Dwelling quietly in the rich club: Brain network determinants of slow cortical fluctuations

Philosophical Transactions of the Royal Society B: Biological Sciences

Volumn 370, Issue 1668, 2015, Pages

  Gollo, Leonardo L ^a   Zalesky, Andrew ^b,c   Matthew Hutchison, R ^d   Van Den Heuvel, Martijn ^e   Breakspear, Michael ^a,f  

^a QIMR BERGHOFER MEDICAL RESEARCH INSTITUTE   (Australia)

^b UNIVERSITY OF MELBOURNE   (Australia)

^c The University of Melbourne   (Australia)

^d HARVARD UNIVERSITY   (United States)

^e UNIVERSITY MEDICAL CENTER UTRECHT   (Netherlands)

^f Metro North Mental Health Service   (Australia)

Author keywords

Brain dynamics; Chronoarchitecture; Connectome; Core periphery axis; Metastability; Prefrontal hierarchy

Indexed keywords

BRAIN; DEVELOPMENTAL BIOLOGY; MORPHOLOGY; PRIMATE; SPATIOTEMPORAL ANALYSIS; TOPOLOGY;

PRIMATES;

ANATOMY AND HISTOLOGY; BIOLOGICAL MODEL; BRAIN; BRAIN MAPPING; HUMAN; NERVE CELL NETWORK; PHYSIOLOGY; PROCEDURES;

BRAIN; BRAIN MAPPING; HUMANS; MODELS, BIOLOGICAL; NERVE NET;

EID: 84926184109     PISSN: 09628436     EISSN: 14712970     Source Type: Journal    
DOI: 10.1098/rstb.2014.0165     Document Type: Article

References (122)

1. Brodmann K. 1909 Vergleichende Lokalisationslehre der Grosshirnrinde in ihren Prinzipien dargestellt auf Grund des Zellenbaues: Barth
2. Catani M, Thiebaut de Schotten M, Slater D, Dell’Acqua F. 2013 Connectomic approaches before the connectome. Neuroimage 80, 2-13. (doi:10. 1016/j.neuroimage.2013.05.109)
3. Zilles K, Amunts K. 2009 Receptor mapping: architecture of the human cerebral cortex. Curr. Opin. Neurol. 22, 331-339. (doi:10.1097/WCO. 0b013e32832d95db)
4. Kötter R, Stephan K, Palomero-Gallagher N, Geyer S, Schleicher A, Zilles K. 2001 Multimodal characterisation of cortical areas by multivariate analyses of receptor binding and connectivity data. Anat. Embryol. 204, 333-349. (doi:10.1007/s0042 90100199)
5. Stephan KE, Zilles K, Kötter R. 2000 Coordinateindependent mapping of structural and functional data by objective relational transformation (ORT). Phil. Trans. R. Soc. Lond. B 355, 37-54. (doi:10. 1098/rstb.2000.0548)
6. Beckmann M, Johansen-Berg H, Rushworth MF. 2009 Connectivity-based parcellation of human cingulate cortex and its relation to functional specialization. J. Neurosci. 29, 1175-1190. (doi:10. 1523/JNEUROSCI.3328-08.2009)
7. Mars RB et al. 2011 Diffusion-weighted imaging tractography-based parcellation of the human parietal cortex and comparison with human and macaque resting-state functional connectivity. J. Neurosci. 31, 4087-4100. (doi:10.1523/JNEUROSCI.5102-10.2011)
8. Bullmore E, Sporns O. 2009 Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186-198. (doi:10.1038/nrn2575)
9. Sporns O, Chialvo DR, Kaiser M, Hilgetag CC. 2004 Organization, development and function of complex brain networks. Trends Cogn. Sci. 8, 418-425. (doi:10.1016/j.tics.2004.07.008)
10. Bartels A, Zeki S. 2004 The chronoarchitecture of the human brain: natural viewing conditions reveal a time-based anatomy of the brain. Neuroimage 22, 419-433. (doi:10.1016/j. neuroimage.2004.01.007)
11. Bartels A, Zeki S. 2005 The chronoarchitecture of the cerebral cortex. Phil. Trans. R. Soc. B 360, 733-750. (doi:10.1098/rstb.2005.1627)
12. Poincare´ H. 1890 Sur le probleme des trois corps et les équations de la dynamique. Acta Math. 13, A271. (doi:10.1007/BF02392514)
13. Dux PE, Marois R. 2009 The attentional blink: a review of data and theory. Attent. Percept. Psychophys. 71, 1683-1700. (doi:10.3758/APP.71.8. 1683)
14. Victor JD, Purpura KP. 1996 Nature and precision of temporal coding in visual cortex: a metric-space analysis. J. Neurophysiol. 76, 1310-1326
15. Tononi G, Sporns O, Edelman GM. 1996 A complexity measure for selective matching of signals by the brain. Proc. Natl Acad. Sci. USA 93, 3422-3427. (doi:10.1073/pnas.93.8.3422)
16. Gottschalk A, Bauer MS, Whybrow PC. 1995 Evidence of chaotic mood variation in bipolar disorder. Arch. Gen. Psychiatry 52, 947-959. (doi:10.1001/archpsyc.1995.039502 30061009)
17. Nakamura T, Kiyono K, Yoshiuchi K, Nakahara R, Struzik ZR, Yamamoto Y. 2007 Universal scaling law in human behavioral organization. Phys. Rev. Lett. 99, 138103. (doi:10.1103/PhysRevLett. 99.138103)
18. Botvinick MM. 2008 Hierarchical models of behavior and prefrontal function. Trends Cogn. Sci. 12, 201-208. (doi:10.1016/j.tics.2008.02.009)
19. Simoncelli EP, Olshausen BA. 2001 Natural image statistics and neural representation. Annu. Rev. Neurosci. 24, 1193-1216. (doi:10.1146/annurev. neuro.24.1.1193)
20. Wark B, Fairhall A, Rieke F. 2009 Timescales of inference in visual adaptation. Neuron 61, 750-761. (doi:10.1016/j.neuron.2009.01.019)
21. Song S, Miller KD, Abbott LF. 2000 Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat. Neurosci. 3, 919-926. (doi:10.1038/78829)
22. Thivierge J-P, Cisek P. 2008 Nonperiodic synchronization in heterogeneous networks of spiking neurons. J. Neurosci. 28, 7968-7978. (doi:10.1523/JNEUROSCI.0870-08.2008)
23. Rubinov M, Sporns O, Thivierge J-P, Breakspear M. 2011 Neurobiologically realistic determinants of self-organized criticality in networks of spiking neurons. PLoS Comput. Biol. 7, e1002038. (doi:10. 1371/journal.pcbi.1002038)
24. O’Reilly R, Frank M. 2006 Making working memory work: a computational model of learning in the prefrontal cortex and basal ganglia. Neural Comput. 18, 283-328. (doi:10.1162/089976606775093909)
25. Fusi S, Drew PJ, Abbott L. 2005 Cascade models of synaptically stored memories. Neuron 45, 599-611. (doi:10.1016/j.neuron.2005.02.001)
26. Kiebel SJ, Daunizeau J, Friston KJ. 2008 A hierarchy of time-scales and the brain. PLoS Comput. Biol. 4, e1000209. (doi:10.1371/journal.pcbi.1000209)
27. Botvinick MM. 2007 Multilevel structure in behaviour and in the brain: a model of Fuster’s hierarchy. Phil. Trans. R. Soc. B 362, 1615-1626. (doi:10.1098/rstb.2007.2056)
28. Fuster JM. 1990 Prefrontal cortex and the bridging of temporal gaps in the perception-action cycle. Ann. NY Acad. Sci. 608, 318-336. (doi:10.1111/j. 1749-6632.1990.tb48901.x)
29. Fuster JM. 2001 The prefrontal cortex—an update: time is of the essence. Neuron 30, 319-333. (doi:10.1016/S0896-6273(01)00285-9)
30. Mesulam M-M. 1998 From sensation to cognition. Brain 121, 1013-1052. (doi:10.1093/brain/121.6. 1013)
31. Badre D, D’Esposito M. 2009 Is the rostro-caudal axis of the frontal lobe hierarchical? Nat. Rev. Neurosci. 10, 659-669. (doi:10.1038/nrn2667)
32. Petrides M. 2005 Lateral prefrontal cortex: architectonic and functional organization. Phil. Trans. R. Soc. B 360, 781-795. (doi:10.1098/rstb. 2005.1631)
33. Bernacchia A, Seo H, Lee D, Wang X-J. 2011 A reservoir of time constants for memory traces in cortical neurons. Nat. Neurosci. 14, 366-372. (doi:10.1038/nn.2752)
34. Murray JD et al. 2014 A hierarchy of intrinsic timescales across primate cortex. Nat. Neurosci. 17, 1661-1663. (doi:10.1038/nn.3862)
35. Hasson U, Yang E, Vallines I, Heeger DJ, Rubin N. 2008 A hierarchy of temporal receptive windows in human cortex. J. Neurosci. 28, 2539-2550. (doi:10. 1523/JNEUROSCI.5487-07.2008)
36. Honey CJ et al. 2012 Slow cortical dynamics and the accumulation of information over long timescales. Neuron 76, 423-434. (doi:10.1016/j.neuron.2012. 08.011)
37. Deco G, Jirsa VK, Robinson PA, Breakspear M, Friston K. 2008 The dynamic brain: from spiking neurons to neural masses and cortical fields. PLoS Comput. Biol. 4, e1000092. (doi:10.1371/journal. pcbi.1000092)
38. Breakspear M, Jirsa V, Deco G. 2010 Computational models of the brain: from structure to function. Neuroimage 52, 727-730. (doi:10.1016/j. neuroimage.2010.05.061)
39. Friston K. 2010 The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11, 127-138. (doi:10.1038/nrn2787)
40. Honey CJ, Kötter R, Breakspear M, Sporns O. 2007 Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc. Natl Acad. Sci. USA 104, 10 240-10 245. (doi:10.1073/pnas.0701519104)
41. Freyer F, Roberts JA, Becker R, Robinson PA, Ritter P, Breakspear M. 2011 Biophysical mechanisms of multistability in resting-state cortical rhythms. J. Neurosci. 31, 6353-6361. (doi:10.1523/JNEUROSCI.6693-10.2011)
42. Friston K, Adams RA, Perrinet L, Breakspear M. 2012 Perceptions as hypotheses: saccades as experiments. Front. Psychol. 3, 151. (doi:10.3389/fpsyg.2012. 00151)
43. Breakspear M, Roberts J, Terry JR, Rodrigues S, Mahant N, Robinson P. 2006 A unifying explanation of primary generalized seizures through nonlinear brain modeling and bifurcation analysis. Cerebr. Cortex 16, 1296-1313. (doi:10.1093/cercor/bhj072)
44. Jirsa VK, Stacey WC, Quilichini PP, Ivanov AI, Bernard C. 2014 On the nature of seizure dynamics. Brain 137, 2210-2230. (doi:10.1093/brain/awu133)
45. Lundstrom BN, Higgs MH, Spain WJ, Fairhall AL. 2008 Fractional differentiation by neocortical pyramidal neurons. Nat. Neurosci. 11, 1335-1342. (doi:10.1038/nn.2212)
46. Buzsáki G, Watson BO. 2012 Brain rhythms and neural syntax: implications for efficient coding of cognitive content and neuropsychiatric disease. Dialogues Clin. Neurosci. 14, 345
47. Buzsáki G, Logothetis N, Singer W. 2013 Scaling brain size, keeping timing: evolutionary preservation of brain rhythms. Neuron 80, 751-764. (doi:10. 1016/j.neuron.2013.10.002)
48. Miller KJ et al. 2010 Dynamic modulation of local population activity by rhythm phase in human occipital cortex during a visual search task. Front. Hum. Neurosci. 4, 197. (doi:10.3389/fnhum.2010. 00197)
49. Fujimoto K, Kaneko K. 2003 How fast elements can affect slow dynamics. Physica D: Nonlinear Phenomena 180, 1-16. (doi:10.1016/S0167-2789(03)00046-0)
50. Langdon AJ, Boonstra TW, Breakspear M. 2011 Multi-frequency phase locking in human somatosensory cortex. Progr. Biophys. Mol. Biol. 105, 58-66. (doi:10.1016/j.pbiomolbio.2010. 09.015)
51. Roberts JA, Robinson PA. 2012 Quantitative theory of driven nonlinear brain dynamics. Neuroimage 62, 1947-1955. (doi:10.1016/j.neuroimage.2012. 05.054)
52. Breakspear M, Stam CJ. 2005 Dynamics of a neural system with a multiscale architecture. Phil. Trans. R. Soc. B 360, 1051-1074. (doi:10.1098/rstb.2005.1643)
53. Breakspear M. 2004 ‘Dynamic’ connectivity in neural systems. Neuroinformatics 2, 205-224. (doi:10. 1385/NI:2:2:205)
54. Felleman DJ, Van Essen DC. 1991 Distributed hierarchical processing in the primate cerebral cortex. Cerebr. Cortex 1, 1-47. (doi:10.1093/cercor/1.1.1)
55. Sporns O. 2011 Networks of the brain. Cambridge, MA: MIT Press
56. Bassett DS, Greenfield DL, Meyer-Lindenberg A, Weinberger DR, Moore SW, Bullmore ET. 2010 Efficient physical embedding of topologically complex information processing networks in brains and computer circuits. PLoS Comput. Biol. 6, e1000748. (doi:10.1371/journal.pcbi.1000748)
57. van den Heuvel MP, Sporns O. 2011 Rich-club organization of the human connectome. J. Neurosci. 31, 15775-15786. (doi:10.1523/JNEUROSCI.3539-11.2011)
58. van den Heuvel MP, Sporns O. 2013 Network hubs in the human brain. Trends Cogn. Sci. 17, 683-696. (doi:10.1016/j.tics.2013.09.012)
59. Harriger L, Van Den Heuvel MP, Sporns O. 2012 Rich club organization of macaque cerebral cortex and its role in network communication. PLoS ONE 7, e46497. (doi:10.1371/journal.pone.0046497)
60. Scholtens LH, Schmidt R, de Reus MA, van den Heuvel MP. 2014 Linking macroscale graph analytical organization to microscale neuroarchitectonics in the macaque connectome. J. Neurosci. 34, 12 192-12 205. (doi:10.1523/JNEUROSCI.0752-14.2014)
61. Towlson EK, Ve´rtes PE, Ahnert SE, Schafer WR, Bullmore ET. 2013 The rich club of the C. elegans neuronal connectome. J. Neurosci. 33, 6380-6387. (doi:10.1523/JNEUROSCI.3784-12.2013)
62. Drevets WC. 2001 Neuroimaging and neuropathological studies of depression: implications for the cognitive-emotional features of mood disorders. Curr. Opin. Neurobiol. 11, 240-249. (doi:10.1016/S0959-4388(00)00203-8)
63. Stephan KE, Kamper L, Bozkurt A, Burns GA, Young MP, Kötter R. 2001 Advanced database methodology for the Collation of Connectivity data on the Macaque brain (CoCoMac). Phil. Trans. R. Soc. Lond. B 356, 1159-1186. (doi:10.1098/rstb.2001.0908)
64. Sporns O, Kötter R. 2004 Motifs in brain networks. PLoS Biol. 2, e369. (doi:10.1371/journal.pbio. 0020369)
65. Gollo LL, Mirasso C, Sporns O, Breakspear M. 2014 Mechanisms of zero-lag synchronization in cortical motifs. PLoS Comput. Biol. 10, e1003548. (doi:10. 1371/journal.pcbi.1003548)
66. Gollo LL, Breakspear M. 2014 The frustrated brain: from dynamics on motifs to communities and networks. Phil. Trans. R. Soc. B 369, 20130532. (doi:10.1098/rstb.2013.0532)
67. van den Heuvel MP, Kahn RS, Goñi J, Sporns O. 2012 High-cost, high-capacity backbone for global brain communication. Proc. Natl Acad. Sci. USA 109, 11 372-11 377. (doi:10.1073/pnas. 1203593109)
68. Papo D, Buldu´ JM, Boccaletti S, Bullmore ET. 2014 How does complex network theory help us to understand the brain? Phil. Trans. R. Soc. B 369, 20130520. (doi:10.1098/rstb.2013.0520)
69. Breakspear M, Terry JR, Friston KJ. 2003 Modulation of excitatory synaptic coupling facilitates synchronization and complex dynamics in a biophysical model of neuronal dynamics. Network: Comput. Neural Syst. 14, 703-732. (doi:10.1088/0954-898X/14/4/305)
70. Larter R, Speelman B, Worth RM. 1999 A coupled ordinary differential equation lattice model for the simulation of epileptic seizures. Chaos 9, 795-804. (doi:10.1063/1.166453)
71. Ashwin P, Buescu J, Stewart I. 1996 From attractor to chaotic saddle: a tale of transverse instability. Nonlinearity 9, 703. (doi:10.1088/0951-7715/9/3/006)
72. Ghosh A, Rho Y, McIntosh AR, Kötter R, Jirsa VK. 2008 Noise during rest enables the exploration of the brain’s dynamic repertoire. PLoS Comput. Biol. 4, e1000196. (doi:10.1371/journal.pcbi.1000196)
73. Belykh I, de Lange E, Hasler M. 2005 Synchronization of bursting neurons: what matters in the network topology. Phys. Rev. Lett. 94, 188101. (doi:10.1103/PhysRevLett.94.188101)
74. Friston K. 2005 A theory of cortical responses. Phil. Trans. R. Soc. B 360, 815-836. (doi:10.1098/rstb. 2005.1622)
75. de Pasquale F, Della Penna S, Snyder AZ, Marzetti L, Pizzella V, Romani GL, Corbetta M. 2012 A cortical core for dynamic integration of functional networks in the resting human brain. Neuron 74, 753-764. (doi:10.1016/j.neuron.2012.03.031)
76. Shanahan M. 2006 A cognitive architecture that combines internal simulation with a global workspace. Conscious. Cogn. 15, 433-449. (doi:10. 1016/j.concog.2005.11.005)
77. Edelman GM, Tononi G. 2000 Reentry and the dynamic core: neural correlates of conscious experience. In Neural correlates of consciousness, empirical and conceptual questions, pp. 139-151. Cambridge, MA: MIT Press
78. Dehaene S, Kerszberg M, Changeux J-P. 1998 A neuronal model of a global workspace in effortful cognitive tasks. Proc. Natl Acad. Sci. USA 95, 14 529-14 534. (doi:10.1073/pnas.95.24.14529)
79. Le Van Quyen M. 2003 Disentangling the dynamic core: a research program for a neurodynamics at the large-scale. Biol. Res. 36, 67-88. (doi:10.4067/S0716-97602003000100006)
80. Borgatti SP, Everett MG. 2000 Models of core/periphery structures. Soc. Netw. 21, 375-395. (doi:10.1016/S0378-8733(99)00019-2)
81. Csermely P, London A, Wu L-Y, Uzzi B. 2013 Structure and dynamics of core/periphery networks. J. Complex Netw. 1, 93-123. (doi:10.1093/comnet/cnt016)
82. Senden M, Deco G, de Reus MA, Goebel R, van den Heuvel MP. 2014 Rich club organization supports a diverse set of functional network configurations. Neuroimage 96, 174-182. (doi:10.1016/j. neuroimage.2014.03.066)
83. Friston KJ. 2000 The labile brain. II. Transients, complexity and selection. Phil. Trans. R. Soc. Lond. B 355, 237-252. (doi:10.1098/rstb.2000.0561)
84. Friston KJ. 2000 The labile brain. III. Transients and spatio-temporal receptive fields. Phil. Trans. R. Soc. Lond. B 355, 253-265. (doi:10.1098/rstb.2000. 0562)
85. Friston K, Breakspear M, Deco G. 2012 Perception and self-organized instability. Front. Comput. Neurosci. 6, 44. (doi:10.3389/fncom.2012.00044)
86. Bassett DS, Wymbs NF, Rombach MP, Porter MA, Mucha PJ, Grafton ST. 2013 Task-based coreperiphery organization of human brain dynamics. PLoS Comput. Biol. 9, e1003171. (doi:10.1371/journal.pcbi.1003171)
87. Ashwin P, Breakspear M. 2001 Anisotropic properties of riddled basins. Phys. Lett. A 280, 139-145. (doi:10.1016/S0375-9601(01)00043-3)
88. Vaishnavi SN, Vlassenko AG, Rundle MM, Snyder AZ, Mintun MA, Raichle ME. 2010 Regional aerobic glycolysis in the human brain. Proc. Natl Acad. Sci. USA 107, 17 757-17 762. (doi:10.1073/pnas. 1010459107)
89. Henry B, Langlands T, Wearne S. 2008 Fractional cable models for spiny neuronal dendrites. Phys. Rev. Lett. 100, 128103. (doi:10.1103/PhysRevLett. 100.128103)
90. Santamaria F, Wils S, De Schutter E, Augustine GJ. 2006 Anomalous diffusion in Purkinje cell dendrites caused by spines. Neuron 52, 635-648. (doi:10. 1016/j.neuron.2006.10.025)
91. Breakspear M. 2002 Nonlinear phase desynchronization in human electroencephalographic data. Hum. Brain Mapp. 15, 175-198. (doi:10.1002/hbm.10011)
92. Freeman WJ, Burke BC, Holmes MD. 2003 Aperiodic phase re-setting in scalp EEG of beta-gamma oscillations by state transitions at alpha-theta rates. Hum. Brain Mapp. 19, 248-272. (doi:10. 1002/hbm.10120)
93. de Pasquale F et al. 2010 Temporal dynamics of spontaneous MEG activity in brain networks. Proc. Natl Acad. Sci. USA 107, 6040-6045. (doi:10.1073/pnas.0913863107)
94. Chang C, Glover GH. 2010 Time-frequency dynamics of resting-state brain connectivity measured with fMRI. Neuroimage 50, 81-98. (doi:10.1016/j.neuroimage.2009.12.011)
95. Hutchison RM, Womelsdorf T, Gati JS, Everling S, Menon RS. 2013 Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaques. Hum. Brain Mapp. 34, 2154-2177. (doi:10.1002/hbm.22058)
96. Leonardi N, Shirer WR, Greicius MD, Van De Ville D. 2014 Disentangling dynamic networks: separated and joint expressions of functional connectivity patterns in time. Hum. Brain Mapp. 35, 5984-5995. (doi:10.1002/hbm.22599)
97. Zalesky A, Fornito A, Cocchi L, Gollo LL, Breakspear M. 2014 Time-resolved resting-state brain networks. Proc. Natl Acad. Sci. USA 111, 10 341-10 346. (doi:10.1073/pnas.1400181111)
98. Hutchison RM et al. 2013 Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage 80, 360-378. (doi:10.1016/j. neuroimage.2013.05.079)
99. Zalesky A, Fornito A, Harding IH, Cocchi L, Yÿcel M, Pantelis C, Bullmore ET. 2010 Whole-brain anatomical networks: does the choice of nodes matter? Neuroimage 50, 970-983. (doi:10.1016/j. neuroimage.2009.12.027)
100. Clark A. 2013 Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behav. Brain Sci. 36, 181-204. (doi:10.1017/S0140525X12000477)
101. Hohwy J. 2013 The predictive mind. Oxford, UK: Oxford University Press
102. Srinivasan MV, Laughlin SB, Dubs A. 1982 Predictive coding: a fresh view of inhibition in the retina. Proc. R. Soc. Lond. B 216, 427-459. (doi:10.1098/rspb.1982.0085)
103. Lee TS, Mumford D. 2003 Hierarchical Bayesian inference in the visual cortex. JOSA A 20, 1434-1448. (doi:10.1364/JOSAA.20.001434)
104. Conant RC, Ross Ashby W. 1970 Every good regulator of a system must be a model of that system. Int. J. Syst. Sci. 1, 89-97. (doi:10.1080/00207727008920220)
105. Roberts JA, Wallis G, Breakspear M. 2013 Fixational eye movements during viewing of dynamic natural scenes. Front. Psychol. 4, 797. (doi:10.3389/fpsyg. 2013.00797)
106. Perry A, Wen W, Lord A, Thalamuthu A, Sachdev P, Breakspear M. 2015 The organisation of the elderly connectome. arXiv:1502.05772 [q-bio.NC]
107. Ashby WR. 1947 Principles of the self-organizing dynamic system. J. Gen. Psychol. 37, 125-128. (doi:10.1080/00221309.1947.9918144)
108. Craig AD. 2002 How do you feel? Interoception: the sense of the physiological condition of the body. Nat. Rev. Neurosci. 3, 655-666. (doi:10.1038/nrn894)
109. Critchley HD, Wiens S, Rotshtein P, Öhman A, Dolan RJ. 2004 Neural systems supporting interoceptive awareness. Nat. Neurosci. 7, 189-195. (doi:10. 1038/nn1176)
110. Gu X, Hof PR, Friston KJ, Fan J. 2013 Anterior insular cortex and emotional awareness. J. Comp. Neurol. 521, 3371-3388. (doi:10.1002/cne.23368)
111. Seth AK. 2013 Interoceptive inference, emotion, and the embodied self. Trends Cogn. Sci. 17, 565-573. (doi:10.1016/j.tics.2013.09.007)
112. Seth AK. 2014 Interoceptive inference: from decision-making to organism integrity. Trends Cogn. Sci. 20, 1-2. (doi:10.1016/j.tics.2014.03.006)
113. Picard F, Friston K. 2014 Predictions, perception, and a sense of self. Neurology 83, 1112-1118. (doi:10.1212/WNL.0000000000000798)
114. van den Heuvel MP, Sporns O, Collin G, Scheewe T, Mandl RC, Cahn W, Goñi J, Hulshoff Pol HE, Kahn RS. 2013 Abnormal rich club organization and functional brain dynamics in schizophrenia. JAMA Psychiatry 70, 783-792. (doi:10.1001/jamapsychiatry.2013.1328)
115. Parker G, Hadzi-Pavlovic D. 1996 Melancholia: a disorder of movement and mood: a phenomenological and neurobiological review. Cambridge, UK: Cambridge University Press
116. Mitchell PB, Frankland A, Hadzi-Pavlovic D, Roberts G, Corry J, Wright A, Loo CK, Breakspear M. 2011 Comparison of depressive episodes in bipolar disorder and in major depressive disorder within bipolar disorder pedigrees. Br. J. Psychiatry 199, 303-309. (doi:10.1192/bjp.bp.110.088823)
117. Roberts G et al. 2013 Reduced inferior frontal gyrus activation during response inhibition to emotional stimuli in youth at high risk of bipolar disorder. Biol. Psychiatry 74, 55-61. (doi:10.1016/j.biopsych.2012. 11.004)
118. Hyett M, Breakspear M, Friston K, Guo C, Parker G. In press. The insula state of melancholia: disconnection of interoceptive and attentional networks. JAMA Psychiatry. (doi:10.1001/jamapsychiatry.2014.2490)
119. Hyett M. 2015 Attention and inference in melancholic depression. PhD thesis, UNSW, Australia
120. Crossley NA, Mechelli A, Scott J, Carletti F, Fox PT, McGuire P, Bullmore ET. 2014 The hubs of the human connectome are generally implicated in the anatomy of brain disorders. Brain 137, 2382-2395. (doi:10.1093/brain/awu132)
121. Kant I. 1781 The critique of pure reason (eBook). Raleigh, NC: Alex Catalogue
122. Honey C, Sporns O, Cammoun L, Gigandet X, Thiran J-P, Meuli R, Hagmann P. 2009 Predicting human resting-state functional connectivity from structural connectivity. Proc. Nat. Acad. Sci. USA 106, 2035-2040. (doi:10.1073/pnas.0811168106)