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研究生: 黃思瑜
Huang, Allison
論文名稱: 利用瞳孔測量儀評估中風大鼠的治療效果
The Use of Pupillometry for Assessing Brain Stimulation Treatment on Stroke in Rats
指導教授: 陳家進
Chen, Jia-Jian
學位類別: 碩士
Master
系所名稱: 工學院 - 生物醫學工程學系
Department of BioMedical Engineering
論文出版年: 2021
畢業學年度: 109
語文別: 英文
論文頁數: 34
中文關鍵詞: 瞳孔測量儀中風腦電刺激
外文關鍵詞: pupillometry, stroke, brain stimulation, near infrared light
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  • 中風是世界上前三大造成死亡和殘障的疾病 。而腦 電刺激是 近期十 分引人 關 注 的 中風治療,其效益的評估自然也十分重要。 瞳孔光反 射是很 常見的中 風診斷 、 功 能 回覆評估方式之一。透過測量瞳孔光反射功 能,我們 可以判 斷腦電刺 激對於 中 風 復 原的效果。
    本研究中,使用了新開發的瞳孔測量儀測量 大鼠的 瞳孔光反 射。此 新式瞳 孔 測 量 儀運用光纖將紅外線雷射光導入植入大鼠頭 部的近紅 外線光 譜儀(N IRS ) 導 管 中,作為瞳孔測量儀的光源。我們將大鼠分 為實驗組 及對照 組。兩組 大鼠皆 接 受 導 管植入手術,以利使用新式的瞳孔測量儀觀 測瞳孔光 反射。 實驗組的 大鼠並 接 受 M CAO (middle cerebral artery occlusion)手術引起缺血性中風。透過比較兩組 大鼠的數據差異,做為判定此瞳孔測量儀是 否具備評 估中風 後復健手 段之效 果 的 能 力。
    透過數據分析,我們可以發現,中風大鼠需 要較長 的時間進 行瞳孔 反應, 並 且 其 收縮比例不若正常大鼠。因此,我們能以此為依據,認定此新式瞳孔測量儀具 有評 估腦電刺激等中風復建手段的能力。
    關鍵詞:瞳孔測量儀、中風、腦電刺激

    According to WHO, stroke is the top three cause of mortality and the main cause of disability around the world. Among varied treatment modalities, brain stimulation has shown great potential for facilitate brain plasticity. Pupillary light reflex has been commonly used by medical professional for brain response assessment. The aim of this study is to develop a new adaptation of pupillometry and utilize light reflex as an assessment tool for brain stimulation on rats with stroke.
    In this study, a new adaptation of pupillometry using near-infrared spectroscopy (NIRS) cannula as a guidance for infrared lighting is developed. This newly developed device utilizes a laser emitter as light source. The light goes through the NIRS cannulas via an optical fiber to light up the pupil, as opposed to traditional pupillometry with external IR lighting setup from the camera or ambient light.
    Rats are divided into 2 groups in our experiment: the control group and experimental group. All rats were implanted with NIRS cannulas to observe pupillary light reflex with the new adaptation of pupillometry. The experimental group received MCAO (middle cerebral artery occlusion) surgery for stroke induction. The result of shows that rat with stroke needs longer response time and the response is weaker than normal rats. Preliminary animal study indicates that proper design of pupillometry could serve as an assessment tool for evaluating the progress of treatment, such as brain stimulation, on subjects with stroke.
    Keywords: pupillometry, stroke, brain stimulation, near infrared light

    摘要 I Abstract II Contents III List of Figures V Chapter I. Introduction 1 1.1 Central nervous system and eyes 1 1.2 Stroke and pupil and disease detection 2 1.3 Clinical intervention of stroke and brain stimulation 3 1.4 Pupillometry 3 1.5 The aims of this study 4 Chapter II. Materials and Methods 5 2.1 Experimental design 5 2.2 MCAO surgery 6 2.3 The implementation of NIRS cannula and brain stimulation electrodes 7 2.4 Setup of pupillometry system 7 2.5. Pupil detection 10 2.6 New adaptation of pupillometry 17 Chapter III. Results 18 3.1 Image processing 18 3.2 Data analysis 23 Chapter IV. Discussion and Conclusions 27 4.1 New adapted pupillometry 27 4.2 Infrared back-illumination pupillometry (iBip) 29 4.3 Conclusion 31 References 33

    1. Alpern, M., McCready Jr, D. W., & Barr, L. (1963). The dependence of the photopupil response on flash duration and intensity. The Journal of general physiology, 47(2), 265-278.
    2. Daoud, A. Chapter 9: Morphological Image Processing. Retrieved from https:// www.slideshare.net/daouddodo/chapter-9-morphological-image-processing
    3. Detection of a Circle in Noisy Image Data. Retrieved from https:// dsp.stackexchange.com/questions/5930/detection-of-a-circle-in-noisy-image-data
    4. Ferrari, G. L., Marques, J. L., Gandhi, R. A., Heller, S. R., Schneider, F. K., Tesfaye, S., & Gamba, H. R. (2010a). Using dynamic pupillometry as a simple screening.
    5. Ferrari, G. L., Marques, J. L., Gandhi, R. A., Heller, S. R., Schneider, F. K., Tesfaye, S., & Gamba, H. R. (2010b). Using dynamic pupillometry as a simple screening tool to detect autonomic neuropathy in patients with diabetes: a pilot study. Biomedical engineering online, 9(1), 26.
    6. Fotiou, D., Stergiou, V., Tsiptsios, D., Lithari, C., Nakou, M., & Karlovasitou, A. (2009). Cholinergic deficiency in Alzheimer's and Parkinson's disease: evaluation with pupillometry. International Journal of Psychophysiology, 73(2), 143-149.
    7. Fregni, F., Boggio, P. S., Mansur, C. G., Wagner, T., Ferreira, M. J., Lima, M. C., . . . Nitsche, M. A. (2005). Transcranial direct current stimulation of the unaffected hemisphere in stroke patients. Neuroreport, 16(14), 1551-1555.
    8. Gandiga, P. C., Hummel, F. C., & Cohen, L. G. (2006). Transcranial DC stimulation (tDCS): a tool for double-blind sham-controlled clinical studies in brain stimulation. Clinical neurophysiology, 117(4), 845-850.
    9. Hummel, F., Celnik, P., Giraux, P., Floel, A., Wu, W.-H., Gerloff, C., & Cohen, L. G. (2005). Effects of non-invasive cortical stimulation on skilled motor function in chronic stroke. Brain, 128(3), 490-499.
    10. Hummel, F., & Cohen, L. G. (2005). Improvement of motor function with noninvasive cortical stimulation in a patient with chronic stroke. Neurorehabilitation and neural repair, 19(1), 14-19.
    11. Hummel, F. C., & Cohen, L. G. (2006). Non-invasive brain stimulation: a new strategy to improve neurorehabilitation after stroke? The Lancet Neurology, 5(8), 708-712.
    12. Loewenfeld, I. E. (1958). Mechanisms of reflex dilatation of the pupil. Documenta Ophthalmologica, 12(1), 185-448.
    13. Lowenfeld, I. (1993). The Pupil; Anatomy, Physiology, and Clinical Applications. Ames IA. In: Iowa State University Press.
    14. Nikolaou, N., & Antoniadis, I. (2003). Application of morphological operators as envelope extractors for impulsive-type periodic signals. Mechanical Systems and Signal Processing, 17(6), 1147-1162.
    15. Radke, R. DIP Lecture 13: Morphological image processing. Retrieved from https://www.youtube.com/watch?v=IcBzsP-fvPo&t=2560s
    16. Rousselet, E., Kriz, J., & Seidah, N. G. (2012). Mouse model of intraluminal MCAO:cerebral infarct evaluation by cresyl violet staining. JoVE (Journal of Visualized Experiments)(69), e4038.
    17. Rubshtein, A. (2012). Detection of a Circle in Noisy Image Data. Retrieved from https://dsp.stackexchange.com/questions/5930/detection-of-a-circle-in-noisy-image-data
    18. Sinha, U. (n.d.). Circle Hough Transform. Retrieved from https://aishack.in/tutorials/circle-hough-transform/
    19. Tweed, D., Cadera, W., & Vilis, T. (1990). Computing three-dimensional eye position quaternions and eye velocity from search coil signals. Vision research, 30(1), 97-110. 40/4 1
    20. Wikipedia, c. (14 June 2020 06:03 UTC). Mathematical morphology. Retrieved from https://en.wikipedia.org/w/index.php? title=Mathematical_morphology&oldid=962461507
    21. Young, D. (2020, July 13, 2020). Hough transform for circles. Retrieved from https:// www.mathworks.com/matlabcentral/fileexchange/26978-hough-transform-for-circles
    22. Yuzgec, O., Prsa, M., Zimmermann, R., & Huber, D. (2018). Pupil Size Coupling to Cortical States Protects the Stability of Deep Sleep via Parasympathetic Modulation. Curr Biol, 28(3), 392-400 e393. doi:10.1016/j.cub.2017.12.049

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