Beta power encodes contextual estimates of temporal event probability in the human brain


Autoři: Alessandro Tavano aff001;  Erich Schröger aff001;  Sonja A. Kotz aff003
Působiště autorů: BioCog, Cognitive Incl. Biological Psychology, Institute of Psychology, University of Leipzig, Leipzig, Germany aff001;  Department of Neuroscience, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany aff002;  Department of Neuropsychology, Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany aff003;  Faculty of Psychology and Neuroscience, Department of Neuropsychology and Psychopharmacology, Maastricht University, Maastricht, The Netherlands aff004
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: 10.1371/journal.pone.0222420

Souhrn

To prepare for an impending event of unknown temporal distribution, humans internally increase the perceived probability of event onset as time elapses. This effect is termed the hazard rate of events. We tested how the neural encoding of hazard rate changes by providing human participants with prior information on temporal event probability. We recorded behavioral and electroencephalographic (EEG) data while participants listened to continuously repeating five-tone sequences, composed of four standard tones followed by a non-target deviant tone, delivered at slow (1.6 Hz) or fast (4 Hz) rates. The task was to detect a rare target tone, which equiprobably appeared at either position two, three or four of the repeating sequence. In this design, potential target position acts as a proxy for elapsed time. For participants uninformed about the target’s distribution, elapsed time to uncertain target onset increased response speed, displaying a significant hazard rate effect at both slow and fast stimulus rates. However, only in fast sequences did prior information about the target’s temporal distribution interact with elapsed time, suppressing the hazard rate. Importantly, in the fast, uninformed condition pre-stimulus power synchronization in the beta band (Beta 1, 15–19 Hz) predicted the hazard rate of response times. Prior information suppressed pre-stimulus power synchronization in the same band, while still significantly predicting response times. We conclude that Beta 1 power does not simply encode the hazard rate, but—more generally—internal estimates of temporal event probability based upon contextual information.

Klíčová slova:

Attention – Behavior – Electroencephalography – Permutation – Probability density – Probability distribution – Scalp – Electrode potentials


Zdroje

1. Bermudez MA, Schultz W. Timing in reward and decision processes. Philos Trans R Soc Lond B Biol Sci. 2014; 369: 20120468. doi: 10.1098/rstb.2012.0468 24446502

2. Bertelson P. The time course of preparation. Q J Exp Psychol. 1967; 19: 272–279. doi: 10.1080/14640746708400102 6074169

3. Bolger D, Coull JT, Schön D. Metrical rhythm implicitly orients attention in time as indexed by improved target detection and left inferior parietal activation. J Cogn Neurosci. 2014; 26: 593–605. doi: 10.1162/jocn_a_00511 24168222

4. Correa Á, Lupiáñez J, Milliken P, Tudela P. Endogenous temporal orienting of attention in detection and discrimination tasks. Percept Psychophys. 2004; 66: 264–278. doi: 10.3758/bf03194878 15129748

5. Correa Á, Nobre AC. Neural modulation by regularity and passage of time. J Neurophysiol. 2008; 100: 1649–1655. doi: 10.1152/jn.90656.2008 18632896

6. Coull JT, Cheng RK, Meck WH. Neuroanatomical and neurochemical substrates of timing. Neuropsychopharmacol. 2011; 36: 3–25.

7. Coull JT, Nobre AC. Dissociating explicit timing from temporal expectations with fMRI. Curr Op Neurobiol. 2008; 18: 137–144. doi: 10.1016/j.conb.2008.07.011 18692573

8. Cui X, Stetson C, Montague PR, Eagleman DM. Ready … Go: Amplitude of the fMRI signal encodes expectation of cue arrival time. Plos Biol. 2009; 7: e1000167. doi: 10.1371/journal.pbio.1000167 19652698

9. Griffin IC, Miniussi C, Nobre AC. Orienting attention in time. Front Biosci. 2001; 6: D660–671. doi: 10.2741/griffin 11282565

10. Janssen P, Shadlen MN. A neural representation of the hazard rate of elapsed time in macaque area LIP. Nat Neurosci. 2005; 8: 234–241. doi: 10.1038/nn1386 15657597

11. Jones MR, Moynihan H, MacKenzie N, Puente J. Temporal aspects of stimulus-driven attending in dynamic arrays. Psychol Sci. 2002; 13: 313–319. doi: 10.1111/1467-9280.00458 12137133

12. Luce RD. Response times: their role in inferring elementary mental organization. Oxford: Oxford University Press; 1986.

13. Nobre K, Coull JT. Attention and time. Oxford: Oxford University Press; 2010.

14. Novak GP, Ritter W, Vaughan JR, Wiznitzer ML. Differentiation of negative event related potentials in an auditory discrimination task. Electroencephalogr Clin Neurophysiol. 1990; 75: 255–275. doi: 10.1016/0013-4694(90)90105-s 1691075

15. Niemi P, Näätänen R. Foreperiod and simple reaction time. Psychol Bull. 1981: 89; 133–162.

16. Vangkilde S, Coull JT, Bundesen C. Great expectations: Temporal expectation modulates perceptual processing speed. J Exp Psychol Hum Percept Perform. 2012; 38: 1183–1119. doi: 10.1037/a0026343 22250866

17. Woodrow H. The measurement of attention. Psychol Monogr. 1914;17: 5.

18. Elithorn A, Lawrence C. Central inhibition: some refractory observations. Q J Exp Psychol. 1955; 11: 211–220.

19. Yang T, Shadlen MN. Probabilistic reasoning by neurons. Nature 2007; 447: 1075–1080. doi: 10.1038/nature05852 17546027

20. Maimon G, Assad JA. A cognitive signal for the proactive timing of action in macaque LIP. Nat Neurosci. 2006; 9: 948–955. doi: 10.1038/nn1716 16751764

21. Genovesio A, Tsujimoto S, Wise SP. Neuronal activity related to elapsed time in prefrontal cortex. J Neurophysiol. 2006; 95: 3281–3285. doi: 10.1152/jn.01011.2005 16421197

22. Cotti J, Rohenkohl G, Stokes M, Nobre AC, Coull JT. Functionally dissociating temporal and motor components of response preparation in left intraparietal sulcus. Neuroimage 2011; 54: 1221–1230. doi: 10.1016/j.neuroimage.2010.09.038 20868756

23. Coull JT, Cotti J, Vidal F. Increasing activity in Left Inferior Parietal Cortex and right Prefrontal Cortex with increasing temporal predictability: An fMRI study of the hazard rate. Proc Soc Behav Sci. 2014; 126: 41–44.

24. Davranche K, Nazarian B, Vidal F, Coull JT. Orienting attention in time activates left intraparietal sulcus for perceptual and motor task goals. J Cogn Neurosci. 2011; 23: 3318–3330. doi: 10.1162/jocn_a_00030 21452942

25. Hultin L, Rossini P, Romani GL, Högstedt P, Tecchio F, Pizzella V. Neuromagnetic localization of the late component of the contingent negative variation. Electroencephalogr Clin Neurophysiol. 1996; 98: 435–448. doi: 10.1016/0013-4694(96)95507-8 8763503

26. Sussman E, Winkler I, Huotilainen M, Ritter W, Näätänen R. Top-down effects can modify the initially stimulus-driven auditory organization. Cog Brain Res. 2002; 13: 393–405.

27. Sussman E, Gumenyuk V. Organization of sequential sounds in auditory memory. NeuroReport 2005; 16: 1519–1523. doi: 10.1097/01.wnr.0000177002.35193.4c 16110282

28. Friston K. A theory of cortical responses. Philos Trans R Soc London Biol Sci. 2005; 360: 815–836.

29. Garrido MI, Kilner JM, Kiebel SJ, Friston KJ. Dynamic causal modeling of the response to frequency deviants. J Neurophysiol. 2009a; 101: 2620–2631.

30. Garrido MI, Kilner JM, Stephan KE, Friston KJ. The mismatch negativity: a review of underlying mechanisms. Clin Neurophysiol. 2009b; 120: 453–463.

31. Jaramillo S, Zador AM. The auditory cortex mediates the perceptual effects of acoustic temporal expectation. Nat Neurosci. 2011; 14: 246–251. doi: 10.1038/nn.2688 21170056

32. Tavano A, Widmann A, Bendixen A, Schröger E. Temporal regularity facilitates higher-order sensory predictions in fast auditory sequences. Eur J Neurosci. 2014; 39: 308–318. doi: 10.1111/ejn.12404 24236753

33. Wacongne C, Changeux JP, Dehaene S. A neuronal model of predictive coding accounting for the mismatch negativity. J Neurosci. 2012; 32: 3665–3678. doi: 10.1523/JNEUROSCI.5003-11.2012 22423089

34. Ahveninen J, Kähkönen S, Pennanen S, Liesivuori J, Ilmoniemi RJ, Jääskeläinen IP. Tryptophan depletion effects on EEG and MEG responses suggest serotonergic modulation of auditory involuntary attention in humans. Neuroimage 2002; 16: 1052–1061. 12202092

35. Chennu S, Noreika V, Gueorguiev D, Blenkmann A, Kochen S, Ibáñez A, Owen AM, Bekinschtein TA. Expectation and Attention in Hierarchical Auditory Prediction. J Neurosci. 2013; 33: 11194–11205. doi: 10.1523/JNEUROSCI.0114-13.2013 23825422

36. Miniussi C, Wilding EL, Coull JT, Nobre AC. Orienting attention in time: modulation of brain potentials. Brain 1999; 122: 1507–1518. doi: 10.1093/brain/122.8.1507 10430834

37. Schröger E, Marzecová A, SanMiguel I. Attention and prediction in human audition: a lesson from cognitive psychophysiology. Eur J Neurosci. 2015; 41: 641–664. doi: 10.1111/ejn.12816 25728182

38. Sokolov EN, Spinks JA, Näätänen R, Lyytinen H. Auditory event-related potentials in the study of the orienting response. In: Sokolov EN, Spinks JA, Näätänen R, Lyytinen H, editors. The orienting response in information processing. Mahwah, NJ: Lawrence Erlbaum; 2002. pp. 241–325.

39. Sussman ES. A new view on the MMN and attention debate: The role of context in processing auditory events. J Psychophysiol. 2007; 21: 164–175.

40. Fujioka T, Ross B, Trainor LJ. Beta-Band Oscillations Represent Auditory Beat and Its Metrical Hierarchy in Perception and Imagery. J Neurosci. 2015; 35: 15187–15198. doi: 10.1523/JNEUROSCI.2397-15.2015 26558788

41. Fujioka T, Trainor LJ, Large EW, Ross B. Internalized timing of isochronous sounds is represented in neuromagnetic β oscillations. J Neurosci. 2012; 32: 1791–1802. doi: 10.1523/JNEUROSCI.4107-11.2012 22302818

42. Merchant H, Grahn J, Trainor LJ, Rohrmeier M, Fitch WT. Finding the beat: a neural perspective across humans and non-human primates. Philos Trans R Soc Lond B Biol Sci. 2015; 370: 20140093. doi: 10.1098/rstb.2014.0093 25646516

43. Arnal LH, Doelling KB, Poeppel D. Delta–beta coupled oscillations underlie temporal prediction accuracy. Cereb Cortex 2015; 25: 3077–3085. doi: 10.1093/cercor/bhu103 24846147

44. Arnal LH, Giraud AL. Cortical oscillations and sensory predictions. Trends Cogn Sci. 2012; 16: 390–398. doi: 10.1016/j.tics.2012.05.003 22682813

45. Patel AD, Iversen JR. The evolutionary neuroscience of musical beat perception: The Action Simulation for Auditory Prediction (ASAP) hypothesis. Front Syst Neurosci. 2014; 8: 57. doi: 10.3389/fnsys.2014.00057 24860439

46. Pfurtscheller G, Graimann B, Huggins JE, Levine SP, Schuh LA. Spatiotemporal patterns of beta desynchronization and gamma synchronization in corticographic data during self-paced movement. Clin Neurophysiol. 2003; 114: 1226–1236. 12842719

47. Engel AK, Fries P. Beta-band oscillations—signalling the status quo? Curr Op Neurobiol. 2010; 20: 156–165. doi: 10.1016/j.conb.2010.02.015 20359884

48. Michalareas G, Vezoli J, vam Pelt S, Schoffelen JM, Kennedy H, Fries P. Alpha-Beta and Gamma rhythms subserve feedback and feedforward influences among human visual cortical areas. Neuron 2016; 89: 384–397. doi: 10.1016/j.neuron.2015.12.018 26777277

49. Lee JH, Whittington MA, Kopell NJ. Top-down beta rhythms support selective attention via interlaminar interaction: a model. PLoS Comput Biol. 2013; 9: e1003164–1003123. doi: 10.1371/journal.pcbi.1003164 23950699

50. Spitzer B, Haegens S. Beyond the Status Quo: A Role for Beta Oscillations in Endogenous Content (Re)Activation. eNeuro 2017; 4: ENEURO.0170-17.2017.

51. Kopell N, Whittington MA, Kramer MA. Neuronal assembly dynamics in the beta1 frequency range permits short-term memory. Proc Natl Acad Sci USA. 2011; 108: 3779–3784. doi: 10.1073/pnas.1019676108 21321198

52. Brovelli A, Ding M, Ledberg A, Chen Y, Nakamura R, Bressler SL. Beta oscillations in a large-scale sensorimotor cortical network: directional influences revealed by Granger causality. Proc Natl Acad Sci USA. 2004; 101: 9849–9854. doi: 10.1073/pnas.0308538101 15210971

53. Leventhal DK, Gage GJ, Schmidt R, Pettibone JR, Case AC, Berke JD. Basal Ganglia Beta oscillations accompany cue utilization. Neuron 2012; 73: 523–536. doi: 10.1016/j.neuron.2011.11.032 22325204

54. Theil H. A rank-invariant method of linear and polynomial regression analysis. I, II, III. Nederl Akad Wetensch Proc. 1950; 53: 386–392, 521–525, 1397–1412.

55. Sen PK. Estimates of the regression coefficient based on Kendall’s tau. J Am Statistical Assoc. 1968; 63: 1379–1389.

56. Hauk O, Davis MH, Ford M, Pulvermüller F, Marslen-Wilson WD. The time course of visual word recognition as revealed by linear regression analysis of ERP data. Neuroimage 2006; 30: 1383–1400. doi: 10.1016/j.neuroimage.2005.11.048 16460964

57. Maris E, Oostenveld R. Nonparametric statistical testing of EEG- and MEG-data. J Neurosci Methods 2007; 164: 177–190. doi: 10.1016/j.jneumeth.2007.03.024 17517438

58. Näätänen R, Picton T. The N1 wave of the human electric and magnetic response to sound: A review and an analysis of the component structure. Psychophysiol. 1987; 24: 375–425.

59. Murray MM, Brunet D, Michel CM. Topographic ERP analyses: a step-by-step tutorial review. Brain Topogr. 2008; 20: 249–264. doi: 10.1007/s10548-008-0054-5 18347966

60. Budd TW, Barry RJ, Gordon E, Rennie C, Michie PT. Decrement of the N1 auditory event-related potential with stimulus repetition: habituation vs. refractoriness. Int J Psychophysiol. 1998; 31: 51–68. 9934621

61. Bendixen A, Prinz W, Horvath J, Trujillo-Barreto NJ, Schröger E. Rapid extraction of auditory feature contingencies. Neuroimage 2008; 41: 1111–1119. doi: 10.1016/j.neuroimage.2008.03.040 18467129

62. Leon MI, Shadlen MN. Representation of time by neurons in the posterior parietal cortex of the macaque. Neuron 2003; 38: 317–327. doi: 10.1016/s0896-6273(03)00185-5 12718864

63. Luft CD, Meeson A, Welchman AE, Kourtzi Z. Decoding the future from past experience: learning shapes predictions in early visual cortex. J Neurophysiol. 2015; 113: 3159–3171. doi: 10.1152/jn.00753.2014 25744884

64. Cowan N. What are the differences between long-term, short-term, and working memory? In: Lacaille J-C, Castellucci VF, Belleville S, Sossin WS, editors. Progress in Brain Research: Essence of Memory. Amsterdam: Elsevier; 2008. pp. 323–338.

65. Cowan NJ, Saults S, Nugent LD. The role of absolute and relative amounts of time in forgetting within immediate memory: the case of tone-pitch comparisons. Psychon Bull Rev 1999; 4: 393–397.

66. Schulze K, Tillmann B. Working memory for pitch, timbre, and words. Memory 2015; 21: 377–395.

67. Todorovic A., Schoffelen J.-M., van Ede F., Maris E., de Lange FP. Temporal Expectation and Attention Jointly Modulate Auditory Oscillatory Activity in the Beta Band. PLoS ONE 2015; 10: e0120288. https://doi.org/10.1371/journal.pone.0120288 25799572

68. Rohenkohl G, Nobre AC. Alpha oscillations related to anticipatory attention follow temporal expectations. J Neurosci. 2011; 31: 14076–14084. doi: 10.1523/JNEUROSCI.3387-11.2011 21976492

69. Wilsch A, Henry MJ, Herrmann B, Maess B, Obleser J. Alpha oscillatory dynamics index temporal expectation benefits in working memory. Cereb Cortex 2015; 25: 1938–1946. doi: 10.1093/cercor/bhu004 24488943

70. Chang A, Bosnyak DJ, Trainor LJ. Unpredicted Pitch Modulates Beta Oscillatory Power during Rhythmic Entrainment to a Tone Sequence. Front Psychol. 2016; 7: 327. doi: 10.3389/fpsyg.2016.00327 27014138

71. Kopell N, Kramer MA, Malerba P, Whittington MA. Are different rhythms good for different functions? Front Human Neurosci. 2010; 4: 187.

72. Gulberti A, Moll CK, Hamel W, Buhmann C, Koeppen JA, Boelmans K, Zittel S, Gerloff C, Westphasl M, Schneider TR, Engel AK. Predictive timing functions of cortical beta oscillations are impaired in Parkinson’s disease and influenced by L-DOPA and deep brain stimulation of the subthalamic nucleus. Impaired beta-band timing functions in PD. Neuroimage: Clinical 2015; 9: 436–449.

73. American National Standards Institute. Specifications for audiometers (ANSI S3.6–1996). New York; 1996.

74. Macmillan NA, Creelman CD. Detection theory: A user’s guide. Cambridge: Cambridge University Press; 1991.

75. Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics. J Neurosci Methods 2004; 134: 9–21. doi: 10.1016/j.jneumeth.2003.10.009 15102499

76. Widmann A, Schröger E, Maess B. Digital filter design for electrophysiological data—a practical approach. J Neurosci Methods 2015; 250: 34–46. doi: 10.1016/j.jneumeth.2014.08.002 25128257

77. Viola FC, Thorne J, Edmonds B, Schneider T, Eichele T, Debener S. Semi-automatic identification of independent components representing EEG artefact. Clin Neurophysiol. 2009; 120: 868–877. doi: 10.1016/j.clinph.2009.01.015 19345611

78. Onton J, Makeig S. Information-based modelling of event-related brain dynamics. Prog Brain Res. 2006; 159: 99–120. doi: 10.1016/S0079-6123(06)59007-7 17071226

79. Rahne T, von Specht H, Mühler R. Sorted averaging. Application to auditory event-related responses. J Neurosci Methods 2008; 172: 74–78. doi: 10.1016/j.jneumeth.2008.04.006 18499265

80. Dien J, Frishkoff GA, Cerbone A, Tucker DM. Parametric analysis of event-related potentials in semantic comprehension: evidence for parallel brain mechanisms. Brain Res Cogn Brain Res. 2003; 15: 137–153. 12429366

81. Oostenveld R, Fries P, Maris E, Schoffelen JM. FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data. Comput Intell Neurosci. 2011; Article ID 156869: doi: 10.1155/2011/156869 21253357

82. Kendall M. A New Measure of Rank Correlation. Biometrika 1938; 30: 81–89.


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