Graph-theoretical analysis for energy landscape reveals the organization of state transitions in the resting-state human cerebral cortex
Autoři:
Jiyoung Kang aff001; Chongwon Pae aff002; Hae-Jeong Park aff001
Působiště autorů:
Center for Systems and Translational Brain Sciences, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea
aff001; Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
aff002; BK21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea
aff003; Department of Cognitive Science, Yonsei University, Seoul, Republic of Korea
aff004
Vyšlo v časopise:
PLoS ONE 14(9)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0222161
Souhrn
The resting-state brain is often considered a nonlinear dynamic system transitioning among multiple coexisting stable states. Despite the increasing number of studies on the multistability of the brain system, the processes of state transitions have rarely been systematically explored. Thus, we investigated the state transition processes of the human cerebral cortex system at rest by introducing a graph-theoretical analysis of the state transition network. The energy landscape analysis of brain state occurrences, estimated using the pairwise maximum entropy model for resting-state fMRI data, identified multiple local minima, some of which mediate multi-step transitions toward the global minimum. The state transition among local minima is clustered into two groups according to state transition rates and most inter-group state transitions were mediated by a hub transition state. The distance to the hub transition state determined the path length of the inter-group transition. The cortical system appeared to have redundancy in inter-group transitions when the hub transition state was removed. Such a hub-like organization of transition processes disappeared when the connectivity of the cortical system was altered from the resting-state configuration. In the state transition, the default mode network acts as a transition hub, while coactivation of the prefrontal cortex and default mode network is captured as the global minimum. In summary, the resting-state cerebral cortex has a well-organized architecture of state transitions among stable states, when evaluated by a graph-theoretical analysis of the nonlinear state transition network of the brain.
Klíčová slova:
Biology and life sciences – Physical sciences – Chemistry – Research and analysis methods – Neuroscience – Computer and information sciences – Network analysis – Mathematics – Probability theory – Anatomy – Medicine and health sciences – Diagnostic medicine – Imaging techniques – Probability distribution – Brain – Brain mapping – Functional magnetic resonance imaging – Neuroimaging – Diagnostic radiology – Magnetic resonance imaging – Radiology and imaging – Neural networks – Physical chemistry – Reaction dynamics – Transition state – Systems science – Nonlinear dynamics – Nonlinear systems – Cerebral cortex
Zdroje
1. Deco G, Tononi G, Boly M, Kringelbach ML. Rethinking segregation and integration: contributions of whole-brain modelling. Nature reviews Neuroscience. 2015;16(7):430–9. doi: 10.1038/nrn3963 26081790
2. Deco G, Jirsa VK. Ongoing cortical activity at rest: criticality, multistability, and ghost attractors. The Journal of neuroscience: the official journal of the Society for Neuroscience. 2012;32(10):3366–75.
3. Cabral J, Kringelbach ML, Deco G. Exploring the network dynamics underlying brain activity during rest. Progress in neurobiology. 2014;114:102–31. doi: 10.1016/j.pneurobio.2013.12.005 24389385
4. Tognoli E, Kelso JA. The metastable brain. Neuron. 2014;81(1):35–48. doi: 10.1016/j.neuron.2013.12.022 24411730
5. Freyer F, Roberts JA, Ritter P, Breakspear M. A canonical model of multistability and scale-invariance in biological systems. PLoS Comput Biol. 2012;8(8):e1002634. doi: 10.1371/journal.pcbi.1002634 22912567
6. Freyer F, Roberts JA, Becker R, Robinson PA, Ritter P, Breakspear M. Biophysical mechanisms of multistability in resting-state cortical rhythms. The Journal of neuroscience: the official journal of the Society for Neuroscience. 2011;31(17):6353–61.
7. Kelso JA. Multistability and metastability: understanding dynamic coordination in the brain. Philos Trans R Soc Lond B Biol Sci. 2012;367(1591):906–18. doi: 10.1098/rstb.2011.0351 22371613
8. Schwartz JL, Grimault N, Hupe JM, Moore BC, Pressnitzer D. Multistability in perception: binding sensory modalities, an overview. Philos Trans R Soc Lond B Biol Sci. 2012;367(1591):896–905. doi: 10.1098/rstb.2011.0254 22371612
9. Breakspear M. Dynamic models of large-scale brain activity. Nature neuroscience. 2017;20(3):340–52. doi: 10.1038/nn.4497 28230845
10. Rabinovich MI, Varona P. Robust transient dynamics and brain functions. Front Comput Neurosci. 2011;5:24. doi: 10.3389/fncom.2011.00024 21716642
11. Monti RP, Hellyer P, Sharp D, Leech R, Anagnostopoulos C, Montana G. Estimating time-varying brain connectivity networks from functional MRI time series. NeuroImage. 2014;103:427–43. doi: 10.1016/j.neuroimage.2014.07.033 25107854
12. Park H-J, Friston KJ, Pae C, Park B, Razi A. Dynamic effective connectivity in resting state fMRI. NeuroImage. 2017.
13. Jeong SO, Pae C, Park HJ. Connectivity-based change point detection for large-size functional networks. NeuroImage. 2016;143:353–63. doi: 10.1016/j.neuroimage.2016.09.019 27622394
14. Hutchison RM, Womelsdorf T, Gati JS, Everling S, Menon RS. Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaques. Hum Brain Mapp. 2013;34(9):2154–77. doi: 10.1002/hbm.22058 22438275
15. Handwerker DA, Roopchansingh V, Gonzalez-Castillo J, Bandettini PA. Periodic changes in fMRI connectivity. NeuroImage. 2012;63(3):1712–9. doi: 10.1016/j.neuroimage.2012.06.078 22796990
16. Chang C, Glover GH. Time-frequency dynamics of resting-state brain connectivity measured with fMRI. NeuroImage. 2010;50(1):81–98. doi: 10.1016/j.neuroimage.2009.12.011 20006716
17. Allen EA, Damaraju E, Plis SM, Erhardt EB, Eichele T, Calhoun VD. Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex. 2014;24(3):663–76. doi: 10.1093/cercor/bhs352 23146964
18. Cribben I, Haraldsdottir R, Atlas LY, Wager TD, Lindquist MA. Dynamic connectivity regression: determining state-related changes in brain connectivity. NeuroImage. 2012;61(4):907–20. doi: 10.1016/j.neuroimage.2012.03.070 22484408
19. Calhoun VD, Miller R, Pearlson G, Adali T. The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron. 2014;84(2):262–74. doi: 10.1016/j.neuron.2014.10.015 25374354
20. Preti MG, Bolton TA, Van De Ville D. The dynamic functional connectome: State-of-the-art and perspectives. NeuroImage. 2017;160:41–54. doi: 10.1016/j.neuroimage.2016.12.061 28034766
21. Kang J, Pae C, Park HJ. Energy landscape analysis of the subcortical brain network unravels system properties beneath resting state dynamics. Neuroimage. 2017;149:153–64. doi: 10.1016/j.neuroimage.2017.01.075 28159684
22. Watanabe T, Hirose S, Wada H, Imai Y, Machida T, Shirouzu I, et al. A pairwise maximum entropy model accurately describes resting-state human brain networks. Nat Commun. 2013;4:1370. doi: 10.1038/ncomms2388 23340410
23. Watanabe T, Hirose S, Wada H, Imai Y, Machida T, Shirouzu I, et al. Energy landscapes of resting-state brain networks. Frontiers in neuroinformatics. 2014;8:12. doi: 10.3389/fninf.2014.00012 24611044
24. Watanabe T, Kan S, Koike T, Misaki M, Konishi S, Miyauchi S, et al. Network-dependent modulation of brain activity during sleep. NeuroImage. 2014;98:1–10. doi: 10.1016/j.neuroimage.2014.04.079 24814208
25. Watanabe T, Masuda N, Megumi F, Kanai R, Rees G. Energy landscape and dynamics of brain activity during human bistable perception. Nat Commun. 2014;5:4765. doi: 10.1038/ncomms5765 25163855
26. Ezaki T, Sakaki M, Watanabe T, Masuda N. Age-related changes in the ease of dynamical transitions in human brain activity. Hum Brain Mapp. 2018;39(6):2673–88. doi: 10.1002/hbm.24033 29524289
27. Gu S, Cieslak M, Baird B, Muldoon SF, Grafton ST, Pasqualetti F, et al. The Energy Landscape of Neurophysiological Activity Implicit in Brain Network Structure. Sci Rep. 2018;8(1):2507. doi: 10.1038/s41598-018-20123-8 29410486
28. Rao F, Karplus M. Protein dynamics investigated by inherent structure analysis. Proceedings of the National Academy of Sciences of the United States of America. 2010;107(20):9152–7. doi: 10.1073/pnas.0915087107 20435910
29. Li CB, Yang H, Komatsuzaki T. Multiscale complex network of protein conformational fluctuations in single-molecule time series. Proceedings of the National Academy of Sciences of the United States of America. 2008;105(2):536–41. doi: 10.1073/pnas.0707378105 18178627
30. Frauenfelder H, Sligar SG, Wolynes PG. The energy landscapes and motions of proteins. Science. 1991;254(5038):1598–603. doi: 10.1126/science.1749933 1749933
31. Gfeller D, De Los Rios P, Caflisch A, Rao F. Complex network analysis of free-energy landscapes. Proceedings of the National Academy of Sciences of the United States of America. 2007;104(6):1817–22. doi: 10.1073/pnas.0608099104 17267610
32. Delvenne JC, Yaliraki SN, Barahona M. Stability of graph communities across time scales. Proceedings of the National Academy of Sciences of the United States of America. 2010;107(29):12755–60. doi: 10.1073/pnas.0903215107 20615936
33. Goldstein M. Viscous Liquids and the Glass Transition: A Potential Energy Barrier Picture. The Journal of Chemical Physics. 1969;51(9):3728–39.
34. Golos M, Jirsa V, Dauce E. Multistability in Large Scale Models of Brain Activity. PLoS Comput Biol. 2015;11(12):e1004644. doi: 10.1371/journal.pcbi.1004644 26709852
35. Deco G, Senden M, Jirsa V. How anatomy shapes dynamics: a semi-analytical study of the brain at rest by a simple spin model. Front Comput Neurosci. 2012;6:68. doi: 10.3389/fncom.2012.00068 23024632
36. Deco G, Jirsa VK, McIntosh AR. Resting brains never rest: computational insights into potential cognitive architectures. Trends in neurosciences. 2013;36(5):268–74. doi: 10.1016/j.tins.2013.03.001 23561718
37. Hansen EC, Battaglia D, Spiegler A, Deco G, Jirsa VK. Functional connectivity dynamics: modeling the switching behavior of the resting state. NeuroImage. 2015;105:525–35. doi: 10.1016/j.neuroimage.2014.11.001 25462790
38. Van Essen DC, Ugurbil K, Auerbach E, Barch D, Behrens TE, Bucholz R, et al. The Human Connectome Project: a data acquisition perspective. NeuroImage. 2012;62(4):2222–31. doi: 10.1016/j.neuroimage.2012.02.018 22366334
39. Bassett DS, Bullmore ET. Small-World Brain Networks Revisited. The Neuroscientist. 2017;26(1):107385841666772–18.
40. Senden M, Reuter N, van den Heuvel MP, Goebel R, Deco G. Cortical rich club regions can organize state-dependent functional network formation by engaging in oscillatory behavior. NeuroImage. 2016.
41. van den Heuvel MP, Sporns O. Rich-club organization of the human connectome. The Journal of neuroscience: the official journal of the Society for Neuroscience. 2011;31(44):15775–86.
42. Honey CJ, Sporns O. Dynamical consequences of lesions in cortical networks. Hum Brain Mapp. 2008;29(7):802–9. doi: 10.1002/hbm.20579 18438885
43. Weissenbacher A, Kasess C, Gerstl F, Lanzenberger R, Moser E, Windischberger C. Correlations and anticorrelations in resting-state functional connectivity MRI: a quantitative comparison of preprocessing strategies. NeuroImage. 2009;47(4):1408–16. doi: 10.1016/j.neuroimage.2009.05.005 19442749
44. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage. 2012;59(3):2142–54. doi: 10.1016/j.neuroimage.2011.10.018 22019881
45. Thomas JB, Brier MR, Bateman RJ, Snyder AZ, Benzinger TL, Xiong C, et al. Functional connectivity in autosomal dominant and late-onset Alzheimer disease. JAMA neurology. 2014;71(9):1111–22. doi: 10.1001/jamaneurol.2014.1654 25069482
46. Taylor JS, Rastle K, Davis MH. Interpreting response time effects in functional imaging studies. NeuroImage. 2014;99:419–33. doi: 10.1016/j.neuroimage.2014.05.073 24904992
47. Desikan RS, Segonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage. 2006;31(3):968–80. doi: 10.1016/j.neuroimage.2006.01.021 16530430
48. Power JD, Cohen AL, Nelson SM, Wig GS, Barnes KA, Church JA, et al. Functional network organization of the human brain. Neuron. 2011;72(4):665–78. doi: 10.1016/j.neuron.2011.09.006 22099467
49. Smith SM, Beckmann CF, Andersson J, Auerbach EJ, Bijsterbosch J, Douaud G, et al. Resting-state fMRI in the Human Connectome Project. NeuroImage. 2013;80:144–68. doi: 10.1016/j.neuroimage.2013.05.039 23702415
50. Nielsen JA, Zielinski BA, Ferguson MA, Lainhart JE, Anderson JS. An evaluation of the left-brain vs. right-brain hypothesis with resting state functional connectivity magnetic resonance imaging. PLoS One. 2013;8(8):e71275. doi: 10.1371/journal.pone.0071275 23967180
51. Yeh FC, Tang AN, Hobbs JP, Hottowy P, Dabrowski W, Sher A, et al. Maximum Entropy Approaches to Living Neural Networks. Entropy. 2010;12(1):89–106.
52. Becker OM, Karplus M. The topology of multidimensional potential energy surfaces: Theory and application to peptide structure and kinetics. The Journal of Chemical Physics. 1997;106(4):1495–517.
53. Csárdi G, Nepusz T. The igraph software package for complex network research. Inter Journal Complex Systems. 2006:1695.
54. Sokal R, Michener C. A statistical method for evaluating systematic relationships. University of Kansas Science Bulletin. 1958;38:1409–38.
55. Cabral J, Kringelbach ML, Deco G. Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms. NeuroImage. 2017;160:84–96. doi: 10.1016/j.neuroimage.2017.03.045 28343985
56. Stagno JR, Liu Y, Bhandari YR, Conrad CE, Panja S, Swain M, et al. Structures of riboswitch RNA reaction states by mix-and-inject XFEL serial crystallography. Nature. 2016.
57. Yuan Y, Tam MF, Simplaceanu V, Ho C. New look at hemoglobin allostery. Chem Rev. 2015;115(4):1702–24. doi: 10.1021/cr500495x 25607981
58. Vesper MD, de Groot BL. Collective dynamics underlying allosteric transitions in hemoglobin. PLoS Comput Biol. 2013;9(9):e1003232. doi: 10.1371/journal.pcbi.1003232 24068910
59. Mihailescu MR, Russu IM. A signature of the T—> R transition in human hemoglobin. Proceedings of the National Academy of Sciences of the United States of America. 2001;98(7):3773–7. doi: 10.1073/pnas.071493598 11259676
60. François-Martin C, Rothman JE, Pincet F. Low energy cost for optimal speed and control of membrane fusion. Proceedings of the National Academy of Sciences of the United States of America. 2017;114(6):1238–41. doi: 10.1073/pnas.1621309114 28115718
61. Ryham RJ, Klotz TS, Yao L, Cohen FS. Calculating Transition Energy Barriers and Characterizing Activation States for Steps of Fusion. Biophysical journal. 2016;110(5):1110–24. doi: 10.1016/j.bpj.2016.01.013 26958888
62. Smirnova YG, Marrink S-J, Lipowsky R, Knecht V. Solvent-Exposed Tails as Prestalk Transition States for Membrane Fusion at Low Hydration. J Am Chem Soc. 2010;132(19):6710–8. doi: 10.1021/ja910050x 20411937
63. Shimabukuro K, Muneyuki E, Yoshida M. An alternative reaction pathway of F1-ATPase suggested by rotation without 80 degrees/40 degrees substeps of a sluggish mutant at low ATP. Biophys J. 2006;90(3):1028–32. doi: 10.1529/biophysj.105.067298 16258036
64. Uemura S, Higuchi H, Olivares AO, De La Cruz EM, Ishiwata S. Mechanochemical coupling of two substeps in a single myosin V motor. Nat Struct Mol Biol. 2004;11(9):877–83. doi: 10.1038/nsmb806 15286720
65. Price CJ, Friston KJ. Degeneracy and cognitive anatomy. Trends Cogn Sci. 2002;6(10):416–21. 12413574
66. Edelman GM, Gally JA. Degeneracy and complexity in biological systems. Proceedings of the National Academy of Sciences of the United States of America. 2001;98(24):13763–8. doi: 10.1073/pnas.231499798 11698650
67. Spiegler A, Hansen EC, Bernard C, McIntosh AR, Jirsa VK. Selective Activation of Resting-State Networks following Focal Stimulation in a Connectome-Based Network Model of the Human Brain. eNeuro. 2016;3(5).
68. Pillai AS, Jirsa VK. Symmetry Breaking in Space-Time Hierarchies Shapes Brain Dynamics and Behavior. Neuron. 2017;94(5):1010–26. doi: 10.1016/j.neuron.2017.05.013 28595045
69. Rabinovich MI, Huerta R, Varona P, Afraimovich VS. Transient cognitive dynamics, metastability, and decision making. PLoS Comput Biol. 2008;4(5):e1000072. doi: 10.1371/journal.pcbi.1000072 18452000
70. Lopez-Persem A, Verhagen L, Amiez C, Petrides M, Sallet J. The Human Ventromedial Prefrontal Cortex: Sulcal Morphology and Its Influence on Functional Organization. J Neurosci. 2019;39(19):3627–39. doi: 10.1523/JNEUROSCI.2060-18.2019 30833514
71. Margulies DS, Ghosh SS, Goulas A, Falkiewicz M, Huntenburg JM, Langs G, et al. Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc Natl Acad Sci U S A. 2016;113(44):12574–9. doi: 10.1073/pnas.1608282113 27791099
72. van den Heuvel MP, Sporns O. Rich-club organization of the human connectome. J Neurosci. 2011;31(44):15775–86. doi: 10.1523/JNEUROSCI.3539-11.2011 22049421
73. Park HJ, Lee JD, Chun JW, Seok JH, Yun M, Oh MK, et al. Cortical surface-based analysis of 18F-FDG PET: measured metabolic abnormalities in schizophrenia are affected by cortical structural abnormalities. Neuroimage. 2006;31(4):1434–44. doi: 10.1016/j.neuroimage.2006.02.001 16540349
74. Greicius MD, Supekar K, Menon V, Dougherty RF. Resting-state functional connectivity reflects structural connectivity in the default mode network. Cereb Cortex. 2009;19(1):72–8. doi: 10.1093/cercor/bhn059 18403396
75. Watanabe T, Rees G. Brain network dynamics in high-functioning individuals with autism. Nature Communications. 2017;8:16048. doi: 10.1038/ncomms16048 28677689
76. Michel CM, Koenig T. EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review. Neuroimage. 2018;180(Pt B):577–93. doi: 10.1016/j.neuroimage.2017.11.062 29196270
77. Alexander DM, Trengove C, van Leeuwen C. Donders is dead: cortical traveling waves and the limits of mental chronometry in cognitive neuroscience. Cogn Process. 2015;16(4):365–75. doi: 10.1007/s10339-015-0662-4 26139038
78. Zhang H, Watrous AJ, Patel A, Jacobs J. Theta and Alpha Oscillations Are Traveling Waves in the Human Neocortex. Neuron. 2018;98(6):1269–81 e4. doi: 10.1016/j.neuron.2018.05.019 29887341
79. Roberts JA, Gollo LL, Abeysuriya RG, Roberts G, Mitchell PB, Woolrich MW, et al. Metastable brain waves. Nat Commun. 2019;10(1):1056. doi: 10.1038/s41467-019-08999-0 30837462
80. Lecrux C, Hamel E. Neuronal networks and mediators of cortical neurovascular coupling responses in normal and altered brain states. Philos Trans R Soc Lond B Biol Sci. 2016;371(1705).
81. Deco G, Ponce-Alvarez A, Mantini D, Romani GL, Hagmann P, Corbetta M. Resting-state functional connectivity emerges from structurally and dynamically shaped slow linear fluctuations. J Neurosci. 2013;33(27):11239–52. doi: 10.1523/JNEUROSCI.1091-13.2013 23825427
82. Stephan KE, Kasper L, Harrison LM, Daunizeau J, den Ouden HE, Breakspear M, et al. Nonlinear dynamic causal models for fMRI. Neuroimage. 2008;42(2):649–62. doi: 10.1016/j.neuroimage.2008.04.262 18565765
83. Vidaurre D, Quinn AJ, Baker AP, Dupret D, Tejero-Cantero A, Woolrich MW. Spectrally resolved fast transient brain states in electrophysiological data. Neuroimage. 2016;126:81–95. doi: 10.1016/j.neuroimage.2015.11.047 26631815
84. Vidaurre D, Smith SM, Woolrich MW. Brain network dynamics are hierarchically organized in time. Proc Natl Acad Sci U S A. 2017;114(48):12827–32. doi: 10.1073/pnas.1705120114 29087305
85. Ezaki T, Watanabe T, Ohzeki M, Masuda N. Energy landscape analysis of neuroimaging data. Philos Trans A Math Phys Eng Sci. 2017;375(2096).
86. Loh M, Rolls ET, Deco G. A dynamical systems hypothesis of schizophrenia. PLoS Comput Biol. 2007;3(11):e228. doi: 10.1371/journal.pcbi.0030228 17997599
87. Cabral J, Fernandes HM, Van Hartevelt TJ, James AC, Kringelbach ML, Deco G. Structural connectivity in schizophrenia and its impact on the dynamics of spontaneous functional networks. Chaos: An Interdisciplinary Journal of Nonlinear Science. 2013;23(4):046111.
Článok vyšiel v časopise
PLOS One
2019 Číslo 9
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
- Nejasný stín na plicích – kazuistika
- Masturbační chování žen v ČR − dotazníková studie
- Je Fuchsova endotelová dystrofie rohovky neurodegenerativní onemocnění?
- Fixní kombinace paracetamol/kodein nabízí synergické analgetické účinky
Najčítanejšie v tomto čísle
- Graviola (Annona muricata) attenuates behavioural alterations and testicular oxidative stress induced by streptozotocin in diabetic rats
- CH(II), a cerebroprotein hydrolysate, exhibits potential neuro-protective effect on Alzheimer’s disease
- Comparison between Aptima Assays (Hologic) and the Allplex STI Essential Assay (Seegene) for the diagnosis of Sexually transmitted infections
- Assessment of glucose-6-phosphate dehydrogenase activity using CareStart G6PD rapid diagnostic test and associated genetic variants in Plasmodium vivax malaria endemic setting in Mauritania