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Annual Review of Biomedical Engineering

Volume 19, 2017

Emerging Frontiers of Neuroengineering: A Network Science of Brain Connectivity

Abstract

Neuroengineering is faced with unique challenges in repairing or replacing complex neural systems that are composed of many interacting parts. These interactions form intricate patterns over large spatiotemporal scales and produce emergent behaviors that are difficult to predict from individual elements. Network science provides a particularly appropriate framework in which to study and intervene in such systems by treating neural elements (cells, volumes) as nodes in a graph and neural interactions (synapses, white matter tracts) as edges in that graph. Here, we review the emerging discipline of network neuroscience, which uses and develops tools from graph theory to better understand and manipulate neural systems from micro- to macroscales. We present examples of how human brain imaging data are being modeled with network analysis and underscore potential pitfalls. We then highlight current computational and theoretical frontiers and emphasize their utility in informing diagnosis and monitoring, brain–machine interfaces, and brain stimulation. A flexible and rapidly evolving enterprise, network neuroscience provides a set of powerful approaches and fundamental insights that are critical for the neuroengineer's tool kit.

Keyword(s): control theory, graph theory, network neuroscience, neuroengineering, neuroimaging
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/content/journals/10.1146/annurev-bioeng-071516-044511
2017-06-21
2024-04-20
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Literature Cited

  1. Chen HI, Jgamadze D, Serruya MD, Cullen DK, Wolf JA, Smith DH. 1.  2016. Neural substrate expansion for the restoration of brain function. Front. Syst. Neurosci. 10:1 [Google Scholar]
  2. Haynes JD, Rees G. 2.  2006. Decoding mental states from brain activity in humans. Nat. Rev. Neurosci. 7:523–34 [Google Scholar]
  3. Christophel TB, Hebart MN, Haynes JD. 3.  2012. Decoding the contents of visual short-term memory from human visual and parietal cortex. J. Neurosci. 32:12983–89 [Google Scholar]
  4. Putze F, Schultz T. 4.  2014. Adaptive cognitive technical systems. J. Neurosci. Methods 234:108–15 [Google Scholar]
  5. Krusienski DJ, Grosse-Wentrup M, Galan F, Coyle D, Miller KJ. 5.  et al. 2011. Critical issues in state-of-the-art brain–computer interface signal processing. J. Neural. Eng. 8:025002 [Google Scholar]
  6. DiLorenzo DJ, Bronzino JD. 6.  2007. Neuroengineering Boca Raton, FL: CRC
  7. Johnson MD, Lim HH, Netoff TI, Connolly AT, Johnson N. 7.  et al. 2013. Neuromodulation for brain disorders: challenges and opportunities. IEEE Trans. Biomed. Eng. 60:610–24 [Google Scholar]
  8. Glaser JI, Kording KP. 8.  2016. The development and analysis of integrated neuroscience data. Front. Comput. Neurosci. 10:11 [Google Scholar]
  9. Valiant LG. 9.  2014. What must a global theory of cortex explain. Curr. Opin. Neurobiol. 25:15–19 [Google Scholar]
  10. Craver CF. 10.  2005. Beyond reduction: mechanisms, multifield integration and the unity of neuroscience. Stud. Hist. Philos. Biol. Biomed. Sci. 36:373–95 [Google Scholar]
  11. Bullmore E, Barnes A, Bassett DS, Fornito A, Kitzbichler M. 11.  et al. 2009. Generic aspects of complexity in brain imaging data and other biological systems. NeuroImage 47:1125–34 [Google Scholar]
  12. Stevenson IH, Kording KP. 12.  2011. How advances in neural recording affect data analysis. Nat. Neurosci. 14:139–42 [Google Scholar]
  13. Betzel RF, Bassett DS. 13.  2016. Multi-scale brain networks. NeuroImage In press. https://doi.org/10.1016/j.neuroimage.2016.11.006 [Crossref]
  14. Long B, Zhang Z, Yu PS. 14.  2010. Relational Data Clustering: Models, Algorithms, and Applications Boca Raton, FL: CRC
  15. Conaco C, Bassett DS, Zhou H, Arcila ML, Degnan SM. 15.  et al. 2012. Functionalization of a protosynaptic gene expression network. PNAS 109:10612–18 [Google Scholar]
  16. Zhang S, Bassett DS, Winkelstein BA. 16.  2016. Stretch-induced network reconfiguration of collagen fibres in the human facet capsular ligament. J. R. Soc. Interface 13:20150883 [Google Scholar]
  17. van den Heuvel MP, Bullmore ET, Sporns O. 17.  2016. Comparative connectomics. Trends Cogn. Sci. 20:345–61 [Google Scholar]
  18. Shih CT, Sporns O, Yuan SL, Su TS, Lin YJ. 18.  et al. 2015. Connectomics-based analysis of information flow in the Drosophila brain. Curr. Biol. 25:1249–58 [Google Scholar]
  19. Bassett DS, Bullmore ET. 19.  2009. Human brain networks in health and disease. Curr. Opin. Neurol. 22:340–47 [Google Scholar]
  20. Fornito A, Bullmore ET. 20.  2015. Connectomics: a new paradigm for understanding brain disease. Eur. Neuropsychopharmacol. 25:733–48 [Google Scholar]
  21. Medaglia JD, Lynall ME, Bassett DS. 21.  2015. Cognitive network neuroscience. J. Cogn. Neurosci. 27:1471–91 [Google Scholar]
  22. Misic B, Sporns O. 22.  2016. From regions to connections and networks: new bridges between brain and behavior. Curr. Opin. Neurobiol. 40:1–7 [Google Scholar]
  23. Vertes PE, Alexander-Bloch AF, Gogtay N, Giedd JN, Rapoport JL, Bullmore ET. 23.  2012. Simple models of human brain functional networks. PNAS 109:5868–73 [Google Scholar]
  24. Vertes PE, Alexander-Bloch AF, Bullmore ET. 24.  2014. Generative models of rich clubs in Hebbian neuronal networks and large-scale human brain networks. Philos. Trans. R. Soc. Lond. B 369:20130531 [Google Scholar]
  25. Betzel RF, Avena-Koenigsberger A, Goñi J, He Y, de Reus MA. 25.  et al. 2016. Generative models of the human connectome. NeuroImage 124:1054–64 [Google Scholar]
  26. Pilosof S, Porter MA, Pascual M, Kefi S. 26.  2016. The multilayer nature of ecological networks. arXiv:1511.04453 [q-bio]
  27. Proulx SR, Promislow DE, Phillips PC. 27.  2005. Network thinking in ecology and evolution. Trends. Ecol. Evol. 20:345–53 [Google Scholar]
  28. Newman MEJ. 28.  2010. Networks: An Introduction Cambridge, MA: MIT Press
  29. Rubinov M, Sporns O. 29.  2010. Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52:1059–69 [Google Scholar]
  30. Pavlovic DM, Vértes PE, Bullmore ET, Schafer WR, Nichols TE. 30.  2014. Stochastic blockmodeling of the modules and core of the Caenorhabditis elegans connectome. PLOS ONE 9:e97584 [Google Scholar]
  31. Simpson SL, Hayasaka S, Laurienti PJ. 31.  2011. Exponential random graph modeling for complex brain networks. PLOS ONE 6:e20039 [Google Scholar]
  32. Lindquist MA, Xu Y, Nebel MB, Caffo BS. 32.  2014. Evaluating dynamic bivariate correlations in resting-state fMRI: a comparison study and a new approach. NeuroImage 101:531–46 [Google Scholar]
  33. Misic B, Betzel RF, Nematzadeh A, Goni J, Griffa A. 33.  et al. 2015. Cooperative and competitive spreading dynamics on the human connectome. Neuron 86:1518–29 [Google Scholar]
  34. Misic B, Sporns O, McIntosh AR. 34.  2014. Communication efficiency and congestion of signal traffic in large-scale brain networks. PLOS Comput. Biol. 10:e1003427 [Google Scholar]
  35. Patel TP, Ventre SC, Geddes-Klein D, Singh PK, Meaney DF. 35.  2014. Single-neuron NMDA receptor phenotype influences neuronal rewiring and reintegration following traumatic injury. J. Neurosci. 34:4200–13 [Google Scholar]
  36. Gratton C, Lee TG, Nomura EM, D'Esposito M. 36.  2013. The effect of theta-burst TMS on cognitive control networks measured with resting state fMRI. Front. Syst. Neurosci. 7:124 [Google Scholar]
  37. Kaiser M. 37.  2011. A tutorial in connectome analysis: topological and spatial features of brain networks. NeuroImage 57:892–907 [Google Scholar]
  38. Fornito A, Zalesky A, Breakspear M. 38.  2013. Graph analysis of the human connectome: promise, progress, and pitfalls. NeuroImage 80:426–44 [Google Scholar]
  39. van Diessen E, Numan T, van Dellen E, van der Kooi AW, Boersma M. 39.  et al. 2015. Opportunities and methodological challenges in EEG and MEG resting state functional brain network research. Clin. Neurophysiol. 126:1468–81 [Google Scholar]
  40. Reijneveld JC, Ponten SC, Berendse HW, Stam CJ. 40.  2007. The application of graph theoretical analysis to complex networks in the brain. Clin. Neurophysiol. 118:2317–31 [Google Scholar]
  41. Bullmore E, Sporns O. 41.  2009. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10:186–98 [Google Scholar]
  42. Bassett DS, Bullmore E. 42.  2006. Small-world brain networks. Neuroscientist 12:512–23 [Google Scholar]
  43. Bullmore ET, Bassett DS. 43.  2011. Brain graphs: graphical models of the human brain connectome. Annu. Rev. Clin. Psychol. 7:113–40 [Google Scholar]
  44. Butts CT. 44.  2009. Revisiting the foundations of network analysis. Science 325:414–16 [Google Scholar]
  45. Zalesky A, Fornito A, Harding IH, Cocchi L, Yucel M. 45.  et al. 2010. Whole-brain anatomical networks: Does the choice of nodes matter. NeuroImage 50:970–83 [Google Scholar]
  46. Bollobas B. 46.  1985. Random Graphs New York: Academic
  47. Bollobas B. 47.  1979. Graph Theory: An Introductory Course Berlin: Springer
  48. Ghrist R. 48.  2014. Elementary Applied Topology Amazon.com: Createspace. v1.0
  49. Avena-Koenigsberger A, Goñi J, Betzel RF, van den Heuvel MP, Griffa A. 49.  et al. 2014. Using Pareto optimality to explore the topology and dynamics of the human connectome. Philos. Trans. R. Soc. Lond. B 369:20130530 [Google Scholar]
  50. Watts DJ, Strogatz SH. 50.  1998. Collective dynamics of ‘small-world’ networks. Nature 393:440–42 [Google Scholar]
  51. Kitzbichler MG, Henson RN, Smith ML, Nathan PJ, Bullmore ET. 51.  2011. Cognitive effort drives workspace configuration of human brain functional networks. J. Neurosci. 31:8259–570 [Google Scholar]
  52. Rubinov M, Bassett DS. 52.  2011. Emerging evidence of connectomic abnormalities in schizophrenia. J. Neurosci. 31:6263–65 [Google Scholar]
  53. Bassett DS, Lynall ME. 53.  2015. Network methods to characterize brain structure and function. Cognitive Neuroscience: The Biology of the Mind M Gazzaniga, RB Ivry, GR Mangun Cambridge, MA: MIT Press. 5th ed. [Google Scholar]
  54. Li Y, Liu Y, Li J, Qin W, Li K. 54.  et al. 2009. Brain anatomical network and intelligence. PLOS Comput. Biol. 5:e1000395 [Google Scholar]
  55. Bassett DS, Bullmore ET. 55.  2016. Small world brain networks revisited. Neuroscientist. In press. https: doi.org/10.1177/1073858416667720 [Crossref]
  56. Sporns O, Betzel RF. 56.  2016. Modular brain networks. Annu. Rev. Psychol. 67:613–40 [Google Scholar]
  57. Porter MA, Onnela JP, Mucha PJ. 57.  2009. Communities in networks. Not. AMS 56:1082–97 [Google Scholar]
  58. Fortunato S. 58.  2010. Community detection in graphs. Phys. Rep. 486:75–174 [Google Scholar]
  59. Everett MG, Borgatti SP. 59.  1999. Peripheries of cohesive subsets. Soc. Netw. 21:397–407 [Google Scholar]
  60. Borgatti SP, Everett MG. 60.  1999. Models of core/periphery structures. Soc. Netw. 21:375–95 [Google Scholar]
  61. Bassett DS, Wymbs NF, Rombach MP, Porter MA, Mucha PJ, Grafton ST. 61.  2013. Task-based core–periphery organization of human brain dynamics. PLOS Comput. Biol. 9:e1003171 [Google Scholar]
  62. van den Heuvel MP, Sporns O. 62.  2011. Rich-club organization of the human connectome. J. Neurosci. 31:15775–86 [Google Scholar]
  63. Bassett DS, Siebenhühner F. 63.  2013. Multiscale network organization in the human brain. Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain MZ Pesenson New York: Wiley [Google Scholar]
  64. Bassett DS, Greenfield DL, Meyer-Lindenberg A, Weinberger DR, Moore SW, Bullmore ET. 64.  2010. Efficient physical embedding of topologically complex information processing networks in brains and computer circuits. PLOS Comput. Biol. 6:e1000748 [Google Scholar]
  65. Bassett DS, Brown JA, Deshpande V, Carlson JM, Grafton ST. 65.  2011. Conserved and variable architecture of human white matter connectivity. NeuroImage 54:1262–79 [Google Scholar]
  66. Muldoon SF, Soltesz I, Cossart R. 66.  2013. Spatially clustered neuronal assemblies comprise the microstructure of synchrony in chronically epileptic networks. PNAS 110:3567–72 [Google Scholar]
  67. Bettencourt LM, Stephens GJ, Ham MI, Gross GW. 67.  2007. Functional structure of cortical neuronal networks grown in vitro. Phys. Rev. E 75:021915 [Google Scholar]
  68. Fulcher BD, Fornito A. 68.  2016. A transcriptional signature of hub connectivity in the mouse connectome. PNAS 113:1435–40 [Google Scholar]
  69. Hilgetag CC, Kaiser M. 69.  2004. Clustered organization of cortical connectivity. Neuroinformatics 2:353–60 [Google Scholar]
  70. Markov NT, Ercsey-Ravasz M, Van Essen DC, Knoblauch K, Toroczkai Z, Kennedy H. 70.  2013. Cortical high-density counterstream architectures. Science 342:1238406 [Google Scholar]
  71. Rubinov M, Ypma R, Watson C, Bullmore E. 71.  2015. Wiring cost and topological participation of the mouse brain connectome. PNAS 112:10032–37 [Google Scholar]
  72. van den Heuvel MP, Scholtens LH, de Reus MA. 72.  2016. Topological organization of connectivity strength in the rat connectome. Brain Struct. Funct. 221:1719–36 [Google Scholar]
  73. Kaiser M. 73.  2015. Neuroanatomy: Connectome connects fly and mammalian brain networks. Curr. Biol. 25:R416–18 [Google Scholar]
  74. Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ. 74.  et al. 2008. Mapping the structural core of human cerebral cortex. PLOS Biol. 6:e159 [Google Scholar]
  75. Deng ZD, McClinctock SM, Lisanby SH. 75.  2015. Brain network properties in depressed patients receiving seizure therapy: a graph theoretical analysis of peri-treatment resting EEG. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2015:2203–6 [Google Scholar]
  76. Toppi J, Ciaramidaro A, Vogel P, Mattia D, Babiloni F. 76.  et al. 2015. Graph theory in brain-to-brain connectivity: a simulation study and an application to an EEG hyperscanning experiment. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2015:2211–14 [Google Scholar]
  77. Zhang Y, Xu P, Guo D, Yao D. 77.  2013. Prediction of SSVEP-based BCI performance by the resting-state EEG network. J. Neural Eng. 10:066017 [Google Scholar]
  78. Bassett DS, Bullmore ET, Meyer-Lindenberg A, Apud JA, Weinberger DR, Coppola R. 78.  2009. Cognitive fitness of cost-efficient brain functional networks. PNAS 106:11747–52 [Google Scholar]
  79. Khambhati AN, Davis KA, Oommen BS, Chen SH, Lucas TH. 79.  et al. 2015. Dynamic network drivers of seizure generation, propagation and termination in human neocortical epilepsy. PLOS Comput. Biol. 11:e1004608 [Google Scholar]
  80. Khambhati A, Davis K, Lucas T, Litt B, Bassett DS. 80.  2016. Virtual cortical resection reveals push–pull network control preceding seizure evolution. Neuron 91:1170–82 [Google Scholar]
  81. Niu H, Wang J, Zhao T, Shu N, He Y. 81.  2012. Revealing topological organization of human brain functional networks with resting-state functional near infrared spectroscopy. PLOS ONE 7:e45771 [Google Scholar]
  82. Zhang J, Lin X, Fu G, Sai L, Chen H. 82.  et al. 2016. Mapping the small-world properties of brain networks in deception with functional near-infrared spectroscopy. Sci. Rep. 6:25297 [Google Scholar]
  83. Bassett DS, Brown J, Deshpande V, Carlson J, Grafton ST. 83.  2011. Conserved and variable architecture of human white matter connectivity. NeuroImage 54:1262–79 [Google Scholar]
  84. Zalesky A, Fornito A, Harding IH, Cocchi L, Yucel M. 84.  et al. 2010. Whole-brain anatomical networks: does the choice of nodes matter. NeuroImage 50:970–83 [Google Scholar]
  85. Zhang Z, Telesford QK, Giusti C, Lim KO, Bassett DS. 85.  2016. Choosing wavelet methods, filters, and lengths for functional brain network construction. PLOS ONE 11:e0157243 [Google Scholar]
  86. Jones DK. 86.  2008. Studying connections in the living human brain with diffusion MRI. Cortex 44:936–52 [Google Scholar]
  87. Hermundstad AM, Brown KS, Bassett DS, Aminoff EM, Frithsen A. 87.  et al. 2014. Structurally-constrained relationships between cognitive states in the human brain. PLOS Comput. Biol. 10:e1003591 [Google Scholar]
  88. Hermundstad AM, Bassett DS, Brown KS, Aminoff EM, Clewett D. 88.  et al. 2013. Structural foundations of resting-state and task-based neural activity in the human brain. PNAS 110:6169–74 [Google Scholar]
  89. Bassett DS, Bullmore E, Verchinski BA, Mattay VS, Weinberger DR, Lindenberg A. 89.  2008. Hierarchical organization of human cortical networks in health and schizophrenia. J. Neurosci. 28:9239–48 [Google Scholar]
  90. Lynall ME, Bassett DS, Kerwin R, McKenna PJ, Kitzbichler M. 90.  et al. 2010. Functional connectivity and brain networks in schizophrenia. J. Neurosci. 30:9477–87 [Google Scholar]
  91. Nomi JS, Uddin LQ. 91.  2015. Developmental changes in large-scale network connectivity in autism. NeuroImage Clin. 7:732–41 [Google Scholar]
  92. Menon V. 92.  2011. Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn. Sci. 15:483–506 [Google Scholar]
  93. Burns SP, Santaniello S, Yaffe RB, Jouny CC, Crone NE. 93.  et al. 2014. Network dynamics of the brain and influence of the epileptic seizure onset zone. PNAS 111:E5321–30 [Google Scholar]
  94. He Y, Chen Z, Gong G, Evans A. 94.  2009. Neuronal networks in Alzheimer's disease. Neuroscientist 15:333–50 [Google Scholar]
  95. Poza J, Garcia M, Gomez C, Bachiller A, Carreres A, Hornero R. 95.  2013. Characterization of the spontaneous electroencephalographic activity in Alzheimer's disease using disequilibria and graph theory. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2013:5990–93 [Google Scholar]
  96. Tijms BM, Wink AM, de Haan W, van der Flier WM, Stam CJ. 96.  et al. 2013. Alzheimer's disease: connecting findings from graph theoretical studies of brain networks. Neurobiol. Aging 34:2023–36 [Google Scholar]
  97. Stam CJ. 97.  2014. Modern network science of neurological disorders. Nat. Rev. Neurosci. 15:683–95 [Google Scholar]
  98. Zhu H, Zhou P, Alcauter S, Chen Y, Cao H. 98.  et al. 2016. Changes of intranetwork and internetwork functional connectivity in Alzheimer's disease and mild cognitive impairment. J. Neural Eng. 13:046008 [Google Scholar]
  99. Petti M, Toppi J, Pichiorri F, Cincotti F, Salinari S. 99.  et al. 2013. Aged-related changes in brain activity classification with respect to age by means of graph indexes [sic]. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2013:4350–53 [Google Scholar]
  100. Sacchet MD, Prasad G, Foland-Ross LC, Thompson PM, Gotlib IH. 100.  2014. Elucidating brain connectivity networks in major depressive disorder using classification-based scoring. Proc. IEEE Int. Symp. Biomed. Imaging 2014:246–49 [Google Scholar]
  101. Toppi J, Anzolin A, Petti M, Cincotti F, Mattia D. 101.  et al. 2014. Investigating statistical differences in connectivity patterns properties at single subject level: a new resampling approach. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2014:6357–60 [Google Scholar]
  102. Zhao C, Zhao M, Yang Y, Gao J, Rao N, Lin P. 102.  2016. The reorganization of human brain networks modulated by driving mental fatigue. IEEE J. Biomed. Health Inform. In press
  103. Toppi J, Mattia D, Anzolin A, Risetti M, Petti M. 103.  et al. 2014. Time varying effective connectivity for describing brain network changes induced by a memory rehabilitation treatment. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2014:6786–89 [Google Scholar]
  104. Ge R, Zhang H, Yao L, Long Z. 104.  2015. Motor imagery learning induced changes in functional connectivity of the default mode network. IEEE Trans. Neural Syst. Rehabil. Eng. 23:138–48 [Google Scholar]
  105. Stoeckel LE, Garrison KA, Ghosh S, Wighton P, Hanlon CA. 105.  et al. 2014. Optimizing real time fMRI neurofeedback for therapeutic discovery and development. NeuroImage Clin. 5:245–55 [Google Scholar]
  106. Banca P, Sousa T, Duarte IC, Branco M. 106.  2015. Visual motion imagery neurofeedback based on the hMT+/V5 complex: evidence for a feedback-specific neural circuit involving neocortical and cerebellar regions. J. Neural Eng. 12:066003 [Google Scholar]
  107. Fels M, Bauer R, Gharabaghi A. 107.  2015. Predicting workload profiles of brain-robot interface and electromygraphic neurofeedback with cortical resting-state networks: personal trait or task-specific challenge. J. Neural Eng. 12:046029 [Google Scholar]
  108. Andersen RA, Kellis S, Klaes C, Aflalo T. 108.  2014. Toward more versatile and intuitive cortical brain–machine interfaces. Curr. Biol. 24:R885–97 [Google Scholar]
  109. Zhang R, Yao D, Valdes-Sosa PA, Li F, Li P. 109.  et al. 2015. Efficient resting-state EEG network facilitates motor imagery performance. J. Neural Eng. 12:066024 [Google Scholar]
  110. Sporns O. 110.  2013. The human connectome: origins and challenges. NeuroImage 80:53–61 [Google Scholar]
  111. Christopoulos VN, Boeff DV, Evans CD, Crowe DA, Amirikian B. 111.  et al. 2012. A network analysis of developing brain cultures. J. Neural Eng. 9:046008 [Google Scholar]
  112. Kafashan M, Ching S. 112.  2015. Optimal stimulus scheduling for active estimation of evoked brain networks. J. Neural Eng. 12:066011 [Google Scholar]
  113. Kanagasabapathi TT, Massobrio P, Barone RA, Tedesco M, Martinoia S. 113.  et al. 2012. Functional connectivity and dynamics of cortical-thalamic networks co-cultured in a dual compartment device. J. Neural Eng. 9:036010 [Google Scholar]
  114. Chang JC, Brewer GJ, Wheeler BC. 114.  2006. Neuronal network structuring induces greater neuronal activity through enhanced astroglial development. J. Neural Eng. 3:217–26 [Google Scholar]
  115. Viallon M, Cuvinciuc V, Delattre B, Merlini L, Barnaure-Nachbar I. 115.  et al. 2015. State-of-the-art MRI techniques in neuroradiology: principles, pitfalls, and clinical applications. Neuroradiology 57:441–67 [Google Scholar]
  116. Beckmann CF, DeLuca M, Devlin JT, Smith SM. 116.  2005. Investigations into resting-state connectivity using independent component analysis. Philos. Trans. R. Soc. Lond. B 360:1001–13 [Google Scholar]
  117. De Luca M, Beckmann CF, De Stefano N, Matthews PM, Smith SM. 117.  2006. fMRI resting state networks define distinct modes of long-distance interactions in the human brain. NeuroImage 29:1359–67 [Google Scholar]
  118. Greicius MD, Supekar K, Menon V, Dougherty RF. 118.  2009. Resting-state functional connectivity reflects structural connectivity in the default mode network. Cereb. Cortex 19:72–78 [Google Scholar]
  119. Aurich NK, Filho JA. 119.  2015. Evaluating the reliability of different preprocessing steps to estimate graph theoretical measures in resting state fMRI data. Front. Neurosci. 9:48 [Google Scholar]
  120. Brooks JCW, Faull OK, Pattinson KTS, Jenkinson M. 120.  2013. Physiological noise in brainstem fmri. Front. Hum. Neurosci. 7:623 [Google Scholar]
  121. Marchitelli R, Minati L, Marizzoni M, Bosch B, Bartrés-Faz D. 121.  et al. 2016. Test–retest reliability of the default mode network in a multi-centric fMRI study of healthy elderly: effects of data-driven physiological noise correction techniques. Hum. Brain Mapp. 37:2114–32 [Google Scholar]
  122. Birn RM, Diamond JB, Smith MA, Bandettini PA. 122.  2006. Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI. NeuroImage 31:1536–48 [Google Scholar]
  123. Shmueli K, van Gelderen P, de Zwart JA, Horovitz SG, Fukunaga M. 123.  et al. 2007. Low-frequency fluctuations in the cardiac rate as a source of variance in the resting-state fMRI BOLD signal. NeuroImage 38:306–20 [Google Scholar]
  124. Glover GH, Li TQ, Ress D. 124.  2000. Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magn. Reson. Med. 44:162–67 [Google Scholar]
  125. Beall EB, Lowe MJ. 125.  2010. The non-separability of physiologic noise in functional connectivity MRI with spatial ICA at 3T. J. Neurosci. Methods 191:263–76 [Google Scholar]
  126. Salimi-Khorshidi G, Douaud G, Beckmann CF, Glasser MF, Griffanti L, Smith SM. 126.  2014. Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers. NeuroImage 90:449–68 [Google Scholar]
  127. Beall EB, Lowe MJ. 127.  2007. Isolating physiologic noise sources with independently determined spatial measures. NeuroImage 37:1286–300 [Google Scholar]
  128. Chang C, Glover GH. 128.  2010. Time-frequency dynamics of resting-state brain connectivity measured with fMRI. NeuroImage 50:81–98 [Google Scholar]
  129. Fox MD, Zhang D, Snyder AZ, Raichle ME. 129.  2009. The global signal and observed anticorrelated resting state brain networks. J. Neurophysiol. 101:3270–83 [Google Scholar]
  130. Murphy K, Birn RM, Handwerker DA, Jones TB, Bandettini PA. 130.  2009. The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced. NeuroImage 44:893–905 [Google Scholar]
  131. Andellini M, Cannat V, Gazzellini S, Bernardi B, Napolitano A. 131.  2015. Test–retest reliability of graph metrics of resting state MRI functional brain networks: a review. J. Neurosci. Methods 253:183–92 [Google Scholar]
  132. Satterthwaite TD, Elliott MA, Gerraty RT, Ruparel K, Loughead J. 132.  et al. 2013. An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. NeuroImage 64:240–56 [Google Scholar]
  133. Ciric R, Wolf DH, Power JD, Roalf DR, Baum G. 133.  et al. 2016. Benchmarking confound regression strategies for the control of motion artifact in studies of functional connectivity. https://doi.org/10.1016/j.neuroimage.2017.03.020 [Crossref]
  134. Friston KJ, Williams S, Howard R, Frackowiak RS, Turner R. 134.  1996. Movement-related effects in fMRI time series. Magn. Reson. Med. 35:346–55 [Google Scholar]
  135. Lemieux L, Salek-Haddadi A, Lund TE, Laufs H, Carmichael D. 135.  2007. Modelling large motion events in fMRI studies of patients with epilepsy. Magn. Reson. Imaging 25:894–901 [Google Scholar]
  136. Tierney TM, Weiss-Croft LJ, Centeno M, Shamshiri EA, Perani S. 136.  et al. 2016. FIACH: a biophysical model for automatic retrospective noise control in fMRI. NeuroImage 124:1009–20 [Google Scholar]
  137. Hagmann P, Jonasson L, Maeder P, Thiran JP, Wedeen VJ, Meuli R. 137.  2006. Understanding diffusion MR imaging techniques: from scalar diffusion–weighted imaging to diffusion tensor imaging and beyond. Radiographics 26:Suppl. 1S205–23 [Google Scholar]
  138. Pestilli F, Yeatman JD, Rokem A, Kay KN, Wandell BA. 138.  2014. Evaluation and statistical inference for human connectomes. Nat. Methods 11:1058–63 [Google Scholar]
  139. Yeh CH, Smith RE, Liang X, Calamante F, Connelly A. 139.  2016. Correction for diffusion MRI fibre tracking biases: the consequences for structural connectomic metrics. NeuroImage 142:150–62 [Google Scholar]
  140. Hutchison RM, Womelsdorf T, Allen EA, Bandettini PA, Calhoun VD. 140.  et al. 2013. Dynamic functional connectivity: promise, issues, and interpretations. NeuroImage 80:360–78 [Google Scholar]
  141. Calhoun VD, Miller R, Pearlson G, Adal T. 141.  2014. The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron 84:262–74 [Google Scholar]
  142. Kopell NJ, Gritton HJ, Whittington MA, Kramer MA. 142.  2014. Beyond the connectome: the dynome. Neuron 83:1319–28 [Google Scholar]
  143. Kivel M, Arenas A, Barthelemy M, Gleeson JP, Moreno Y, Porter MA. 143.  2014. Multilayer networks. J. Complex Netw. 2:203–71 [Google Scholar]
  144. Holme P, Saramaki J. 144.  2012. Temporal networks. Phys. Rep. 519:97–125 [Google Scholar]
  145. Mucha PJ, Richardson T, Macon K, Porter MA, Onnela JP. 145.  2010. Community structure in time-dependent, multiscale, and multiplex networks. Science 328:876–78 [Google Scholar]
  146. Bassett DS, Porter MA, Wymbs NF, Grafton ST, Carlson JM, Mucha PJ. 146.  2013. Robust detection of dynamic community structure in networks. Chaos 23:013142 [Google Scholar]
  147. Reinkensmeyer DJ, Burdet E, Casadio M, Krakauer JW, Kwakkel G. 147.  et al. 2016. Computational neurorehabilitation: modeling plasticity and learning to predict recovery. J. Neuroeng. Rehabil. 13:42 [Google Scholar]
  148. Bassett DS, Wymbs NF, Porter MA, Mucha PJ, Carlson JM, Grafton ST. 148.  2011. Dynamic reconfiguration of human brain networks during learning. PNAS 108:7641–46 [Google Scholar]
  149. Mantzaris AV, Bassett DS, Wymbs NF, Estrada E, Porter MA. 149.  et al. 2013. Dynamic network centrality summarizes learning in the human brain. J. Complex Netw. 1:83–92 [Google Scholar]
  150. Bassett DS, Wymbs NF, Porter MA, Mucha PJ, Grafton ST. 150.  2014. Cross-linked structure of network evolution. Chaos 24:013112 [Google Scholar]
  151. Bassett DS, Yang M, Wymbs NF, Grafton ST. 151.  2015. Learning-induced autonomy of sensorimotor systems. Nat. Neurosci. 18:744–51 [Google Scholar]
  152. Heitger MH, Ronsse R, Dhollander T, Dupont P, Caeyenberghs K, Swinnen SP. 152.  2012. Motor learning–induced changes in functional brain connectivity as revealed by means of graph-theoretical network analysis. NeuroImage 61:633–50 [Google Scholar]
  153. Mattar MG, Thompson-Schill SL, Bassett DS. 153.  2016. The network architecture of value learning. arXiv:1607.04169 [q-bio]
  154. Karuza EA, Thompson-Schill SL, Bassett DS. 154.  2016. Local patterns to global architectures: influences of network topology on human learning. Trends Cogn. Sci. 20:629–40 [Google Scholar]
  155. Muldoon SF, Bassett DS. 155.  2016. Network and multilayer network approaches to understanding human brain dynamics. Philos. Sci. 83:710–20 [Google Scholar]
  156. Nicosia V, Skardal PS, Latora V, Arenas A. 156.  2014. Spontaneous synchronization driven by energy transport in interconnected networks. arXiv:1405.5855v2 [nlin]
  157. Brookes MJ, Tewarie PK, Hunt BA, Robson SE, Gascoyne LE. 157.  et al. 2016. A multi-layer network approach to MEG connectivity analysis. NeuroImage 132:425–38 [Google Scholar]
  158. De Domenico M, Sasai S, Arenas A. 158.  2016. Mapping multiplex hubs in human functional brain network. Front. Neurosci. 10:326 [Google Scholar]
  159. Kolaczyk ED. 159.  2009. Statistical Analysis of Network Data: Methods and Models Berlin: Springer
  160. Simpson SL, Lyday RG, Hayasaka S, Marsh AP, Laurienti PJ. 160.  2013. A permutation testing framework to compare groups of brain networks. Front. Comput. Neurosci. 7:171 [Google Scholar]
  161. Winkler AM, Webster MA, Vidaurre D, Nichols TE, Smith SM. 161.  2015. Multi-level block permutation. NeuroImage 123:253–68 [Google Scholar]
  162. Ginestet CE, Simmons A. 162.  2011. Statistical parametric network analysis of functional connectivity dynamics during a working memory task. NeuroImage 55:688–704 [Google Scholar]
  163. Bassett DS, Nelson BG, Mueller BA, Camchong J, Lim KO. 163.  2012. Altered resting state complexity in schizophrenia. NeuroImage 59:2196–207 [Google Scholar]
  164. Betzel RF, Medaglia JD, Papadopoulos L, Baum G, Gur RC. 164.  et al. 2016. The modular organization of human anatomical brain networks: accounting for the cost of wiring. Netw. Neurosci In press
  165. Ramsay JO, Silverman BW. 165.  2005. Functional Data Analysis Berlin: Springer
  166. Ramsay JO, Silverman BW. 166.  2002. Applied Functional Data Analysis: Methods and Case Studies Berlin: Springer
  167. Ramsay JO. 167.  2006. Functional Data Analysis New York: Wiley
  168. Papadopoulos L, Puckett J, Daniels KE, Bassett DS. 168.  2016. Evolution of network architecture in a granular material under compression. Phys. Rev. E 94:032908 [Google Scholar]
  169. Bassett DS, Owens ET, Porter MA, Manning ML, Daniels KE. 169.  2015. Extraction of force-chain network architecture in granular materials using community detection. Soft Matter 11:2731–44 [Google Scholar]
  170. Lepage KQ, Ching S, Kramer MA. 170.  2013. Inferring evoked brain connectivity through adaptive perturbation. J. Comput. Neurosci. 34:303–18 [Google Scholar]
  171. Shehzad Z, Kelly C, Reiss PT, Craddock RC, Emerson JW. 171.  et al. 2014. A multivariate distance-based analytic framework for connectome-wide association studies. NeuroImage 93:74–94 [Google Scholar]
  172. Simpson SL, Moussa MN, Laurienti PJ. 172.  2012. An exponential random graph modeling approach to creating group-based representative whole-brain connectivity networks. NeuroImage 60:1117–26 [Google Scholar]
  173. Klimm F, Bassett DS, Carlson JM, Mucha PJ. 173.  2014. Resolving structural variability in network models and the brain. PLOS Comput. Biol. 10:e1003491 [Google Scholar]
  174. Ganmor E, Segev R, Schneidman E. 174.  2011. The architecture of functional interaction networks in the retina. J. Neurosci. 31:3044–54 [Google Scholar]
  175. Ganmor E, Segev R, Schneidman E. 175.  2011. Sparse low-order interaction network underlies a highly correlated and learnable neural population code. PNAS 108:9679–84 [Google Scholar]
  176. Giusti C, Ghrist R, Bassett DS. 176.  2016. Two's company, three (or more) is a simplex: algebraic-topological tools for understanding higher-order structure in neural data. J. Comput. Neurosci. 41:1–14 [Google Scholar]
  177. Giusti C, Pastalkova E, Curto C, Itskov V. 177.  2015. Clique topology reveals intrinsic geometric structure in neural correlations. PNAS 112:13455–60 [Google Scholar]
  178. Curto C. 178.  2016. What can topology tell us about the neural code?. arXiv:1605.01905 [q-bio]
  179. Sizemore A, Giusti C, Bassett DS. 179.  2016. Classification of weighted networks through mesoscale homological features. J. Complex Netw. 2016:cnw013 [Google Scholar]
  180. Kim E, Kang H, Lee H, Lee HJ, Suh MW. 180.  et al. 2014. Morphological brain network assessed using graph theory and network filtration in deaf adults. Hear. Res. 315:88–98 [Google Scholar]
  181. Liu YY, Slotine JJ, Barabási AL. 181.  2011. Controllability of complex networks. Nature 473:167–73 [Google Scholar]
  182. Pasqualetti F, Zampieri S, Bullo F. 182.  2014. Controllability metrics, limitations and algorithms for complex networks. IEEE Trans. Control Netw. Syst. 1:40–52 [Google Scholar]
  183. Schiff SJ. 183.  2011. Neural Control Engineering: The Emerging Intersection Between Control Theory and Neuroscience Cambridge, MA: MIT Press
  184. Motter AE. 184.  2015. Networkcontrology. Chaos 25:097621 [Google Scholar]
  185. Gu S, Pasqualetti F, Cieslak M, Telesford QK, Yu AB. 185.  et al. 2015. Controllability of structural brain networks. Nat. Commun. 6:8414 [Google Scholar]
  186. Betzel RF, Gu S, Medaglia JD, Pasqualetti F, Bassett DS. 186.  2016. Optimally controlling the human connectome: the role of network topology. Sci. Rep. 29:30770 [Google Scholar]
  187. Gu S, Betzel RF, Cieslak M, Delio PR, Grafton ST. 187.  et al. 2017. Optimal trajectories of brain state transitions. NeuroImage 148:305–17 [Google Scholar]
  188. Cornelius SP, Kath WL, Motter AE. 188.  2013. Realistic control of network dynamics. Nat. Commun. 4:1942 [Google Scholar]
  189. Medaglia JD, Gu S, Pasqualetti F, Ashare RL, Lerman C. 189.  et al. 2016. Cognitive control in the controllable connectome. arXiv:1606.09185 [q-bio]
  190. Tang E, Giusti C, Baum G, Gu S, Kahn AE. 190.  et al. 2016. Structural drivers of diverse neural dynamics and their evolution across development. arXiv:1607.01010 [q-bio.NC]
  191. Ching S, Ritt JT. 191.  2013. Control strategies for underactuated neural ensembles driven by optogenetic stimulation. Front. Neural Circuits 7:54 [Google Scholar]
  192. Muldoon SF, Pasqualetti F, Gu S, Cieslak M, Grafton ST. 192.  et al. 2016. Stimulation-based control of dynamic brain networks. PLOS Comput. Biol. 12:e1005076 [Google Scholar]
  193. Hao S, Subramanian S, Jordan A, Santaniello S, Yaffe R. 193.  et al. 2014. Computing network-based features from intracranial EEG time series data: application to seizure focus localization. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2014:5812–15 [Google Scholar]
  194. Santaniello S, Burns SP, Golby AJ, Singer JM, Anderson WS, Sarma SV. 194.  2011. Quickest detection of drug-resistant seizures: an optimal control approach. Epilepsy Behav. 22:S49–60 [Google Scholar]
  195. Ching S, Brown EN, Kramer MA. 195.  2012. Distributed control in a mean-field cortical network model: implications for seizure suppression. Phys. Rev. E 86:021920 [Google Scholar]
  196. Crofts JJ, Higham DJ, Bosnell R, Jbabdi S, Matthews PM. 196.  et al. 2011. Network analysis detects changes in the contralesional hemisphere following stroke. NeuroImage 54:161–69 [Google Scholar]
  197. Wymbs NF, Bassett DS, Mucha PJ, Porter MA, Grafton ST. 197.  2012. Motor chunking is correlated with activation of the human sensorimotor putamen. Neuron 74:936–46 [Google Scholar]
  198. Onnela JP, Rauch SL. 198.  2016. Harnessing smartphone-based digital phenotyping to enhance behavioral and mental health. Neuropsychopharmacology 41:1691–96 [Google Scholar]
  199. Torous J, Kiang MV, Lorme J, Onnela JP. 199.  2016. New tools for new research in psychiatry: a scalable and customizable platform to empower data driven smartphone research. JMIR Ment. Health 3:e16 [Google Scholar]
  200. Beagan JA, Gilgenast TG, Kim J, Plona Z, Norton HK. 200.  et al. 2016. Local genome topology can exhibit an incompletely rewired 3D folding state during somatic cell reprogramming. Cell Stem Cell 18:611–24 [Google Scholar]
  201. Murphy AC, Muldoon SF, Baker D, Lastowka A, Bennett B. 201.  et al. 2016. Structure, function, and control of the musculoskeletal network. arXiv. 2016
  202. Baldassano SN, Bassett DS. 202.  2016. Topological distortion and reorganized modular structure of gut microbial co-occurrence networks in inflammatory bowel disease. Sci. Rep. 6:26087 [Google Scholar]
  203. Steinway SN, Biggs MB, Loughran TPJ, Papin JA, Albert R. 203.  2015. Inference of network dynamics and metabolic interactions in the gut microbiome. PLOS Comput. Biol. 11:e1004338 [Google Scholar]
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Emerging Frontiers of Neuroengineering: A Network Science of Brain Connectivity
Annual Review of Biomedical Engineering 19, 327 (2017); https://doi.org/10.1146/annurev-bioeng-071516-044511
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