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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林發暄 | |
| dc.contributor.author | Fu-Hua Yang | en |
| dc.contributor.author | 楊馥華 | zh_TW |
| dc.date.accessioned | 2021-06-15T01:18:13Z | - |
| dc.date.available | 2011-08-22 | |
| dc.date.copyright | 2011-08-22 | |
| dc.date.issued | 2011 | |
| dc.date.submitted | 2011-08-16 | |
| dc.identifier.citation | 1. Hultsch, D.F. and S.W.S. MacDonald, Intraindividual variability in performance as a theoretical window onto cognitive aging. New frontiers in cognitive aging, ed. R.A. Dixon, L. Backman, and L.-G. Nilsson. 2004, Oxford: Oxford University Press.
2. MacDonald, S.W., L. Nyberg, and L. Backman, Intra-individual variability in behavior: links to brain structure, neurotransmission and neuronal activity. Trends Neurosci, 2006. 29(8): p. 474-80. 3. Walhovd, K.B. and A.M. Fjell, White matter volume predicts reaction time instability. Neuropsychologia, 2007. 45(10): p. 2277-84. 4. Burton, C.L., et al., Intraindividual variability in physical and emotional functioning: comparison of adults with traumatic brain injuries and healthy adults. Clin Neuropsychol, 2002. 16(3): p. 264-79. 5. Li, S.C., et al., Transformations in the couplings among intellectual abilities and constituent cognitive processes across the life span. Psychol Sci, 2004. 15(3): p. 155-63. 6. Stuss, D.T., et al., Staying on the job: the frontal lobes control individual performance variability. Brain, 2003. 126(Pt 11): p. 2363-80. 7. Wiesenfeld, K. and F. Moss, Stochastic resonance and the benefits of noise: from ice ages to crayfish and SQUIDs. Nature, 1995. 373(6509): p. 33-6. 8. Welford, A.T. and J.M.T. Brebner, Reaction times. 1980. 9. Whelan, R., Effective analysis of reaction time data. The Psychological Record, 2008. 58(3): p. 475-482. 10. Welford, A.T., Reaction time, speed of performance, and age. Ann N Y Acad Sci, 1988. 515: p. 1-17. 11. Gerson, A.D., L.C. Parra, and P. Sajda, Cortical origins of response time variability during rapid discrimination of visual objects. Neuroimage, 2005. 28(2): p. 342-53. 12. Schall, J.D., Neural correlates of decision processes: neural and mental chronometry. Curr Opin Neurobiol, 2003. 13(2): p. 182-6. 13. Cook, E.P. and J.H. Maunsell, Dynamics of neuronal responses in macaque MT and VIP during motion detection. Nat Neurosci, 2002. 5(10): p. 985-94. 14. Hanes, D.P. and J.D. Schall, Neural control of voluntary movement initiation. Science, 1996. 274(5286): p. 427-30. 15. Thompson, K.G., et al., Perceptual and motor processing stages identified in the activity of macaque frontal eye field neurons during visual search. J Neurophysiol, 1996. 76(6): p. 4040-55. 16. Churchland, M.M., et al., Neural variability in premotor cortex provides a signature of motor preparation. J Neurosci, 2006. 26(14): p. 3697-712. 17. Niemi, P., Naatanen, Risto, Foreperiod and simple reaction time. Psychol. Bull., 1981. 89(1): p. 133-162. 18. Castellanos, F.X., et al., Varieties of attention-deficit/hyperactivity disorder-related intra-individual variability. Biol Psychiatry, 2005. 57(11): p. 1416-23. 19. Weissman, D.H., et al., The neural bases of momentary lapses in attention. Nat Neurosci, 2006. 9(7): p. 971-8. 20. Fox, M.D., et al., Intrinsic fluctuations within cortical systems account for intertrial variability in human behavior. Neuron, 2007. 56(1): p. 171-84. 21. Roitman, J.D. and M.N. Shadlen, Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. J Neurosci, 2002. 22(21): p. 9475-89. 22. MacDonald, S.W., et al., Increased response-time variability is associated with reduced inferior parietal activation during episodic recognition in aging. J Cogn Neurosci, 2008. 20(5): p. 779-86. 23. Ratcliff, R., Methods for dealing with reaction time outliers. Psychol Bull, 1993. 114(3): p. 510-32. 24. Hamalainen, M.S. and R.J. Ilmoniemi, Interpreting magnetic fields of the brain: minimum norm estimates. Med Biol Eng Comput, 1994. 32(1): p. 35-42. 25. Fuchs, M., et al., An improved boundary element method for realistic volume-conductor modeling. IEEE Trans Biomed Eng, 1998. 45(8): p. 980-97. 26. Available from: http://www.nmr.mgh.harvard.edu/martinos/flashHome.php. 27. Yamagishi, N., et al., Attentional changes in pre-stimulus oscillatory activity within early visual cortex are predictive of human visual performance. Brain Res, 2008. 1197: p. 115-22. 28. Foxe, J.J., G.V. Simpson, and S.P. Ahlfors, Parieto-occipital ~10 Hz activity reflects anticipatory state of visual attention mechanisms. Neuroreport, 1998. 9: p. 3929-3933. 29. Jensen, O. and A. Mazaheri, Shaping functional architecture by oscillatory alpha activity: gating by inhibition. Front Hum Neurosci, 2010. 4: p. 186. 30. van Dijk, H., et al., Prestimulus oscillatory activity in the alpha band predicts visual discrimination ability. J Neurosci, 2008. 28(8): p. 1816-23. 31. Haegens, S., B.F. Handel, and O. Jensen, Top-down controlled alpha band activity in somatosensory areas determines behavioral performance in a discrimination task. J Neurosci, 2011. 31(14): p. 5197-204. 32. Molins, A., et al., Quantification of the benefit from integrating MEG and EEG data in minimum l2-norm estimation. Neuroimage, 2008. 42(3): p. 1069-77. 33. Rockland, K.L.S., J.H. Kaas, and P. A., Extrastriate cortex in primates. 1997. 34. Ress, D., B.T. Backus, and D.J. Heeger, Activity in primary visual cortex predicts performance in a visual detection task. Nat Neurosci, 2000. 3(9): p. 940-5. 35. Romei, V., et al., Spontaneous fluctuations in posterior alpha-band EEG activity reflect variability in excitability of human visual areas. Cereb Cortex, 2008. 18(9): p. 2010-8. 36. Sauseng, P., et al., Spontaneous locally restricted EEG alpha activity determines cortical excitability in the motor cortex. Neuropsychologia, 2009. 47(1): p. 284-8. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/42631 | - |
| dc.description.abstract | Intra-individual variability in behaviors can be an important indicator of central nervous system integrity. This study aims at understanding the origins of the behavioral reaction time (RT) variability across trials using magnetoencephalography (MEG) in a two-choice reaction time visuomotor task. We classify trials into the fast-response (FR) and the slow-response (SR) groups according to the RTs and we study the oscillatory activity and evoked responses. We found that the pre-stimulus alpha band (8-14 Hz) oscillatory power (0.4 s before the visual stimulus onset) around right posterior sensors was significantly higher in the SR group than in the FR group (p<0.001). The visual and motor evoked responses have significantly smaller amplitude in the SR group than in the FR group. With respect to the onset of the visual stimulus, the peak timing difference between FR and SR groups was only 0~8 ms in the visual cortex and 85 ms in the motor cortex. These results suggest that the posterior alpha power may modulate the brain activity in visual and motor cortices to cause inter-trial RT variability. Such a modulation can be observed after 150 ms from the visual stimulus onset by MEG. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T01:18:13Z (GMT). No. of bitstreams: 1 ntu-100-R98548040-1.pdf: 1071901 bytes, checksum: a8200c597c4c693e31c56fbd8b5e0840 (MD5) Previous issue date: 2011 | en |
| dc.description.tableofcontents | 摘要 ........................................................................................ ii
ABSTRACT ............................................................................. iii CONTENTS ............................................................................ iv List of Figures and Table ....................................................... vi CHAPTER 1 INTRODUCTION..................................................... 1 1.1 Background and Problem Statement ................................. 2 1.2 Literature Review ...............................................................3 1.3 Objectives of Study ........................................................... 5 CHAPTER 2 METHOD ............................................................... 7 2.1 Materials ............................................................................8 2.1.1 Experiment Paradigm ..................................................... 8 2.1.2 Stimuli ............................................................................ 8 2.1.3 Participants .................................................................... 9 2.1.4 Data Acquisition ............................................................. 9 2.2 Data Analysis ................................................................... 10 2.2.1 Preprocessing ............................................................... 10 2.2.2 Classification of Trials ................................................... 10 2.2.3 Analysis of Oscillatory Response .................................... 11 2.2.4 Analysis of Evoked Response ......................................... 12 CHAPTER 3 RESULT ................................................................. 14 3.1 Behavioral Responses ........................................................ 15 3.2 Oscillatory Power .............................................................. 16 3.3 Latencies of Evoked Response ........................................... 18 3.4 Magnitudes of Evoked Response ........................................ 23 CHAPTER 4 DISCUSSION ........................................................... 24 4.1 Alpha Oscillations .............................................................. 25 4.2 Latencies of Evoked Response ............................................ 25 4.3 Magnitudes of Evoked Response ........................................ 27 REFERENCES ............................................................................. 29 | |
| dc.language.iso | en | |
| dc.subject | 腦磁波 | zh_TW |
| dc.subject | 反應時間 | zh_TW |
| dc.subject | 行為變異度 | zh_TW |
| dc.subject | α振盪 | zh_TW |
| dc.subject | 視覺 | zh_TW |
| dc.subject | 運動 | zh_TW |
| dc.subject | behavioral variability | en |
| dc.subject | visuomotor task | en |
| dc.subject | alpha power | en |
| dc.subject | reaction time | en |
| dc.subject | magnetoencephalography | en |
| dc.title | 腦磁活動與自發運動反應時間差異之關係 | zh_TW |
| dc.title | Neuromagnetic correlates of behavioral variability in voluntary visuomotor tasks | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 99-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 蔡尚岳,林益如 | |
| dc.subject.keyword | 反應時間,行為變異度,α振盪,視覺,運動,腦磁波, | zh_TW |
| dc.subject.keyword | reaction time,behavioral variability,alpha power,visuomotor task,magnetoencephalography, | en |
| dc.relation.page | 31 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2011-08-16 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 醫學工程學研究所 | zh_TW |
| 顯示於系所單位: | 醫學工程學研究所 | |
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