Irreversibility as a signature of non-equilibrium phase transition in large-scale human brain networks: An fMRI study
Jing Wang(王菁), Kejian Wu(吴克俭), Jiaqi Dong(董家奇), and Lianchun Yu(俞连春)†
School of Physical Science and Technology, Lanzhou Center for Theoretical Physics, Key Laboratory of Theoretical Physics of Gansu Province, Key Laboratory of Quantum Theory and Applications of Ministry of Education, Gansu Provincial Research Center for Basic Disciplines of Quantum Physics, Lanzhou University, Lanzhou 730000, China
Abstract It has been argued that the human brain, as an information-processing machine, operates near a phase transition point in a non-equilibrium state, where it violates detailed balance leading to entropy production. Thus, the assessment of irreversibility in brain networks can provide valuable insights into their non-equilibrium properties. In this study, we utilized an open-source whole-brain functional magnetic resonance imaging (fMRI) dataset from both resting and task states to evaluate the irreversibility of large-scale human brain networks. Our analysis revealed that the brain networks exhibited significant irreversibility, violating detailed balance, and generating entropy. Notably, both physical and cognitive tasks increased the extent of this violation compared to the resting state. Regardless of the state (rest or task), interactions between pairs of brain regions were the primary contributors to this irreversibility. Moreover, we observed that as global synchrony increased within brain networks, so did irreversibility. The first derivative of irreversibility with respect to synchronization peaked near the phase transition point, characterized by the moderate mean synchronization and maximized synchronization entropy of blood oxygenation level-dependent (BOLD) signals. These findings deepen our understanding of the non-equilibrium dynamics of large-scale brain networks, particularly in relation to their phase transition behaviors, and may have potential clinical applications for brain disorders.
Fund: Project supported by the Fundamental Research Funds for the Central Universities (Grant Nos. lzujbky-2021-62 and lzujbky-2024-jdzx06), the National Natural Science Foundation of China (Grant No. 12247101), the Natural Science Foundation of Gansu Province, China (Grant Nos. 22JR5RA389 and 23JRRA1740), and the ‘111 Center’ Fund (Grant No. B20063).
Jing Wang(王菁), Kejian Wu(吴克俭), Jiaqi Dong(董家奇), and Lianchun Yu(俞连春) Irreversibility as a signature of non-equilibrium phase transition in large-scale human brain networks: An fMRI study 2025 Chin. Phys. B 34 058703
[1] Gnesotto F S, Mura F, Gladrow J and Broedersz C P 2018 Rep. Prog. Phys. 81 066601 [2] Battle C, Broedersz C P, Fakhri N, Geyer V F, Howard J, Schmidt C F and MacKintosh F C 2016 Science 352 604 [3] Zia R K P and Schmittmann B 2007 J. Stat. Mech. Theory Exp. 2007 P07012 [4] Seifert U 2012 Rep. Prog. Phys. 75 126001 [5] Seifert U 2005 Phys. Rev. Lett. 95 040602 [6] Kawai R, Parrondo J M R and den Broeck C V 2007 Phys. Rev. Lett. 98 080602 [7] Lynn C W, Holmes C M, Bialek W and Schwab D J 2022 Phys. Rev. Lett. 129 118101 [8] Lynn C W, Cornblath E J, Papadopoulos L, Bertolero M A and Bassett D S 2021 Proc. Natl. Acad. Sci. USA 118 e2109889118 [9] Sanz Perl Y, Bocaccio H, Pallavicini C, Pérez-Ipiña I, Laureys S, Laufs H, Kringelbach M, Deco G and Tagliazucchi E 2021 Phys. Rev. E 104 014411 [10] Harris J J, Jolivet R and Attwell D 2012 Neuron 75 762 [11] Yu L and Yu Y 2017 J. Neurosci. Res. 95 2253 [12] Erecínska M and Silver I A 1989 J. Cereb. Blood Flow Metab. 9 2 [13] Norberg K and Siejö B K 1975 Brain Res. 86 45 [14] Du F, Zhu X H, Zhang Y, Friedman M, Zhang N, Uǧurbil K and Chen W 2008 Proc. Natl. Acad. Sci. USA 105 6409 [15] Deco G, Sanz Perl Y, de la Fuente L, Sitt J D, Yeo B T T, Tagliazucchi E and Kringelbach M L 2023 Netw. Neurosci. 7 966 [16] Deco G, Sanz Perl Y, Bocaccio H, Tagliazucchi E and Kringelbach M L 2022 Commun. Biol. 5 572 [17] de la Fuente L A, Zamberlan F, Bocaccio H, Kringelbach M, Deco G, Sanz Perl Y, Pallavicini C and Tagliazucchi E 2023 Cereb. Cortex 33 1856 [18] Ibáñez A 2022 Trends Cogn. Sci. 26 1031 [19] Zanin M, Güntekin B, Aktürk T, Hanoǧlu L and Papo D 2019 Front. Physiol. 10 1619 [20] Bassett D S and Sporns O 2017 Nat. Neurosci. 20 353 [21] Lv G, Xu T, Chen F, Zhu P, Wang M and He G 2024 Chin. Phys. B 33 028704 [22] Beggs J M 2022 The Cortex and the Critical Point: Understanding the Power of Emergence (Cambridge, MA: The MIT Press) pp. 51-71 [23] Beggs J M and Plenz D 2003 J. Neurosci. 23 11167 [24] Haldeman C and Beggs J M 2005 Phys. Rev. Lett. 94 058101 [25] ShewWL, Yang H, Petermann T, Roy R and Plenz D 2009 J. Neurosci. 29 15595 [26] Beggs J M 2008 Phil. Trans. R. Soc. A 366 329 [27] Wang R, Lin P, Liu M,Wu Y, Zhou T and Zhou C 2019 Phys. Rev. Lett. 123 038301 [28] Song B, Ma N, Liu G, Zhang H, Yu L, Liu L and Zhang J 2019 J. Neural Eng. 16 056002 [29] Haimovici A, Tagliazucchi E, Balenzuela P and Chialvo D R 2013 Phys. Rev. Lett. 110 178101 [30] Zhou X, Ma N, Song B, Wu Z, Liu G, Liu L, Yu L and Feng J 2021 Front. Comput. Neurosci. 15 641335 [31] Wang R, Liu M, Cheng X,Wu Y, Hildebrandt A and Zhou C 2021 Proc. Natl. Acad. Sci. USA 118 e2022288118 [32] Xu L, Feng J and Yu L 2022 Hum. Brain Mapp. 43 2534 [33] Torres J J and Marro J 2015 Sci. Rep. 5 12216 [34] Noa C E F, Harunari P E, de Oliveira M J and Fiore C E 2019 Phys. Rev. E 100 012104 [35] Aguilera M, Igarashi M and Shimazaki H 2023 Nat. Commun. 14 3685 [36] Zhang Y and Barato A C 2016 J. Stat. Mech. Theory Exp. 2016 113207 [37] Huang X, Xu K, Chu C, Jiang T and Yu S 2017 J. Neurosci. 37 10481 [38] Van Essen D C, Smith S M, Barch D M, Behrens T E J, Yacoub E, Uǧurbil K and Consortium W U M H 2013 NeuroImage 80 62 [39] Glasser M F, Smith S M, Marcus D S, Andersson J L, Auerbach E J, Behrens T E, Coalson T S, HarmsMP, Jenkinson M, Moeller S, Robinson E C, Sotiropoulos S N, Xu J, Yacoub E, Uǧurbil K and Van Essen D C 2016 Nat. Neurosci. 19 1175 [40] Glasser M F, Sotiropoulos S N, Wilson J A, Coalson T S, Fischl B, Andersson J L, Xu J, Jbabdi S, Webster M, Polimeni J R, Van Essen D C, Jenkinson M and Consortium W U-M H 2013 NeuroImage 80 105 [41] Smith S M, Nichols T E, Vidaurre D, Winkler A M, Behrens T E, Glasser M F, Uǧurbil K, Barch D M, Van Essen D C and Miller K L 2015 Nat. Neurosci. 18 1565 [42] Liu T T, Nalci A and Falahpour M 2017 NeuroImage 150 213 [43] Schaefer A, Kong R, Gordon E M, Laumann T O, Zuo X N, Holmes A J, Eickhoff S B and Yeo B T T 2018 Cereb. Cortex 28 3095 [44] Yeo B T, Krienen F M, Sepulcre J, Sabuncu M R, Lashkari D, Hollinshead M, Roffman J L, Smoller J W, Zöllei L, Polimeni J R, Fischl B, Liu H and Buckner R L 2011 J. Neurophysiol. 106 1125 [45] Lynn C W, Holmes C M, Bialek W and Schwab D J 2022 Phys. Rev. E 106 034102 [46] Ge H and Qian H 2013 Phys. Rev. E 87 062125 [47] Palmer S E, Marre O, Berry M J and Bialek W 2015 Proc. Natl. Acad. Sci. USA 112 6908 [48] Schneidman E, Berry M J, Segev R and Bialek W 2006 Nature 440 1007 [49] Strong S P, Koberle R, de Ruyter van Steveninck R R and Bialek W 1998 Phys. Rev. Lett. 80 197 [50] Tagliazucchi E, Balenzuela P, Fraiman D and Chialvo D R 2012 Front. Physiol. 3 15 [51] Yang H, Shew W L, Roy R and Plenz D 2012 J. Neurosci. 32 1061 [52] Zhang J, Wu K, Dong J, Feng J and Yu, L 2024 Neural Netw. 184 107100 [53] Crochik L and Tomé T 2005 Phys. Rev. E 72 057103 [54] Sterling P and Laughlin S 2015 Principles of Neural Design (Cambridge, MA: The MIT Press) pp. 57-104 [55] Balasubramanian V 2015 Proc. IEEE Inst. Electr. Electron. Eng. 103 1346 [56] Tomé T and de Oliveira M J 2012 Phys. Rev. Lett. 108 020601 [57] Lu W, Du X, Wang J, Zeng L, Ye L, Xiang S, Zheng Q, Zhang J, Xu N, Feng J and DTB Consortium 2024 Nat. Comput. Sci. 4 890 [58] Kawazura Y and Yoshida Z 2010 Phys. Rev. E 82 066403 [59] Niu W, Huang X, Xu K, Jiang T and Yu S 2019 Neuroscience 412 190 [60] Zimmern V 2020 Front. Neural Circuits 14 54
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.