Sporns, O., Tononi, G. & Kötter, R. The human connectome: a structural description of the human brain. PLoS Comput. Biol. 1, e42 (2005).
PubMed PubMed Central Article CAS Google Scholar
Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186–198 (2009).
CAS PubMed Article Google Scholar
DeWeerdt, S. How to map the brain. Nature 571, S6 (2019).
CAS PubMed Article Google Scholar
Sporns, O. The future of network neuroscience. Netw. Neurosci. 1, 1–2 (2017).
Insel, T. R., Landis, S. C. & Collins, F. S. The NIH Brain Initiative. Science 340, 687–688 (2013).
CAS PubMed PubMed Central Article Google Scholar
Amunts, K. et al. The Human Brain Project: creating a European research infrastructure to decode the human brain. Neuron 92, 574–581 (2016).
CAS PubMed Article Google Scholar
Bassett, D. S. & Sporns, O. Network neuroscience. Nat. Neurosci. 20, 353–364 (2017).
CAS PubMed PubMed Central Article Google Scholar
Sejnowski, T. J., Churchland, P. S. & Movshon, J. A. Putting big data to good use in neuroscience. Nat. Neurosci. 17, 1440–1441 (2014).
CAS PubMed PubMed Central Article Google Scholar
Rubinov, M. & Sporns, O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059–1069 (2010).
Poldrack, R. A. et al. Scanning the horizon: towards transparent and reproducible neuroimaging research. Nat. Rev. Neurosci. 18, 115–126 (2017).
CAS PubMed PubMed Central Article Google Scholar
Van den Heuvel, M. P., Bullmore, E. T. & Sporns, O. Comparative connectomics. Trends Cogn. Sci. 20, 345–361 (2016).
Passingham, R. E., Stephan, K. E. & Kötter, R. The anatomical basis of functional localization in the cortex. Nat. Rev. Neurosci. 3, 606–616 (2002).
CAS PubMed Article Google Scholar
Mars, R. B., Passingham, R. E. & Jbabdi, S. Connectivity fingerprints: from areal descriptions to abstract spaces. Trends Cogn. Sci. 22, 1026–1037 (2018).
PubMed PubMed Central Article Google Scholar
Sporns, O., Honey, C. J. & Kötter, R. Identification and classification of hubs in brain networks. PLoS ONE 2, e1049 (2007).
PubMed PubMed Central Article Google Scholar
Van Den Heuvel, M. P., Kahn, R. S., Goñi, J. & Sporns, O. High-cost, high-capacity backbone for global brain communication. Proc. Natl Acad. Sci. USA 109, 11372–11377 (2012).
PubMed PubMed Central Article Google Scholar
Hilgetag, C.-C., Burns, G. A., O’Neill, M. A., Scannell, J. W. & Young, M. P. Anatomical connectivity defines the organization of clusters of cortical areas in the macaque and the cat. Philos. Trans. Roy. Soc. Lond. B 355, 91–110 (2000).
Sporns, O. & Betzel, R. F. Modular brain networks. Annu. Rev. Psychol. 67, 613–640 (2016).
Sporns, O. Network attributes for segregation and integration in the human brain. Curr. Opin. Neurobiol. 23, 162–171 (2013).
CAS PubMed Article Google Scholar
Chung, J. et al. Statistical connectomics. Annu. Rev. Stat. 8, 463–492 (2021).
Fornito, A., Zalesky, A. & Bullmore, E. Fundamentals of Brain Network Analysis Ch. 10 (Academic, 2016).
Klimm, F., Bassett, D. S., Carlson, J. M. & Mucha, P. J. Resolving structural variability in network models and the brain. PLoS Comput. Biol. 10, e1003491 (2014). This study proposes to comprehensively benchmark observed networks with respect to a spectrum of null models, thereby providing a more complete feature profile.
PubMed PubMed Central Article CAS Google Scholar
Watts, D. J. & Strogatz, S. H. Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998).
CAS PubMed Article Google Scholar
Humphries, M. D. & Gurney, K. Network ‘small-world-ness’: a quantitative method for determining canonical network equivalence. PLoS ONE 3, e0002051 (2008).
PubMed Article CAS Google Scholar
Newman, M. E. & Girvan, M. Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004).
Esfahlani, F. Z. et al. Modularity maximization as a flexible and generic framework for brain network exploratory analysis. NeuroImage 244, 118607 (2021).
Rubinov, M. & Sporns, O. Weight-conserving characterization of complex functional brain networks. NeuroImage 56, 2068–2079 (2011).
MacMahon, M. & Garlaschelli, D. Community detection for correlation matrices. Phys. Rev. X 5, 21006 (2015).
Colizza, V., Flammini, A., Serrano, M. A. & Vespignani, A. Detecting rich-club ordering in complex networks. Nat. Phys. 2, 110–115 (2006).
Alstott, J., Panzarasa, P., Rubinov, M., Bullmore, E. & Vértes, P. A unifying framework for measuring weighted rich clubs by integrating randomized controls. Sci. Rep. 4, 7525 (2014).
Im, K., Paldino, M. J., Poduri, A., Sporns, O. & Grant, P. E. Altered white matter connectivity and network organization in polymicrogyria revealed by individual gyral topology-based analysis. NeuroImage 86, 182–193 (2014).
Maslov, S. & Sneppen, K. Specificity and stability in topology of protein networks. Science 296, 910–913 (2002).
CAS PubMed Article Google Scholar
Betzel, R. F. & Bassett, D. S. Specificity and robustness of long-distance connections in weighted, interareal connectomes. Proc. Natl Acad. Sci. USA 115, E4880–E4889 (2018). This study introduces a constrained rewiring model that preserves density and degree sequence, and approximately preserves the connection length distribution and length–weight relationship.
CAS PubMed PubMed Central Article Google Scholar
Sporns, O. & Kötter, R. Motifs in brain networks. PLoS Biol. 2, e369 (2004).
PubMed PubMed Central Article CAS Google Scholar
Kale, P., Zalesky, A. & Gollo, L. L. Estimating the impact of structural directionality: how reliable are undirected connectomes? Net. Neurosci. 2, 259–284 (2018).
Suárez, L. E., Richards, B. A., Lajoie, G. & Misic, B. Learning function from structure in neuromorphic networks. Nat. Mach. Intell. 3, 771–786 (2021).
Erdős, P. & Rényi, A. On the evolution of random graphs. Publ. Math. Inst. Hung. Acad. Sci. 5, 17–60 (1960).
Gilbert, E. N. Random graphs. Ann. Math. Stat. 30, 1141–1144 (1959).
Kaiser, M. & Hilgetag, C. C. Nonoptimal component placement, but short processing paths, due to long-distance projections in neural systems. PLoS Comput. Biol. 2, e95 (2006).
PubMed PubMed Central Article CAS Google Scholar
Ercsey-Ravasz, M. et al. A predictive network model of cerebral cortical connectivity based on a distance rule. Neuron 80, 184–197 (2013).
CAS PubMed PubMed Central Article Google Scholar
Betzel, R. F. et al. Generative models of the human connectome. NeuroImage 124, 1054–1064 (2016).
Goulas, A., Betzel, R. F. & Hilgetag, C. C. Spatiotemporal ontogeny of brain wiring. Sci. Adv. 5, eaav9694 (2019).
CAS PubMed PubMed Central Article Google Scholar
Oldham, S. et al. Modeling spatial, developmental, physiological, and topological constraints on human brain connectivity. Preprint at bioRxiv https://doi.org/10.1101/2021.09.29.462379 (2021).
Barabási, A.-L. & Albert, R. Emergence of scaling in random networks. Science 286, 509–512 (1999).
Lancaster, G., Iatsenko, D., Pidde, A., Ticcinelli, V. & Stefanovska, A. Surrogate data for hypothesis testing of physical systems. Phys. Rep. 748, 1–60 (2018).
Daunizeau, J., David, O. & Stephan, K. Dynamic causal modelling: a critical review of the biophysical and statistical foundations. NeuroImage 58, 312–322 (2011).
留言 (0)