Robust prediction of individual creative ability from brain functional connectivity

Edited by Olaf Sporns, Indiana University, Bloomington, IN, and accepted by Editorial Board Member Michael S. Gazzaniga December 4, 2017 (received for review July 31, 2017)
January 16, 2018
115 (5) 1087-1092

Significance

People’s capacity to generate creative ideas is central to technological and cultural progress. Despite advances in the neuroscience of creativity, the field lacks clarity on whether a specific neural architecture distinguishes the highly creative brain. Using methods in network neuroscience, we modeled individual creative thinking ability as a function of variation in whole-brain functional connectivity. We identified a brain network associated with creative ability comprised of regions within default, salience, and executive systems—neural circuits that often work in opposition. Across four independent datasets, we show that a person’s capacity to generate original ideas can be reliably predicted from the strength of functional connectivity within this network, indicating that creative thinking ability is characterized by a distinct brain connectivity profile.

Abstract

People’s ability to think creatively is a primary means of technological and cultural progress, yet the neural architecture of the highly creative brain remains largely undefined. Here, we employed a recently developed method in functional brain imaging analysis—connectome-based predictive modeling—to identify a brain network associated with high-creative ability, using functional magnetic resonance imaging (fMRI) data acquired from 163 participants engaged in a classic divergent thinking task. At the behavioral level, we found a strong correlation between creative thinking ability and self-reported creative behavior and accomplishment in the arts and sciences (r = 0.54). At the neural level, we found a pattern of functional brain connectivity related to high-creative thinking ability consisting of frontal and parietal regions within default, salience, and executive brain systems. In a leave-one-out cross-validation analysis, we show that this neural model can reliably predict the creative quality of ideas generated by novel participants within the sample. Furthermore, in a series of external validation analyses using data from two independent task fMRI samples and a large task-free resting-state fMRI sample, we demonstrate robust prediction of individual creative thinking ability from the same pattern of brain connectivity. The findings thus reveal a whole-brain network associated with high-creative ability comprised of cortical hubs within default, salience, and executive systems—intrinsic functional networks that tend to work in opposition—suggesting that highly creative people are characterized by the ability to simultaneously engage these large-scale brain networks.

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Acknowledgments

This research was supported by Grant RFP-15-12 from the Imagination Institute (www.imagination-institute.org), funded by the John Templeton Foundation. Q.C. and J.Q. were supported by National Science Foundation of China Grants 31571137 and 31470981. The opinions expressed in this publication are those of the authors and do not necessarily reflect the view of the Imagination Institute or the John Templeton Foundation.

Supporting Information

Supporting Information (PDF)
Dataset_S01 (TXT)
Dataset_S02 (TXT)

References

1
RE Beaty, M Benedek, PJ Silvia, DL Schacter, Creative cognition and brain network dynamics. Trends Cogn Sci 20, 87–95 (2016).
2
MA Runco, S Acar, Divergent thinking as an indicator of creative potential. Creat Res J 24, 66–75 (2012).
3
JA Plucker, Is the proof in the pudding? Reanalyses of Torrance’s (1958 to present) longitudinal data. Creat Res J 12, 103–114 (1999).
4
A Abraham, The promises and perils of the neuroscience of creativity. Front Hum Neurosci 7, 246 (2013).
5
G Gonen-Yaacovi, et al., Rostral and caudal prefrontal contribution to creativity: A meta-analysis of functional imaging data. Front Hum Neurosci 7, 465 (2013).
6
X Wu, et al., A meta-analysis of neuroimaging studies on divergent thinking using activation likelihood estimation. Hum Brain Mapp 36, 2703–2718 (2015).
7
DL Zabelina, JR Andrews-Hanna, Dynamic network interactions supporting internally-oriented cognition. Curr Opin Neurobiol 40, 86–93 (2016).
8
PT Sowden, A Pringle, L Gabora, The shifting sands of creative thinking: Connections to dual-process theory. Think Reason 21, 40–60 (2014).
9
RE Jung, BS Mead, J Carrasco, RA Flores, The structure of creative cognition in the human brain. Front Hum Neurosci 7, 330 (2013).
10
LQ Uddin, Salience processing and insular cortical function and dysfunction. Nat Rev Neurosci 16, 55–61 (2015).
11
RE Beaty, M Benedek, SB Kaufman, PJ Silvia, Default and executive network coupling supports creative idea production. Sci Rep 5, 10964 (2015).
12
M Ellamil, C Dobson, M Beeman, K Christoff, Evaluative and generative modes of thought during the creative process. Neuroimage 59, 1783–1794 (2012).
13
MD Fox, et al., The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci USA 102, 9673–9678 (2005).
14
A Anticevic, et al., The role of default network deactivation in cognition and disease. Trends Cogn Sci 16, 584–592 (2012).
15
ES Finn, et al., Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity. Nat Neurosci 18, 1664–1671 (2015).
16
MD Rosenberg, ES Finn, D Scheinost, RT Constable, MM Chun, Characterizing attention with predictive network models. Trends Cogn Sci 21, 290–302 (2017).
17
X Shen, et al., Using connectome-based predictive modeling to predict individual behavior from brain connectivity. Nat Protoc 12, 506–518 (2017).
18
MD Rosenberg, et al., A neuromarker of sustained attention from whole-brain functional connectivity. Nat Neurosci 19, 165–171 (2016).
19
PJ Silvia, et al., Assessing creativity with divergent thinking tasks: Exploring the reliability and validity of new subjective scoring methods. Psychol Aesthetics Creativity Arts 2, 68–85 (2008).
20
E Jauk, M Benedek, AC Neubauer, The road to creative achievement: A latent variable model of ability and personality predictors. Eur J Pers 28, 95–105 (2014).
21
X Shen, F Tokoglu, X Papademetris, RT Constable, Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. Neuroimage 82, 403–415 (2013).
22
JH Steiger, Tests for comparing elements of a correlation matrix. Psychol Bull 87, 245–251 (1980).
23
MW Cole, DS Bassett, JD Power, TS Braver, SE Petersen, Intrinsic and task-evoked network architectures of the human brain. Neuron 83, 238–251 (2014).
24
S Liu, et al., Brain activity and connectivity during poetry composition: Toward a multidimensional model of the creative process. Hum Brain Mapp 36, 3351–3372 (2015).
25
AL Pinho, Ö de Manzano, P Fransson, H Eriksson, F Ullén, Connecting to create: Expertise in musical improvisation is associated with increased functional connectivity between premotor and prefrontal areas. J Neurosci 34, 6156–6163 (2014).
26
A Dezfouli, BW Balleine, Habits, action sequences and reinforcement learning. Eur J Neurosci 35, 1036–1051 (2012).
27
D Vatansever, et al., Varieties of semantic cognition revealed through simultaneous decomposition of intrinsic brain connectivity and behaviour. Neuroimage 158, 1–11 (2017).
28
D Vatansever, DK Menon, EA Stamatakis, Default mode contributions to automated information processing. Proc Natl Acad Sci USA 114, 12821–12826 (2017).
29
RE Beaty, AP Christensen, M Benedek, PJ Silvia, DL Schacter, Creative constraints: Brain activity and network dynamics underlying semantic interference during idea production. Neuroimage 148, 189–196 (2017).
30
LJ Hearne, JB Mattingley, L Cocchi, Functional brain networks related to individual differences in human intelligence at rest. Sci Rep 6, 32328 (2016).
31
EG Chrysikou, MJ Weber, SL Thompson-Schill, A matched filter hypothesis for cognitive control. Neuropsychologia 62, 341–355 (2014).
32
A Fink, et al., The creative brain: Investigation of brain activity during creative problem solving by means of EEG and FMRI. Hum Brain Mapp 30, 734–748 (2009).
33
M Benedek, et al., To create or to recall? Neural mechanisms underlying the generation of creative new ideas. Neuroimage 88, 125–133 (2014).
34
A Fink, et al., Training of verbal creativity modulates brain activity in regions associated with language- and memory-related demands. Hum Brain Mapp 36, 4104–4115 (2015).
35
M Benedek, et al., Creating metaphors: The neural basis of figurative language production. Neuroimage 90, 99–106 (2014).
36
SH Carson, JB Peterson, DM Higgins, Reliability, validity, and factor structure of the creative achievement questionnaire. Creat Res J 17, 37–50 (2005).
37
M Batey, A Furnham, X Safiullina, Intelligence, general knowledge and personality as predictors of creativity. Learn Individ Differ 20, 532–535 (2010).
38
J Diedrich, et al., Assessment of real-life creativity: The inventory of creative activities and achievements (ICAA). Psychol Aesthetics Creativity Arts
39
S Whitfield-Gabrieli, A Nieto-Castanon, Conn: A functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect 2, 125–141 (2012).

Information & Authors

Information

Published in

Go to Proceedings of the National Academy of Sciences
Go to Proceedings of the National Academy of Sciences
Proceedings of the National Academy of Sciences
Vol. 115 | No. 5
January 30, 2018
PubMed: 29339474

Classifications

Submission history

Published online: January 16, 2018
Published in issue: January 30, 2018

Keywords

  1. connectome
  2. creativity
  3. divergent thinking
  4. fMRI

Acknowledgments

This research was supported by Grant RFP-15-12 from the Imagination Institute (www.imagination-institute.org), funded by the John Templeton Foundation. Q.C. and J.Q. were supported by National Science Foundation of China Grants 31571137 and 31470981. The opinions expressed in this publication are those of the authors and do not necessarily reflect the view of the Imagination Institute or the John Templeton Foundation.

Notes

This article is a PNAS Direct Submission. O.S. is a guest editor invited by the Editorial Board.

Authors

Affiliations

Roger E. Beaty1 [email protected]
Department of Psychology, Harvard University, Cambridge, MA 02143;
Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104;
Alexander P. Christensen
Department of Psychology, University of North Carolina at Greensboro, Greensboro, NC 27402;
Department of Psychology, Yale University, New Haven, CT 06520;
Mathias Benedek
Department of Psychology, University of Graz, 8010 Graz, Austria;
Qunlin Chen
School of Psychology, Southwest University, Chongqing 400715, China;
Andreas Fink
Department of Psychology, University of Graz, 8010 Graz, Austria;
Jiang Qiu
School of Psychology, Southwest University, Chongqing 400715, China;
Thomas R. Kwapil
Department of Psychology, University of Illinois at Urbana–Champaign, Champaign, IL 61820
Michael J. Kane
Department of Psychology, University of North Carolina at Greensboro, Greensboro, NC 27402;
Paul J. Silvia
Department of Psychology, University of North Carolina at Greensboro, Greensboro, NC 27402;

Notes

1
To whom correspondence should be addressed. Email: [email protected].
Author contributions: R.E.B., M.B., A.F., J.Q., T.R.K., M.J.K., and P.J.S. designed research; R.E.B., A.P.C., M.B., Q.C., A.F., J.Q., and P.J.S. performed research; M.D.R. contributed new reagents/analytic tools; R.E.B., Y.N.K., A.P.C., and M.D.R. analyzed data; and R.E.B., Y.N.K., A.P.C., M.D.R., M.B., Q.C., T.R.K., M.J.K., and P.J.S. wrote the paper.

Competing Interests

The authors declare no conflict of interest.

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    Robust prediction of individual creative ability from brain functional connectivity
    Proceedings of the National Academy of Sciences
    • Vol. 115
    • No. 5
    • pp. 823-E1075

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