數理資優大腦白質網路結構分析之研究

White Matter Network Connectome of Mathematical and Scientific Talents

10.6251/BEP.201903_50(3).0001

郭靜姿(Ching-Chih Kuo)；陳學志(Hsueh-Chih Chen)；梁庚辰(Keng-Chen Liang)；高淑芬(Susan Shur-Fen Gau)；吳清麟(Ching-Lin Wu)

智力 ； 圖形理論 ； 數理資優 ； 擴散張量影像 ； Diffusion tensor imaging ； Graph theory ； Intelligence ； Mathematical and scientific talents

教育心理學報

50卷3期（2019 / 03 / 01）

389 - 406

繁體中文

近期研究已普遍指出數理資優個體的大腦結構與常人不同。然而，多數研究較著重於認知功能與單一大腦區域的運作之關係。本研究首從圖形理論取向探討數理資優、智力與大腦白質網路結構之關係。參與者為42位神經功能正常的成年男性，數理資優與一般成人各21人，其年齡分別為年齡為21.00 ± 1.67歲及年齡為21.48 ± 2.29歲。研究者採用魏氏成人智力測驗第三版評估個體智商，並進行擴散張量影像掃瞄，再依此建構大腦白質網路，使用圖形理論計算大腦網路屬性及各節點之連結效率。結果發現，數理資優組在大腦局部區域內節點之間的傳遞效率較佳，並以左側額上回尤甚。進一步控制數理資優與一般組的智力均等，數理資優組在大腦局部區域內的節點訊息傳輸效率仍占有優勢，並以涉及空間處理能力的左側枕上回群聚程度最佳。其後，分別分析數理資優與一般組在智力與大腦網路拓樸屬性之相關結果指出，僅一般成人作業智力與大腦網路全局效率為正相關，數理資優成人的智力與大腦網路連結則無直接關聯。上述結果不僅提供數理資優與常人在大腦白質結構差異情形的實徵證據，更從大腦拓樸網路之取向區辨數理資優、數理能力以及智力之異同，將可做為往後評估資賦優異的參考。

Recent studies have highlighted that people with mathematical and scientific talents develop a different brain structure from those with typical development. However, most of these studies have focused on the relationship between cognitive functions of the brain and the operation of a single area of the brain. This study explores the connections among the network structures with relation to mathematical and scientific talents, intelligence, and white matter. The study recruited 42 men with normal nerve functions. The experimental group comprised 21 participants with mathematical and scientific talents and an age of 21.00 ± 1.67 years; the control group comprised 21 participants with typical developmental and an age of 21.48 ± 2.29 years. The mathematical and scientific talent and typical developmental groups consisted of 21 people each. The researchers adopted the third version of the Wechsler Adult Intelligence Test to evaluate individual intelligence, conduct diffusion tensor imaging of participants, and construct a network of white matter to analyze the overall network attributes and nodal efficiencies using graph theory. The results show that the communication efficiency among the nodes inside the local region is relatively better in people with mathematical and scientific talents, particularly the in the superior prefrontal gyrus. Moreover, when intelligence was equal between the two groups, the mathematical and scientific talent group outperformed the other in terms of node efficiency in local regions and the clustering coefficient in the superior occipital gyrus. The relationship between the topological properties and the intelligence of the mathematical and scientific talent group and the typical developmental group showed that only the intelligence of the typical developmental group was positively connected with integrated efficiency across several regions of the brain; however, no direct correlation was shown in the mathematical and scientific talents group. The results not only provided empirical evidence for the disparity in white matter structure between mathematical and scientific talent and typical development groups but also distinguished mathematical and scientific talents, mathematical ability, and intelligence based on the topological network of the brain, which can be used in future assessments for people with mathematical and scientific talents.

1. 郭靜姿, C. C.,林慶波, C. P.,張馨仁, H. J.,周坤賢, K. H.,曾琦芬, C. F. C.,張玉佩, Y. P.,林燁虹, Y. H.(2012)。高中數理能力優異班學生與普通班學生大腦結構及性別差異之研究。教育科學研究期刊，57(2)，25-64。
   連結：
2. 郭靜姿, C. C.,張馨仁, H. J.,張玉佩, Y. P.,周坤賢, K. H.,林燁虹, Y. H.,陳雪君, H. C.,林慶波, C. P.(2012)。高中數理資優班學生心理特質與大腦結構之研究。教育心理學報，43(4)，805-832。
   連結：
3. Achard, S.,Salvador, R.,Whitcher, B.,Suckling, J.,Bullmore, E.(2006).A resilient, low frequency, small-world human brain functional network with highly connected association cortical hubs.Journal of Neuroscience,26,63-72.
4. Bai, F.,Shu, N.,Yuan, Y.,Shi, Y.,Yu, H.,Wu, D.,Zhang, Z.(2012).Topologically convergent and divergent structural connectivity patterns between patients with remitted geriatric depression and amnestic mild cognitive impairment.Journal of Neuroscience,32,4307-4318.
5. Bellec, P.,Lavoie-Courchesne, S.,Dickinson, P.,Lerch, J. P.,Zijdenbos, A. P.,Evans, A. C.(2012).The pipeline system for Octave and Matlab (PSOM): A lightweight scripting framework and execution engine for scientific workflows.Frontiers in Neuroinformatics,6,1-18.
6. Berg, D. H.(2008).Working memory and arithmetic calculation in children: The contributory roles of processing speed, short-term memory, and reading.Journal of Experimental Child Psychology,99,288-308.
7. Bullmore, E.,Sporns, O.(2009).Complex brain networks: Graph theoretical analysis of structural and functional systems.Nature Reviews Neuroscience,10,186-198.
8. Cui, Z.,Zhong, S.,Xu, P.,He, Y.,Gong, G.(2013).PANDA: A pipeline toolbox for analyzing brain diffusion images.Frontiers in Human Neuroscience,21,7-42.
9. Danker, J. F.,Anderson, J. R.(2007).The roles of prefrontal and posterior parietal cortex in algebra problem solving: A case of using cognitive modeling to inform neuroimaging data.NeuroImage,35,1365-1377.
10. Davidson, J. E.,Sternberg, R. J.(2003).The psychology of problem solving.Cambridge, UK:Cambridge University Press.
11. Desco, M.,Navas-Sanchez, F. J.,Sanchez-Gonzalez, J.,Reig, S.,Robles, O.,Franco, C.,Arango, C.(2011).Mathematically gifted adolescents use more extensive and more bilateral areas of the fronto-parietal network than controls during executive functioning and fluid reasoning tasks.NeuroImage,57,281-292.
12. Frangou, S.,Chitins, X.,Williams, S. C. R.(2004).Mapping IQ and gray matter density in healthy young people.NeuroImage,23,800-805.
13. Geake, J. G.,Hansen, P. C.(2005).Neural correlates of intelligence as revealed by fMRI of fluid analogies.NeuroImage,26,555-564.
14. Gong, G.,Rosa-Neto, P.,Carbonell, F.,Chen, Z. J.,He, Y.,Evans, A. C.(2009).Age and gender-related differences in the cortical anatomical network.Journal of Neuroscience,29,15684-15693.
15. Grabner, R. H.,Ansari, D.,Reishofer, G.,Stern, E.,Ebner, F.,Neuper, C.(2007).Individual differences in mathematical competence predict parietal brain activation during mental calculation.NeuroImage,38,346-356.
16. Grabowska, A.(2017).Sex on the brain: Are gender-dependent structural and functional differences associated with behavior?.Journal of Neuroscience Research,95,200-212.
17. Hagmann, P.,Cammoun. L.,Gigandet, X.,Meuli, R.,Honey, C. J.,Wedeen, V. J.,Sporns, O.(2008).Mapping the structural core of human cerebral cortex.PLOS Biology,6,e159.
18. Hanakawa, T.,Honda, M.,Okada, T.,Fukuyama, H.,Shibasaki, H.(2003).Neural correlates underlying mental calculation in abacus experts: A functional magnetic resonance imaging study.NeuroImage,19,296-307.
19. He, Y.,Chen, Z. J.,Gong, G.,Evans, A. C.(2009).Neuronal networks in Alzheimer's disease.Neuroscientist,15,333-350.
20. Heller, K. A.(Ed.),Moenks, F. J.(Ed.),Passow, A. H.(Ed.)(1993).International handbook of research and development of giftedness and talent.New York, NY:Pergamon Press.
21. Hoard, M. K.,Geary, D. C.,Byrd-Craven, J.,Nugent, L.(2008).Mathematical cognition in intellectually precocious first graders.Developmental Neuropsychology,33,251-276.
22. Ischebeck, A.,Zamarian, L.,Egger, K.,Schocke, M.,Delazer, M.(2007).Imaging early practice effects in arithmetic.NeuroImage,36,993-1003.
23. Jung, R. E.,Grazioplene, R.,Caprihan, A.,Chavez, R. S.,Haier, R. J.(2010).White matter integrity, creativity, and psychopathology: Disentangling constructs with diffusion tensor imaging.PLoS One,5,e9818.
24. Jung, R. E.,Haier, R. J.(2007).The parieto-frontal integration theory (PFIT) of intelligence: Converging neuroimaging evidence.Behavioral & Brain Sciences,30,135-154.
25. Kuo, C. C.,Liang, K. C.,Tseng, Christine C. F.,Gau, Susan S. F.(2014).Comparison of the cognitive profiles and social adjustment between mathematically and scientifically talented students and students with Asperger's syndrome.Research in Autism Spectrum Disorders,8,838-850.
26. Latora, V.,Marchiori, M.(2001).Efficient behavior of small-world networks.Physical Review Letters,87(19),198701.
27. Lee, K. H.,Choi, Y. Y.,Gray, J. R.,Cho, S. H.,Chae, J. H.,Lee, S.,Kim, K.(2006).Neural correlates of superior intelligence: stronger recruitment of posterior parietal cortex.NeuroImage,29,578-586.
28. Leemans, A.,Jones, D. K.(2009).The B‐matrix must be rotated when correcting for subject motion in DTI data.Magnetic Resonance in Medicine,61(6),1336-1349.
29. Leikin, M.,Waisman, I.,Leikin, R.(2013).How brain research can contribute to the evaluation of mathematical giftedness.Psychological Test and Assessment Modeling,55,415-437.
30. Leikin, R.(Ed.),Berman, A.(Ed.),Koichu, B.(Ed.)(2009).Creativity in mathematics and the education of gifted students.Rotterdam, the Netherlands:Sense Publisher.
31. Li, Y. H.,Liu, Y.,Qin, W.,Li, K. C.,Yu, C. S.,Jiang, T. Z.(2009).Brain anatomical network and intelligence.PLOS Computational Biology,5,e1000395.
32. Mazoyer, B.,Zago, L.,Mellet, E.,Bricogne, S.,Etard, O.,Houde, O.,Tzourio-Mazoyer, N.(2001).Cortical networks for working memory and executive functions sustain the conscious resting state in man.Brain Research Bulletin,54(3),287-298.
33. Meyer, M. L.,Salimpoor, V. N.,Wu, S. S.,Geary, D. C.,Menon, V.(2010).Differential contribution of specific working memory components to mathematics achievement in 2nd and 3rd graders.Learning and Individual Differences,20,101-109.
34. Narr, K. L.,Woods, R. P.,Thompson, P. M.,Szeszko, P.,Robinson, D.,Dimtcheva1, T.,Bilder, R. M.(2007).Relationships between IQ and regional cortical gray matter thickness in healthy adults.Cerebral Cortex,17,2163-2171.
35. Navas-Sanchez, F. J.,Aleman-Gomez, Y.,Sanchez-Gonzalez, J.,Guzman-De-Villoria, J. A.,Franco, C.,Robles, O.,Arango, C.,Desco, M.(2014).White matter microstructure correlates of mathematical giftedness and intelligence quotient.Human Brain Mapping,35,2619-2631.
36. O'Boyle, M. W,Cunnington, R.,Silk, T. J.,Vaughan, D.,Jackson, G.,Syngeniotis, A.,Egan, G. F.(2005).Mathematically gifted male adolescents activate a unique brain network during mental rotation.Cognitive Brain Research,25,583-587.
37. Prescott, J.,Gavrilescu, M.,Cunnington, R.,O’Boyle, M. W,Egan, G. F.(2010).Enhanced brain connectivity in math-gifted adolescents: an fMRI study using mental rotation.Cognitive Neuroscience,1,277-288.
38. Raichle, M. E.,MacLeod, A. M.,Snyder, A. Z.,Powers, W. J.,Gusnard, D. A.,Shulman, G. L.(2001).A default mode of brain function.Proceedings of the National Academy of Sciences of the United States of America,98(2),676-682.
39. Reiss, A. L.,Abrams, M. T.,Singer, H. S.,Ross, J. L.,Denckla, M. B.(1996).Brain development, gender and IQ in children. A volumetric imaging study.Brain,119,1763.
40. Schmithorst, V. J.,Brown, R. D.(2004).Empirical validation of the triple-code model of numerical processing for complex math operations using functional MRI and group Independent Component Analysis of the mental addition and subtraction of fractions.NeuroImage,22,1414-1420.
41. Singh, H.,O’Boyle, M. W(2004).Interhemispheric interaction during visual information processing in mathematically gifted youth, average ability adolescents and college students.Neuropsychology,18,371-377.
42. Smith, S. M.,Jenkinson, M.,Woolrich, M. W.,Beckmann, C. F.,Behrens, T. E.,Johansen-Berg, H.,Matthews, P. M.(2004).Advances in functional and structural MR image analysis and implementation as FSL.NeuroImage,23,208-219.
43. Sporns, O.,Chialvo, D. R.,Kaiser, M.,Hilgetag, C. C.(2004).Organization, development and function of complex brain networks.Trends in Cognitive Sciences,8,418-425.
44. Taber, Keith S(Ed.),Sumida, Manabu(Ed.)(2015).International perspectives on science education for the gifted: Key issues and challenges.U.K., London:Routledge.
45. Taub, G. E.,Floyd, R. G.,Keith, T. Z.,McGrew, K. S.(2008).Effects of general and broad cognitive abilities on mathematics achievement.School Psychology Quarterly,23,187-198.
46. Tzourio-Mazoyer, N.,Landeau, B.,Papathanassiou, D.,Crivello, F.,Etard, O.,Delcroix, N.,Joliot, M.(2002).Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain.NeuroImage,15,273-289.
47. van den Heuvel, M. P.,Stam, C. J.,Boersma, M.,Hulshoff Pol, H. E.(2008).Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain.NeuroImage,43,528-539.
48. van den Heuvel, M. P.,Stam, C. J.,Kahn, R. S.,Hulshoff Pol, H. E.(2009).Efficiency of functional brain networks and intellectual performance.The Journal of Neuroscience,29(23),7619-7624.
49. Wang, J.,Wang, X.,Xia, M.,Liao, X.,Evans, A.,He, Y.(2015).GRETNA: A graph theoretical network analysis toolbox for imaging connectomics.Frontiers in Human Neuroscience,9,386.
50. Wang, L. Q.,Song, M.,Jiang, T. Z.,Zhang, Y. T.,Yu, C. S.(2011).Regional homogeneity of the resting-state brain activity correlates with individual intelligence.Neuroscience Letters,488,275-278.
51. Wang, R.,Benner, T.,Sorensen, A. G.,Wedeen, V. J.(2007).Diffusion toolkit: A software package for diffusion imaging data processing and tractography.Proceedings of the International Society for Magnetic Resonance in Medicine,15,3720.
52. Watts, D. J.,Strogatz, S. H.(1998).Collective dynamics of ‘small-world’ networks.Nature,393,440-442.
53. Wilke, M.,Sohn, J. H.,Byars, A. W.,Holland, S. K.(2003).Bright spots: Correlations of gray matter volume with IQ in a normal pediatric population.NeuroImage,20,202-215.
54. Wu, C. L.,Zhong, S. Y.,Chan, Y. C.,Chen, H. C.,Gong, G. L.,He, Y.,Li, P.(2016).White-matter structural connectivity underlying human laughter-related traits processing.Frontiers in Psychology,7,1637.
55. Wu, C. L.,Zhong, S.,Chan, Y. C.,Chen, H.C.,He, Y.(2018).White-matter structural connectivity in relation to humor styles: An exploratory study.Frontiers in Psychology,9,1654.
56. Wu. C. L.,Zhong, S. Y.,Chen, H. C.(2016).Discriminating remote and close association with relation to white-matter structural connectivity.PloS One,11(10),e0165053.
57. Zhang, L.,Gan, J. Q.,Wang, H.(2015).Mathematically gifted adolescents mobilize enhanced workspace configuration of theta cortical network during deductive reasoning.Neuroscience,289,334-348.
58. Zhou, X.,Chen, C.,Zang, Y.,Dong, Q.,Chen, C.,Qiao, S.,Gong, Q.(2007).Dissociated brain organization for single-digit addition and multiplication.NeuroImage,35,871-880.
59. 陳榮華, Y. H.,陳心怡, H. Y.(2002).魏氏成人智力量表第三版（WAIS-III）中文版.臺北=Taiwan, Taipei:中國行為科學社=Chinses Behavioral Science Corporation.

1. 蔡明富,廖釗君,陳錦雪,郭靜姿,張書豪,林燁虹,于曉平(2020)。臺灣中小學資優教育銜接與資優學生學習適應研究。教育心理學報，51(3)，415-442。
2. 郭靜姿,吳清麟(2020)。數理資優能力、智力與大腦區域同質性之相關研究。教育心理學報，51(3)，443-456。
3. 胡瑀,孟瑛如(2021)。國小注意力缺陷過動症學生、學習障礙伴隨注意力缺陷過動症問題學生與一般學生在魏氏兒童智力量表第五版（WISC-V）表現差異之探討。特殊教育學報，54，31-60。
4. 許芮瑄,吳孟育(2021)。後真相時代下的賦權知能～媒體素養課程設計之分享。資優教育季刊，155，25-32。
5. (2023)。技術型高中學習障礙學生在魏氏兒童智力量表第五版表現之初探。特殊教育季刊，168，25-36。