现代舞训练与弦乐训练对脑灰质体积的差异影响

doi:10.3724/SP.J.1042.2018.01165

摘要/Abstract

摘要：

目前舞蹈与音乐两种训练对脑灰质结构影响的差异尚不明确。本研究利用基于体素的形态学分析方法(voxel-based morphometry, VBM), 比较现代舞训练被试、弦乐训练被试与对照组被试的脑结构磁共振数据。结果表明现代舞训练组在涉及感觉运动控制的皮层、皮层下结构及小脑多个区域出现灰质体积的显著增加与减少; 弦乐训练组则在与音乐训练直接相关的听-动-读皮层出现灰质体积的显著增加。这一发现提示现代舞训练可能系统性地影响广泛脑区的灰质结构, 弦乐训练可能局部地改变了具体功能脑区的灰质结构, 两种训练对脑灰质结构的影响模式存在差异。

关键词: 现代舞训练, 弦乐训练, 基于体素的形态学分析(VBM)

Abstract:

The discrepant effects of dance and music training on gray matter volume are still unknown. In this study, We used voxel-based morphometry (VBM) method to analyze the structural magnetic resonance imaging (sMRI) data of modern dancers, string instrument players and controls subjects. Our results showed increased gray matter volume (GMV) among cortical, subcortical and the cerebellum areas within the modern dancers and localized cortical regions in the string instrument players respectively. Moreover, among the three groups only modern dancers showed decreased GMV between cortical and subcortical regions. The results suggested a systematical and widespread effects of modern dance training as well as an effector-specific training outcome in the auditory-motor-semantic cortex of the string instrument players.

Key words: modern dance training, string instrument training, voxel-based morphometry (VBM)

中图分类号:

B845

李谷静, 李薪, 贺辉, 罗程, 尧德中. (2018). 现代舞训练与弦乐训练对脑灰质体积的差异影响. 心理科学进展 , 26(7), 1165-1173.

LI Gujing, LI Xin, HE Hui, LUO Cheng, YAO Dezhong. (2018). Modern dance training and string instrument training have different effects on grey matter architecture. Advances in Psychological Science, 26(7), 1165-1173.

图/表 3

参考文献 50

1	段旭君 . ( 2013). 基于大尺度脑网络分析方法的脑可塑性研究(博士学位论文). 电子科技大学, 成都.
2	蒋存梅 . ( 2016). 音乐心理学. 上海: 华东师范大学出版社.
3	马清 . ( 2000). 音乐理论与管弦乐基础. 北京: 北京大学出版社.
4	平心 . ( 2004). 舞蹈心理学. 北京: 高等教育出版社.
5	吕艺生 . ( 2003). 舞蹈学导论. 上海: 上海音乐出版社.
6	覃嫔 . ( 2018). 舞蹈艺术的训练研究. 北京: 北京理工大学出版社.
7	周临舒, 赵怀阳, 蒋存梅 . ( 2017). 音乐表演训练对神经可塑性的影响: 元分析研究. 心理科学进展, 25( 11), 1877-1887. doi: 10.3724/SP.J.1042.2017.01877 URL
8	Ashburner, J., & Friston K. J, . ( 2000). Voxel-based morphometry--the methods. NeuroImage, 11, 805-821. doi: 10.1006/nimg.2000.0582 URL
9	Ashburner, J., & Friston K. J, . ( 2005). Unified segmentation. NeuroImage, 26( 3), 839-851. doi: 10.1016/j.neuroimage.2005.02.018 URL
10	Bangert M., Peschel T., Schlaug G., Rotte M., Drescher D., Hinrichs H., .. Altenmüller E . ( 2006). Shared networks for auditory and motor processing in professional pianists: Evidence from fMRI conjunction. NeuroImage, 30( 3), 917-926. doi: 10.1016/j.neuroimage.2005.10.044 URL pmid: 16380270
11	Bangert, M., & Schlaug G. , ( 2006). Specialization of the specialized in features of external human brain morphology. European Journal of Neuroscience, 24( 6), 1832-1834. doi: 10.1111/j.1460-9568.2006.05031.x URL pmid: 1700494617004946
12	Baumann S., Koeneke S., Schmidt C. F., Meyer M., Lutz K., & Jancke L . ( 2007). A network for audio-motor coordination in skilled pianists and non-musicians. Brain Research, 1161, 65-78. doi: 10.1016/j.brainres.2007.05.045 URL pmid: 17603027
13	Bermudez P., Lerch J. P., Evans A. C., & Zatorre R. J . ( 2009). Neuroanatomical correlates of musicianship as revealed by cortical thickness and voxel-based morphometry. Cerebral Cortex, 19( 7), 1583-1596. doi: 10.1093/cercor/bhn196 URL pmid: 19073623
14	Bostan A. C., Dum R. P., & Strick P. L . ( 2013). Cerebellar networks with the cerebral cortex and basal ganglia. Trends in Cognitive Sciences, 17( 5), 241-254. doi: 10.1016/j.tics.2013.03.003 URL
15	Brown S., Martinez M. J., & Parsons L. M . ( 2006). The neural basis of human dance. Cerebral Cortex, 16( 8), 1157-1167. doi: 10.1093/cercor/bhj057 URL pmid: 16221923
16	Burzynska A. Z., Finc K., Taylor B. K., Knecht A. M., & Kramer A. F . ( 2017). The dancing brain: Structural and functional signatures of expert dance training. Frontiers in Human Neuroscience, 11, 566. doi: 10.3389/fnhum.2017.00566 URL pmid: 5711858
17	Calvo-Merino B., Glaser D. E., Grèzes J., Passingham R. E., & Haggard P . ( 2005). Action observation and acquired motor skills: An FMRI study with expert dancers. Cerebral Cortex, 15( 8), 1243-1249. doi: 10.1093/cercor/bhi007 URL pmid: 15616133
18	Cross E. S., Hamilton A. F., & Grafton S. T . ( 2006). Building a motor simulation de novo: Observation of dance by dancers. NeuroImage, 31( 3), 1257-1267. doi: 10.1016/j.neuroimage.2006.01.033 URL
19	Draganski B., Gaser C., Busch V., Schuierer G., Bogdahn U., & May A . ( 2004). Neuroplasticity: Changes in grey matter induced by training. Nature, 427( 6972), 311-312. doi: 10.1038/427311a URL
20	Giacosa C., Karpati F. J., Foster N. E. V., Penhune V. B., & Hyde K. L . ( 2016). Dance and music training have different effects on white matter diffusivity in sensorimotor pathways. NeuroImage, 135, 273-286. doi: 10.1016/j.neuroimage.2016.04.048 URL pmid: 27114054
21	Groussard M., Rauchs G., Landeau B., Viader F., Desgranges B., Eustache F., & Platel H . ( 2010). The neural substrates of musical memory revealed by fMRI and two semantic tasks. NeuroImage, 53( 4), 1301-1309. doi: 10.1016/j.neuroimage.2010.07.013 URL pmid: 20627131
22	Groussard M., Viader F., Landeau B., Desgranges B., Eustache F., & Platel H . ( 2014). The effects of musical practice on structural plasticity: The dynamics of grey matter changes. Brain and Cognition, 90, 174-180. doi: 10.1016/j.bandc.2014.06.013 URL pmid: 25127369
23	Han Y., Yang H., Lv Y. T., Zhu C. Z., He Y., Tang H. H., .. Dong Q . ( 2009). Gray matter density and white matter integrity in pianists' brain: A combined structural and diffusion tensor MRI study. Neuroscience Letters, 459( 1), 3-6. doi: 10.1016/j.neulet.2008.07.056 URL pmid: 18672026
24	Hänggi J., Koeneke S., Bezzola L., & Jäncke L . ( 2010). Structural neuroplasticity in the sensorimotor network of professional female ballet dancers. Human Brain Mapping, 31( 8), 1196-1206. doi: 10.1002/hbm.20928 URL pmid: 20024944
25	Huang H. Y., Wang J. J., Seger C., Min L., Feng D., Wu X. Y., .. Huang R. W . ( 2017). Long-term intensive gymnastic training induced changes in intra- and inter-network functional connectivity: An independent component analysis. Brain Structure and Function, 223( 1), 131-144. doi: 10.1007/s00429-017-1479-y URL pmid: 28733834
26	Huang R. W., Lu M., Song Z., & Wang J . ( 2015). Long-term intensive training induced brain structural changes in world class gymnasts. Brain Structure and Function, 220( 2), 625-644. doi: 10.1007/s00429-013-0677-5 URL pmid: 24297657
27	Hutchinson S., Lee L. H. L., Gaab N., & Schlaug G . ( 2003). Cerebellar volume of musicians. Cerebral Cortex, 13( 9), 943-949. doi: 10.1093/cercor/13.9.943 URL
28	Hyde K. L., Peretz I., & Zatorre R. J . ( 2008). Evidence for the role of the right auditory cortex in fine pitch resolution. Neuropsychologia, 46( 2), 632-639. doi: 10.1016/j.neuropsychologia.2007.09.004 URL pmid: 17959204
29	Jola C., McAleer P., Grosbras M. H., Love S. A., Morison G., & Pollick F. E . ( 2013). Uni- and multisensory brain areas are synchronised across spectators when watching unedited dance recordings. i-Perception, 4( 4), 265-284. doi: 10.1068/i0536 URL pmid: 24349687
30	Jones J., Adlam A., Benattayallah A., & Milton F . ( 2017, July). Working memory training increases recruitment of the middle frontal gyrus in children. Poster session presented at the Conference of Experimental Psychology Society, Reading, UK.
31	Karpati F. J., Giacosa C., Foster N. E. V., Penhune V. B., & Hyde K. L . ( 2017). Dance and music share gray matter structural correlates. Brain Research, 1657, 62-73. doi: 10.1016/j.brainres.2016.11.029 URL pmid: 27923638
32	Kheradmand, A., & Zee D. S, . ( 2011). Cerebellum and ocular motor control. Frontiers in Neurology, 2, 53. doi: 10.3389/fneur.2011.00053 URL pmid: 3164106
33	Koelsch, S., & Siebel W. A, . ( 2005). Towards a neural basis of music perception. Trends in Cognitive Sciences, 9( 12), 578-584. doi: 10.1016/j.tics.2010.01.002 URL
34	Lahav A., Saltzman E., & Schlaug G . ( 2007). Action representation of sound: Audiomotor recognition network while listening to newly acquired actions. Journal of Neuroscience, 27( 2), 308-314. doi: 10.1523/JNEUROSCI.4822-06.2007 URL
35	Laufer I., Negishi M., Lacadie C. M., Papademetris X., & Constable R. T . ( 2011). Dissociation between the activity of the right middle frontal gyrus and the middle temporal gyrus in processing semantic priming. PLoS One, 6( 8), e22368. doi: 10.1371/journal.pone.0022368 URL pmid: 21829619
36	Li G. J., He H., Huang M. T., Zhang X. X., Lu J., Lai Y. X., .. Yao D. Z . ( 2015). Identifying enhanced cortico- basal ganglia loops associated with prolonged dance training. Scientific Reports, 5, 10271. doi: 10.1038/srep10271 URL pmid: 26035693
37	Li S. Y., Han Y., Wang D. Y., Yang H., Fan Y. B., Lv Y. T., .. He Y . ( 2010). Mapping surface variability of the central sulcus in musicians. Cerebral Cortex, 20( 1), 25-33. doi: 10.1093/cercor/bhp074 URL pmid: 19433652
38	Maguire E. A., Gadian D. G., Johnsrude I. S., Good C. D., Ashburner J., Frackowiak R. S. J., & Frith C. D . ( 2000) Navigation-related structural change in the hippocampi of taxi drivers. Proceedings of the National Academy of Sciences of the United States of America, 97( 8), 4398-4403. doi: 10.1073/pnas.070039597 URL pmid: 10716738
39	Mutschler I., Schulze-Bonhage A., Glauche V., Demandt E., Speck O., & Ball T . ( 2007). A rapid sound-action association effect in human insular cortex. PLoS One, 2( 2), e259. doi: 10.1371/journal.pone.0000259 URL pmid: 17327919
40	Nichols, T. E . ( 2012). Multiple testing corrections, nonparametric methods, and random field theory. NeuroImage, 62( 2), 811-815. doi: 10.1016/j.neuroimage.2012.04.014 URL pmid: 22521256
41	Oldfield, R. C . ( 1971). The assessment and analysis of handedness: The Edinburgh inventory. Neuropsychologia, 9( 1), 97-113. doi: 10.1016/0028-3932(71)90067-4 URL pmid: 5146491
42	Ono Y., Nomoto Y., Tanaka S., Sato K., Shimada S., Tachibana A., .. Noah J. A . ( 2014). Frontotemporal oxyhemoglobin dynamics predict performance accuracy of dance simulation gameplay: Temporal characteristics of top-down and bottom-up cortical activities. NeuroImage, 85, 461-470. doi: 10.1016/j.neuroimage.2013.05.071 URL pmid: 23707582
43	Öztürk A. H., Tasçioglu B., Aktekin M., Kurtoglu Z., & Erden I . ( 2002). Morphometric comparison of the human corpus callosum in professional musicians and non- musicians by using in vivo magnetic resonance imaging. Journal of Neuroradiology, 29( 1), 29-34. doi: 10.1002/jmri.10067 URL pmid: 11984475
44	Rüber T., Lindenberg R., & Schlaug G . ( 2015). Differential adaptation of descending motor tracts in musicians. Cerebral Cortex, 25( 6), 1490-1498. doi: 10.1093/cercor/bht331 URL pmid: 24363265
45	Schlaug G., Jancke L., Huang Y., & Steinmetz H . ( 1995). In vivo evidence of structural brain asymmetry in musicians. Science, 267( 5198), 699-701. doi: 10.1126/science.7839149 URL
46	Schneider P., Scherg M., Dosch H. G., Specht H. J., Gutschalk A., & Rupp A . ( 2002). Morphology of Heschl's gyrus reflects enhanced activation in the auditory cortex of musicians. Nature Neuroscience, 5( 7), 688-694. doi: 10.1038/nn871 URL pmid: 12068300
47	Shibasaki H., Sadato N., Lyshkow H., Yonekura Y., Honda M., Nagamine T., .. Konishi J . ( 1993). Both primary motor cortex and supplementary motor area play an important role in complex finger movement. Brain, 116, 1387-1398. doi: 10.1093/brain/116.6.1387 URL pmid: 8293277
48	Sluming V., Barrick T., Howard M., Cezayirli E., Mayes A., & Roberts N . ( 2002). Voxel-based morphometry reveals increased gray matter density in Broca's area in male symphony orchestra musicians. NeuroImage, 17( 3), 1613-1622. doi: 10.1006/nimg.2002.1288 URL pmid: 12414299
49	Taubert M., Draganski B., Anwander A., Muller K., Horstmann A., Villringer A., & Ragert P . ( 2010). Dynamic properties of human brain structure: Learning-related changes in cortical areas and associated fiber connections. Journal of Neuroscience, 30( 35), 11670-11677. doi: 10.1523/JNEUROSCI.2567-10.2010 URL pmid: 20810887
50	Turner R. S., Grafton S. T., Votaw J. R., Delong M. R., & Hoffman J. M . ( 1998). Motor subcircuits mediating the control of movement velocity: A PET study. Journal of Neurophysiology, 80( 4), 2162-2176. doi: 10.1097/00005072-199810000-00010 URL pmid: 9772269

人口学变量	现代舞训练组	弦乐训练组	对照组	p
性别(男/女)	5/13	7/13	8/17	0.892
年龄(岁)	19.00 ± 1.41	19.05 ± 1.19	19.24 ± 0.87	0.765 df (2,60)
教育水平(年)	12.83 ± 1.33	13.05 ± 1.09	13.20 ± 1.11	0.574 df (2,60)
训练年限(年)	11.44 ± 3.24	11.33 ± 2.72	—	0.282 df (36)

人口学变量	现代舞训练组	弦乐训练组	对照组	p
性别(男/女)	5/13	7/13	8/17	0.892
年龄(岁)	19.00 ± 1.41	19.05 ± 1.19	19.24 ± 0.87	0.765 df (2,60)
教育水平(年)	12.83 ± 1.33	13.05 ± 1.09	13.20 ± 1.11	0.574 df (2,60)
训练年限(年)	11.44 ± 3.24	11.33 ± 2.72	—	0.282 df (36)

脑区	MNI坐标			体素个数	F(2,60)值 (最大点)	dan-con	dan- con (p)	dan- con (t₄₁)	mus-con	mus- con (p)	mus- con (t₄₃)	mus-dan	mus-dan (p)	mus- dan (t₃₆)
脑区	x	y	z	体素个数	F(2,60)值 (最大点)	dan-con	dan- con (p)	dan- con (t₄₁)	mus-con	mus- con (p)	mus- con (t₄₃)	mus-dan	mus-dan (p)	mus- dan (t₃₆)
Cerebelum_ Crus1_R	33	-58	-40	32	9.99	p < 0.05	0.01029	2.69		0.07979	-1.79	p < 0.001	0.00029	-4.01
Frontal_ Med_Orb_R	6	40	-6	82	11.29	p < 0.001	0.00039	3.86		0.74462	-0.33	p < 0.001	0.00058	-3.76
Thalamus_R	12	-21	-1	267	18.05	p < 0.001	0.00028	-3.97		0.16760	1.40	p < 0.001	0.00001	5.26
Thalamus_L	-10	-16	1	240	13.33	p < 0.001	0.00036	-3.89		0.26093	1.14	p < 0.001	0.00001	5.26
Temporal_ Sup_R	55	-15	1	131	13.47		0.74732	0.32	p < 0.001	0.00002	4.80	p < 0.001	0.00090	3.61
Putamen_R	25	4	13	112	12.88	p < 0.001	0.00009	4.33		0.29734	1.05	p < 0.01	0.00233	-3.27
Supp_Motor_ Area_R	6	1	63	128	11.83	p < 0.01	0.00161	-3.38		0.34007	0.96	p < 0.001	0.00001	5.12
Precentral_L	-34	-6	58	120	12.12	p < 0.01	0.00564	-2.92	p < 0.05	0.04677	2.05	p < 0.001	0.00004	4.68
Frontal_ Mid_R	42	-3	57	71	11.30		0.23886	-1.20	p < 0.01	0.00225	3.24	p < 0.001	0.00003	4.77

脑区	MNI坐标			体素个数	F(2,60)值 (最大点)	dan-con	dan- con (p)	dan- con (t₄₁)	mus-con	mus- con (p)	mus- con (t₄₃)	mus-dan	mus-dan (p)	mus- dan (t₃₆)
脑区	x	y	z	体素个数	F(2,60)值 (最大点)	dan-con	dan- con (p)	dan- con (t₄₁)	mus-con	mus- con (p)	mus- con (t₄₃)	mus-dan	mus-dan (p)	mus- dan (t₃₆)
Cerebelum_ Crus1_R	33	-58	-40	32	9.99	p < 0.05	0.01029	2.69		0.07979	-1.79	p < 0.001	0.00029	-4.01
Frontal_ Med_Orb_R	6	40	-6	82	11.29	p < 0.001	0.00039	3.86		0.74462	-0.33	p < 0.001	0.00058	-3.76
Thalamus_R	12	-21	-1	267	18.05	p < 0.001	0.00028	-3.97		0.16760	1.40	p < 0.001	0.00001	5.26
Thalamus_L	-10	-16	1	240	13.33	p < 0.001	0.00036	-3.89		0.26093	1.14	p < 0.001	0.00001	5.26
Temporal_ Sup_R	55	-15	1	131	13.47		0.74732	0.32	p < 0.001	0.00002	4.80	p < 0.001	0.00090	3.61
Putamen_R	25	4	13	112	12.88	p < 0.001	0.00009	4.33		0.29734	1.05	p < 0.01	0.00233	-3.27
Supp_Motor_ Area_R	6	1	63	128	11.83	p < 0.01	0.00161	-3.38		0.34007	0.96	p < 0.001	0.00001	5.12
Precentral_L	-34	-6	58	120	12.12	p < 0.01	0.00564	-2.92	p < 0.05	0.04677	2.05	p < 0.001	0.00004	4.68
Frontal_ Mid_R	42	-3	57	71	11.30		0.23886	-1.20	p < 0.01	0.00225	3.24	p < 0.001	0.00003	4.77

[1]	张才蕙, 叶渐桥, 杨静. 汉语作为第二语言学习的脑机制[J]. 心理科学进展, 2023, 31(5): 747-758.
[2]	刘文华, 温秀娟, 陈灵, 杨瑞, 胡逸儒. 奖励期待和结果评估的脑电成分在精神疾病研究中的应用[J]. 心理科学进展, 2023, 31(5): 783-799.
[3]	张婕, 张火垠, 李红, 雷怡. 睡眠对恐惧学习的影响及其认知神经机制[J]. 心理科学进展, 2023, 31(4): 631-640.
[4]	刘文彬, 漆正堂, 刘微娜. 不同感觉功能对抑郁的影响及其神经机制[J]. 心理科学进展, 2023, 31(4): 641-656.
[5]	孔祥祯, 张凤翔, 蒲艺. 空间导航的脑网络基础和调控机制[J]. 心理科学进展, 2023, 31(3): 330-337.
[6]	刘沛菡, 张火垠, 张旭凯, 李红, 雷怡. 急性疼痛与慢性疼痛对奖赏加工的影响及神经机制[J]. 心理科学进展, 2023, 31(3): 402-415.
[7]	马原啸, 陈旭. 焦虑易感群体焦虑识别与消退中催产素的作用[J]. 心理科学进展, 2023, 31(1): 10-19.
[8]	俞稼钰, 靳羽西, 梁丹丹. 自闭症谱系障碍人群词义加工的脑激活模式：基于fMRI研究的元分析[J]. 心理科学进展, 2022, 30(11): 2448-2460.
[9]	王榕, 陈小异, 杜雪, 蒋军. 经皮迷走神经刺激对抑制控制的调节机制[J]. 心理科学进展, 2022, 30(10): 2269-2277.
[10]	胡小勇, 杜棠艳, 李兰玉, 王甜甜. 低社会经济地位影响自我调节的神经机制[J]. 心理科学进展, 2022, 30(10): 2278-2290.
[11]	张思源, 李雪冰. 不同频率经颅交流电刺激在精神疾病中的应用[J]. 心理科学进展, 2022, 30(9): 2053-2066.
[12]	邹迪, 李红, 王福顺. 唤醒定义探析及其认知神经生理基础[J]. 心理科学进展, 2022, 30(9): 2020-2033.
[13]	梁飞, 江瑶, 肖婷炜, 董洁, 王福顺. 基本情绪的神经基础：来自fMRI与机器视觉技术研究的证据[J]. 心理科学进展, 2022, 30(8): 1832-1843.
[14]	周振友, 孔丽, 陈楚侨. 精神分裂症肠道微生物与脑影像和临床表征的关系[J]. 心理科学进展, 2022, 30(8): 1856-1869.
[15]	李海虹, 尚思源, 谢晓非. 利他行为的遗传基础: 来自定量遗传学和分子遗传学的证据[J]. 心理科学进展, 2022, 30(7): 1574-1588.