Causal density and integrated information as measures of conscious level

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences

Volumn 369, Issue 1952, 2011, Pages 3748-3767

  Seth, Anil K ^a   Barrett, Adam B ^a   Barnett, Lionel ^a  

^a UNIVERSITY OF SUSSEX   (United Kingdom)

Author keywords

Causal density; Consciousness; Integrated information

Indexed keywords

CAUSAL DENSITY; CONSCIOUSNESS; DYNAMICAL COMPLEXITY; DYNAMICAL FEATURES; INTEGRATED INFORMATIONS; QUANTITATIVE MEASURES; RECENT PROGRESS; SIMULATION RESULT; SYSTEM'S DYNAMICS;

INTEGRATION;

ARTICLE; BIOLOGICAL MODEL; CONSCIOUSNESS; INFORMATION SCIENCE; MULTIVARIATE ANALYSIS;

CONSCIOUSNESS; INFORMATION THEORY; MODELS, NEUROLOGICAL; MULTIVARIATE ANALYSIS;

EID: 80054968423     PISSN: 1364503X     EISSN: None     Source Type: Journal    
DOI: 10.1098/rsta.2011.0079     Document Type: Review

References (48)

1. Seth, A. K. 2010 The grand challenge of consciousness. Front. Psychol. 1, 1-2. (doi:10.3389/fpsyg.2010.00005)
2. Melloni, L., Molina, C., Pena, M., Torres, D., Singer, W. & Rodriguez, E. 2007 Synchronization of neural activity across cortical areas correlates with conscious perception. J. Neurosci. 27, 2858-2865. (doi:10.1523/JNEUROSCI.4623-06.2007) (Pubitemid 46438979)
3. Seth, A. K., McKinstry, J. L., Edelman, G. M. & Krichmar, J. L. 2004 Visual binding through reentrant connectivity and dynamic synchronization in a brain-based device. Cereb. Cortex 14, 1185-1199. (doi:10.1093/cercor/bhh079) (Pubitemid 39391195)
4. Balduzzi, D. & Tononi, G. 2009 Qualia: the geometry of integrated information. PLoS Comput. Biol. 5, e1000462. (doi:10.1371/journal.pcbi.1000462)
5. Tononi, G., Sporns, O. & Edelman, G. M. 1994 A measure for brain complexity: relating functional segregation and integration in the nervous system. Proc. Natl Acad. Sci. USA 91, 5033-5037. (doi:10.1073/pnas.91.11.5033) (Pubitemid 24177786)
6. Tononi, G. 2008 Consciousness as integrated information: a provisional manifesto. Biol. Bull. 215, 216-242. (doi:10.2307/25470707)
7. Seth, A. K., Izhikevich, E., Reeke, G. N. & Edelman, G. M. 2006 Theories and measures of consciousness: an extended framework. Proc. Natl Acad. Sci. USA 103, 10799-10 804. (doi:10.1073/pnas.0604347103) (Pubitemid 44078056)
8. Barrett, A. B. & Seth, A. K. 2011 Practical measures of integrated information for time series data. PLoS Comput. Biol., 7, e1001052. (doi:10.1371/journal.Pcbi.1001052)
9. Geweke, J. 1982 Measurement of linear dependence and feedback between multiple time series. J. Am. Statist. Assoc. 77, 304-313. (doi:10.2307/2287238)
10. Granger, C. W. J. 1969 Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37, 424-438. (doi:10.2307/1912791)
11. Seth, A. K. 2010 A MATLAB toolbox for Granger causal connectivity analysis. J. Neurosci. Meth. 186, 262-273. (doi:10.1016/j.jneumeth.2009.11.020)
12. Shanahan, M. 2008 Dynamical complexity in small-world networks of spiking neurons. Phys. Rev. E 78, 041924. (doi:10.1103/PhysRevE.78.041924)
13. Harris, K. D. 2005 Neural signatures of cell assembly organization. Nat. Rev. Neurosci. 6, 399-407. (doi:10.1038/nrn1669) (Pubitemid 40593485)
14. Barrett, A. B., Barnett, L. & Seth, A. K. 2010 Multivariate granger causality and generalized variance. Phys. Rev. E 81, 041907. (doi:10.1103/PhysRevE.81.041907)
15. Ladroue, C., Guo, S., Kendrick, K. & Feng, J. 2009 Beyond element-wise interactions: identifying complex interactions in biological processes. PLoS ONE 4, e6899. (doi:10.1371/journal.pone.0006899)
16. Guo, S., Seth, A. K., Kendrick, K. M., Zhou, C. & Feng, J. 2008 Partial Granger causality-eliminating exogenous inputs and latent variables. J Neurosci. Meth. 172, 79-93. (doi:10.1016/j.jneumeth.2008.04.011)
17. Marinazzo, D., Liao, W., Chen, H. & Stramaglia, S. In press, Nonlinear connectivity by Granger causality. Neuroimage. (doi:10.1016/j. neuroimage.2010.01.099)
18. Schreiber, T. 2000 Measuring information transfer. Phys. Rev. Lett. 85, 461-464. (doi:10.1103/PhysRevLett.85.461)
19. Barnett, L., Barrett, A. B. & Seth, A. K. 2009 Granger causality and transfer entropy are equivalent for gaussian variables. Phys. Rev. Lett. 103, 238701. (doi:10.1103/PhysRev Lett.103.238701)
20. Hvlavácková-Schindler, K., Palus, M., Vejmelka, M. & Bhattacharya, J. 2007 Causality detection based on information-theoretic approaches in time series analysis. Phys. Rep. 441, 1-46. (doi:10.1016/j. physrep.2006.12.004)
21. Tononi, G. & Sporns, O. 2003 Measuring information integration. BMC Neurosci. 4, 31.
22. Balduzzi, D. & Tononi, G. 2008 Integrated information in discrete dynamical systems: motivation and theoretical framework. PLoS Comput. Biol. 4, e1000091. (doi:10.1371/journal. pcbi.1000091)
23. Watts, D. J. & Strogatz, S. H. 1998 Collective dynamics of 'small world' networks. Nature 393, 440-442. (doi:10.1038/30918) (Pubitemid 28292183)
24. Watts, D. J. 1999 Small worlds. Princeton, NJ: Princeton University Press.
25. Horn, R. A. & Johnson, C. R. 1985 Matrix analysis. New York, NY: Cambridge University Press.
26. Barnett, L., Buckley, C. L. & Bullock, S. 2009 Neural complexity and structural connectivity. Phys. Rev. E 79, 051914. (doi:10.1103/PhysRevE.79. 051914)
27. Wennekers, T. & Ay, N. 2001 Dynamical properties of strongly interacting markov chains. Neural Netw. 16, 1483-1497.
28. Wennekers, T. & Ay, N. 2005 Finite state automata resulting from temporal information maximization and a temporal learning rule. Neural Comput. 17, 2258-2290. (doi:10.1162/0899766054615671) (Pubitemid 41186998)
29. Ding, M., Bressler, S., Yang, W. & Liang, H. 2000 Short-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive modeling: data preprocessing, model validation, and variability assessment. Biol. Cybern. 83, 35-45. (doi:10.1007/s004229900137)
30. Geweke, J. 1984 Measures of conditional linear dependence and feedback between time series. J. Am. Statist. Assoc. 79, 907-915. (doi:10.2307/2288723)
31. Palus, M.(1997) On entropy rates of dynamical systems and Gaussian processes.Physica A 227, 301-308(doi:10.1016/S0375-9601(97)00079-0) (Pubitemid 127430442)
32. Palus, M., Komarek, V. Hrncir, Z. & prochazka, T. 1999 Is nonlinearity relevant for detecting changes in EEG? Theory Biosci. 118, 179-188. (Pubitemid 30072062)
33. Hlinka, J., Palus, M., Vejmelka, M., Martini, D. & Corbetta, M. 2011 Functional connectivity in resting-state fMRI: is linear correlation sufficient? Neuroimage 54, 2218-2225. (doi:10.1016/j.neuroimage.2010.08.042)
34. McIntosh, A. R. & Gonzalez-Lima, F. 1994 Structural equation modeling and its application to network analysis in functional brain imaging. Hum. Brain Map. 2, 2-22. (doi:10.1002/hbm.460020104) (Pubitemid 124000439)
35. Ekstrom, A. 2010 How and when the fMRI BOLD signal relates to underlying neural activity: the danger in dissociation. Brain Res. Rev. 62, 233-244. (doi:10.1016/j.brainresrev.2009.12.004)
36. Aguirre, G. K., Zarahn, E. & D'esposito, M. 1998 The variability of human, BOLD hemodynamic responses. Neuroimage 8, 360-369. (doi:10.1006/nimg. 1998.0369) (Pubitemid 28545935)
37. David, O., Guillemain, I., Saillet, S., Reyt, S., Deransart, C., Segebarth, C. & Depaulis, A. 2008 Identifying neural drivers with functional MRI: an electrophysiological validation. PLoS Biol. 6, 2683-2697. (doi:10.1371/journal.pbio.0060315)
38. Roebroeck, A., Formisano, E. & Goebel, R. In press. The identification of interacting networks in the brain using fMRI: model selection, causality and deconvolution. Neuroimage. (doi:10.1016/j.neuroimage.2009.09.036)
39. Ryali, S., Supekar, K., Chen, T. & Menon, V. 2011 Multivariate dynamical systems models for estimating causal interactions in fMRI. Neuroimage 54, 807-823. (doi:10.1016/j.neuroimage. 2010.09.052)
40. Massimini, M., Boly, M., Casali, A., Rosanova, M. & Tononi, G. 2009 A perturbational approach for evaluating the brain's capacity for consciousness. Prog. Brain Res. 177, 201-214. (doi:10.1016/S0079-6123(09)17714-2)
41. Martínez-Montes, E., Vega-Hernández, M., Sánchez-Bornot, J. M. & Valdés-Sosa, P. A. 2008 Identifying complex brain networks using penalized regression methods. J. Biol. Phys. 34, 315-323. (doi:10.1007/s10867-008-9077-0)
42. Srinivasan, R., Russell, D. P., Edelman, G. M. & Tononi, G. 1999 Increased synchronization of magnetic responses during conscious perception. J. Neurosci. 19, 5435-5448. (Pubitemid 29300209)
43. Tononi, G. & Koch, C. 2008 The neural correlates of consciousness: an update. Ann. NY Acad. Sci. 1124, 239-261. (doi:10.1196/annals.1440.004) (Pubitemid 351506781)
44. Seth, A. K. 2009 Explanatory correlates of consciousness: theoretical and computational challenges. Cogn. Comput. 1, 50-63. (doi:10.1007/s12559-009-9007- x)
45. Burgess, A. P., Rehman, J. & Williams, J. D. 2003 Changes in neural complexity during the perception of 3D images using random dot stereograms. Int. J. Psychophysiol. 48, 35-42. (doi:10.1016/S0167-8760(03)00002-3) (Pubitemid 36407684)
46. Branston, N. M., El-Deredy, W. & McGlone, F. P. 2005 Changes in neural complexity of the EEG during a visual oddball task. Clin. Neurophysiol. 116, 151-159. (doi:10.1016/j.clinph. 2004.07.015) (Pubitemid 40260431)
47. van Putten, M. J. A. M. & Stam, C. J. 2001 Application of a neural complexity measure to multichannel eeg. Phys. Lett. A 281, 131-141. (doi:10.1016/S0375-9601(01)00121-9) (Pubitemid 32233629)
48. James, W. 1904 Does consciousness exist? J. Phil. Psychol. Sci. Meth. 1, 477-491. (doi:10.2307/2011942)