The modulation of neural gain facilitates a transition between functional segregation and integration in the brain

eLife

Volumn 7, Issue , 2018, Pages

  Shine, James M ^a,b   Aburn, Matthew J ^c   Breakspear, Michael ^c,d   Poldrack, Russell A ^a  

^a STANFORD UNIVERSITY   (United States)

^b UNIVERSITY OF SYDNEY   (Australia)

^c QIMR BERGHOFER MEDICAL RESEARCH INSTITUTE   (Australia)

^d Metro North Mental Health Service   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BRAIN; HUMAN; HUMAN EXPERIMENT; MODULATION; ARTIFICIAL NEURAL NETWORK; COGNITION; COMPUTER SIMULATION; NERVE CELL; NERVE CELL NETWORK; PHYSIOLOGY;

BRAIN; COGNITION; COMPUTER SIMULATION; NERVE NET; NEURAL NETWORKS (COMPUTER); NEURONS;

EID: 85043511735     PISSN: None     EISSN: 2050084X     Source Type: Journal    
DOI: 10.7554/eLife.31130     Document Type: Article

References (75)

1. Aldecoa R, Marín I. 2013. Surprise maximization reveals the community structure of complex networks. Scientific Reports 3:1060. DOI: https://doi.org/10.1038/srep01060
2. Aston-Jones G, Cohen JD. 2005. An integrative theory of locus coeruleus-norepinephrine function: adaptive gain and optimal performance. Annual Review of Neuroscience 28:403-450. DOI: https://doi.org/10.1146/annurev. neuro.28.061604.135709
3. Barttfeld P, Uhrig L, Sitt JD, Sigman M, Jarraya B, Dehaene S. 2015. Signature of consciousness in the dynamics of resting-state brain activity. PNAS 112:887-892. DOI: https://doi.org/10.1073/pnas.1418031112
4. Bassett DS, Yang M, Wymbs NF, Grafton ST. 2015. Learning-induced autonomy of sensorimotor systems. Nature Neuroscience 18:744-751. DOI: https://doi.org/10.1038/nn.3993
5. Bertolero MA, Yeo BTT, D’Esposito M. 2017. The diverse club. Nature Communications 8:1277. DOI: https://doi. org/10.1038/s41467-017-01189-w
6. Breakspear M, Heitmann S, Daffertshofer A. 2010. Generative models of cortical oscillations: neurobiological implications of the kuramoto model. Frontiers in Human Neuroscience 4:190. DOI: https://doi.org/10.3389/ fnhum.2010.00190
7. Breakspear M. 2017. Dynamic models of large-scale brain activity. Nature Neuroscience 20:340-352. DOI: https://doi.org/10.1038/nn.4497
8. Bullmore E, Sporns O. 2012. The economy of brain network organization. Nature Reviews Neuroscience 13:336-349. DOI: https://doi.org/10.1038/nrn3214
9. Chang C, Glover GH. 2010. Time-frequency dynamics of resting-state brain connectivity measured with fMRI. NeuroImage 50:81-98. DOI: https://doi.org/10.1016/j.neuroimage.2009.12.011
10. Chialvo DR. 2010. Emergent complex neural dynamics. Nature Physics 6:744-750. DOI: https://doi.org/10.1038/ nphys1803
11. Cocchi L, Gollo LL, Zalesky A, Breakspear M. 2017. Criticality in the brain: A synthesis of neurobiology, models and cognition. Progress in Neurobiology 158:132-152. DOI: https://doi.org/10.1016/j.pneurobio.2017.07.002
12. Cohen JR, D’Esposito M. 2016. The segregation and integration of distinct brain networks and their relationship to cognition. Journal of Neuroscience 36:12083-12094. DOI: https://doi.org/10.1523/JNEUROSCI.2965-15. 2016
13. Curto C, Sakata S, Marguet S, Itskov V, Harris KD. 2009. A simple model of cortical dynamics explains variability and state dependence of sensory responses in urethane-anesthetized auditory cortex. Journal of Neuroscience 29:10600-10612. DOI: https://doi.org/10.1523/JNEUROSCI.2053-09.2009
14. Deco G, Jirsa V, McIntosh AR, Sporns O, Kötter R. 2009. Key role of coupling, delay, and noise in resting brain fluctuations. PNAS 106:10302-10307. DOI: https://doi.org/10.1073/pnas.0901831106
15. Deco G, Jirsa VK, McIntosh AR. 2013. Resting brains never rest: computational insights into potential cognitive architectures. Trends in Neurosciences 36:268-274. DOI: https://doi.org/10.1016/j.tins.2013.03.001
16. Deco G, Jirsa VK. 2012. Ongoing cortical activity at rest: criticality, multistability, and ghost attractors. Journal of Neuroscience 32:3366-3375. DOI: https://doi.org/10.1523/JNEUROSCI.2523-11.2012
17. Deco G, Kringelbach ML, Jirsa VK, Ritter P. 2017. The dynamics of resting fluctuations in the brain: metastability and its dynamical cortical core. Scientific Reports 7:3095. DOI: https://doi.org/10.1038/s41598-017-03073-5
18. Deco G, Tononi G, Boly M, Kringelbach ML. 2015a. Rethinking segregation and integration: contributions of whole-brain modelling. Nature Reviews Neuroscience 16:430-439. DOI: https://doi.org/10.1038/nrn3963
19. Deco G, Tononi G, Boly M, Kringelbach ML. 2015b. Rethinking segregation and integration: contributions of whole-brain modelling. Nature Reviews Neuroscience 16:430-439. DOI: https://doi.org/10.1038/nrn3963
20. Eldar E, Cohen JD, Niv Y. 2013. The effects of neural gain on attention and learning. Nature Neuroscience 16:1146-1153. DOI: https://doi.org/10.1038/nn.3428
21. Estrada E, Hatano N. 2008. Communicability in complex networks. Physical Review E 77:036111. DOI: https:// doi.org/10.1103/PhysRevE.77.036111
22. Fitzhugh R. 1961. Impulses and physiological states in theoretical models of nerve membrane. Biophysical Journal 1:445-466. DOI: https://doi.org/10.1016/S0006-3495(61)86902-6
23. Foster BL, He BJ, Honey CJ, Jerbi K, Maier A, Saalmann YB. 2016. Spontaneous Neural Dynamics and Multi- scale Network Organization. Frontiers in Systems Neuroscience 10:7. DOI: https://doi.org/10.3389/fnsys.2016. 00007
24. Freeman WJ. 1979. Nonlinear gain mediating cortical stimulus-response relations. Biological Cybernetics 33:237-247. DOI: https://doi.org/10.1007/BF00337412
25. Fries P. 2005. A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends in Cognitive Sciences 9:474-480. DOI: https://doi.org/10.1016/j.tics.2005.08.011
26. Fries P. 2015. Rhythms for Cognition: Communication through coherence. Neuron 88:220-235. DOI: https://doi. org/10.1016/j.neuron.2015.09.034
27. Friston KJ, Mechelli A, Turner R, Price CJ. 2000. Nonlinear responses in fMRI: the Balloon model, Volterra kernels, and other hemodynamics. NeuroImage 12:466-477. DOI: https://doi.org/10.1006/nimg.2000.0630
28. Ghosh A, Rho Y, McIntosh AR, Kötter R, Jirsa VK. 2008. Noise during rest enables the exploration of the brain’s dynamic repertoire. PLoS Computational Biology 4:e1000196. DOI: https://doi.org/10.1371/journal.pcbi. 1000196
29. Godwin D, Barry RL, Marois R. 2015. Breakdown of the brain’s functional network modularity with awareness. PNAS 112:3799-3804. DOI: https://doi.org/10.1073/pnas.1414466112
30. Gollo LL, Zalesky A, Hutchison RM, van den Heuvel M, Breakspear M. 2015. Dwelling quietly in the rich club:brain network determinants of slow cortical fluctuations. Philosophical Transactions of the Royal Society B:Biological Sciences 370:20140165. DOI: https://doi.org/10.1098/rstb.2014.0165
31. Hearne LJ, Cocchi L, Zalesky A, Mattingley JB. 2017. Reconfiguration of Brain Network Architectures between Resting-State and Complexity-Dependent Cognitive Reasoning. The Journal of Neuroscience 37:0485-17-0488411. DOI: https://doi.org/10.1523/JNEUROSCI.0485-17.2017
32. Heitmann S, Breakspear M. 2017. Handbook for the Brain Dynamics Toolbox Version 2017c. In: QIMR Berghofer Medical Research Institute. ISBN: 9781549720703
33. Honey CJ, Kötter R, Breakspear M, Sporns O. 2007. Network structure of cerebral cortex shapes functional connectivity on multiple time scales. PNAS 104:10240-10245. DOI: https://doi.org/10.1073/pnas.0701519104
34. Honey CJ, Sporns O, Cammoun L, Gigandet X, Thiran JP, Meuli R, Hagmann P. 2009. Predicting human resting- state functional connectivity from structural connectivity. PNAS 106:2035-2040. DOI: https://doi.org/10.1073/ pnas.0811168106
35. Hutchison RM, Womelsdorf T, Allen EA, Bandettini PA, Calhoun VD, Corbetta M, Della Penna S, Duyn JH, Glover GH, Gonzalez-Castillo J, Handwerker DA, Keilholz S, Kiviniemi V, Leopold DA, de Pasquale F, Sporns O, Walter M, Chang C. 2013. Dynamic functional connectivity: promise, issues, and interpretations. NeuroImage 80:360-378. DOI: https://doi.org/10.1016/j.neuroimage.2013.05.079
36. Hwang K, Bertolero MA, Liu WB, D’Esposito M. 2017. The human thalamus is an integrative Hub for functional brain networks. The Journal of Neuroscience 37:5594-5607. DOI: https://doi.org/10.1523/JNEUROSCI.0067-17.2017
37. Izhikevich E, FitzHugh R. 2006. FitzHugh-Nagumo model. Scholarpedia 1:1349. DOI: https://doi.org/10.4249/ scholarpedia.1349
38. Jirsa VK, Sporns O, Breakspear M, Deco G, McIntosh AR. 2010. Towards the virtual brain: network modeling of the intact and the damaged brain. Archives Italiennes De Biologie 148:189-205.
39. Joshi S, Li Y, Kalwani RM, Gold JI. 2016. Relationships between Pupil Diameter and Neuronal Activity in the Locus Coeruleus, Colliculi, and Cingulate Cortex. Neuron 89:221-234. DOI: https://doi.org/10.1016/j.neuron. 2015.11.028
40. Kuramoto Y. 1984. Chemical Oscillations, Waves, and Turbulence. DOI: https://doi.org/10.1007/978-3-642-69689-3
41. Kötter R. 2004. Online retrieval, processing, and visualization of primate connectivity data from the CoCoMac database. Neuroinformatics 2:127-144. DOI: https://doi.org/10.1385/NI:2:2:127
42. Lisman JE, Jensen O. 2013. The q-g neural code. Neuron 77:1002-1016. DOI: https://doi.org/10.1016/j.neuron. 2013.03.007
43. Mišić B, Betzel RF, Nematzadeh A, Goñi J, Griffa A, Hagmann P, Flammini A, Ahn YY, Sporns O. 2015. Cooperative and competitive spreading dynamics on the human connectome. Neuron 86:1518-1529. DOI: https://doi.org/10.1016/j.neuron.2015.05.035
44. Murphy PR, O’Connell RG, O’Sullivan M, Robertson IH, Balsters JH. 2014. Pupil diameter covaries with BOLD activity in human locus coeruleus. Human Brain Mapping 35:4140-4154. DOI: https://doi.org/10.1002/hbm. 22466
45. Park HJ, Friston K. 2013. Structural and functional brain networks: from connections to cognition. Science 342:1238411. DOI: https://doi.org/10.1126/science.1238411
46. Reimer J, Froudarakis E, Cadwell CR, Yatsenko D, Denfield GH, Tolias AS. 2014. Pupil fluctuations track fast switching of cortical states during quiet wakefulness. Neuron 84:355-362. DOI: https://doi.org/10.1016/j. neuron.2014.09.033
47. Reimer J, McGinley MJ, Liu Y, Rodenkirch C, Wang Q, McCormick DA, Tolias AS. 2016. Pupil fluctuations track rapid changes in adrenergic and cholinergic activity in cortex. Nature Communications 7:13289. DOI: https:// doi.org/10.1038/ncomms13289
48. Robbins TW, Arnsten AF. 2009. The neuropsychopharmacology of fronto-executive function: monoaminergic modulation. Annual Review of Neuroscience 32:267-287. DOI: https://doi.org/10.1146/annurev.neuro.051508. 135535
49. Roberts JA, Boonstra TW, Breakspear M. 2015. The heavy tail of the human brain. Current Opinion in Neurobiology 31:164-172. DOI: https://doi.org/10.1016/j.conb.2014.10.014
50. Roberts JA, Perry A, Roberts G, Mitchell PB, Breakspear M. 2017. Consistency-based thresholding of the human connectome. NeuroImage 145:118-129. DOI: https://doi.org/10.1016/j.neuroimage.2016.09.053
51. Rubinov M, Sporns O. 2010. Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52:1059-1069. DOI: https://doi.org/10.1016/j.neuroimage.2009.10.003
52. Rüemelin W. 1982. Numerical Treatment of Stochastic Differential Equations. SIAM Journal on Numerical Analysis 19:604-613. DOI: https://doi.org/10.1137/0719041
53. Safaai H, Neves R, Eschenko O, Logothetis NK, Panzeri S. 2015. Modeling the effect of locus coeruleus firing on cortical state dynamics and single-trial sensory processing. PNAS 112:12834-12839. DOI: https://doi.org/10. 1073/pnas.1516539112
54. Sanz Leon P, Knock SA, Woodman MM, Domide L, Mersmann J, McIntosh AR, Jirsa V. 2013. The Virtual Brain: a simulator of primate brain network dynamics. Frontiers in Neuroinformatics 7:10. DOI: https://doi.org/10.3389/ fninf.2013.00010
55. Sara SJ. 2009. The locus coeruleus and noradrenergic modulation of cognition. Nature Reviews Neuroscience 10:211-223. DOI: https://doi.org/10.1038/nrn2573
56. Servan-Schreiber D, Printz H, Cohen JD. 1990. A network model of catecholamine effects: gain, signal-to-noise ratio, and behavior. Science 249:892-895. DOI: https://doi.org/10.1126/science.2392679
57. Shine JM, Bissett PG, Bell PT, Koyejo O, Balsters JH, Gorgolewski KJ, Moodie CA, Poldrack RA. 2016a. The dynamics of functional brain networks: integrated network states during cognitive task performance. Neuron 92:544-554. DOI: https://doi.org/10.1016/j.neuron.2016.09.018
58. Shine JM, Koyejo O, Bell PT, Gorgolewski KJ, Gilat M, Poldrack RA. 2015. Estimation of dynamic functional connectivity using Multiplication of Temporal Derivatives. NeuroImage 122:399-407. DOI: https://doi.org/10. 1016/j.neuroimage.2015.07.064
59. Shine JM, Koyejo O, Poldrack RA. 2016b. Temporal metastates are associated with differential patterns of time- resolved connectivity, network topology, and attention. PNAS 113:9888-9891. DOI: https://doi.org/10.1073/ pnas.1604898113
60. Shine JM, Poldrack RA. 2017. Principles of dynamic network reconfiguration across diverse brain states. NeuroImage. DOI: https://doi.org/10.1016/j.neuroimage.2017.08.010
61. Shine JM, van den Brink RL, Hernaus D, Nieuwenhuis S, Poldrack RA. 2018. Catecholaminergic manipulation alters dynamic network topology across cognitive states. Network Neuroscience. DOI: https://doi.org/10.1162/ netn_a_00042
62. Shine JM. 2018. Gain_topology. eeed0a2. Github. https://github.com/macshine/ gain_topology
63. Siegel M, Warden MR, Miller EK. 2009. Phase-dependent neuronal coding of objects in short-term memory. PNAS 106:21341-21346. DOI: https://doi.org/10.1073/pnas.0908193106
64. Sporns O. 2013. The human connectome: origins and challenges. NeuroImage 80:53-61. DOI: https://doi.org/ 10.1016/j.neuroimage.2013.03.023
65. Stefanescu RA, Jirsa VK. 2008. A low dimensional description of globally coupled heterogeneous neural networks of excitatory and inhibitory neurons. PLoS Computational Biology 4:e1000219. DOI: https://doi.org/ 10.1371/journal.pcbi.1000219
66. Stefanescu RA, Jirsa VK. 2011. Reduced representations of heterogeneous mixed neural networks with synaptic coupling. Physical Review E 83:026204. DOI: https://doi.org/10.1103/PhysRevE.83.026204
67. Stringer C, Pachitariu M, Steinmetz NA, Okun M, Bartho P, Harris KD, Sahani M, Lesica NA. 2016. Inhibitory control of correlated intrinsic variability in cortical networks. eLife 5:e19695. DOI: https://doi.org/10.7554/eLife. 19695
68. Tononi G, Sporns O, Edelman GM. 1994. A measure for brain complexity: relating functional segregation and integration in the nervous system. PNAS 91:5033-5037. DOI: https://doi.org/10.1073/pnas.91.11.5033
69. van den Heuvel MP, Sporns O. 2013. An anatomical substrate for integration among functional networks in human cortex. Journal of Neuroscience 33:14489-14500. DOI: https://doi.org/10.1523/JNEUROSCI.2128-13. 2013
70. Varela F, Lachaux JP, Rodriguez E, Martinerie J. 2001. The brainweb: phase synchronization and large-scale integration. Nature Reviews Neuroscience 2:229-239. DOI: https://doi.org/10.1038/35067550
71. Waterhouse BD, Sessler FM, Cheng JT, Woodward DJ, Azizi SA, Moises HC, Jung-Tung C. 1988. New evidence for a gating action of norepinephrine in central neuronal circuits of mammalian brain. Brain Research Bulletin 21:425-432. DOI: https://doi.org/10.1016/0361-9230(88)90154-2
72. Westphal AJ, Wang S, Rissman J. 2017. Episodic memory retrieval benefits from a less modular brain network organization. The Journal of Neuroscience 37:3523-3531. DOI: https://doi.org/10.1523/JNEUROSCI.2509-16. 2017
73. Woolrich MW, Stephan KE. 2013. Biophysical network models and the human connectome. NeuroImage 80:330-338. DOI: https://doi.org/10.1016/j.neuroimage.2013.03.059
74. Zalesky A, Fornito A, Cocchi L, Gollo LL, Breakspear M. 2014. Time-resolved resting-state brain networks. PNAS 111:10341-10346. DOI: https://doi.org/10.1073/pnas.1400181111
75. Zamora-López G, Chen Y, Deco G, Kringelbach ML, Zhou C. 2016. Functional complexity emerging from anatomical constraints in the brain: the significance of network modularity and rich-clubs. Scientific Reports 6:38424. DOI: https://doi.org/10.1038/srep38424