簡易檢索 / 詳目顯示

研究生: 呂軒慧
Lu, Shiuan-Huei
論文名稱: 探討精細動作訓練結合擴增實境對失憶型輕度認知障礙在記憶能力之療效
The Effects of Fine Motor Training with Augmented Reality on Memory in elder adults with Amnestic Mild Cognitive impairment (aMCI)
指導教授: 蘇芳慶
Su, Fong-Chin
共同指導教授: 白明奇
Pai, Ming-Chyi
郭立杰
Kuo, Li-Chieh
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: 2019
畢業學年度: 107
語文別: 英文
論文頁數: 65
中文關鍵詞: 失憶型輕度認知障礙 (aMCI)精細動作手指按壓擴增實境(AR)
外文關鍵詞: amnestic mild cognitive impairment (aMCI), fine motor training, finger tapping, augmented reality (AR)
相關次數: 點閱:117下載:1
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 全球失智症患者數量逐年增加,最常見的失智症為阿茲海默症 (Alzheimer’s disease, AD),其臨床前期為失憶型輕度認知障礙 (amnestic mild cognitive impairment, aMCI),為情節記憶缺陷。研究發現失憶型輕度認知障礙的預設模式網絡 (default mode network, DMN)異常區域與類澱粉蛋白斑塊 (beta amyloid, Aβ) 堆積腦區有高度重疊,像是運動輔助區 (supplementary motor area, SMA)、前額葉 (prefrontal, PF) 以及頂葉,可以在執行手指按壓動作時被活化。此外,擴增實境技術 (augmented reality, AR)可提升使用者的學習表現及記憶力。
    本研究探討精細動作訓練結合擴增實境對aMCI族群在記憶力的療效,以及訓練後腦部活化的改變。30位aMCI分為兩組各15位,實驗組 (AR-mPETS group) 使用改良式手指按壓評估訓練系統結合擴增實境技術,進行4到6周精細動作訓練,每次30分鐘,一周2到3次,共12次,控制組未進行精細動作訓練,且在此研究期間沒有參與其他研究實驗。全部受試者在前測和4到6周後的後測評估霍普金斯語言學習測驗修改版 (Hopkins Verbal Learning Test- Revised, HVLT-R)、普渡手功能測驗 (Purdue Pegboard Test)以及近紅外光譜儀器 (Near-infrared spectroscopy, NIRS)。
    訓練組在HVLT-R以及普渡手功能測驗相較於控制組有顯著進步,在AR- (modified pressing and evaluation training system, mPETS)且顳葉皮質區的腦部活化經過訓練後,進行AR-mPETS困難任務與簡單任務的差異有顯著活化。本研究發現,aMCI在AR-mPETS訓練後,情節記憶力以及手功能都有明顯的進步,而且與認知及記憶力相關的顳葉區域在執行困難任務時也有明顯的活化。研究中的訓練策略以及內容可以提升aMCI注意力以及記憶力。未來研究可以增加樣本數量、更嚴謹的實驗設計以及增加後續追蹤評估,以擴展研究發現。

    The number of people with dementia rapidly increased over the world. The most common disease of dementia is Alzheimer’s disease (AD), which preclinical stage is amnestic mild cognitive impairment (aMCI) deficient in episodic memory. It is found that aberrant functional connectivity within the default mode network (DMN) is in a high degree of spatial overlap with patterns of beta amyloid (Aβ) plaque deposition in aMCI. The aberrant functional connectivity within DMN of brain regions, such as SMA (supplementary motor area), PF (prefrontal) and parietal lobe, could be activated by finger tapping tasks. Furthermore, the technology of augmented reality (AR) had the great potential for users to increase their performance and retention.
    The study was to investigate the effects of fine motor training with AR on memory for aMCI, and to examine the change of brain activation after the fine motor training with AR. Thirty elders with aMCI were recruited and divided into experimental group and control group. The experimental group conducted training program by using the modified Pressing Evaluation and Training Systems with AR (AR-mPETS) twice or thrice a week, for a total of 12 training sessions. The control group did not receive any other research during this period. All participants performed Hopkins Verbal Learning Test- Revised (HVLT-R), Purdue Pegboard Test and the Near-infrared spectroscopy (NIRS) evaluation at the intervals of 4 to 6-weeks.
    Both between-group and within-group comparisons revealed highly effects of AR-mPETS training on episodic memory. The brain activation of the temporal cortex significantly increased in the difference between hard mode and easy mode of AR-mPETS at the post-evaluation between groups. Our findings suggested that aMCI patients could enhance episodic memory and increase the brain activation of the primary auditory cortex in the temporal lobe after AR-mPETS training. The strategies of our training program might enhance the attentional functions and memory ability. Further investigations with a larger sample, more controlled trial, and adding follow-up evaluation are needed to extend these findings.

    摘 要 I Abstract IV List of Figure IX List of Table XI Chapter 1 Introduction 1 1.1 Background 1 1.2 The amnestic mild cognitive impairment (aMCI) 2 1.3 Default mode network (DMN) in aMCI 3 1.4 The possibility of fine motor training applied to the cognitive impairment patients 4 1.5 The technology of augmented reality (AR) 5 1.6 Motivation 6 1.7 Purpose 7 Chapter 2 Materials and Methods 8 2.1 Participants 8 2.2 Experimental equipment for augmented reality technology with modified Pressing Evaluation and Training Systems (AR-mPETS) 9 2.3 Experimental setting and procedure 12 2.3.1. The experimental setting 12 2.3.2. The experimental procedure 12 2.4 AR-mPETS training 14 2.5 Outcome measures 16 2.5.1. Primary outcome measure 17 2.5.2. Secondary outcome measures 18 2.5.2.1 Purdue Pegboard Test 18 2.5.2.2 Near-infrared spectroscopy (NIRS) 19 2.6 Data Analysis 20 2.7 Statistical Analysis 21 Chapter 3 Results 23 3.1 Participant characteristics 23 3.2 Effects of fine motor training with AR in clinical assessments 26 3.2.1. Primary outcome 27 3.2.1.1. HVLT-R total recall score 27 3.2.1.2. HVLT-R delay recall score 28 3.2.1.3. HVLT-R retention (%) 29 3.2.1.4. HVLT-R RDI 30 3.2.2. Secondary outcomes 31 3.2.2.1. Purdue right hand 31 3.2.2.2. Purdue left hand 32 3.2.2.3. Purdue both hands 33 3.2.2.4. Purdue assembly 34 3.3 Effects of fine motor training with AR in cortical activation evaluated from NIRS 35 3.3.1. Prefrontal cortex (PF) 37 3.3.2. Premotor cortex (PM) 39 3.3.3. Supplementary motor area (SMA) 41 3.3.4. Primary motor cortex (M1) 43 3.3.5. Primary auditory cortex (A) 45 3.3.6. Somatosensory cortex (S) 47 Chapter 4 Discussions 50 4.1. The effect of fine motor training with AR on HVLT-R 50 4.2. The effect of fine motor training with AR on Purdue 51 4.3. Brain activations before and after fine motor training with AR 52 4.4. Limitations 55 References 57

    1. 台灣失智症協會 (March 2019).認識失智症. Retrieved June 22, 2019 from http://www.tada2002.org.tw/About/IsntDementia
    2. Arnáiz, E., & Almkvist, O. (2003). Neuropsychological features of mild cognitive impairment and preclinical Alzheimer's disease. Acta Neurologica Scandinavica, 107, 34-41.
    3. Barsom, E., Graafland, M., & Schijven, M. (2016). Systematic review on the effectiveness of augmented reality applications in medical training. Surgical endoscopy, 30(10), 4174-4183.
    4. Baumeister, J., Ssin, S. Y., ElSayed, N. A., Dorrian, J., Webb, D. P., Walsh, J. A., . . . Kohler, M. (2017). Cognitive Cost of Using Augmented Reality Displays. IEEE transactions on visualization and computer graphics, 23(11), 2378-2388.
    5. Belleville, S., Clement, F., Mellah, S., Gilbert, B., Fontaine, F., & Gauthier, S. (2011). Training-related brain plasticity in subjects at risk of developing Alzheimer’s disease. Brain, 134(6), 1623-1634.
    6. Buckner, R. L., Andrews‐Hanna, J. R., & Schacter, D. L. (2008). The brain's default network. Annals of the New York Academy of Sciences, 1124(1), 1-38.
    7. Butler, M., McCreedy, E., Nelson, V. A., Desai, P., Ratner, E., Fink, H. A., . . . Brasure, M. (2018). Does cognitive training prevent cognitive decline?: a systematic review. Annals of internal medicine, 168(1), 63-68.
    8. Carius, D., Andrä, C., Clauß, M., Ragert, P., Bunk, M., & Mehnert, J. (2016). Hemodynamic response alteration as a function of task complexity and expertise—an fNIRS study in jugglers. Frontiers in human neuroscience, 10, 126.
    9. Dai, T. H., Liu, J. Z., Sahgal, V., Brown, R. W., & Yue, G. H. (2001). Relationship between muscle output and functional MRI-measured brain activation. Experimental brain research, 140(3), 290-300.
    10. Dannenberg, H., Alexander, A. S., Robinson, J. C., & Hasselmo, M. E. (2019). The Role of Hierarchical Dynamical Functions in Coding for Episodic Memory and Cognition. Journal of cognitive neuroscience, 1-19.
    11. De Crescenzio, F., Fantini, M., Persiani, F., Di Stefano, L., Azzari, P., & Salti, S. (2010). Augmented reality for aircraft maintenance training and operations support. IEEE Computer Graphics and Applications, 31(1), 96-101.
    12. de Jager, C. A., Schrijnemaekers, A.-C. M., Honey, T. E., & Budge, M. M. (2009). Detection of MCI in the clinic: evaluation of the sensitivity and specificity of a computerised test battery, the Hopkins Verbal Learning Test and the MMSE. Age and ageing, 38(4), 455-460.
    13. Dennis, E. L., & Thompson, P. M. (2014). Functional brain connectivity using fMRI in aging and Alzheimer’s disease. Neuropsychology review, 24(1), 49-62.
    14. Finn, M., & McDonald, S. (2011). Computerised cognitive training for older persons with mild cognitive impairment: a pilot study using a randomised controlled trial design. Brain Impairment, 12(3), 187-199.
    15. Fitzpatrick-Lewis, D., Warren, R., Ali, M. U., Sherifali, D., & Raina, P. (2015). Treatment for mild cognitive impairment: a systematic review and meta-analysis. CMAJ open, 3(4), E419.
    16. Garry, M. I., Kamen, G., & Nordstrom, M. A. (2004). Hemispheric differences in the relationship between corticomotor excitability changes following a fine-motor task and motor learning. Journal of neurophysiology, 91(4), 1570-1578.
    17. Hampstead, B. M., Stringer, A. Y., Stilla, R. F., Deshpande, G., Hu, X., Moore, A. B., & Sathian, K. (2011). Activation and effective connectivity changes following explicit-memory training for face–name pairs in patients with mild cognitive impairment: a pilot study. Neurorehabilitation and neural repair, 25(3), 210-222.
    18. Hogervorst, E., Combrinck, M., Lapuerta, P., Rue, J., Swales, K., & Budge, M. (2002). The Hopkins verbal learning test and screening for dementia. Dementia and Geriatric Cognitive Disorders, 13(1), 13-20.
    19. Homae, F., Watanabe, H., Otobe, T., Nakano, T., Go, T., Konishi, Y., & Taga, G. (2010). Development of global cortical networks in early infancy. Journal of Neuroscience, 30(14), 4877-4882.
    20. Kashou, N. H., Giacherio, B. M., Nahhas, R. W., & Jadcherla, S. R. (2016). Hand-grasping and finger tapping induced similar functional near-infrared spectroscopy cortical responses. Neurophotonics, 3(2), 025006.
    21. Kay, C. D., Seidenberg, M., Durgerian, S., Nielson, K. A., Smith, J. C., Woodard, J. L., & Rao, S. M. (2017). Motor timing intraindividual variability in amnestic mild cognitive impairment and cognitively intact elders at genetic risk for Alzheimer’s disease. Journal of clinical and experimental neuropsychology, 39(9), 866-875.
    22. Khachaturian, Z. S. (1985). Diagnosis of Alzheimer's disease. Archives of neurology, 42(11), 1097-1105.
    23. Kontaxopoulou, D., Beratis, I. N., Fragkiadaki, S., Pavlou, D., Yannis, G., Economou, A., . . . Papageorgiou, S. G. (2017). Incidental and intentional memory: their relation with attention and executive functions. Archives of Clinical Neuropsychology, 32(5), 519-532.
    24. Kuboyama, N., Nabetani, T., Shibuya, K.-i., Machida, K., & Ogaki, T. (2004). The effect of maximal finger tapping on cerebral activation. Journal of physiological anthropology and applied human science, 23(4), 105-110.
    25. Lacourse, M. G., Orr, E. L., Cramer, S. C., & Cohen, M. J. (2005). Brain activation during execution and motor imagery of novel and skilled sequential hand movements. Neuroimage, 27(3), 505-519.
    26. Lee, J., Lee, B., Park, Y., & Kim, Y. (2015). Effects of combined fine motor skill and cognitive therapy to cognition, degree of dementia, depression, and activities of daily living in the elderly with Alzheimer’s disease. Journal of physical therapy science, 27(10), 3151-3154.
    27. Light, C. M., Chappell, P. H., & Kyberd, P. J. (2002). Establishing a standardized clinical assessment tool of pathologic and prosthetic hand function: normative data, reliability, and validity. Archives of physical medicine and rehabilitation, 83(6), 776-783.
    28. Lin, P.-Y., Lin, S.-I., & Chen, J.-J. J. (2011). Functional near infrared spectroscopy study of age-related difference in cortical activation patterns during cycling with speed feedback. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 20(1), 78-84.
    29. Lu, C.-F., Liu, Y.-C., Yang, Y.-R., Wu, Y.-T., & Wang, R.-Y. (2015). Maintaining gait performance by cortical activation during dual-task interference: a functional near-infrared spectroscopy study. PloS one, 10(6), e0129390.
    30. Lv, Y., Margulies, D. S., Villringer, A., & Zang, Y.-F. (2013). Effects of finger tapping frequency on regional homogeneity of sensorimotor cortex. PloS one, 8(5), e64115.
    31. Malek-Ahmadi, M. (2016). Reversion from mild cognitive impairment to normal cognition. Alzheimer Disease & Associated Disorders, 30(4), 324-330.
    32. Marner, M. R., Irlitti, A., & Thomas, B. H. (2013). Improving procedural task performance with augmented reality annotations. Paper presented at the 2013 IEEE International Symposium on Mixed and Augmented Reality (ISMAR).
    33. Mason, M. F., Norton, M. I., Van Horn, J. D., Wegner, D. M., Grafton, S. T., & Macrae, C. N. (2007). Wandering minds: the default network and stimulus-independent thought. Science, 315(5810), 393-395.
    34. Mazoyer, B., Zago, L., Mellet, E., Bricogne, S., Etard, O., Houdé, O., . . . Tzourio-Mazoyer, N. (2001). Cortical networks for working memory and executive functions sustain the conscious resting state in man. Brain research bulletin, 54(3), 287-298.
    35. Millán-Calenti, J. C., Lorenzo-López, L., Alonso-Búa, B., de Labra, C., González-Abraldes, I., & Maseda, A. (2016). Optimal nonpharmacological management of agitation in Alzheimer’s disease: challenges and solutions. Clinical interventions in aging, 11, 175.
    36. Morihiro, M., Tsubone, T., & Wada, Y. (2009). Relation between NIRS signal and motor capability. Paper presented at the 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
    37. Mormino, E. C., Smiljic, A., Hayenga, A. O., H. Onami, S., Greicius, M. D., Rabinovici, G. D., . . . Madison, C. M. (2011). Relationships between beta-amyloid and functional connectivity in different components of the default mode network in aging. Cerebral Cortex, 21(10), 2399-2407.
    38. Nelson, D. L., & Jepson-Thomas, J. (2003). Occupational Form, Occupational Performance, and a Conceptual Framework for Theraputic Performance. 87-155.
    39. Neumann, U., & Majoros, A. (1998). Cognitive, performance, and systems issues for augmented reality applications in manufacturing and maintenance. Paper presented at the Proceedings. IEEE 1998 Virtual Reality Annual International Symposium (Cat. No. 98CB36180).
    40. Oh, H., & Jagust, W. J. (2013). Frontotemporal network connectivity during memory encoding is increased with aging and disrupted by beta-amyloid. Journal of Neuroscience, 33(47), 18425-18437.
    41. Ohsugi, H., Ohgi, S., Shigemori, K., & Schneider, E. B. (2013). Differences in dual-task performance and prefrontal cortex activation between younger and older adults. BMC neuroscience, 14(1), 10.
    42. Pangelinan, M. M., Zhang, G., VanMeter, J. W., Clark, J. E., Hatfield, B. D., & Haufler, A. J. (2011). Beyond age and gender: relationships between cortical and subcortical brain volume and cognitive-motor abilities in school-age children. Neuroimage, 54(4), 3093-3100.
    43. Pantoni, L., Poggesi, A., Diciotti, S., Valenti, R., Orsolini, S., Della Rocca, E., . . . Salvadori, E. (2017). Effect of attention training in mild cognitive impairment patients with subcortical vascular changes: the RehAtt Study. Journal of Alzheimer's Disease, 60(2), 615-624.
    44. Perry, R. J., & Hodges, J. R. (1999). Attention and executive deficits in Alzheimer's disease: A critical review. Brain, 122(3), 383-404.
    45. Petersen, R. C. (2004). Mild cognitive impairment as a diagnostic entity. Journal of internal medicine, 256(3), 183-194.
    46. Petersen, R. C. (2011). Clinical practice. Mild cognitive impairment. The New England journal of medicine, 364(23), 2227.
    47. Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Tangalos, E. G., & Kokmen, E. (1999). Mild cognitive impairment: clinical characterization and outcome. Archives of neurology, 56(3), 303-308.
    48. Qi, Z., Wu, X., Wang, Z., Zhang, N., Dong, H., Yao, L., & Li, K. (2010). Impairment and compensation coexist in amnestic MCI default mode network. Neuroimage, 50(1), 48-55.
    49. Raichle, M. E. (2015). The brain's default mode network. Annual review of neuroscience, 38, 433-447.
    50. Ribeiro, F., Guerreiro, M., & De Mendonça, A. (2007). Verbal learning and memory deficits in mild cognitive impairment. Journal of clinical and experimental neuropsychology, 29(2), 187-197.
    51. Rizzi, L., Missiaggia, L., & Roriz Cruz, M. (2018). 1-42. NeuroMolecular Medicine, 20(4), 491-497.
    52. Rodakowski, J., Saghafi, E., Butters, M. A., & Skidmore, E. R. (2015). Non-pharmacological interventions for adults with mild cognitive impairment and early stage dementia: An updated scoping review. Molecular aspects of medicine, 43, 38-53.
    53. Rojas, G. J., Villar, V., Iturry, M., Harris, P., Serrano, C. M., Herrera, J. A., & Allegri, R. F. (2013). Efficacy of a cognitive intervention program in patients with mild cognitive impairment. International Psychogeriatrics, 25(5), 825-831.
    54. Rosenberry, R., Munson, M., Chung, S., Samuel, T. J., Patik, J., Tucker, W. J., . . . Nelson, M. D. (2018). Age‐related microvascular dysfunction: novel insight from near‐infrared spectroscopy. Experimental physiology, 103(2), 190-200.
    55. Savulich, G., Piercy, T., Brühl, A., Fox, C., Suckling, J., Rowe, J. B., . . . Sahakian, B. J. (2017). Focusing the neuroscience and societal implications of cognitive enhancers. Clinical Pharmacology & Therapeutics, 101(2), 170-172.
    56. Schrijnemaekers, A., de Jager, C. A., Hogervorst, E., & Budge, M. (2006). Cases with mild cognitive impairment and Alzheimer’s disease fail to benefit from repeated exposure to episodic memory tests as compared with controls. Journal of clinical and experimental neuropsychology, 28(3), 438-455.
    57. Serra, L., Cercignani, M., Mastropasqua, C., Torso, M., Spanò, B., Makovac, E., . . . Caltagirone, C. (2016). Longitudinal changes in functional brain connectivity predicts conversion to Alzheimer’s disease. Journal of Alzheimer's Disease, 51(2), 377-389.
    58. Serrano-Pozo, A., Frosch, M. P., Masliah, E., & Hyman, B. T. (2011). Neuropathological alterations in Alzheimer disease. Cold Spring Harbor perspectives in medicine, 1(1), a006189.
    59. Shibuya, K., Kuboyama, N., & Tanaka, J. (2014). Changes in ipsilateral motor cortex activity during a unilateral isometric finger task are dependent on the muscle contraction force. Physiological measurement, 35(3), 417.
    60. Soechting, J. F., & Flanders, M. (1997). Flexibility and repeatability of finger movements during typing: analysis of multiple degrees of freedom. Journal of computational neuroscience, 4(1), 29-46.
    61. Suzuki, T., Shimada, H., Makizako, H., Doi, T., Yoshida, D., Tsutsumimoto, K., . . . Park, H. (2012). Effects of multicomponent exercise on cognitive function in older adults with amnestic mild cognitive impairment: a randomized controlled trial. BMC neurology, 12(1), 128.
    62. Takeda, A., Loveman, E., Clegg, A., Kirby, J., Picot, J., Payne, E., & Green, C. (2006). A systematic review of the clinical effectiveness of donepezil, rivastigmine and galantamine on cognition, quality of life and adverse events in Alzheimer's disease. International journal of geriatric psychiatry, 21(1), 17-28.
    63. Theleritis, C., Siarkos, K., Katirtzoglou, E., & Politis, A. (2017). Pharmacological and nonpharmacological treatment for apathy in Alzheimer disease: a systematic review across modalities. Journal of geriatric psychiatry and neurology, 30(1), 26-49.
    64. Tundis, R., Bonesi, M., Menichini, F., & R Loizzo, M. (2016). Recent knowledge on medicinal plants as source of cholinesterase inhibitors for the treatment of dementia. Mini reviews in medicinal chemistry, 16(8), 605-618.
    65. Wagner, A. D., Schacter, D. L., Rotte, M., Koutstaal, W., Maril, A., Dale, A. M., . . . Buckner, R. L. (1998). Building memories: remembering and forgetting of verbal experiences as predicted by brain activity. Science, 281(5380), 1188-1191.
    66. Wang, C., Pan, Y., Liu, Y., Xu, K., Hao, L., Huang, F., . . . Guo, W. (2018). Aberrant default mode network in amnestic mild cognitive impairment: a meta-analysis of independent component analysis studies. Neurological Sciences, 39(5), 919-931.
    67. Wei, X.-h., & Ji, L.-l. (2014). Effect of handball training on cognitive ability in elderly with mild cognitive impairment. Neuroscience letters, 566, 98-101.
    68. Yan, H., Zhang, Y., Chen, H., Wang, Y., & Liu, Y. (2013). Altered effective connectivity of the default mode network in resting-state amnestic type mild cognitive impairment. Journal of the International Neuropsychological Society, 19(4), 400-409.
    69. Yan, J. H., Rountree, S., Massman, P., Doody, R. S., & Li, H. (2008). Alzheimer’s disease and mild cognitive impairment deteriorate fine movement control. Journal of Psychiatric Research, 42(14), 1203-1212.
    70. Yan, J. H., Thomas, J. R., Stelmach, G. E., & Thomas, K. T. (2000). Developmental features of rapid aiming arm movements across the lifespan. Journal of motor behavior, 32(2), 121-140.
    71. Zang, Y., Jiang, T., Lu, Y., He, Y., & Tian, L. (2004). Regional homogeneity approach to fMRI data analysis. Neuroimage, 22(1), 394-400.
    72. Zhang, H., Wang, Z., Wang, J., Lyu, X., Wang, X., Liu, Y., . . . Yu, X. (2019). Computerized multi-domain cognitive training reduces brain atrophy in patients with amnestic mild cognitive impairment. Translational psychiatry, 9(1), 48.
    73. Zhen, D., Xia, W., Yi, Z. Q., Zhao, P. W., Zhong, J. G., Shi, H. C., . . . Pan, P. L. (2018). Alterations of brain local functional connectivity in amnestic mild cognitive impairment. Translational neurodegeneration, 7(1), 26.

    下載圖示 校內:2024-08-15公開
    校外:2024-08-15公開
    QR CODE