, L.C., Thompson, P.M. & Gotlib, I.H. Support vector machine classification of major depressive disorder using diffusion-weighted neuroimaging and graph theory. Front. Psychiatry 6, 21 (2015).
Albert, R. & Thakar, J. Boolean modeling: a logic-based dynamic approach for understanding signaling and regulatory networks and for making useful predictions. Wiley Interdiscip. Rev. Syst. Biol. Med. 6, 353–369 (2014).
Wendling, F., Benquet, P., Bartolomei, F. & Jirsa, V. Computational models of epileptiform activity. J. Neurosci. Methods 260, 233–251 (2016).
Watanabe, T., Masuda, N., Megumi, F., Kanai, R. & Rees, G. Energy landscape and dynamics of brain activity during human bistable perception. Nat. Commun. 5, 4765 (2014).
Hermundstad, A.M. et al. Structurally constrained relationships between cognitive states in the human brain. PLoS Comput. Biol. 10, e1003591 (2014).
Harrington, D.L. et al. Network topology and functional connectivity disturbances precede the onset of Huntington's disease. Brain 138, 2332–2346 (2015).
Falk, E.B. et al. Self-affirmation alters the brain's response to health messages and subsequent behavior change. Proc. Natl. Acad. Sci. USA 112, 1977–1982 (2015).
Gratton, C., Lee, T.G., Nomura, E.M. & D'Esposito, M. The effect of theta-burst TMS on cognitive control networks measured with resting state fMRI. Front. Syst. Neurosci. 7, 124 (2013).
Crofts, J.J. et al. Network analysis detects changes in the contralesional hemisphere following stroke. Neuroimage 54, 161–169 (2011).
Ramsey, L.E. et al. Normalization of network connectivity in hemispatial neglect recovery. Ann. Neurol. 80, 127–141 (2016).
Lewis, P.M., Thomson, R.H., Rosenfeld, J.V. & Fitzgerald, P.B. Brain neuromodulation techniques: a review. Neuroscientist 22, 406–421 (2016).
Johnson, M.D. et al. Neuromodulation for brain disorders: challenges and opportunities. IEEE Trans. Biomed. Eng. 60, 610–624 (2013).
Kailath, T. Linear Systems (Prentice Hall, 1979).
Liu, Y.Y., Slotine, J.J. & Barabási, A.L. Controllability of complex networks. Nature 473, 167–173 (2011).
Pasqualetti, F., Zampieri, S. & Bullo, F. Controllability metrics, limitations and algorithms for complex networks. IEEE Trans. Contr. Netw. Syst. 1, 40–52 (2014).
Gu, S. et al. Controllability of structural brain networks. Nat. Commun. 6, 8414 (2015).
Santaniello, S. et al. Therapeutic mechanisms of high-frequency stimulation in Parkinson's disease and neural restoration via loop-based reinforcement. Proc. Natl. Acad. Sci. USA 112, E586–E595 (2015).
Ching, S. et al. Real-time closed-loop control in a rodent model of medically induced coma using burst suppression. Anesthesiology 119, 848–860 (2013).
Khambhati, A.N., Davis, K.A., Lucas, T.H., Litt, B. & Bassett, D.S. Virtual cortical resection reveals push-pull network control preceding seizure evolution. Neuron 91, 1170–1182 (2016).
Wheeler, D.W. et al. Hippocampome.org: a knowledge base of neuron types in the rodent hippocampus. eLife 4, e09960 (2015).
Markram, H. et al. Reconstruction and simulation of neocortical microcircuitry. Cell 163, 456–492 (2015).
Schneider, C.J., Bezaire, M. & Soltesz, I. Toward a full-scale computational model of the rat dentate gyrus. Front. Neural Circuits 6, 83 (2012).
van den Heuvel, M.P., Bullmore, E.T. & Sporns, O. Comparative Connectomics. Trends Cogn. Sci. 20, 345–361 (2016).
Goulas, A. et al. Comparative analysis of the macroscale structural connectivity in the macaque and human brain. PLoS Comput. Biol. 10, e1003529 (2014).
Li, L. et al. Mapping putative hubs in human, chimpanzee and rhesus macaque connectomes via diffusion tractography. Neuroimage 80, 462–474 (2013).
Battiston, F., Nicosia, V., Chavez, M. & Latora, V. Multilayer motif analysis of brain networks. Preprint at https://arxiv.org/abs/1606.09115 (2016).
Wolf, L., Goldberg, C., Manor, N., Sharan, R. & Ruppin, E. Gene expression in the rodent brain is associated with its regional connectivity. PLoS Comput. Biol. 7, e1002040 (2011).
French, L. & Pavlidis, P. Relationships between gene expression and brain wiring in the adult rodent brain. PLoS Comput. Biol. 7, e1001049 (2011).
Richiardi, J. et al. BRAIN NETWORKS. Correlated gene expression supports synchronous activity in brain networks. Science 348, 1241–1244 (2015).
Wang, G.Z. et al. Correspondence between resting-state activity and brain gene expression. Neuron 88, 659–666 (2015).
Fulcher, B.D. & Fornito, A. A transcriptional signature of hub connectivity in the mouse connectome. Proc. Natl. Acad. Sci. USA 113, 1435–1440 (2016).
Rubinov, M., Ypma, R.J., Watson, C. & Bullmore, E.T. Wiring cost and topological participation of the mouse brain connectome. Proc. Natl. Acad. Sci. USA 112, 10032–10037 (2015).
Uddin, L.Q. Idiosyncratic connectivity in autism: developmental and anatomical considerations. Trends Neurosci. 38, 261–263 (2015).
Hernandez, L.M., Rudie, J.D., Green, S.A., Bookheimer, S. & Dapretto, M. Neural signatures of autism spectrum disorders: insights into brain network dynamics. Neuropsychopharmacology 40, 171–189 (2015).
Kuiper, J.S. et al. Social relationships and cognitive decline: a systematic review and meta-analysis of longitudinal cohort studies. Int. J. Epidemiol. 45, 1169–1206 (2016).
Mier, D. & Kirsch, P. Social-cognitive deficits in schizophrenia. Curr. Top. Behav. Neurosci. https://dx.doi.org/10.1007/7854_2015_427 (2016).
Tost, H., Champagne, F.A. & Meyer-Lindenberg, A. Environmental influence in the brain, human welfare and mental health. Nat. Neurosci. 18, 1421–1431 (2015).
Byrge, L., Sporns, O. & Smith, L.B. Developmental process emerges from extended brain-body-behavior networks. Trends Cogn. Sci. 18, 395–403 (2014).
Gibson, G. The environmental contribution to gene expression profiles. Nat. Rev. Genet. 9, 575–581 (2008).
Pescosolido, B. et al. The social symbiome framework: linking genes-to-global cultures in public health using network science. in Handbook of Applied Systems Science (ed. Neal, Z.P.) 25–48 (Routledge, 2015).
Murphy, A.C. et al. Explicitly linking regional activation and function connectivity: community structure of weighted networks with continuous annotation. Preprint at https://arxiv.org/abs/1611.07962 (2016).
Ganmor, E., Segev, R. & Schneidman, E. Sparse low-order interaction network underlies a highly correlated and learnable neural population code. Proc. Natl. Acad. Sci. USA 108, 9679–9684 (2011).
Giusti, C., Pastalkova, E., Curto, C. & Itskov, V. Clique topology reveals intrinsic geometric structure in neural correlations. Proc. Natl. Acad. Sci. USA 112, 13455–13460 (2015).
Bassett, D.S. et al. Dynamic reconfiguration of human brain networks during learning. Proc. Natl. Acad. Sci. USA 108, 7641–7646 (2011).
Braun, U. et al. Dynamic reconfiguration of frontal brain networks during executive cognition in humans. Proc. Natl. Acad. Sci. USA 112, 11678–11683 (2015).
Betzel, R.F., Gu, S., Medaglia, J.D., Pasqualetti, F. & Bassett, D.S. Optimally controlling the human connectome: the role of network topology. Sci. Rep. 6, 30770 (2016).
Muldoon, S.F. et al. Stimulation-based control of dynamic brain networks. PLoS Comput. Biol. 12, e1005076 (2016).
Muldoon, S.F. et al. GABAergic inhibition shapes interictal dynamics in awake epileptic mice. Brain 138, 2875–2890 (2015).
Feldt Muldoon, S., Soltesz, I. & Cossart, R. Spatially clustered neuronal assemblies comprise the microstructure of synchrony in chronically epileptic networks. Proc. Natl. Acad. Sci. USA 110, 3567–3572 (2013).
Burns, S.P. et al. Network dynamics of the brain and influence of the epileptic seizure onset zone. Proc. Natl. Acad. Sci. USA 111, E5321–E5330 (2014).
Ching, S., Brown, E.N. & Kramer, M.A. Distributed control in a mean-field cortical network model: implications for seizure suppression. Phys. Rev. E 86, 021920 (2012).
Simony, E. et al. Dynamic reconfiguration of the default mode network during narrative comprehension. Nat. Commun. 7, 12141 (2016).
Schmaelzle, R. et al. Brain connectivity dynamics during social interaction reflect social network structure. Preprint at https://doi.org/10.1101/096420 (2017).
The authors gratefully acknowledge helpful comments by A. Avena-Koenigsberger, R. Betzel, L. Chai and G. Rosenthal. D.S.B. acknowledges support from the John D. and Catherine T. MacArthur Foundation, the Alfred P. Sloan Foundation, the National Science Foundation (BCS-1430087, NCS BCS-1631550, CAREER PHY-1554488) and the US National Institutes of Health (R01-HD086888, R21-M MH-106799, R01NS099348). O.S. acknowledges support from the Indiana Clinical Translational Sciences Institute (NIH UL1TR0011808), the J.S. McDonnell Foundation (220020387), the National Science Foundation (1636892) and the US National Institutes of Health (R01-AT009036, R01-B022574 and P30-AG010133).
The authors declare no competing financial interests.
About this article
Cite this article
Bassett, D., Sporns, O. Network neuroscience. Nat Neurosci 20, 353–364 (2017). https://doi.org/10.1038/nn.4502
This article is cited by
Meeting report for the 2022 UC Irvine Center for neural circuit mapping conference: linking brain function to cell types and circuits
Molecular Psychiatry (2023)
The role of the angular gyrus in semantic cognition: a synthesis of five functional neuroimaging studies
Brain Structure and Function (2023)
Mathematics of Control, Signals, and Systems (2023)
Genome-wide association study of the human brain functional connectome reveals strong vascular component underlying global network efficiency
Scientific Reports (2022)
Nature Communications (2022)