(35.175.212.130) 您好!臺灣時間:2021/05/15 10:36
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:李慶鴻
研究生(外文):Ching-Hung Lee
論文名稱:用類神經網路模型研究基底神經核和說話之神經關聯性
論文名稱(外文):Study of neural correlates between speech production and the basal ganglia with neural network model
指導教授:吳炤民
指導教授(外文):Chao-Min Wu
學位類別:碩士
校院名稱:國立中央大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:124
中文關鍵詞:說話基底神經核DIVAGODIVA
外文關鍵詞:Speech productionBasal gangliaDirection Into Velocities ArticulatorGradient Order DIVA
相關次數:
  • 被引用被引用:0
  • 點閱點閱:24
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
「說話」是一個極其複雜的動作,除了需要構音器官彼此協調運作之外,大腦神經訊息的傳遞和對構音器官的控制也格外重要,隨著功能性磁振造影 (Functional Magnetic Resonance Imaging, fMRI)、擴散張量影像 (Diffusion Tensor Imaging, DTI) 等技術的成熟,大腦與說話的神經關聯性逐漸被揭開,除了皮質區域的控制功能,皮質下組織對說話的影響也開始受到關注和重視,然而針對基底神經核 (Basal ganglia) 的研究仍存在著限制,此外許多言語障礙也沒有找到有效的治療方式。因此,本研究的目的在研究基底神經核和說話之間的關聯性,藉由數學計算模型模擬基底神經核參與說話運動的過程,並從異常狀態下的模擬結果來預測可能發生的言語障礙現象,最後結合本實驗室先前研究的構音模型,找出言語治療的有效方法。
本研究使用以類神經網路為基礎之大腦訊號模型-GODIVA (Gradient Order Direction Into Velocities Articulator),該模型模擬左下額葉溝 (Left inferior frontal sulcus)、前運動輔助區 (Pre-supplementary motor area)、額葉島蓋 (Frontal operculum) 以及尾狀核 (Caudate) 迴路,來產生說話所需的大腦訊號。而實驗方法為延伸該模型加入運動輔助區(Supplementary motor area)、殼核 (Putamen) 迴路等新的區塊至模型中,再模擬三種情形下大腦產生說話的過程,並與原版模型做比較。在正常情形部分,與原版相比第一音節會因為經過尾狀核迴路而增加約100毫秒的延遲時間,不過能提升模擬結果的準確度,顯示基底神經核對序列性的說話任務具有控制能力。在異常情形下,多巴胺 (Dopamine) 濃度的異常會影響多巴胺受體第一型 (D1) 和第二型 (D2) 的活動程度,第一型會導致所有音節的延遲,然而不同的音節會有不同的延遲程度;第二型會使刺激音節轉移的強度降低,導致音節轉移的時間點向後延遲;在白質纖維 (White matter fiber) 受損情形下,也會導致刺激音節轉移的強度降低,出現音節無法轉移的現象。上述異常情形皆表示基底神經核的受損會導致說話運動障礙,然而造成障礙的主因並非皆由單一因素所造成,也可能為多重原因影響之下所導致的結果。
最後,將修改後的GODIVA模型,與本實驗室先前開發具有中文聲調之構音模型-DIVA模型結合,達到訊號由大腦下達指令,構音器模擬字詞之功能,透過真實發聲探討實際言語障礙與大腦神經的關聯性,然而受限於GODIVA模型考慮的控制參數,以及DIVA模型發聲構造不夠完善,只能做到模擬母音以及部分子音,和產生類似口吃重複發聲的現象。未來希望能加入更多控制因素,包含加入尾狀核迴路之間接路徑,模擬產生失語症等言語障礙,以及將DIVA聲道模型切割的更細緻,達到構音器精準模擬所有發音。
“Speech production” is an extremely complex action. In addition to the coordination of articulators, the transmission of neural signals and control of articulators are also important. With the advent of functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), the neural correlation between brain and speech has gradually been uncovered. Apart from the control function of cortical areas, the influence of subcortical areas on speech has received more and more attention. However, the research methods on the basal ganglia (BG) is still limited, and there are no effective treatments for many speech disorders so far. Therefore, the purpose of this study is to investigate the correlation between basal ganglia and speech production. By simulating the process of the basal ganglia participating in speech production with a computational model, we predict the possible motor speech disorders from simulation results under abnormal conditions. Finally, we combined our model with the articulation model previously studied in our laboratory to find out effective ways for speech therapy.
GODIVA (Gradient Order Direction Into Velocities Articulator), a neural network based model, simulated the left inferior frontal sulcus, the pre-supplementary motor area, the frontal operculum and the caudate nucleus circuits to generate brain signals used in speech production in this study. Our approach was to add new blocks such as the supplementary motor area and the putamen nucleus circuits to the model and then simulate the process of speech production in three different conditions. After that, we compared simulation results with the original GODIVA model. In normal condition, even though passing through caudate nucleus circuit causes a delay with the first syllable at about 100 milliseconds compared to the original GODIVA model, the accuracy of the simulation results could be significantly improved. It suggests that the basal ganglia have control ability for sequential speech tasks. In another condition, abnormal level of dopamine would affect the activity of dopamine receptor D1 and D2. D1 would cause delays in all syllables, but the delays of syllables would be different. D2 would decrease the intensity of the syllable switching activity, causing delays to the switch point of syllable. In the last condition, white matter fiber impairments would also reduce the intensity of the activity so that the syllable could not be changed to the next one. The above-mentioned abnormal conditions suggest that the impairment of basal ganglia would lead to motor speech disorders. The major source of the disorder is not always originated from a single factor, but may also from the results of multiple factors.
Finally, we combined the GODIVA model with the DIVA model previously developed in our laboratory with Chinese tones to build the function that the GODIVA model transmits brain signal instruction to control DIVA model for producing speech sound. In this way, we could find out the correlation between brain and motor speech disorder. Unfortunately, due to the limit of number of control parameters used in the GODIVA model and integrity of vocal tract used in DIVA model, the model only can simulate vowels and some consonants, and produce stuttered-like repeated sounds. In future studies, we hope more factors could be considered such as the indirect pathway of caudate nucleus circuit, so the model could simulate other motor speech disorder such as aphasia. Besides, the vocal tract shape of the DIVA model could be modified to accurately simulate all syllables.
中 華 民 國 一 零 九 年 六 月 I
中文摘要 V
Abstract VIII
致謝 X
目錄 XI
圖目錄 XIV
表目錄 XVIII
第一章  緒論 1
1.1研究動機: 1
1.2文獻探討: 2
1.3研究目的: 11
1.4論文架構: 12
第二章  說話運動及模型比較 14
2.1說話運動流程: 14
2.2語言運動階層-DIVA模型 16
2.3基底神經核 19
2.4說話排序及控制階層– GODIVA模型 23
2.5其他模型與模型比較 27
第三章  GODIVA模型修改 34
3.1 修改方式: 34
3.2 臨床特徵: 35
3.2.1語言音韻序列區 36
3.2.2語言結構框架區 37
3.2.3語言聲音映射區 37
3.2.4運動輔助區 38
3.2.5基底神經核迴路 38
3.3模型數學特徵: 40
3.3.1語言音韻序列區 (左側額下溝 IFS) 41
3.3.2語言結構框架區 (前運動輔助區 preSMA) 44
3.3.3基底神經核計畫迴路 (尾狀核迴路) 46
3.3.4語言聲音映射區 SSM (額葉島蓋) 49
3.3.5運動輔助區 (運動輔助區 SMA) 51
3.3.6基底神經核運動迴路 (殼核迴路) 53
第四章  實驗方法及設備 57
4.1 MIX-GODIVA模型使用流程: 57
4.2 DIVA模型以及模型整合: 61
4.2.1加入中文聲調之DIVA模型 61
4.2.2模型整合 62
第五章  實驗結果與討論 66
5.1 MIX-GODIVA模型結果: 66
5.1.1正常情形模擬結果 66
5.1.2正常情形與原版比較 73
5.1.3異常情形-多巴胺濃度異常 76
5.1.4異常情形-多巴胺受體第一型與第二型 (D1R & D2R) 79
5.1.5異常情形-白質纖維 (White Mater Fiber, WMF) 82
5.1.6討論 83
5.2模型整合: 86
5.2.1單母音、雙母音以及CV結構 87
5.2.2類口吃模擬-重複發音 89
第六章  結論與未來展望 93
6.1結論: 93
6.2未來展望: 95
附錄A 98
附錄B 99
參考文獻 101
Alario, F.-X., Chainay, H., Lehericy, S., & Cohen, L. (2006). The role of the supplementary motor area (SMA) in word production. Brain Research, 1076, 129-143.
Alexander, G. E., Delong, M. R., & Strick, P. L. (1986). Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annual Review of Neuroscience, 9, 357-381.
Asakawa, T., Fang, H., Sugiyama, K., Nozaki, T., Kobayashi, S., Hong, Z., Suzuki, K., Mori, N., Yang, Y., Hua, F., Ding, G., Wen, G., Namba, H., & Xia, Y. (2016). Human behavioral assessments in current research of Parkinson’s disease. Neuroscience & Biobehavioral Reviews, 68, 741-772.
Atsushi, N., Hironobu, T., & Masahiko, T. (2002). Functional significance of the cortico-subthalamo-pallidal ‘hyperdirect’ pathway. Neuroscience Research, 43, 111-117.
Bernard, J. A., Russell, C. E., Newberry, R. E., Goen, J. R. M., & Mittal, V. A. (2017). Patients with schizophrenia show aberrant patterns of basal ganglia activation: Evidence from ALE meta-analysis. Neuroimage Clinical, 14, 450-463.
Boecker, H., Dagher, A., Ceballos-Baumann, A. O., Passingham, R. E., Samuel, M., Friston, K. J., Poline, J.-B., Dettmers, C., Conrad, B., & Brooks, D. J. (1998). Role of the human rostral supplementary motor area and the basal ganglia in motor sequence control: Investigations with H2O15 PET. J. Neurophysiol, 79, 1070-1080.
Bohland, J. W., Bullock, D., & Guenther, F. H. (2010). Neural representations and mechanisms for the performance of simple speech sequences. Journal of Cognitive Neuroscience, 22(7), 1504-1529.
Bohland, J. W., & Guenther, F. H. (2006). An fMRI investigation of syllable sequence production. NeuroImage, 32, 821-841.
Brown, J. W., Bullock, D., & Grossberg, D. (2004). How Laminar Frontal Cortex and Basal Ganglia Circuits Interact to Control Planned and Reactive Saccades. Neural Networks, 17, 471-510.
Brown, S., Ingham, R. J., Ingham, J. C., Laird, A. R., & Fox, P. T. (2005). Stuttered and fluent speech production: An ALE meta-analysis of functional neuroimaging studies. Human Brain Mapping, 25, 105-117.
Bullock, D., & Rhodes, B. J. (2002). Competitive queuing for planning and serial performance. In M. Arbib (Ed.), The Handbook of Brain Theory and Neural Networks (2nd ed.). Cambridge, MA: The MIT Press.
Civier, O., Bullock, D., Max, L., Guenther, F. H. (2013). Computational modeling of stuttering caused by impairments in a basal ganglia thalamo-cortical circuit involved in syllable selection and initiation. Brain & Language, 126, 263-278.
Cohen, M. X., & Frank, M. J. (2009). Neurocomputational models of basal ganglia function in learning, memory and choice. Behavioural Brain Research, 199, 141-156.
Darley, F. L., Aronson, A. E., & Brown, J. R. (1975). Motor speech disorder. Philadelphia, Saunders.
Devlin, J. T., Matthews, P. M., Rushworth, M. F. S. (2003). Semantic processing in the left inferior prefrontal cortex: A combined functional magnetic resonance imaging and transcranial magnetic stimulation study. Journal of Cognitive Neuroscience, 15(1), 71-84.
Doyon, J., Bellec, P., Amsel, R., Penhune, V., Monchi, O., Carrier, J., Lehéricy, S., & Benali, H. (2009). Contributions of the basal ganglia and functionally related brain structures to motor learning. Behavioural Brain Research, 199, 61-75.
Dronkers, N. F. (1996). A new brain region for coordinating speech articulation. Nature, 384(14), 159-161.
Duffy, J. R. (1995). Motor speech disorders: Substrates, differential diagnosis, and management (1st ed.). America, Mosby Inc.
Florio, T. M., Scarnati, E., Rosa, I., Censo, D. D., Ranieri, B., Cimini, A., Galante, A., & Alecci, M. (2018). The basal ganglia: More than just a switching device. CNS Neuroscience & Therapeutics, 1–8.
Foerde, K., & Shohamy, D. (2011). The role of the basal ganglia in learning and memory: Insight from Parkinson’s disease. Neurobiology of Learning and Memory, 96, 624-636.
Giraud, A. L., Neumann, K., Bachoud-Levi, A.-C., Gudenberg, A. W., Euler, H. A., & Lanfermann, H., Preibisch, C. (2008). Severity of dysfluency correlates with basal ganglia activity in persistent developmental stuttering. Brain and Language, 104, 190-199.
Gough, P. M., Nobre, A. C., & Devlin, J. T. (2005). Dissociating linguistic processes in the left inferior frontal cortex with transcranial magnetic stimulation. The Journal of Neuroscience, 25(35), 8010-8016.
Graybiel, A. M., Aosaki, T., Flaherty, A. W., & Kimura, M. (1994). The basal ganglia and adaptive motor control. Science, 23, 1826-1831.
Guenther, F. H. (1994). A neural network model of speech acquisition and motor equivalent speech production. Biological Cybernetics, 72, 43-53.
Guenther, F. H., & Ghosh, S. S. (2003). A model of cortical and cerebellar function in speech. In Proceedings of the XVth International Congress of Phonetic Sciences.
Guenther, F. H., Ghosh, S. S., & Tourville, J. A. (2006). Neural modeling and imaging of the cortical interactions underlying syllable production. Brain and Language, 96, 280-301.
Guenther, F. H., & Vladusich, T. (2012). A neural theory of speech acquisition and production. Journal of Neurolinguistics, 25, 408-422.
Hertrich, I., Dietrich, S., & Ackermann, H. (2016). The role of the supplementary motor area for speech and language processing. Neuroscience and Biobehavioral Reviews, 68, 602-610.
Hikosaka, O., & Isoda, M. (2010). Switching from automatic to controlled behavior: Cortico-basal ganglia mechanisms. Trends in Cognitive Sciences, 14(4), 154-161.
Hikosaka, O., Takikawa, Y., & Kawagoe, R. (2000). Role of the basal ganglia in the control of purposive saccadic eye movements. Physiological Reviews, 80(3), 954-978.
Hillis, A. E., Work, M., Barker, P. B., Jacobs, M. A., Breese, E. L., & Maurer, K. (2004). Re-examining the brain regions crucial for orchestrating speech articulation. Brain, 127, 1479-1487.
Jahfari, S., Waldorp, L., Wildenberg, W. P. M. van den, Scholte, H. S., Ridderinkhof, K. R., & Forstmann, B. U. (2011). Effective connectivity reveals important roles for both the hyperdirect (fronto-subthalamic) and the indirect (fronto-striatal-pallidal) fronto-basal ganglia pathways during response inhibition. The Journal of Neuroscience, 31(18), 6891-6899.
Jonas, S. (1981). The supplementary motor region and speech emission. Journal of Communication Disorders, 14, 349-373.
Lu, C. M., Peng, D. L., Chen, C. S., Ning, N., Ding, G. S., Li, K. C., Yang, Y. H., & Lin, C. L. (2010). Altered effective connectivity and anomalous anatomy in the basal ganglia-thalamocortical circuit of stuttering speakers. Cortex, 46, 49-67.
Mahon, S., Deniau, J.-M., & Charpier, S. (2003). Various synaptic activities and firing patterns in cortico-striatal and striatal neurons in vivo. Journal of Physiology – Paris, 97, 557-566.
Mass, E., Mailend, M.-L., & Guenther, F. H. (2015). Feedforward and feedback control in apraxia of speech: Effects of noise masking on vowel production. Journal of Speech, Language, and Hearing Research, 58, 185-200.
Maeda, S. (1982). A digital simulation method of the vocal-tract system. Speech Communication, 1, 199-229.
Middleton, F. A., & Strick, P. L. (2000). Basal ganglia and cerebellar loops: Motor and cognitive circuits. Brain Research Reviews, 31, 236-250.
Nixon, P., Lazarova, J., Hodinott-Hill, I., Gough, P., & Passingham, R. (2004). The inferior frontal gyrus and phonological processing: An investigation using rTMS. Journal of Cognitive Neuroscience, 16(2), 289-300.
Segawa, J. A., Tourville, J. A., Beal, D. S., & Guenther, F. H. (2015). The neural correlates of speech motor sequence learning. Journal of Cognitive Neuroscience, 27(4), 819-831.
Terband, H., Maassen, B., Guenther, F. H., & Brumberg, J. (2014). Auditory-motor interactions in pediatric motor speech disorders: Neurocomputational modeling of disordered development. Journal of Communication Disorders, 47, 17-33.
Terband, H., Rodd, J., & Mass, E. (2019). Testing hypotheses about underlying deficit of apraxia of speech through computational neural modeling with the DIVA model. International Journal of Speech-Language Pathology, 1-12.
Watkins, K. E., Vargha-Khadem, F., Ashburner, J., Passingham, R. E., Connelly, A., Friston, K. J., Frackowiak, R. S. J., Mishkin, M., & Gadian, D. G. (2002). MRI analysis of an inherited speech and language disorder: Structural brain abnormalities. Brain, 125, 465-478.
Willis, G. L., & Armstrong, S. M. (1998). Orphan neurones and amine excess: The functional neuropathology of Parkinsonism and neuropsychiatric disease. Brain Research Reviews, 27, 177-242.
Wu, C.-M., Wang, T.-W., & Lee, M.-H. (2015). Study of neural mechanism of Mandarin vowel perception and diphthong production with neural network model. Journal of the Phonetic Society of Japan, 19(2), 115-123.
Ziegler, W., Kilian, B., & Deger, K. (1997). The role of the left mesial frontal cortex in fluent speech: Evidence from a case of left supplementary motor area hemorrhage. Neuropsychologia, 35(9), 1197-1208.
洪國軒 (2018). 整合大腦與構音之類神經網路模型模擬中文字詞之產生, 碩士論文, 國立中央大學電機工程學系。
黃華民 (2008). 臨床神經解剖學基礎, 合記書局, 台灣台北。
楊淑蘭 (2011). 口吃—理論與實務工作, 心理出版社股份有限公司, 台灣台北。
電子全文 電子全文(網際網路公開日期:20230801)
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top