Skip to main content
SpringerLink
Log in

Flexible reconfiguration of functional brain networks as a potential neural mechanism of creativity

  • Original Research
  • Published:
Brain Imaging and Behavior Aims and scope Submit manuscript

Abstract

Creativity relies on the reorganizing of multimodal information and flexible switching between different modes of thinking, suggesting an association between creativity and the reconfiguration of functional brain networks. Here, we investigated global and regional brain dynamics in high-creative (HCG, N = 22) and a low-creative (LCG, N = 20) groups during a divergent creative thinking task. We found that during the creative thinking task, the HCG demonstrated higher global network flexibility, as compared to the LCG. In addition, creative thinking in the HCG was associated with significantly higher regional flexibility in the medial superior temporal gyrus, superior parietal lobule, precuneus, nucleus accumbens, and the ventral inferior frontal gyrus. Interestingly, the LCG demonstrated decreased regional flexibility in the medial superior temporal gyrus, superior parietal lobule, and the ventral inferior frontal gyrus. We also found that the changes in global and regional flexibility in the creative compared with the control tasks were good features allowing for distinguishing between the HCG and the LCG. Taken together, these findings provide evidence that divergent creative thinking is associated with flexible reconfiguration of brain networks involved in verbal, working memory, and reward processing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Bassett, D. S., Wymbs, N. F., Porter, M. A., Mucha, P. J., Carlson, J. M., & Grafton, S. T. (2011). Dynamic reconfiguration of human brain networks during learning. Proceedings of the National Academy of Sciences, 108(18), 7641–7646.

    CAS  Google Scholar 

  • Beaty, Chen, Q., Christensen, A. P., Qiu, J., Silvia, P. J., & Schacter, D. L. (2018a). Brain networks of the imaginative mind: Dynamic functional connectivity of default and cognitive control networks relates to openness to experience. Human Brain Mapping, 39(2), 811–821.

    PubMed  Google Scholar 

  • Beaty, Christensen, A. P., Benedek, M., Silvia, P. J., & Schacter, D. L. (2017). Creative constraints: Brain activity and network dynamics underlying semantic interference during idea production. Neuroimage, 148, 189–196.

    PubMed  Google Scholar 

  • Beaty, Kenett, Y. N., Christensen, A. P., Rosenberg, M. D., Benedek, M., Chen, Q., et al. (2018b). Robust prediction of individual creative ability from brain functional connectivity. Proceedings of the National Academy of Sciences, 115(5), 1087–1092.

    CAS  Google Scholar 

  • Beaty, Seli, P., & Schacter, D. L. (2019). Network neuroscience of creative cognition: Mapping cognitive mechanisms and individual differences in the creative brain. Current Opinion in Behavioral Sciences, 27, 22–30.

    PubMed  Google Scholar 

  • Biau, G., & Scornet, E. (2016). A random forest guided tour. Test, 25(2), 197–227.

    Google Scholar 

  • Braun, U., Schäfer, A., Walter, H., Erk, S., Romanczuk-Seiferth, N., Haddad, L., et al. (2015). Dynamic reconfiguration of frontal brain networks during executive cognition in humans. Proceedings of the National Academy of Sciences, 112(37), 11678–11683.

    CAS  Google Scholar 

  • Calhoun, V. D., Miller, R., Pearlson, G., & Adalı, T. (2014). The chronnectome: Time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron, 84(2), 262–274.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Caspers, S., Schleicher, A., Bacha-Trams, M., Palomero-Gallagher, N., Amunts, K., & Zilles, K. (2012). Organization of the human inferior parietal lobule based on receptor architectonics. Cerebral Cortex, 23(3), 615–628.

    PubMed  PubMed Central  Google Scholar 

  • Castro, D. C., & Bruchas, M. R. (2019). A motivational and Neuropeptidergic hub: Anatomical and functional diversity within the nucleus Accumbens Shell. Neuron, 102(3), 529–552.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Cavanna, A. E., & Trimble, M. R. (2006). The precuneus: A review of its functional anatomy and behavioural correlates. Brain, 129(3), 564–583.

    PubMed  Google Scholar 

  • Chen, X., & Ishwaran, H. (2012). Random forests for genomic data analysis. Genomics, 99(6), 323–329.

    CAS  PubMed  Google Scholar 

  • Constantinidis, C., & Klingberg, T. (2016). The neuroscience of working memory capacity and training. Nature Reviews Neuroscience, 17(7), 438–449.

    CAS  PubMed  Google Scholar 

  • Dietrich, A. (2015). How creativity happens in the brain: Springer.

  • Dietrich, A., & Kanso, R. (2010). A review of EEG, ERP, and neuroimaging studies of creativity and insight. Psychological Bulletin, 136(5), 822–848.

    PubMed  Google Scholar 

  • Douw, L., Wakeman, D. G., Tanaka, N., Liu, H., & Stufflebeam, S. M. (2016). State-dependent variability of dynamic functional connectivity between frontoparietal and default networks relates to cognitive flexibility. Neuroscience, 339, 12–21.

    CAS  PubMed  Google Scholar 

  • Ellamil, M., Dobson, C., Beeman, M., & Christoff, K. (2012). Evaluative and generative modes of thought during the creative process. Neuroimage, 59(2), 1783–1794.

    PubMed  Google Scholar 

  • Fan, L., Li, H., Zhuo, J., Zhang, Y., Wang, J., Chen, L., et al. (2016). The human brainnetome atlas: A new brain atlas based on connectional architecture. Cerebral Cortex, 26(8), 3508–3526.

    PubMed  PubMed Central  Google Scholar 

  • Gansler, D. A., Moore, D. W., Susmaras, T. M., Jerram, M. W., Sousa, J., & Heilman, K. M. (2011). Cortical morphology of visual creativity. Neuropsychologia, 49(9), 2527–2532.

    PubMed  Google Scholar 

  • Gao, Z., Zhang, D., Liang, A., Liang, B., Wang, Z., Cai, Y., et al. (2017). Exploring the associations between intrinsic brain connectivity and creative ability using functional connectivity strength and connectome analysis. Brain Connectivity, 7(9), 590–601.

    PubMed  Google Scholar 

  • Guildford, J., Christensen, P., Merrifield, P., & Wilson, R. (1978). Alternate uses: Manual of instructions and interpretation. Orange: Sheridan Psychological Services.

    Google Scholar 

  • Hellyer, P. J., Scott, G., Shanahan, M., Sharp, D. J., & Leech, R. (2015). Cognitive flexibility through metastable neural dynamics is disrupted by damage to the structural connectome. Journal of Neuroscience, 35(24), 9050–9063.

    CAS  PubMed  Google Scholar 

  • Hennessey, B. A., & Amabile, T. M. (2010). Creativity. Annual Review of Psychology, 61(1), 569–598.

    PubMed  Google Scholar 

  • Jung-Beeman, M., Bowden, E. M., Haberman, J., Frymiare, J. L., Arambel-Liu, S., Greenblatt, R., Reber, P. J., & Kounios, J. (2004). Neural activity when people solve verbal problems with insight. PLoS Biology, 2(4), e97.

    PubMed  PubMed Central  Google Scholar 

  • Kenett, Y. N., Levy, O., Kenett, D. Y., Stanley, H. E., Faust, M., & Havlin, S. (2018). Flexibility of thought in high creative individuals represented by percolation analysis. Proceedings of the National Academy of Sciences, 115(5), 867–872.

    CAS  Google Scholar 

  • Kim, K. H. (2006). Can we trust creativity tests? A review of the Torrance tests of creative thinking (TTCT). Creativity Research Journal, 18(1), 3–14.

    Google Scholar 

  • Koenigs, M., Barbey, A. K., Postle, B. R., & Grafman, J. (2009). Superior parietal cortex is critical for the manipulation of information in working memory. Journal of Neuroscience, 29(47), 14980–14986.

    CAS  PubMed  Google Scholar 

  • Kounios, J., Fleck, J. I., Green, D. L., Payne, L., Stevenson, J. L., Bowden, E. M., & Jung-Beeman, M. (2008). The origins of insight in resting-state brain activity. Neuropsychologia, 46(1), 281–291.

    PubMed  Google Scholar 

  • Li, J., Zhang, D., Liang, A., Liang, B., Wang, Z., Cai, Y., … Jiao, B. (2017). High transition frequencies of dynamic functional connectivity states in the creative brain. Scientific Reports, 7.

  • Liu, M., Wang, M., Wang, J., & Li, D. (2013). Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: Application to the recognition of orange beverage and Chinese vinegar. Sensors and Actuators B: Chemical, 177, 970–980.

    CAS  Google Scholar 

  • Luo, Z., Zeng, L.-L., Qin, J., Hou, C., Shen, H., & Hu, D. (2019). Functional Parcellation of human brain Precuneus using density-based clustering. Cerebral cortex.

  • Madore, K. P., Addis, D. R., & Schacter, D. L. (2015). Creativity and memory: Effects of an episodic-specificity induction on divergent thinking. Psychological Science, 26(9), 1461–1468.

    PubMed  Google Scholar 

  • Madore, K. P., Thakral, P. P., Beaty, R. E., Addis, D. R., & Schacter, D. L. (2017). Neural mechanisms of episodic retrieval support divergent creative thinking. Cerebral Cortex, 29(1), 150–166.

    PubMed Central  Google Scholar 

  • Marron, T. R., Lerner, Y., Berant, E., Kinreich, S., Shapira-Lichter, I., Hendler, T., & Faust, M. (2018). Chain free association, creativity, and the default mode network. Neuropsychologia, 118, 40–58.

    PubMed  Google Scholar 

  • Mattar, M. G., Betzel, R. F., & Bassett, D. S. (2016). The flexible brain. Brain, 139(8), 2110–2112.

    PubMed  Google Scholar 

  • Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878.

    CAS  PubMed  Google Scholar 

  • Nijstad, B. A., De Dreu, C. K., Rietzschel, E. F., & Baas, M. (2010). The dual pathway to creativity model: Creative ideation as a function of flexibility and persistence. European Review of Social Psychology, 21(1), 34–77.

    Google Scholar 

  • Pedersen, M., Zalesky, A., Omidvarnia, A., & Jackson, G. D. (2018). Multilayer network switching rate predicts brain performance. Proceedings of the National Academy of Sciences, 115(52), 13376–13381.

    CAS  Google Scholar 

  • Raven, J. (2000). The Raven's progressive matrices: Change and stability over culture and time. Cognitive Psychology, 41(1), 1–48.

    CAS  PubMed  Google Scholar 

  • Ruparel, N. H., Shahane, N. M., & Bhamare, D. P. (2013). Learning from small data set to build classification model: A survey. Internationla Journal of Computer Applications, 975(8887), 23–26.

    Google Scholar 

  • Saggar, M., Quintin, E.-M., Bott, N. T., Kienitz, E., Chien, Y.-H., Hong, D. W., et al. (2017). Changes in brain activation associated with spontaneous improvization and figural creativity after design-thinking-based training: A longitudinal fMRI study. Cerebral Cortex, 27(7), 3542–3552.

    PubMed  Google Scholar 

  • Shah, C., Erhard, K., Ortheil, H. J., Kaza, E., Kessler, C., & Lotze, M. (2013). Neural correlates of creative writing: An fMRI study. Human Brain Mapping, 34(5), 1088–1101.

    PubMed  Google Scholar 

  • Shen, W., Yuan, Y., Liu, C., & Luo, J. (2017). The roles of the temporal lobe in creative insight: An integrated review. Thinking & Reasoning, 23(4), 321–375.

    Google Scholar 

  • Shine, J. M., Bissett, P. G., Bell, P. T., Koyejo, O., Balsters, J. H., Gorgolewski, K. J., Moodie, C. A., & Poldrack, R. A. (2016a). The dynamics of functional brain networks: Integrated network states during cognitive task performance. Neuron, 92(2), 544–554.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Shine, J. M., Breakspear, M., Bell, P. T., Martens, K. A. E., Shine, R., Koyejo, O., et al. (2019). Human cognition involves the dynamic integration of neural activity and neuromodulatory systems. Nature Neuroscience, 22(2), 289–296.

    CAS  PubMed  Google Scholar 

  • Shine, J. M., Koyejo, O., Bell, P. T., Gorgolewski, K. J., Gilat, M., & Poldrack, R. A. (2015). Estimation of dynamic functional connectivity using multiplication of temporal derivatives. Neuroimage, 122, 399–407.

    PubMed  Google Scholar 

  • Shine, J. M., Koyejo, O., & Poldrack, R. A. (2016b). Temporal metastates are associated with differential patterns of time-resolved connectivity, network topology, and attention. Proceedings of the National Academy of Sciences, 113(35), 9888–9891.

    CAS  Google Scholar 

  • Simonton, D. K. (2003). Scientific creativity as constrained stochastic behavior: The integration of product, person, and process perspectives. Psychological Bulletin, 129(4), 475–494.

    PubMed  Google Scholar 

  • Sun, J., Liu, Z., Rolls, E. T., Chen, Q., Yao, Y., Yang, W., et al. (2018). Verbal creativity correlates with the temporal variability of brain networks during the resting state. Cerebral Cortex, 29(3), 1047–1058.

    Google Scholar 

  • Sun, J., Zhang, Q., Li, Y., Meng, J., Chen, Q., Yang, W., … Qiu, J. (2019). Plasticity of the resting-state brain: Static and dynamic functional connectivity change induced by divergent thinking training. Brain imaging and behavior, 1-9.

  • Takeuchi, H., Taki, Y., Sassa, Y., Hashizume, H., Sekiguchi, A., Fukushima, A., & Kawashima, R. (2010). Regional gray matter volume of dopaminergic system associate with creativity: Evidence from voxel-based morphometry. Neuroimage, 51(2), 578–585.

    PubMed  Google Scholar 

  • Tang, X., Pang, J., Nie, Q.-Y., Conci, M., Luo, J., & Luo, J. (2015). Probing the cognitive mechanism of mental representational change during chunk decomposition: A parametric fMRI study. Cerebral Cortex, 26(7), 2991–2999.

    PubMed  Google Scholar 

  • Tik, M., Sladky, R., Luft, C. D. B., Willinger, D., Hoffmann, A., Banissy, M. J., Bhattacharya, J., & Windischberger, C. (2018). Ultra-high-field fMRI insights on insight: Neural correlates of the aha!-moment. Human Brain Mapping, 39(8), 3241–3252.

    PubMed  PubMed Central  Google Scholar 

  • Xie, H., Zheng, C. Y., Handwerker, D. A., Bandettini, P. A., Calhoun, V. D., Mitra, S., & Gonzalez-Castillo, J. (2019). Efficacy of different dynamic functional connectivity methods to capture cognitively relevant information. Neuroimage, 188, 502–514.

    PubMed  Google Scholar 

Download references

Acknowledgments

This study was funded by the Natural Science Foundation of China (No. 31600907) and the scholarship offered by China Scholarship Council.

Author information

Authors and Affiliations

  1. Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, 510631, China

    Junchao Li, Zengjian Wang, Bingqing Jiao, Yibo Wang, Huawei Xu, Hui Yang, Yingying Huang, Yan Sun, Peng Zhang, Rengui Yu, Ming Liu & Delong Zhang

  2. Cognition Schizophrenia Imaging Lab, Institute of Psychiatry Psychology and Neuroscience, King’s College London, London, UK

    Natasza Orlov

  3. Department of Psychology, University of Roehampton, London, UK

    Natasza Orlov

  4. Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA

    Natasza Orlov

  5. Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA

    Natasza Orlov

Authors
  1. Junchao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Natasza Orlov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zengjian Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bingqing Jiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yibo Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Huawei Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hui Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yingying Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yan Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Peng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Rengui Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Ming Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Delong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Junchao Li, Delong Zhang and Ming Liu conceived the study; Junchao Li performed the data analysis; Junchao Li, Delong Zhang and Natasza Orlov wrote the paper; Junchao Li, Zengjian Wang, Bingqing Jiao, Yibo Wang, Huawei Xu, Yingying Huang, Yan Sun, Hui Yang, Peng Zhang, and Rengui Yu collected the MRI data; and all authors have approved the final manuscript.

Corresponding authors

Correspondence to Ming Liu or Delong Zhang.

Ethics declarations

Conflict of interest

The authors declare that there are no conflicts of interest.

Ethical approval

This study was approved by the Review Board of South China Normal University in Guangzhou, China.

Informed consent

All the participants were recruited from South China Normal University. All the volunteers provided informed written consent.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Highlights

• We employed a dynamic module detection algorithm to investigate the distinct changes in the reconfiguration flexibility of functional brain networks during the creative task in high-creative and low-creative individuals.

• Compared to the low-creative group, the high-creative group demonstrated a significantly higher global and regional flexibility during the creative task.

• When comparing the creative versus the control task, the high-creative group demonstrated significant increases in regional flexibility, while the low-creative group demonstrated significant decreases in regional flexibility.

• The changes in global and regional flexibility between the creative and control tasks are good features allowing distinguishing between the high-creative and low-creative groups.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, J., Orlov, N., Wang, Z. et al. Flexible reconfiguration of functional brain networks as a potential neural mechanism of creativity. Brain Imaging and Behavior 15, 1944–1954 (2021). https://doi.org/10.1007/s11682-020-00388-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11682-020-00388-2

Keywords

Search

Navigation