研究生: |
林高弘 Lin, Kao-Hung |
---|---|
論文名稱: |
作業前靜息態腦波源訊號連結和高爾夫推桿表現的關係 The relationship between pre-task resting-state EEG source connectivity and golf putting performance |
指導教授: |
洪聰敏
Hung, Tsung-Min |
口試委員: | 張育愷 黃崇儒 |
口試日期: | 2021/01/26 |
學位類別: |
碩士 Master |
系所名稱: |
體育學系 Department of Physical Education |
論文出版年: | 2021 |
畢業學年度: | 109 |
語文別: | 中文 |
論文頁數: | 45 |
中文關鍵詞: | 靜息態大腦活動 、精準性運動表現 、連結網路 、功能性連結分析 |
英文關鍵詞: | resting-state brain activity, precision sport performance, brain connectivity networks, functional connectivity analysis |
DOI URL: | http://doi.org/10.6345/NTNU202100415 |
論文種類: | 學術論文 |
相關次數: | 點閱:95 下載:7 |
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目的:過去探討大腦功能的研究主要都在關心作業誘發的大腦活動,然而,越來越多的證據指出,在沒有刺激和休息狀態下的大腦活動也是具有功能性意義的。在精準性運動的腦波研究主要都在探討動作執行準備期的大腦活動,不過靜息態腦波和運動表現的關係還並不明確。因此,本研究欲探討作業前靜息態腦波和推桿表現的關係,特別是源訊號層級的功能性連結。方法:本研究招募三十二位高爾夫球選手,測量參與者靜息態腦波源訊號連結和推桿表現。相關分析用來探討靜息態腦波源訊號連結和推桿表現的關連。結果:左中額葉迴 (left middle frontal gyrus; L. MFG) 和上顳葉迴 (right superior temporal gyrus; R. STG) 間在Beta頻率段的連結強度與推桿進洞率有顯著正相關 (r = 0.72, p = 3.07 × 10-6)。結論:本研究使用源訊號分析進一步探討與運動表現有關的神經機制,並較精確定位出額葉與右顳葉區連結的訊號來源。這代表靜息態腦波功能性連結可以作為一個區別個體化推桿表現的工具。本研究展示了視覺空間相關的訊息與工作記憶或是與動作準備相關的歷程互相配合,在運動表現中的重要性。
Purpose: Former research in cognitive neuroscience domain were mainly focused on task-induced brain activity. However, accumulative evidences showed that our brain is active and functioning meaningfully even at resting-state. In the precision sport domain, most of the EEG study were focusing on task-related brain activity. The relationship between resting-state EEG and precision sports performance is still unclear. Therefore, the current research investigated the relationship between resting-state EEG and golf putting performance, especially by using the measure of source space functional connectivity. Methods: 32 golfers were recruited. Participants’ resting-state EEG and putting performance were measured. The relationship between resting-state EEG and putting performance was analysed by Spearman’s correlation analysis. Results: The connectivity strength between left middle frontal gyrus and right superior temporal gyrus in beta frequency is positively correlated with putting performance (r = 0.72, p = 3.07 × 10-6). Conclusions: The current research uses source connectivity to analyse the relationship between resting-state EEG and putting performance. It shows that resting-state EEG can detect the individual differences of putting performance. The results show that visual-spatial processing with working memory or motor preparation mechanism is crucial for leading to superior sport performance.
高士竣, 洪聰敏, & 黃崇儒. (2009). 較佳精準運動表現中專注的腦波特徵. 中華體育
季刊, 23(3), 1-16.
Babiloni, C., Del Percio, C., Iacoboni, M., Infarinato, F., Lizio, R., Marzano, N., Crespi, G.,
Dassù, F., Pirritano, M., Gallamini, M., & Eusebi, F.,( 2008). Golf putt outcomes are
predicted by sensorimotor cerebral EEG rhythms. The Journal of Physiology, 586 (1),
131–139.
Babiloni, C., Marzano, N., Iacoboni, M., Infarinato, F., Aschieri, P., Buffo, P., Cibelli, G.,
Soricelli, A., Eusebi, F., & Del Percio, C. (2010). Resting state cortical rhythms in athletes: a high-resolution EEG study. Brain Research Bulletin, 81(1), 149 –156.
Baldassare, A., Lewis, C. M., Committeri, G., Snyder, A. Z., Romani, G. L., &
Corbetta, M., (2012). Individual variability in functional connectivity predicts
performance of a perceptual task. Proceedings of the National Academy of
Science of the United State of America, 109, 3516–3521.
Bastos, A. M., & Schoffelen, J. M. (2016). A tutorial review of functional connectivity
analysis methods and their interpretational pitfalls. Frontiers in systems
neuroscience, 9, 175.
Baumeister, J., Reinecke, K., Liesen, H., & Weiss, M. (2008). Cortical activity of skilled
performance in a complex sports related motor task. European Journal of Applied Physiology, 104, 625–631.
Bear, M. F., Connors, B. W., & Paradiso, M. A. (2007). Neuroscience: Exploring the brain.
3rd ed. Philadelphia: Lippincott Williams & Wilkins.
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and
powerful approach to multiple testing. Journal of the Royal statistical society: series B
(Methodological), 57(1), 289-300.
Baumeister, J., Reinecke, K., Liesen, H., & Weiss, M. (2008). Cortical activity of skilled
performance in a complex sports related motor task. Eur J Appl Physiol, 104(4),
625-631. doi:10.1007/s00421-008-0811-x
Brookes, M. J., Woolrich, M., Luckhoo, H., Price, D., Hale, J. R., Stephenson, M. C., . . .
Morris, P. G. (2011). Investigating the electrophysiological basis of resting state
networks using magnetoencephalography. Proceedings of the national academy of
sciences, 108(40), 16783-16788.
Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of
structural and functional systems. Nature reviews neuroscience, 10(3), 186-198
Buszard, T., Farrow, D., Zhu, F. F., & Masters, R. S. (2016). The relationship between
working memory capacity and cortical activity during performance of a novel motor
task. Psychology of Sport and Exercise, 22, 247-254.
Cantou, P., Platel, H., Desgranges, B., & Groussard, M. (2018). How motor, cognitive and
musical expertise shapes the brain: Focus on fMRI and EEG resting-state functional
connectivity. Journal of Chemical Neuroanatomy, 89, 60-68.
Chang, C. Y., Chen, Y. H., & Yen, N. S. (2018). Nonlinear neuroplasticity corresponding to
sports experience: A voxel‐based morphometry and resting‐state functional connectivity
study. Human Brain Mapping, 39(11), 4393-4403.
Chang, C.-Y., & Hung, T.-M. (2020). Understanding and Controlling Cortical Activity for
Superior Performance. Kinesiology Review, 1(aop), 1-10.
Chatrian, G. E., Lettich, E., & Nelson, P. L. (1985). Ten percent electrode system for
topographic studies of spontaneous and evoked EEG activity. American Journal of
EEG Technology, 25, 83–92.
Cheng, M.-Y., Huang, C.-J., Chang, Y.-K., Koester, D., Schack, T., & Hung, T.-M. (2015).
Sensorimotor rhythm neurofeedback enhances golf putting performance. Journal of
Sport and Exercise Psychology, 37(6), 626-636.
Christie, S., di Fronso, S., Bertollo, M., & Werthner, P. (2017). Individual Alpha Peak
Frequency in Ice Hockey Shooting Performance. Frontiers in Psychology, 8, 762.
Cole, M. W., Bassett, D. S., Power, J. D., Braver, T. S., & Petersen, S. E. (2014). Intrinsic
and task-evoked network architectures of the human brain. Neuron, 83, 238 –251.
Cooke, A. (2013). Readying the head and steadying the heart: A review of cortical and
cardiac studies of preparation for action in sport. International Review of Sport and
Exercise Psychology, 6(1), 122-138.
Curtis, C. E., & D'Esposito, M. (2003). Persistent activity in the prefrontal cortex during
working memory. Trends in cognitive sciences, 7(9), 415-423.
Damoiseaux, J. S., Rombouts, S. A., Barkhof, F., Scheltens, P., & Stam, C. J. (2006).
Consistent resting-state networks across healthy subjects. Proceedings of the National
Academy of Science of the United State of America, 103, 13848–53.
Deco, G., Jirsa, V. K., & McIntosh, A. R., (2011). Emerging concepts for the dynamical
organization of resting-state activity in the brain. Nature Review Neuroscience, 12,
43–56.
Deeny, S. P., Haufler, A. J., Saffer, M., & Hatfield, B. D. (2009). Electroencephalographic
coherence during visuomotor performance: a comparison of cortico-cortical
communication in experts and novices. Journal of Motor Behavior, 41(2), 106 –116.
Deeny, S. P., Hillman, C. H., Janelle, C. M., & Hatfield, B. D. (2003). Cortico–cortical
communication and superior performance in skilled marksmen: An EEG coherence
analysis. Journal of Sport and Exercise Psychology, 25, 188–204.
Del Percio, C., Babiloni, C., Bertollo, M., Marzano, N., Iacoboni, M., Infarinato, F., Lizio,
R., Stocchi, M., Robazza, C., Cibelli, G., Comani, S., & Eusebi, F. (2009). Visuo-
attentional and sensorimotor alpha rhythms are related to visuomotor performance in
athletes. Human Brain Mapping, 30, 3527–3540.
Del Percio, C., Infarinato, F., Marzano, N., Iacoboni, M., Aschieri, P., Lizio, R., &
Babiloni, C. (2011). Reactivity of alpha rhythms to eyes opening is lower in athletes
than non-athletes: A high-resolution EEG study. International Journal of
Psychophysiology, 82, 240–247.
Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single
trial EEG dynamics including independent component analysis. Journal of
Neuroscience Methods, 134, 9 –21.
Di, X., Zhu, S., Jin, H., Wang, P., Ye, Z., Zhou, K., ... & Rao, H. (2012). Altered resting brain
function and structure in professional badminton players. Brain connectivity, 2(4),
225-233.
Doppelmayr, M., Finkenzeller, T., & Sauseng, P. (2008). Frontal midline theta in the
pre-shot phase of rifle shooting: differences between experts and
novices. Neuropsychologia, 46(5), 1463-1467.
Dubois, J., & Adolphs, R. (2016). Building a science of individual differences from fMRI.
Trends in cognitive sciences, 20(6), 425-443.
Ellison, A., Schindler, I., Pattison, L. L., & Milner, A. D. (2004). An exploration of the role of
the superior temporal gyrus in visual search and spatial perception using
TMS. Brain, 127(10), 2307-2315.
Friston, K. J. (2011). Functional and effective connectivity: A review. Brain Connectivity,
1(1), 13-36.
Genovese, C. R., Lazar, N. A., & Nichols, T., (2002). Thresholding of statistical maps in
functional neuroimaging using the false discovery rate. NeuroImage, 15(4), 870 – 878.
Gharabaghi, A., Berger, M. F., Tatagiba, M., & Karnath, H. O. (2006). The role of the right
superior temporal gyrus in visual search—insights from intraoperative electrical
stimulation. Neuropsychologia, 44(12), 2578-2581.
Gong, A., Liu, J., Li, F., Liu, F., Jiang, C., & Fu, Y. (2017). Correlation Between Resting-
state Electroencephalographic Characteristics and Shooting Performance.
Neuroscience, 366, 172-183.
Gong, A., Liu, J., Lu, L., Wu, G., Jiang, C., & Fu, Y. (2019). Characteristic differences
between the brain networks of high-level shooting athletes and non-athletes calculated
using the phase-locking value algorithm. Biomedical Signal Processing and Control,
51, 128-137.
Guerra-Carrillo, B., Mackey, A. P., & Bunge, S. A. (2014). Resting-state fMRI: a window
into human brain plasticity. The Neuroscientist, 20(5), 522-533.
Guo, Z., Huang, X., Wang, M., Jones, J. A., Dai, Z., Li, W., Liu, P., & Liu, H. (2016).
Regional homogeneity of intrinsic brain activity correlates with auditory-motor processing of vocal pitch errors. NeuroImage, 142, 565–575.
Hampson, M., Driesen, N. R., Skudlarski, P., Gore, J. C., & Constable, R. T. (2006). Brain
connectivity related to working memory performance. Journal of Neuroscience, 26,
13338–13343.
Hardmeier, M., Hatz, F., Bousleiman, H., Schindler, C., Stam, C. J., & Fuhr, P. (2014).
Reproducibility of functional connectivity and graph measures based on the phase lag
index (PLI) and weighted phase lag index (wPLI) derived from high resolution EEG.
PLoS One, 9, e108648.
Hassan, M., Dufor, O., Merlet, I., Berrou, C., & Wendling, F. (2014). EEG Source
Connectivity Analysis: From Dense Array Recordings to Brain Networks. PLoS ONE,
9(8), e105041.
Hatfield, B. D. (2018). Brain dynamics and motor behavior: A case for efficiency and
refinement for superior performance. Kinesiology Review, 7(1), 42–50.
Hatfield, B.D., & Hillman, C.H. (2001). The psychophysiology of sport: A mechanistic
understanding of the psychology of superior performance. In R.N. Singer, C.H.
Hausenblas, & C.M. Janelle (Eds.), Handbook of sport psychology (2nd ed., pp.
362–386). New York, NY: John Wiley & Sons, Inc.
Hatfield, B. D., Landers, D. M., & Ray, W. J. (1984). Cognitive processes during self-paced
motor performance: An electroencephalographic profile of skilled marksmen. Journal
of Sport and Exercise Psychology, 6(1), 42-59.
Hermens, D. F., Soei, E. X., Clarke, S. D., Kohn, M. R., Gordon, E., & Williams, L. M.
(2005). Resting EEG theta 554 activity predicts cognitive performance in
attention-deficit hyperactivity disorder. Pediatric Neurology, 32, 248–56.
Hung, C. L., Hung, T. M., & Chang, C. W. (2009). Comparison of Best and Worst
Performance on EEG Coherence in Skilled Dart Players. Psychophysiology, 46, 135-135.
Kao, S. C., Huang, C. J., & Hung, T. M. (2013). Frontal midline theta is a specific indicator
of optimal attentional engagement during skilled putting performance. Journal of Sport
and Exercise Psychology, 35(5), 470-478.
Karamacoska, D., Barry, R. J., & Steiner, G. Z. (2017). Resting state intrinsic EEG impacts
on go stimulus‐response processes. Psychophysiology, 54, 894–903.
Karamacoska, D., Barry, R. J., & Steiner, G. Z. (2019). Using principal components analysis
to examine resting state eeg in relation to task performance. Psychophysiology, e13327.
Klein, C., Liem, F., Hänggi, J., Elmer, S., & Jäncke, L. (2015). The “silent” imprint of musical
training. Human Brain Mapping, 37, 536.
Langer, N., Pedroni, A., Gianotti, L. R., Hänggi, J., Knoch, D., & Jäncke, L. (2012).
Functional brain network efficiency predicts intelligence. Human brain mapping,
33(6), 1393-1406.
Li, G., He, H., Huang, M., Zhang, X., Lu, J., Lai, Y., ... & Yao, D. (2015). Identifying
enhanced cortico-basal ganglia loops associated with prolonged dance
training. Scientific reports, 5(1), 1-11.
Marquetand, J., Vannoni, S., Carboni, M., Hegner, Y. L., Stier, C., Braun, C., & Focke, N. K.
(2019). Reliability of Magnetoencephalography and High-Density
Electroencephalography Resting-State Functional Connectivity Metrics. Brain
Connectivity, 9(7).
Mennes, M., Zuo, X. N., Kelly, C., Di Martino, A., Zang, Y. F., Biswal, B., Castellanos, F. X.,
& Milham, M. P. (2011). Linking inter-individual differences in neural activation and
behavior to intrinsic brain dynamics. NeuroImage, 54, 2950–2959.
Michel, C. M., & Brunet, D. (2019). EEG source imaging: a practical review of the analysis
steps. Frontiers in neurology, 10, 325.
Michel, C. M., & Murray, M. M. (2012). Towards the utilization of EEG as a brain imaging
tool. NeuroImage, 61(2), 371-385.
Mirifar, A., Beckmann, J., & Ehrlenspiel, F. (2017). Neurofeedback as supplementary
training for optimizing athletes' performance: A systematic review with implications
for future research. Neuroscience & Biobehavioral Review, 75, 419-432.
Mulert, C., Jager, L., Schmitt, R., Bussfeld, P., Pogarell, O., Möller, H. J., Juckel, G., &
Hegerl, U. (2004). Integration of fMRI and simultaneous EEG: towards a comprehensive
understanding of localization and time-course of brain activity in target detection.
NeuroImage, 22, 83–94.
Neubauer, A. C., & Fink, A. (2009). Intelligence and neural efficiency. Neuroscience &
Biobehavioral Reviews, 33(7), 1004-1023.
Nolte, G., Bai, O., Wheaton, L., Mari, Z., Vorbach, S., & Hallett, M. (2004). Identifying true
brain interaction from EEG data using the imaginary part of coherency. Clinical
neurophysiology, 115(10), 2292-2307.
Palva, S., & Palva, J. M. (2012). Discovering oscillatory interaction networks with M/EEG:
Challenges and breakthroughs. Trends in Cognitive Sciences, 16, 219–230.
Parasuraman, R., & Jiang, Y. (2012). Individual differences in cognition, affect, and
performance: Behavioral, neuroimaging, and molecular genetic
approaches. Neuroimage, 59(1), 70-82.
Pascual-Marqui, R. D., Lehmann, D., Koukkou, M., Kochi, K., Anderer, P., Saletu, B.,
Tanaka, H., Hirata, K., John, E. R., Prichep, L., Biscay-Lirio, R. & Kinoshita, T.
(2011). Assessing interactions in the brain with exact low-resolution electromagnetic
tomography. Philosophical Transactions of the Royal Society A, 369, 3768–3784.
Pedersen, J. R., Johannsen, P., Bak, C. K., Kofoed, B., Saermark, K., & Gjedde, A. (1998).
Origin of human motor readiness field linked to left middle frontal gyrus by MEG and
PET. Neuroimage, 8(2), 214-220.
Pernet, C. R., Wilcox, R. R., & Rousselet, G. A. (2013). Robust correlation analyses: false
positive and power validation using a new open source matlab toolbox. Frontiers in
psychology, 3, 606.
Pontifex, M. B., Miskovic, V., & Laszlo, S. (2017). Evaluating the efficacy of fully
automated approaches for the selection of eyeblink ICA
components. Psychophysiology, 54(5), 780-791.
Poole, V. N., Robinson, M. E., Singleton, O., DeGutis, J., Milberg, W. P.,
McGlinchey, R. E., Salat, D. H., & Esterman, M. (2016). Intrinsic functional
connectivity predicts individual differences in distractibility. Neuropsychologia,
86, 176 –182.
Raichle, M. E. (2006). The brain’s dark energy. Science, 314, 1249–50.
Raichle, M. E. (2015). The brain’s default mode network. Annual Review of Neuroscience, 38,
433–447.
Raichle, M.E. (2010). Two views of brain function. Trends in Cognitive Sciences, 14,
180 –190.
Raichle, M. E., & Mintun, M. A. (2006). Brain work and brain imaging. Annual Review of
Neuroscience, 29, 449–76.
Raichlen, D. A., Bharadwaj, P. K., Fitzhugh, M. C., Haws, K. A., Torre, G. A., Trouard, T. P.,
& Alexander, G. E. (2016). Differences in resting state functional connectivity between
young adult endurance athletes and healthy controls. Frontiers in human
neuroscience, 10, 610.
Sadaghiani S., & Kleinschmidt A. (2013). Functional interactions between intrinsic brain
activity and behavior. NeuroImage, 80, 379–386.
Seeley, W. W., Menon, V., Schatzberg, A. F., Keller, J., Glover, G. H., Kenna, H.,
Reiss, A. L., & Greicius, M. D. (2007). Dissociable intrinsic connectivity networks for
salience processing and executive control. Journal of Neuroscience, 27, 2349–56
Shulman, G. L., Fiez, J. A., Corbetta, M., Buckner, R. L., Miezin, F. M., Raichle, M. E., &
Petersen, S. E. (1997). Common blood flow changes across visual tasks: II. Decreases in
cerebral cortex. Journal of Cognitive Neuroscience, 9, 648–63.
Sitaram, R., Ros, T., Stoeckel, L., Haller, S., Scharnowski, F., Lewis-Peacock, J., . . . Oblak,
E. (2017). Closed-loop brain training: the science of neurofeedback. Nature reviews
neuroscience, 18(2), 86-100.
Sockeel, S., Schwartz, D., Pelegrini-Issac, M., & Benali, H. (2016). Large scale functional
networks identified from resting-state EEG using spatial ICA. PLoS One, 11, e0146845.
Sporns, O. (2010). Dynamic patterns in spontaneous neural activity. Networks of the Brain,
149-178.
Tavor, I., Parker, J. O., Mars, R. B., Smith, S. M., Behrens, T. E., & Jbabdi, S. (2016). Task
free MRI predicts individual differences in brain activity during task performance.
Science, 352(6282), 216–220.
Tian, L., Ren, J., & Zang, Y. (2012). Regional homogeneity of resting state fMRI signals
predicts stop signal task performance. NeuroImage, 60(1), 539e544.
Van de Steen, F., Faes, L., Karahan, E., Songsiri, J., Valdes-Sosa, P. A., & Marinazzo, D.
(2019). Critical comments on EEG sensor space dynamical connectivity analysis. Brain
topography, 32(4), 643-654.
Van Den Heuvel, M. P., Mandl, R. C., Kahn, R. S., & Hulshoff Pol, H. E. (2009).
Functionally linked resting‐state networks reflect the underlying structural connectivity
architecture of the human brain. Human brain mapping, 30(10), 3127-3141.
Vitacco, D., Brandeis, D., Pascual-Marqui, R., & Martin, E. (2002). Correspondence of
event-related potential tomography and functional magnetic resonance imaging during
language processing. Human Brain Mapping, 17, 4–12.
Von Bastian, C. C., Langer, N., Jäncke, L., & Oberauer, K. (2013). Effects of working
memory training in young and old adults. Memory & cognition, 41(4), 611-624.
Wang, K. P., Cheng, M. Y., Chen, T. T., Huang, C. J., Schack, T., & Hung, T. M. (2020).
Elite golfers are characterized by psychomotor refinement in cognitive-motor
processes. Psychology of Sport and Exercise, 50, 101739.
Wang, J., Lu, M., Fan, Y., Wen, X., Zhang, R., Wang, B., ... & Huang, R. (2016). Exploring
brain functional plasticity in world class gymnasts: a network analysis. Brain Structure
and Function, 221(7), 3503-3519.
Wang, Z., Zhang, D., Liang, B., Chang, S., Pan, J., Huang, R., & Liu, M. (2016). Prediction of
Biological Motion Perception Performance from Intrinsic Brain Network Regional
Efficiency. Frontiers in Human Neuroscience, 10, 552.
Whitton, A. E., Deccy, S., Ironside, M. L., Kumar, P., Beltzer, M., & Pizzagalli, D. A. (2018).
Electroencephalography source functional connectivity reveals abnormal high-frequency
communication among large-scale functional networks in depression. Biological
Psychiatry: Cognitive Neuroscience and Neuroimaging, 3(1), 50–58.
Worrell, G. A., Lagerlund, T. D., Sharbrough, F. W., Brinkmann, B. H., Busacker, N. E.,
Cicora, K. M., & O’Brien, T. J. (2000). Localization of the epileptic focus by
low-resolution electromagnetic tomography in patients with a lesion demonstrated by
MRI. Brain Topography, 12, 273–282.
Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology:
Lessons from machine learning. Perspectives on Psychological Science, 12(6),
1100-1122.
Zhang, D., & Raichle, M. E. (2010). Disease and the brain’s dark energy. Nature Review
Neurology, 6, 15–28.
Zhu, F. F., Maxwell, J. P., Hu, Y., Zhang, Z. G., Lam, W. K., Poolton, J. M., & Masters, R. S.
(2010). EEG activity during the verbal-cognitive stage of motor skill
acquisition. Biological psychology, 84(2), 221-227.