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Baseline Measures of EEG Power as Correlates of the Verbal and Nonverbal Components of Creativity and Intelligence

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Intense studies of the neurophysiological correlates of creativity in recent years have identified a connection between baseline brain activity (including the DMN (default mode network)) and measures of creativity. However, the specific components of this activity determining high levels of verbal and nonverbal creativity and the importance of intellectual abilities for successful solution of experimental creative tasks still remain unclear. The balance in the baseline activity of the frontal and rear parts of the cortex may reflect individual problem-solving style while oscillations in different frequency ranges may serve as indicators of this balance. We analyzed the frequency-spatial organization of the baseline EEG and identified power differences in the δ, θ, α2, and β2 rhythms in groups differing in terms of measures of the originality of responses on testing for verbal and nonverbal creativity. Higher creative capacities corresponded to higher power levels of low-frequency biopotentials in the frontal parts of the cortex and decreases in α-rhythm power in the rear sectors. “Pretuning” of cortical activity to verbal originality was apparent mainly in the temporal and central-parietal areas of the cortex, and pretuning to imaginal originality was apparent for the parietal-occipital areas. The contribution of the visuospatial component of intelligence to changes in cortical activity linked with imaginal creativity was greater in the δ and α2 rhythms, while the contribution of the verbal component of intelligence to “pretuning” of cortical neural systems to verbal creativity was greater in the θ and β2 ranges. Thus, analysis of the frequency-spatial organization of cerebral cortical activity may be a useful tool for detecting the role of intellectual abilities and emotional-motivational regulation in the formation of different strategies for achieving high levels of creativity.

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References

  1. R. E. Beaty, M. Benedek, R. W. Wilkins, and E. Jauk, “Creativity and the default network: A functional connectivity analysis of the creative brain at rest,” Neuropsychologia, 64, 92–98 (2014), https://doi.org/10.1016/j.neuropsychologia.2014.09.019.

    Article  PubMed  PubMed Central  Google Scholar 

  2. A.-L. Schuler, M. Tik, R. Sladky, et al., “Modulations in resting state networks of subcortical structures linked to creativity,” Neuroimage, 195, 311–319 (2019), https://doi.org/10.1016/j.neuroimage.2019.03.017.

    Article  PubMed  Google Scholar 

  3. L. Shi, J. Sun, Y. Xia, et al., “Large-scale brain network connectivity underlying creativity in resting-state and task fMRI: Cooperation between default network and frontal-parietal network,” Biol. Psychol., 135, 102–111 (2018), https://doi.org/10.1016/j.biopsycho.2018.03.005.

    Article  PubMed  Google Scholar 

  4. J. Sun, Z. Liu, E. T. Rolls, et al., “Verbal creativity correlates with the temporal variability of brain networks during the resting state,” Cereb. Cortex, 29, No. 3, 1047–1058 (2019), https://doi.org/10.3389/fpsyg.2019.00894.

    Article  PubMed  Google Scholar 

  5. W. Zhu, Q. Chen, L. Xia, et al., “Common and distinct brain networks underlying verbal and visual creativity,” Hum. Brain Mapp., 38, No. 4, 2094–2111 (2017), https://doi.org/10.1002/hbm.23507.

    Article  PubMed  PubMed Central  Google Scholar 

  6. R. E. Beaty, P. Seli, and D. L. Schacter, “Network neuroscience of creative cognition: Mapping cognitive mechanisms and individual differences in the creative brain,” Curr. Opin. Behav. Sci., 27, 22–30 (2019), https://doi.org/10.1016/j.cobeha.2018.08.013.

    Article  PubMed  Google Scholar 

  7. R. Gulbinaite, H. van Rijn, and M. X. Cohen, “Fronto-parietal network oscillations reveal relationship between working memory capacity and cognitive control,” Front. Hum. Neurosci., 8, 761 (2014), https://doi.org/10.3389/fnhum.2014.00761.

    Article  PubMed  PubMed Central  Google Scholar 

  8. J. Heinonen, J. Numminen, Y. Hlushchuk, et al., “Default mode and executive networks areas: Association with the serial order in divergent thinking,” PLoS One, 11, No. 9, e0162234 (2016), https://doi.org/10.1371/journal.pone.0162234.

  9. R. E. Beaty, Y. N. Kenett, A. P. Christensen, et al., “Robust prediction of individual creative ability from brain functional connectivity,” Proc. Natl. Acad. Sci. USA, 115, No. 5, 1087–1092 (2018), https://doi.org/10.1073/pnas.1713532115.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Q. Feng, L. He, W. Yang, et al., “Verbal creativity is correlated with the dynamic reconfiguration of brain networks in the resting state,” Front. Psychol., 10, 894 (2019), https://doi.org/10.3389/fpsyg.2019.00894.

    Article  PubMed  PubMed Central  Google Scholar 

  11. W. Li, J. Yang, Q. Zhang, et al., “The association between resting functional connectivity and visual creativity,” Sci. Rep., 6, 25395 (2016), https://doi.org/10.1038/srep25395.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. K. M. Mihov, M. Denzler, and J. Förster, “Hemispheric specialization and creative thinking: a meta-analytic review of lateralization of creativity,” Brain Cogn., 72, No. 3, 442–448 (2010), https://doi.org/10.1016/j.bandc.2009.12.007.

    Article  PubMed  Google Scholar 

  13. L. Aziz-Zadeh, S. L. Liew, and F. Dandekar, “Exploring the neural correlates of visual creativity,” Soc. Cogn. Affect. Neurosci., 8, 475–480 (2013), https://doi.org/10.1093/scan/nss021.

    Article  PubMed  Google Scholar 

  14. J. Hahm, K. K. Kim, S. H. Park, and H. M. Lee, “Brain areas subserving Torrance tests of creative thinking: an functional magnetic resonance imaging study,” Dement. Neurocogn. Disord., 16, No. 2, 48–53 (2017), https://doi.org/10.12779/dnd.2017.16.2.48.

  15. M. Benedek, R. E. Jung, and O. Vartanian, “The neural bases of creativity and intelligence: Common ground and differences,” Neuropsychologia, 118, Part A, 1–3 (2018), https://doi.org/10.1016/j.neuropsychologia.2018.09.006.

  16. E. Frith, D. B. Elbich, A. P. Christensen, et al., “Intelligence and creativity share a common cognitive and neural basis,” J. Exp. Psychol. Gen., (2020), https://doi.org/10.1037/xge0000958.

  17. R. E. Jung and R. J. Haier, “The Parieto-Frontal Integration Theory (P-FIT) of intelligence: converging neuroimaging evidence,” Behav. Brain Sci., 30, No. 2, 135–154 (2007), https://doi.org/10.1017/S0140525X07001185.

    Article  PubMed  Google Scholar 

  18. K. H. Lee, Y. Y. Choi, J. R. Gray, et al., “Neural correlates of superior intelligence: Stronger recruitment of posterior parietal cortex,” NeuroImage, 29, 578–586 (2006), https://doi.org/10.1016/j.neuroimage.2005.07.036.

    Article  PubMed  Google Scholar 

  19. G. S. P. Pamplona, G. S. S. Neto, S. R. E. Rosset, et al., “Analyzing the association between functional connectivity of the brain and intellectual performance,” Front. Hum. Neurosci., 9, 1–11 (2015), https://doi.org/10.3389/fnhum.2015.00061.

    Article  Google Scholar 

  20. Y. N. Kenett, J. D. Medaglia, R. E. Beaty, et al., “Driving the brain towards creativity and intelligence: A network control theory analysis,” Neuropsychologi, 118, Part A, 79–90 (2018), https://doi.org/10.1016/j.neuropsychologia.2018.01.001.

  21. O. M. Razumnikova, “Relationship between the frequency-spatial parameters of the baseline EEG and levels of intelligence and creativity,” Zh. Vyssh. Nerv. Deyat., 59, No. 6, 686–695 (2009).

    Google Scholar 

  22. C. S. Herrmann, D. Strüber, R. F. Helfrich, A. K. Engel, “EEG oscillations: From correlation to causality,” Int. J. Psychophysiol., 103, 12–21 (2016), https://doi.org/10.1016/j.ijpsycho.2015.02.003.

    Article  PubMed  Google Scholar 

  23. C. E. J. Stevens and D. L. Zabelina, “Creativity comes in waves: An EEG-focused exploration of the creative brain,” Curr. Opin. Behav. Sci., 27, 154–162 (2019), https://doi.org/10.31234/osf.io/ke6wq.

  24. O. Takeshi, T. Aihara, T. Shimokawa, and O. Yamashita, “Large-scale brain network associated with creative insight: combined voxel-based morphometry and resting-state functional connectivity analyses,” Sci. Rep., 8, 6477 (2018), https://doi.org/10.1038/s41598-018-24981-0.

    Article  CAS  Google Scholar 

  25. M. Benedek, E. Jauk, M. Sommer, et al., “Intelligence, creativity, and cognitive control: The common and differential involvement of executive functions in Intelligence and creativity,” Intelligence, 46, 73–83 (2014), https://doi.org/10.1016/j.intell.2014.05.007.

    Article  PubMed  PubMed Central  Google Scholar 

  26. M. Benedek, S. Bergner, T. Könen, et al., “EEG alpha synchronization is related to top-down processing in convergent and divergent thinking,” Neuropsychologia, 49, 3505–3511 (2011), https://doi.org/10.1016/j.neuropsychologia.2011.09.004.

    Article  PubMed  PubMed Central  Google Scholar 

  27. C. Lustenberger, M. R. Boyle, A. A. Foulser, et al., “Functional role of frontal alpha oscillations in creativity,” Cortex, 67, 74–82 (2015), https://doi.org/10.1016/j.cortex.2015.03.012.

    Article  PubMed  PubMed Central  Google Scholar 

  28. B. Erickson, M. Truelove-Hill, Y. Oh, et al., “Resting-state brain oscillations predict trait-like cognitive styles,” Neuropsychologia, 120, 1–8 (2018), https://doi.org/10.1016/j.neuropsychologia.2018.09.014.

    Article  PubMed  Google Scholar 

  29. J. Kounios, J. I. Fleck, D. L. Green, et al., “The origins of insight in resting-state brain activity,” Neuropsychologia, 46, 281–291 (2008), https://doi.org/10.1016/j.neuropsychologia.2007.07.013.

    Article  PubMed  Google Scholar 

  30. P. M. Briley, E. B. Liddle, M. J. Groom, et al., “Development of human electrophysiological brain networks,” J. Neurophysiol., 120, No. 6, 3122–3130 (2018), https://doi.org/10.1152/jn.00293.2018.

    Article  PubMed  PubMed Central  Google Scholar 

  31. V. Costa, “The EEG as an index of neuromodulator balance in memory and mental illness,” Front. Neurosci., 8, 63 (2014), https://doi.org/10.3389/fnins.2014.00063.

    Article  Google Scholar 

  32. E. A. Solomon, J. E. Kragel, M. R. Sperling, et al., “Widespread theta synchrony and high-frequency desynchronization underlies enhanced cognition,” Nat. Commun., 8, 1704 (2017), https://doi.org/10.1038/s41467-017-01763-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. C. D. Hacker, A. Z.Snyder, M. Pahwa, et al., “Frequency-specific electrophysiologic correlates of resting state fMRI networks,” Neuroimage, 149, 446–457 (2017), https://doi.org/10.1016/j.neuroimage.2017.01.054.

    Article  PubMed  Google Scholar 

  34. O. M. Razumnikova, “Features of the selection of information in creative thought. Psychology, Zh. Vyssh. Shkol. Ekonom., 6, No. 3, 134–161 (2009).

  35. O. M. Razumnikova and K. D. Krivonogova, “Electroencephalographic correlates of the activity of the frontoparietal system as predictors of verbal intelligence and non-verbal creativity,” Russ. Psychol. J., 16, No. 2/1, 45–59 (2019), https://doi.org/10.21702/rpj.2019.2.1.4.

  36. O. M. Razumnikova, Methods for Determining Creativity, NGTU, Novosibirsk (2002).

    Google Scholar 

  37. A. Sunavsky and J. Poppenk, “Neuroimaging predictors of creativity in healthy adults,” NeuroImage, 206, 116292 (2020), https://doi.org/10.1016/j.neuroimage.2019.116292.

    Article  PubMed  Google Scholar 

  38. O. M. Razumnikova, V. A. Kagan, and N. V. Panova, “Age-related dynamics of measures of verbal and imaginal creativity among schoolchildren,” Kompleks. Issled. Detstva, 2, No. 2, 72–79 (2020), https://doi.org/10.33910/2687-0223-2020-2-2-72-79.

  39. A. P. Christensen, M. Benedek, P. Silvia, and R. Beaty, “Executive and default network connectivity reflects conceptual interference during creative imagery generation,” PsyArXiv Preprints (2019), https://doi.org/10.31234/osf.io/n438d.

  40. N. Boot, M. Baas, E. Mühlfeld, et al., “Widespread neural oscillations in the delta band dissociate rule convergence from rule divergence during creative idea generation,” Neuropsychologia, 104, 8–17 (2017), https://doi.org/10.1016/j.neuropsychologia.2017.07.033.

    Article  PubMed  Google Scholar 

  41. P. S. Foster, J. B. Williamson, and D. W. Harrison, “The ruff figural fluency test: heightened right frontal lobe delta activity as a function of performance,” Arch. Clin. Neuropsychol., 20, 427–434 (2005), https://doi.org/10.1016/j.acn.2004.09.010.

    Article  PubMed  Google Scholar 

  42. B. Dunst, M. Benedek, E. Jauk, et al., “Neural efficiency as a function of task demands,” Intelligence, 42, No. 100, 22–30 (2014), https://doi.org/10.1016/j.intell.2013.09.005.

  43. R. J. Haier, B. V. Siegel, K. H. Nuechterlein, et al., “Cortical glucose metabolic rate correlates of abstract reasoning and attention studied with positron emission tomography,” Intelligence, 12, 199–217 (1988)

    Article  Google Scholar 

  44. G. G. Knyazev, “Motivation, emotion, and their inhibitory control mirrored in brain oscillations,” Neurosci. Biobehav. Rev., 31, No. 3, 377–395 (2007), https://doi.org/10.1016/j.neubiorev.2006.10.004.

    Article  PubMed  Google Scholar 

  45. J. D. Kropotov, “Frontal midline theta rhythm,” in: Quantitative EEG, Event-Related Potentials and Neurotherapy (2009), pp. 77–95.

  46. Q. Luo, X. Cheng, T. Holroyd, et al., “Theta band activity in response to emotional expressions and its relationship with gamma band activity as revealed by MEG and advanced beamformer source imaging,” Front. Hum. Neurosci., 7, 940 (2014), https://doi.org/10.3389/fnhum.2013.00940.

    Article  PubMed  PubMed Central  Google Scholar 

  47. M. Doppelmayr, W. Klimesch, K. Hödlmoser, et al., “Intelligence related upper alpha desynchronization in a semantic memory task,” Brain Res. Bull., 66, No. 2, 171–177 (2005), https://doi.org/10.1016/j.brainresbull.2005.04.007.

    Article  CAS  PubMed  Google Scholar 

  48. R. Fellinger, W. Grube, A. Zaune, et al., “Evoked traveling alpha waves predict visual-semantic categorization-speed,” NeuroImage, 59, No. 4, 3379–3388 (2012), https://doi.org/10.1016/j.neuroimage.2011.11.010.

  49. L. Drijvers, A. Özyürek, and O. Jensen, “Alpha and beta oscillations index semantic congruency between speech and gestures in clear and degraded speech,” J. Cogn. Neurosci., 30, No. 8, 1086–1097 (2018), https://doi.org/10.1162/jocn_a_01301.

    Article  PubMed  Google Scholar 

  50. R. Terporten, J.-M. Schoffelen, B. Dai, et al., “The relation between alpha/beta oscillations and the encoding of sentence induced contextual information,” Sci. Rep., 9, 20255 (2019), https://doi.org/10.1038/s41598-019-56600-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. M. D. Fox, A. Z. Snyder, J. L. Vincent, et al., “The human brain is intrinsically organized into dynamic, anticorrelated functional networks,” Proc. Natl. Acad. Sci. USA, 102, No. 27, 9673–9678 (2005), https://doi.org/10.1073/pnas.0504136102.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. F. Riemer, R. Grüner, J. Beresniewicz, et al., “Dynamic switching between intrinsic and extrinsic mode networks as demands change from passive to active processing,” Sci. Rep., 10, No. 1, 21463 (2020), https://doi.org/10.1038/s41598-020-78579-6.

  53. C. E. Stevens, Jr. and D. L. Zabelina, “Classifying creativity: Applying machine learning techniques to divergent thinking EEG data,” Neuroimage, 219, 116990 (2020), https://doi.org/10.1016/j.neuroimage.2020.116990.

    Article  PubMed  Google Scholar 

  54. R. Khalil, A. A. Karim, A. Kondinska, and B. Godde, “Effects of transcranial direct current stimulation of left and right inferior frontal gyrus on creative divergent thinking are moderated by changes in inhibition control,” Brain Struct. Funct., 225, No. 6, 1691–1704 (2020), https://doi.org/10.1007/s00429-020-02081-y.

    Article  PubMed  PubMed Central  Google Scholar 

  55. E. Hertenstein, E. Waibel, L. Frase, et al., “Modulation of creativity by transcranial direct current stimulation,” Brain Stimul., 12, No. 5, 1213–1221 (2019), https://doi.org/10.1016/j.brs.2019.06.004.

    Article  PubMed  Google Scholar 

  56. T. Ivancovsky, J. Kurman, H. Morio, and S. Shamay-Tsoory, “Transcranial direct current stimulation (tDCS) targeting the left inferior frontal gyrus: Effects on creativity across cultures,” Soc. Neurosci., 14, No. 3, 277–285 (2019), https://doi.org/10.1080/17470919.2018.1464505.

    Article  PubMed  Google Scholar 

  57. C. Lucchiari, P. M. Sala, and M. E. Vanutelli, “Promoting creativity through transcranial direct current stimulation (tDCS). A critical review,” Front. Behav. Neurosci., 2, No. 12, 167 (2018), https://doi.org/10.3389/fnbeh.2018.00167.

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Translated from Rossiiskii Fiziologicheskii Zhurnal imeni I. M. Sechenova, Vol. 107, No. 8, pp. 955–972, August, 2021.

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Razumnikova, O.M. Baseline Measures of EEG Power as Correlates of the Verbal and Nonverbal Components of Creativity and Intelligence. Neurosci Behav Physi 52, 124–134 (2022). https://doi.org/10.1007/s11055-022-01214-6

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