التبديل الديناميكي بين شبكات الدماغ يتنبأ بالقدرة الإبداعية

النتائج

تركيب العينة والنتائج السلوكية

الشكل 1 | خصائص مجموعات البيانات. A العدد الإجمالي للمشاركين ونطاقات الأعمار لكل مجموعة بيانات. تشير موضع النقطة السوداء على كل خط إلى العمر الوسيط لتلك المجموعة. B توزيع الأداء الإبداعي بناءً على درجات الأصالة التي قيمها البشر في مهام التفكير المتباين في كل مجموعة بيانات.

العلاقة بين تكرار تبديل الشبكة والأداء الإبداعي

العلاقة بين تردد التبديل والذكاء

نظرًا للعلاقة الشرطية بين الإبداع والذكاء، قمنا بتطبيق نهج تحليل ميتا لتحديد ما إذا كان الذكاء، الذي يُعتبر مهمة تحكم، يرتبط أيضًا بالفصل والتكامل بين الشبكة الافتراضية (DMN) والشبكة التنفيذية (ECN)، كما توقعنا (ووجدنا) بالنسبة للإبداع. وبالتالي، تم إجراء نفس التحليلات بين 6 مجموعات بيانات. ، انظر الجدول التكميلي 3) الذي شمل درجات الذكاء بالإضافة إلى درجات الإبداع. لم يكن حجم التأثير الكلي ذا دلالة إحصائية، ، دون تباين كبير، ، على النقيض من ذلك، كان حجم التأثير العام للعلاقة بين تردد التبديل والأداء الإبداعي كبيرًا بين نفس مجموعات البيانات الست. ، فرق تقدير المعامل بين النموذجين (الذكاء مقابل الإبداع) ذو دلالة إحصائية. ، مما يدل على

الأداء الإبداعي وتوازن فصل وتكامل شبكة الوضع الافتراضي والشبكة التنفيذية

مجموعات البيانات	الموقع		وزن r -القيمة [95% فترة الثقة]
جامعة شمال كارولينا	الولايات المتحدة الأمريكية	فم	6.92%	0.26 [0.10، 0.41]
UG_S1	النمسا		5.25%	0.26 [0.03، 0.48]
UG_S2	النمسا		٤.٢٤٪	0.28 [0.02، 0.54]
UG_S3	النمسا	⟵ •	3.75%	0.44 [0.16، 0.72]
شكراً	اليابان	–	11.36%	0.09 [0.03، 0.15]
SLIM_S1	الصين	–	18.14%	-0.02 [-0.14, 0.10]
SLIM_S2	الصين		12.58%	0.24 [0.09، 0.40]
جي بي بي_س1	الصين		16.72%	0.09 [-0.04, 0.22]
جي بي بي_س2	الصين		17.21%	0.16 [0.04، 0.29]
كيو	كندا		3.81%	0.22 [-0.03, 0.48]
نموذج RE ذو المستويين لجميع مجموعات البيانات:			100.00%	0.17 [0.08، 0.27]

الشكل 3 | مخططات الغابة لعشر أحجام تأثير مدرجة في التحليل التلوي. مخطط الغابة لأحجام التأثير للارتباط بين الأداء الإبداعي وتكرار التبديل (محسوب باستخدام بيانات fMRI في حالة الراحة). على الجانب الأيسر، تعرض مخططات الغابة اسم وموقع كل مجموعة بيانات؛ على يسار الخط العمودي المتقطع (الذي يمثل عدم وجود تأثير)، توفر مخططات الغابة الوزن، التأثير الملحوظ، و فترة الثقة، حيث يكون حجم المربع متناسبًا مع الأوزان المحددة بواسطة عكس تباين مجموعات البيانات؛ الخط السفلي يظهر حجم التأثير الكلي (يمين) واختبار التباين (يسار).

مهم ، مما يشير إلى أن التعاون المعتدل – لا أكثر ولا أقل – بين الشبكة الديناميكية للدماغ (DMN) والشبكة التنفيذية (ECN) قد يكون مفيدًا في توليد الأفكار الإبداعية. قد تشير هذه النتيجة إلى أن القدرة الإبداعية تستفيد من البيانات متعددة المراكز،

إمكانية التكرار

التحقق الخارجي باستخدام بيانات fMRI المستندة إلى المهام

أ

نقاش

أشارت الأبحاث السابقة إلى أن DMN قد يكون متورطًا بشكل مركزي في استرجاع الذكريات النشطة والعمليات الترابطية التي تنتج أفكارًا متنوعة، بينما قد تعمل ECN على توجيه العمليات الترابطية من خلال المراقبة، والحد، وعمليات التحكم من أعلى إلى أسفل ذات الصلة . تشرح هذه الدراسات أدوار DMN وECN والشبكات الأخرى في الإدراك الإبداعي في مرحلتين: التوليد والتقييم . اقترحت نظريات الإبداع السابقة أن

القيود والاتجاهات المستقبلية

الاستنتاجات

طرق

مجموعات البيانات

التقييم السلوكي

اكتساب الصورة ومعالجتها

التحليل الوظيفي الديناميكي

تحليلات داخل مجموعة البيانات

النهج التحليلي الشامل

إعادة الإنتاجية

التحقق الخارجي

الإحصائيات وإمكانية التكرار

ملخص التقرير

توفر البيانات

توفر الشيفرة

References

1. Beaty, R. E., Benedek, M., Silvia, P. J. & Schacter, D. L. Creative cognition and brain network dynamics. Trends Cogn. Sci. 20, 87-95 (2016).
2. Benedek, M., Beaty, R. E., Schacter, D. L. & Kenett, Y. N. The role of memory in creative ideation. Nat. Rev. Psychol. 2, 246-257 (2023).
3. Mayseless, N., Eran, A. & Shamay-Tsoory, S. G. Generating original ideas: the neural underpinning of originality. Neuroimage 116, 232-239 (2015).
4. Beaty, R. E. & Kenett, Y. N. Associative thinking at the core of creativity. Trends Cogn. Sci. 27, 671-683 (2023).
5. Lloyd-Cox, J., Chen, Q. & Beaty, R. E. The time course of creativity: multivariate classification of default and executive network contributions to creative cognition over time. Cortex 156, 90-105(2022).
6. Bendetowicz, D. et al. Two critical brain networks for generation and combination of remote associations. Brain 141, 217-233 (2018).
7. Marron, T. R. et al. Chain free association, creativity, and the default mode network. Neuropsychologia 118, 40-58 (2018).
8. Heinonen, J. et al. Default mode and executive networks areas: association with the serial order in divergent thinking. PLoS ONE 11, e0162234 (2016).
9. Andrews-Hanna, J. R., Smallwood, J. & Spreng, R. N. The default network and self-generated thought: component processes, dynamic control, and clinical relevance. Ann. N. Y. Acad. Sci. 1316, 29-52 (2014).
10. Andrews-Hanna, J. R., Reidler, J. S., Huang, C. & Buckner, R. L. Evidence for the default network’s role in spontaneous cognition. J. Neurophysiol. 104, 322-335 (2010).
11. Ovando-Tellez, M. et al. Brain connectivity-based prediction of reallife creativity is mediated by semantic memory structure. Sci. Adv. 8, eabl4294 (2022).
12. Marron, T. R., Berant, E., Axelrod, V. & Faust, M. Spontaneous cognition and its relationship to human creativity: a functional connectivity study involving a chain free association task. Neuroimage 220, 117064 (2020).
13. Shofty, B. et al. The default network is causally linked to creative thinking. Mol. Psychiatry 27, 1848-1854 (2022).
14. Raffaelli, Q. et al. Creative minds at rest: creative individuals are more associative and engaged with their idle thoughts. Creat. Res. J. 36, 396-412 (2024).
15. Shamay-Tsoory, S. G., Adler, N., Aharon-Peretz, J., Perry, D. & Mayseless, N. The origins of originality: the neural bases of creative thinking and originality. Neuropsychologia 49, 178-185 (2011).
16. Wang, M., Hao, N., Ku, Y., Grabner, R. H. & Fink, A. Neural correlates of serial order effect in verbal divergent thinking. Neuropsychologia 99, 92-100 (2017).
17. Kleinmintz, O. M. et al. Participation of the left inferior frontal gyrus in human originality. Brain Struct. Funct. 223, 329-341 (2018).
18. Chen, Q. et al. A meta-analysis of the effects of transcranial direct current stimulation on creative thinking. Psychol. Aesthet. Creat. Arts. https://doi.org/10.1037/aca0000704 (2024).
19. Herault, C. et al. Creative connections: the neural correlates of semantic relatedness are associated with creativity. Commun. Biol. 7, 810 (2024).
20. Ovando-Tellez, M. et al. An investigation of the cognitive and neural correlates of semantic memory search related to creative ability. Commun. Biol. 5, 604 (2022).
21. Ellamil, M., Dobson, C., Beeman, M. & Christoff, K. Evaluative and generative modes of thought during the creative process. Neuroimage 59, 1783-1794 (2012).
22. Wu, X. et al. A meta-analysis of neuroimaging studies on divergent thinking using activation likelihood estimation. Hum. Brain Mapp. 36, 2703-2718 (2015).
23. Benedek, M. et al. To create or to recall? Neural mechanisms underlying the generation of creative new ideas. Neuroimage 88, 125-133 (2014).
24. Benedek, M. et al. Creating metaphors: the neural basis of figurative language production. Neuroimage 90, 99-106 (2014).
25. Matheson, H. E., Kenett, Y. N., Gerver, C. & Beaty, R. E. Representing creative thought: a representational similarity analysis of creative idea generation and evaluation. Neuropsychologia 187, 108587 (2023).
26. Bartoli, E. et al. Default mode network electrophysiological dynamics and causal role in creative thinking. Brain 147, 3409-3425 (2024).
27. Beaty, R. E. et al. Robust prediction of individual creative ability from brain functional connectivity. Proc. Natl Acad. Sci. USA 115, 1087-1092 (2018).
28. Takeuchi, H. et al. The association between resting functional connectivity and creativity. Cereb. Cortex 22, 2921-2929 (2012).
29. Beaty, R. E. et al. Creativity and the default network: a functional connectivity analysis of the creative brain at rest. Neuropsychologia 64, 92-98 (2014).
30. Shi, L. 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).
31. Frith, E. et al. Intelligence and creativity share a common cognitive and neural basis. J. Exp. Psychol. Gen. 150, 609-632 (2021).
32. Patil, A. U. et al. Static and dynamic functional connectivity supports the configuration of brain networks associated with creative cognition. Sci. Rep. 11, 165 (2021).
33. Ren, Z. et al. Connectome-based predictive modeling of creativity anxiety. Neuroimage 225, 117469 (2021).
34. Beaty, R. E., Benedek, M., Kaufman, S. B. & Silvia, P. J. Default and executive network coupling supports creative idea production. Sci. Rep. 5, 10964 (2015).
35. Kleinmintz, O. M., Ivancovsky, T. & Shamay-Tsoory, S. G. The twofold model of creativity: the neural underpinnings of the generation and evaluation of creative ideas. Curr. Opin. Behav. Sci. 27, 131-138 (2019).
36. Fox, K. C. & Christoff, K. The Oxford Handbook of Spontaneous Thought: Mind-wandering, Creativity, and Dreaming (Oxford University Press, 2018).
37. Braun, U. et al. Dynamic reconfiguration of frontal brain networks during executive cognition in humans. Proc. Natl Acad. Sci. USA 112, 11678-11683 (2015).
38. Kenett, Y. N., Chrysikou, E. G., Bassett, D. S. & Thompson-Schill, S. L. Neural dynamics during the generation and evaluation of creative and non-creative ideas. Preprint at bioRxiv https://doi.org/10.1101/ 2024.04.15.589621 (2024).
39. Christoff, K., Irving, Z. C., Fox, K. C., Spreng, R. N. & AndrewsHanna, J. R. Mind-wandering as spontaneous thought: a dynamic framework. Nat. Rev. Neurosci. 17, 718-731 (2016).
40. Benedek, M. & Jauk, E. Spontaneous and controlled processes in creative cognition in The Oxford Handbook of Spontaneous Thought: MindWandering, Creativity, Dreaming and Clinical Conditions (eds Christoff, K. & Fox, K. C. R.) 285-298 (Oxford University Press, 2018).
41. Chrysikou, E. G. Creativity in and out of (cognitive) control. Curr. Opin. Behav. Sci. 27, 94-99 (2019).
42. Tromp, C. Integrated constraints in creativity: foundations for a unifying model. Rev. Gen. Psychol. 27, 41-61 (2023).
43. Sun, J. et al. Verbal creativity correlates with the temporal variability of brain networks during the resting state. Cereb. Cortex 29, 1047-1058 (2018).
44. Sporns, O. Network attributes for segregation and integration in the human brain. Curr. Opin. Neurobiol. 23, 162-171 (2013).
45. Shine, J. M. et al. The dynamics of functional brain networks: integrated network states during cognitive task performance. Neuron 92, 544-554 (2016).
46. Orwig, W. et al. Creativity at rest: exploring functional network connectivity of creative experts. Netw. Neurosci. 7, 1022-1033 (2023).
47. Spreng, R. N., Stevens, W. D., Chamberlain, J. P., Gilmore, A. W. & Schacter, D. L. Default network activity, coupled with the frontoparietal control network, supports goal-directed cognition. Neuroimage 53, 303-317 (2010).
48. Cole, M. W., Ito, T., Bassett, D. S. & Schultz, D. H. Activity flow over resting-state networks shapes cognitive task activations. Nat. Neurosci. 19, 1718-1726 (2016).
49. Tromp, C. Creativity from constraint exploration and exploitation. Psychol. Rep. 127, 1818-1843 (2024).
50. Girn, M., Mills, C., Roseman, L., Carhart-Harris, R. L. & Christoff, K. Updating the dynamic framework of thought: creativity and psychedelics. Neuroimage 213, 116726 (2020).
51. Beaty, R. E., Seli, P. & Schacter, D. L. Network neuroscience of creative cognition: mapping cognitive mechanisms and individual differences in the creative brain. Curr. Opin. Behav. Sci. 27, 22-30 (2019).
52. Cohen, J. R. The behavioral and cognitive relevance of time-varying, dynamic changes in functional connectivity. Neuroimage 180, 515-525 (2018).
53. Wang, R. et al. Segregation, integration, and balance of large-scale resting brain networks configure different cognitive abilities. Proc. Natl Acad. Sci. USA 118, e2022288118 (2021).
54. Kenett, Y. N., Betzel, R. F. & Beaty, R. E. Community structure of the creative brain at rest. Neuroimage 210, 116578 (2020).
55. Capouskova, K., Zamora-López, G., Kringelbach, M. L. & Deco, G. Integration and segregation manifolds in the brain ensure cognitive flexibility during tasks and rest. Hum. Brain Mapp. 44, 6349-6363 (2023).
56. Zhuang, K. et al. Diverse functional interaction driven by controldefault network hubs supports creative thinking. Cereb. Cortex 33, 11206-11224 (2023).
57. Li, J. et al. High transition frequencies of dynamic functional connectivity states in the creative brain. Sci. Rep. 7, 46072 (2017).
58. Cohen, J. R. & D’Esposito, M. The segregation and integration of distinct brain networks and their relationship to cognition. J. Neurosci. 36, 12083-12094 (2016).
59. Xu, J. et al. Anxious brain networks: a coordinate-based activation likelihood estimation meta-analysis of resting-state functional connectivity studies in anxiety. Neurosci. Biobehav. Rev. 96, 21-30 (2019).
60. Jahani, A. et al. Twinned neuroimaging analysis contributes to improving the classification of young people with autism spectrum disorder. Sci. Rep. 14, 20120 (2024).
61. Acar, S. & Runco, M. A. Divergent thinking: new methods, recent research, and extended theory. Psychol. Aesthet. Creat. Arts 13, 153-158 (2019).
62. Karwowski, M. et al. Is creativity without intelligence possible? A necessary condition analysis. Intelligence 57, 105-117 (2016).
63. Gerwig, A. et al. The relationship between intelligence and divergent thinking-a meta-analytic update. J. Intell. 9, 23 (2021).
64. Borenstein, M., Hedges, L. V., Higgins, J. P. & Rothstein, H. R. Introduction to Meta-analysis (Wiley, 2021).
65. Beaty, R. E., Merseal, H. M. & Zeitlen, D. C. in The Routledge International Handbook of Creative Cognition, 433-452 (Routledge, 2023).
66. Shen, X. et al. Using connectome-based predictive modeling to predict individual behavior from brain connectivity. Nat. Protoc. 12, 506-518 (2017).
67. Benedek, M. & Fink, A. Toward a neurocognitive framework of creative cognition: the role of memory, attention, and cognitive control. Curr. Opin. Behav. Sci. 27, 116-122 (2019).
68. Volle, E. Associative and Controlled Cognition in Divergent Thinking: Theoretical, Experimental, Neuroimaging Evidence, and New Directions, 333-362 (Cambridge University Press, 2018).
69. Beaty, R. E., Silvia, P. J. & Benedek, M. Brain networks underlying novel metaphor production. Brain Cogn. 111, 163-170 (2017).
70. Da Mota, P. A. et al. The dynamics of the improvising brain: a study of musical creativity using jazz improvisation. Preprint at bioRxiv https://doi.org/10.1101/2020.01.29.924415 (2020).
71. Limb, C. J. & Braun, A. R. Neural substrates of spontaneous musical performance: an FMRI study of jazz improvisation. PLoS ONE 3, e1679 (2008).
72. Liu, S. Y. et al. Brain activity and connectivity during poetry composition: toward a multidimensional model of the creative process. Hum. Brain Mapp. 36, 3351-3372 (2015).
73. Matheson, H. E. & Kenett, Y. N. The role of the motor system in generating creative thoughts. Neuroimage 213, 116697 (2020).
74. Chen, Q. et al. Brain hemispheric involvement in visuospatial and verbal divergent thinking. Neuroimage 202, 116065 (2019).
75. He, R. et al. The impact of knowledge on poetry composition: an fMRI investigation. Brain Lang. 235, 105202 (2022).
76. Bilder, R. M. & Knudsen, K. S. Creative cognition and systems biology on the edge of chaos. Front. Psychol. 5, 112333 (2014).
77. Simonton, D. K. Creative thought as blind-variation and selectiveretention: combinatorial models of exceptional creativity. Phys. Life Rev. 7, 156-179 (2010).
78. Boot, N., Baas, M., van Gaal, S., Cools, R. & De Dreu, C. K. W. Creative cognition and dopaminergic modulation of fronto-striatal networks: integrative review and research agenda. Neurosci. Biobehav. Rev. 78, 13-23 (2017).
79. Amabile, T. Componential Theory of Creativity (Harvard Business School, 2011).
80. Lambert, P. A. The ontological emergence of creativity https://www. researchgate.net/publication/329210950_The_Ontological_ Emergence_of_Creativity (2018).
81. Ding, K., He, R., Wang, X., Chen, Q. & Kenett, Y. N. Recognizing ideas generated in a creative task: the roles of the hippocampus and medial prefrontal cortex in facilitating self-generated learning. Cereb. Cortex 34, bhae219 (2024).
82. Wang, X. et al. Semantic associative abilities and executive control functions predict novelty and appropriateness of idea generation. Commun. Biol. 7, 703 (2024).
83. Kenett, Y. N. et al. Developing a neurally informed ontology of creativity measurement. Neuroimage 221, 117166 (2020).
84. Gerver, C. R., Griffin, J. W., Dennis, N. A. & Beaty, R. E. Memory and creativity: a meta-analytic examination of the relationship between memory systems and creative cognition. Psychon. Bull. Rev. 30, 2116-2154 (2023).
85. Green, A. E., Beaty, R. E., Kenett, Y. N. & Kaufman, J. C. The process definition of creativity. Creat. Res. J. 36, 544-572 (2024).
86. Japardi, K., Bookheimer, S., Knudsen, K., Ghahremani, D. G. & Bilder, R. M. Functional magnetic resonance imaging of divergent and convergent thinking in Big-C creativity. Neuropsychologia 118, 59-67 (2018).
87. Kam, J. W. et al. Default network and frontoparietal control network theta connectivity supports internal attention. Nat. Hum. Behav. 3, 1263-1270 (2019).
88. Zanesco, A. P., Denkova, E. & Jha, A. P. Self-reported mind wandering and response time variability differentiate prestimulus electroencephalogram microstate dynamics during a sustained attention task. J. Cogn. Neurosci. 33, 28-45 (2021).
89. Liu, S., Abdellaoui, A., Verweij, K. J. & van Wingen, G. A. Replicable brain-phenotype associations require large-scale neuroimaging data. Nat. Hum. Behav. 7, 1344-1356 (2023).
90. Marek, S. et al. Reproducible brain-wide association studies require thousands of individuals. Nature 603, 654-660 (2022).
91. Gratton, C., Nelson, S. M. & Gordon, E. M. Brain-behavior correlations: two paths toward reliability. Neuron 110, 1446-1449 (2022).
92. Kong, X.-Z., Zhang, C., Liu, Y. & Pu, Y. Scanning reproducible brainwide associations: sample size is all you need? Psychoradiology 2, 67-68 (2022).
93. Rosenberg, M. D. & Finn, E. S. How to establish robust brain-behavior relationships without thousands of individuals. Nat. Neurosci. 25, 835-837 (2022).
94. Spisak, T., Bingel, U. & Wager, T. D. Multivariate BWAS can be replicable with moderate sample sizes. Nature 615, E4-E7 (2023).
95. Helwegen, K., Libedinsky, I. & van den Heuvel, M. P. Statistical power in network neuroscience. Trends Cogn. Sci. 27, 282-301 (2023).
96. Bryce, N. V. et al. Brain parcellation selection: an overlooked decision point with meaningful effects on individual differences in resting-state functional connectivity. Neuroimage 243, 118487 (2021).
97. Messé, A. Parcellation influence on the connectivity-based structure-function relationship in the human brain. Hum. Brain Mapp. 41, 1167-1180 (2020).
98. Kong, R. Q. et al. A network correspondence toolbox for quantitative evaluation of novel neuroimaging results. Preprint at bioRxiv https:// doi.org/10.1101/2024.06.17.599426 (2024).
99. Beaty, R. E. et al. Personality and complex brain networks: the role of openness to experience in default network efficiency. Hum. Brain Mapp. 37, 773-779 (2016).
100. Jauk, E., Benedek, M., Dunst, B. & Neubauer, A. C. The relationship between intelligence and creativity: new support for the threshold hypothesis by means of empirical breakpoint detection. Intelligence 41, 212-221 (2013).
101. Jauk, E., Neubauer, A. C., Dunst, B., Fink, A. & Benedek, M. Gray matter correlates of creative potential: a latent variable voxel-based morphometry study. Neuroimage 111, 312-320 (2015).
102. Fink, A. et al. Modulation of resting-state network connectivity by verbal divergent thinking training. Brain Cogn. 128, 1-6 (2018).
103. Poppenk, J. Multimodal brain data and core dimensions of creativity. Data Brief 29, 105176 (2020).
104. Chen, Q. et al. Longitudinal alterations of frontoparietal and frontotemporal networks predict future creative cognitive ability. Cereb. Cortex 28, 103-115 (2018).
105. Liu, Z. et al. Neural and genetic determinants of creativity. Neuroimage 174, 164-176 (2018).
106. Takeuchi, H. et al. Originality of divergent thinking is associated with working memory-related brain activity: evidence from a large sample study. Neuroimage 216, 116825 (2020).
107. Orwig, W., Diez, I., Vannini, P., Beaty, R. & Sepulcre, J. Creative connections: computational semantic distance captures individual creativity and resting-state functional connectivity. J. Cogn. Neurosci. 33, 499-509 (2021).
108. Silvia, P. J. et al. Assessing creativity with divergent thinking tasks: exploring the reliability and validity of new subjective scoring methods. Psychol. Aesthet. Creat. Arts 2, 68-85 (2008).
109. Esteban, O. et al. fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat. Methods 16, 111-116 (2019).
110. Gorgolewski, K. et al. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python. Front. Neuroinform. 5, 13 (2011).
111. Patel, A. X. et al. A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series. Neuroimage 95, 287-304 (2014).
112. Drysdale, A. T. et al. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat. Med. 23, 28-38 (2017).
113. Schaefer, A. et al. Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cereb. Cortex 28, 3095-3114 (2018).
114. Rubinov, M. & Sporns, O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059-1069 (2010).
115. Guimera, R. & Amaral, L. A. N. Functional cartography of complex metabolic networks. Nature 433, 895-900 (2005).
116. Pernet, C. R., Wilcox, R. & Rousselet, G. A. Robust correlation analyses: false positive and power validation using a new open source Matlab toolbox. Front. Psychol. 3, 606 (2013).
117. Viechtbauer, W. Conducting meta-analyses in with the metafor package. J. Stat. Softw. 36, 1-48 (2010).
118. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation, 2013).
119. Costafreda, S. G. Pooling FMRI data: meta-analysis, mega-analysis and multi-center studies. Front. Neuroinform. 3, 33 (2009).
120. Davis, S. W., Stanley, M. L., Moscovitch, M. & Cabeza, R. Restingstate networks do not determine cognitive function networks: a commentary on Campbell and Schacter (2016). Lang. Cogn. Neurosci. 32, 669-673 (2017).
121. Finn, E. S. Is it time to put rest to rest? Trends Cogn. Sci. 25, 1021-1032 (2021).

شكر وتقدير

مساهمات المؤلفين

المصالح المتنافسة

معلومات إضافية

Dynamic switching between brain networks predicts creative ability

Results

Sample composition and behavioral findings

Fig. 1 | Datasets characteristics. A The total number of participants and the age ranges of each dataset. The position of the black dot on each line indicates the median age of that dataset. B The distribution of creative performance based on human-rated originality scores on divergent thinking tasks in each dataset.

The relationship between network switching frequency and creative performance

The relationship between switching frequency and intelligence

Given the conditional relationship between creativity and intelligence, we applied a meta-analytic approach to determine whether intelligence, regarded as a control task, also relates to segregation and integration of DMN and ECN, as we expected (and found) for creativity. Thus, the same analyses were performed among 6 datasets ( , see Supplementary Table 3) which included intelligence scores in addition to creativity scores. The overall effect size was not significant, , with no significant heterogeneity, , . In contrast, the overall effect size for the relationship between switching frequency and creative performance was significant among the same 6 datasets, , . The parameter estimate difference between the two models (Intelligence v.s. Creativity) is significant, , indicating

Creative performance and the balance of DMN-ECN segregation and integration