請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/16548
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 曾文毅(Wen-Yih Isaac Tseng) | |
dc.contributor.author | Shih-Ni Chen | en |
dc.contributor.author | 陳詩霓 | zh_TW |
dc.date.accessioned | 2021-06-07T18:20:11Z | - |
dc.date.copyright | 2020-08-27 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2020-07-29 | |
dc.identifier.citation | Albert, M. S., DeKosky, S. T., Dickson, D., Dubois, B., Feldman, H. H., Fox, N. C., . . . Petersen, R. C. (2011). The diagnosis of mild cognitive impairment due to Alzheimer's disease: recommendations from the National Institute on Aging‐Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimer's Dementia, 7(3), 270-279. Alexander, A. L., Hurley, S. A., Samsonov, A. A., Adluru, N., Hosseinbor, A. P., Mossahebi, P., . . . Field, A. S. (2011). Characterization of cerebral white matter properties using quantitative magnetic resonance imaging stains. Brain connectivity, 1(6), 423-446. Amlien, I., Fjell, A. (2014). Diffusion tensor imaging of white matter degeneration in Alzheimer’s disease and mild cognitive impairment. Neuroscience, 276, 206-215. Ardila, A., Ostrosky-Solis, F., Rosselli, M., Gómez, C. (2000). Age-related cognitive decline during normal aging: the complex effect of education. Archives of clinical neuropsychology, 15(6), 495-513. Ashburner, J., Friston, K. J. (2000). Voxel-based morphometry—the methods. Neuroimage, 11(6), 805-821. Basser, P. J., Pajevic, S., Pierpaoli, C., Duda, J., Aldroubi, A. (2000). In vivo fiber tractography using DT‐MRI data. Magnetic resonance in medicine, 44(4), 625-632. Bennett, I. J., Madden, D. J., Vaidya, C. J., Howard, D. V., Howard Jr, J. H. (2010). Age‐related differences in multiple measures of white matter integrity: A diffusion tensor imaging study of healthy aging. Human brain mapping, 31(3), 378-390. Bischkopf, J., Busse, A., Angermeyer, M. (2002). Mild cognitive impairment 1–a review of prevalence, incidence and outcome according to current approaches. Acta Psychiatrica Scandinavica, 106(6), 403-414. Blennow, K., Hampel, H. (2003). CSF markers for incipient Alzheimer's disease. The lancet neurology, 2(10), 605-613. Boyle, P. A., Yu, L., Wilson, R. S., Gamble, K., Buchman, A. S., Bennett, D. A. (2012). Poor decision making is a consequence of cognitive decline among older persons without Alzheimer’s disease or mild cognitive impairment. PloS one, 7(8). Brueggen, K., Dyrba, M., Cardenas-Blanco, A., Schneider, A., Fliessbach, K., Buerger, K., . . . Priller, J. (2019). Structural integrity in subjective cognitive decline, mild cognitive impairment and Alzheimer’s disease based on multicenter diffusion tensor imaging. Journal of neurology, 266(10), 2465-2474. Busse, A., Hensel, A., Gühne, U., Angermeyer, M., Riedel-Heller, S. (2006). Mild cognitive impairment: long-term course of four clinical subtypes. Neurology, 67(12), 2176-2185. Callaghan, P. T., Coy, A., MacGowan, D., Packer, K. J., Zelaya, F. O. (1991). Diffraction-like effects in NMR diffusion studies of fluids in porous solids. Nature, 351(6326), 467-469. Carmichael, O. T., Aizenstein, H. A., Davis, S. W., Becker, J. T., Thompson, P. M., Meltzer, C. C., Liu, Y. (2005). Atlas-based hippocampus segmentation in Alzheimer's disease and mild cognitive impairment. Neuroimage, 27(4), 979-990. Chang, Y.-L., Chen, T.-F., Shih, Y.-C., Chiu, M.-J., Yan, S.-H., Tseng, W.-Y. I. (2015). Regional cingulum disruption, not gray matter atrophy, detects cognitive changes in amnestic mild cognitive impairment subtypes. Journal of Alzheimer's Disease, 44(1), 125-138. Chang, Y.-L., Yen, Y.-S., Chen, T.-F., Yan, S.-H., Tseng, W.-Y. I. (2016). Clinical Dementia Rating Scale Detects White Matter Changes in Older Adults at Risk for Alzheimer’s Disease. Journal of Alzheimer's Disease, 50(2), 411-423. Chen, Y. J., Lo, Y. C., Hsu, Y. C., Fan, C. C., Hwang, T. J., Liu, C. M., . . . Hwu, H. G. (2015). Automatic whole brain tract‐based analysis using predefined tracts in a diffusion spectrum imaging template and an accurate registration strategy. Human brain mapping, 36(9), 3441-3458. Cherbuin, N., Sargent-Cox, K., Easteal, S., Sachdev, P., Anstey, K. J. (2015). Hippocampal atrophy is associated with subjective memory decline: The PATH Through Life study. The American journal of geriatric psychiatry, 23(5), 446-455. Convit, A., De Leon, M., Tarshish, C., De Santi, S., Tsui, W., Rusinek, H., George, A. (1997). Specific hippocampal volume reductions in individuals at risk for Alzheimer’s disease. Neurobiology of aging, 18(2), 131-138. Delis, D., Kramer, J., Kaplan, E., Ober, B. (2000). Manual for the California verbal learning test (CVLT-II). San Antonio, TX: The Psychological Corporation. Dietrich, O., Raya, J. G., Reeder, S. B., Reiser, M. F., Schoenberg, S. O. (2007). Measurement of signal‐to‐noise ratios in MR images: influence of multichannel coils, parallel imaging, and reconstruction filters. Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine, 26(2), 375-385. Douaud, G., Jbabdi, S., Behrens, T. E., Menke, R. A., Gass, A., Monsch, A. U., . . . Matthews, P. M. (2011). DTI measures in crossing-fibre areas: increased diffusion anisotropy reveals early white matter alteration in MCI and mild Alzheimer's disease. Neuroimage, 55(3), 880-890. Engvig, A., Fjell, A. M., Westlye, L. T., Skaane, N. V., Sundseth, Ø., Walhovd, K. B. (2012). Hippocampal subfield volumes correlate with memory training benefit in subjective memory impairment. Neuroimage, 61(1), 188-194. Flak, M. M., Hol, H. R., Hernes, S. S., Chang, L., Ernst, T., Engvig, A., . . . Knapskog, A.-B. (2018). Cognitive profiles and atrophy ratings on MRI in senior patients with mild cognitive impairment. Frontiers in aging neuroscience, 10, 384. Friman, O., Farneback, G., Westin, C.-F. (2006). A Bayesian approach for stochastic white matter tractography. IEEE transactions on medical imaging, 25(8), 965-978. Gauthier, S., Reisberg, B., Zaudig, M., Petersen, R. C., Ritchie, K., Broich, K., . . . Chertkow, H. (2006). Mild cognitive impairment. The lancet, 367(9518), 1262-1270. Goldman, W., Price, J., Storandt, M., Grant, E., McKeel, D., Rubin, E., Morris, J. (2001). Absence of cognitive impairment or decline in preclinical Alzheimer’s disease. Neurology, 56(3), 361-367. Goldstein, F. C., Mao, H., Wang, L., Ni, C., Lah, J. J., Levey, A. I. (2009). White matter integrity and episodic memory performance in mild cognitive impairment: a diffusion tensor imaging study. Brain imaging and behavior, 3(2), 132-141. Hampel, H., Frank, R., Broich, K., Teipel, S. J., Katz, R. G., Hardy, J., . . . Hoessler, Y. C. (2010). Biomarkers for Alzheimer's disease: academic, industry and regulatory perspectives. Nature reviews Drug discovery, 9(7), 560-574. Henneges, C., Reed, C., Chen, Y.-F., Dell’Agnello, G., Lebrec, J. (2016). Describing the sequence of cognitive decline in Alzheimer’s disease patients: Results from an observational study. Journal of Alzheimer's Disease, 52(3), 1065-1080. Hong, Y. J., Lee, J.-H., Choi, E. J., Han, N., Kim, J. E., Park, S.-H., . . . Kang, D.-W. (2020). Efficacies of Cognitive Interventions in the Elderly with Subjective Cognitive Decline: A Prospective, Three-Arm, Controlled Trial. Journal of clinical neurology (Seoul, Korea), 16(2), 304. Hong, Y. J., Yoon, B., Shim, Y. S., Ahn, K. J., Yang, D. W., Lee, J.-H. (2015). Gray and white matter degenerations in subjective memory impairment: comparisons with normal controls and mild cognitive impairment. Journal of Korean medical science, 30(11), 1652-1658. Hsu, Y.-C., Hsu, C.-H., Tseng, W.-Y. I. (2012). A large deformation diffeomorphic metric mapping solution for diffusion spectrum imaging datasets. Neuroimage, 63(2), 818-834. Hsu, Y. C., Lo, Y. C., Chen, Y. J., Wedeen, V. J., Isaac Tseng, W. Y. (2015). NTU‐DSI‐122: A diffusion spectrum imaging template with high anatomical matching to the ICBM‐152 space. Human brain mapping, 36(9), 3528-3541. Hua, M., Chang, B., Lin, K., Yang, J., Lu, S., Chen, S. (2005). Wechsler memory scale. Chinese Behavioral Science Corporation, Taipei. Huckans, M., Hutson, L., Twamley, E., Jak, A., Kaye, J., Storzbach, D. (2013). Efficacy of cognitive rehabilitation therapies for mild cognitive impairment (MCI) in older adults: working toward a theoretical model and evidence-based interventions. Neuropsychology review, 23(1), 63-80. Jack, C. R., Petersen, R. C., Xu, Y. C., O’Brien, P. C., Smith, G. E., Ivnik, R. J., . . . Kokmen, E. (1999). Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment. Neurology, 52(7), 1397-1397. Jak, A. J., Bondi, M. W., Delano-Wood, L., Wierenga, C., Corey-Bloom, J., Salmon, D. P., Delis, D. C. (2009). Quantification of five neuropsychological approaches to defining mild cognitive impairment. The American Journal of Geriatric Psychiatry, 17(5), 368-375. Jak, A. J., Urban, S., McCAULEY, A., Bangen, K. J., Delano-Wood, L., Corey-Bloom, J., Bondi, M. W. (2009). Profile of hippocampal volumes and stroke risk varies by neuropsychological definition of mild cognitive impairment. Journal of the International Neuropsychological Society, 15(6), 890-897. Jessen, F., Amariglio, R. E., Van Boxtel, M., Breteler, M., Ceccaldi, M., Chételat, G., . . . Van Der Flier, W. M. (2014). A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer's disease. Alzheimer's Dementia, 10(6), 844-852. Kuo, L.-W., Chen, J.-H., Wedeen, V. J., Tseng, W.-Y. I. (2008). Optimization of diffusion spectrum imaging and q-ball imaging on clinical MRI system. Neuroimage, 41(1), 7-18. Lachman, M. E. (2006). Perceived control over aging-related declines: Adaptive beliefs and behaviors. Current Directions in Psychological Science, 15(6), 282-286. Lee, D., Fletcher, E., Martinez, O., Ortega, M., Zozulya, N., Kim, J., . . . DeCarli, C. (2009). Regional pattern of white matter microstructural changes in normal aging, MCI, and AD. Neurology, 73(21), 1722-1728. Li, X.-y., Tang, Z.-c., Sun, Y., Tian, J., Liu, Z.-y., Han, Y. (2016). White matter degeneration in subjective cognitive decline: a diffusion tensor imaging study. Oncotarget, 7(34), 54405. Lo, Y.-C., Soong, W.-T., Gau, S. S.-F., Wu, Y.-Y., Lai, M.-C., Yeh, F.-C., . . . Tseng, W.-Y. I. (2011). The loss of asymmetry and reduced interhemispheric connectivity in adolescents with autism: a study using diffusion spectrum imaging tractography. Psychiatry Research: Neuroimaging, 192(1), 60-66. Loewenstein, D. A., Acevedo, A., Small, B. J., Agron, J., Crocco, E., Duara, R. (2009). Stability of different subtypes of mild cognitive impairment among the elderly over a 2-to 3-year follow-up period. Dementia and geriatric cognitive disorders, 27(5), 418-423. Markowitsch, H. J., Pritzel, M. (1985). The neuropathology of amnesia. Progress in Neurobiology, 25(3), 189-287. Mathern, G. W., Babb, T. L., Leite, J. P., Pretorius, J. K., Yeoman, K. M., Kuhlman, P. A. (1996). The pathogenic and progressive features of chronic human hippocampal epilepsy. Epilepsy research, 26(1), 151-161. McKhann, G. M., Knopman, D. S., Chertkow, H., Hyman, B. T., Jack Jr, C. R., Kawas, C. H., . . . Mayeux, R. (2011). The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging‐Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimer's Dementia, 7(3), 263-269. McKinley, S., Levine, M. (1998). Cubic spline interpolation. College of the Redwoods, 45(1), 1049-1060. Morris, J. C., Storandt, M., Miller, J. P., McKeel, D. W., Price, J. L., Rubin, E. H., Berg, L. (2001). Mild cognitive impairment represents early-stage Alzheimer disease. Archives of neurology, 58(3), 397-405. Nakamura, K. (2008). A “super-aged” society and the “locomotive syndrome”. In: Springer. Nathan, P. J., Lim, Y. Y., Abbott, R., Galluzzi, S., Marizzoni, M., Babiloni, C., . . . Farotti, L. (2017). Association between CSF biomarkers, hippocampal volume and cognitive function in patients with amnestic mild cognitive impairment (MCI). Neurobiology of aging, 53, 1-10. Nordberg, A. (2004). PET imaging of amyloid in Alzheimer's disease. The lancet neurology, 3(9), 519-527. Nussbaum, R. L., Ellis, C. E. (2003). Alzheimer's disease and Parkinson's disease. New england journal of medicine, 348(14), 1356-1364. Olchik, M. R., Farina, J., Steibel, N., Teixeira, A. R., Yassuda, M. S. (2013). Memory training (MT) in mild cognitive impairment (MCI) generates change in cognitive performance. Archives of gerontology and geriatrics, 56(3), 442-447. Park, S., Ryu, S.-H., Yoo, Y., Yang, J.-J., Kwon, H., Youn, J.-H., . . . Lee, J.-Y. (2018). Neural predictors of cognitive improvement by multi-strategic memory training based on metamemory in older adults with subjective memory complaints. Scientific reports, 8(1), 1-11. Peng, Z., Jiang, H., Wang, X., Huang, K., Zuo, Y., Wu, X., . . . Yang, L. (2019). The Efficacy of Cognitive Training for Elderly Chinese Individuals with Mild Cognitive Impairment. BioMed Research International, 2019. Petersen, R. C. (2004). Mild cognitive impairment as a diagnostic entity. Journal of internal medicine, 256(3), 183-194. Petersen, R. C. (2007). Mild cognitive impairment. Continuum: Lifelong Learning in Neurology, 13(2), 15-38. Petersen, R. C., Doody, R., Kurz, A., Mohs, R. C., Morris, J. C., Rabins, P. V., . . . Winblad, B. (2001). Current concepts in mild cognitive impairment. Archives of neurology, 58(12), 1985-1992. Price, J. L., Morris, J. C. (1999). Tangles and plaques in nondemented aging and “preclinical” Alzheimer's disease. Annals of Neurology: Official Journal of the American Neurological Association and the Child Neurology Society, 45(3), 358-368. Quiñonero-Candela, J., Rasmussen, C. E. (2005). A unifying view of sparse approximate Gaussian process regression. Journal of Machine Learning Research, 6(Dec), 1939-1959. Rabin, L. A., Smart, C. M., Crane, P. K., Amariglio, R. E., Berman, L. M., Boada, M., . . . Ellis, K. A. (2015). Subjective cognitive decline in older adults: an overview of self-report measures used across 19 international research studies. Journal of Alzheimer's Disease, 48(s1), S63-S86. Reijnders, J., van Heugten, C., van Boxtel, M. (2013). Cognitive interventions in healthy older adults and people with mild cognitive impairment: a systematic review. Ageing research reviews, 12(1), 263-275. Ridge, P. G., Mukherjee, S., Crane, P. K., Kauwe, J. S., Consortium, A. s. D. G. (2013). Alzheimer’s disease: analyzing the missing heritability. PloS one, 8(11). Saykin, A., Wishart, H., Rabin, L., Santulli, R., Flashman, L., West, J., . . . Mamourian, A. (2006). Older adults with cognitive complaints show brain atrophy similar to that of amnestic MCI. Neurology, 67(5), 834-842. Scola, E., Bozzali, M., Agosta, F., Magnani, G., Franceschi, M., Sormani, M. P., . . . Filippi, M. (2010). A diffusion tensor MRI study of patients with MCI and AD with a 2-year clinical follow-up. Journal of Neurology, Neurosurgery Psychiatry, 81(7), 798-805. Shattuck, D. W., Mirza, M., Adisetiyo, V., Hojatkashani, C., Salamon, G., Narr, K. L., . . . Toga, A. W. (2008). Construction of a 3D probabilistic atlas of human cortical structures. Neuroimage, 39(3), 1064-1080. Singh-Manoux, A., Kivimaki, M., Glymour, M. M., Elbaz, A., Berr, C., Ebmeier, K. P., . . . Dugravot, A. (2012). Timing of onset of cognitive decline: results from Whitehall II prospective cohort study. Bmj, 344, d7622. Sperling, R. A., Aisen, P. S., Beckett, L. A., Bennett, D. A., Craft, S., Fagan, A. M., . . . Montine, T. J. (2011). Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimer's Dementia, 7(3), 280-292. Swardfager, W., Cogo-Moreira, H., Masellis, M., Ramirez, J., Herrmann, N., Edwards, J. D., . . . Nestor, S. M. (2018). The effect of white matter hyperintensities on verbal memory: Mediation by temporal lobe atrophy. Neurology, 90(8), e673-e682. Tierney, M., Szalai, J., Snow, W., Fisher, R., Nores, A., Nadon, G., . . . George-Hyslop, P. S. (1996). Prediction of probable Alzheimer's disease in memory-impaired patients: A prospective longitudinal study. Neurology, 46(3), 661-665. Van Norden, A., Fick, W., De Laat, K., Van Uden, I., Van Oudheusden, L., Tendolkar, I., . . . De Leeuw, F. (2008). Subjective cognitive failures and hippocampal volume in elderly with white matter lesions. Neurology, 71(15), 1152-1159. Visser, P., Verhey, F., Hofman, P., Scheltens, P., Jolles, J. (2002). Medial temporal lobe atrophy predicts Alzheimer's disease in patients with minor cognitive impairment. Journal of Neurology, Neurosurgery Psychiatry, 72(4), 491-497. Wang, Y., West, J. D., Flashman, L. A., Wishart, H. A., Santulli, R. B., Rabin, L. A., . . . Saykin, A. J. (2012). Selective changes in white matter integrity in MCI and older adults with cognitive complaints. Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease, 1822(3), 423-430. Wedeen, V. J., Hagmann, P., Tseng, W. Y. I., Reese, T. G., Weisskoff, R. M. (2005). Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging. Magnetic resonance in medicine, 54(6), 1377-1386. Wedeen, V. J., Wang, R., Schmahmann, J. D., Benner, T., Tseng, W.-Y. I., Dai, G., . . . de Crespigny, A. J. (2008). Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers. Neuroimage, 41(4), 1267-1277. Weimer, D. L., Sager, M. A. (2009). Early identification and treatment of Alzheimer's disease: social and fiscal outcomes. Alzheimer's Dementia, 5(3), 215-226. Wolf, H., Jelic, V., Gertz, H. J., Nordberg, A., Julin, P., Wahlund, L. O. (2003). A critical discussion of the role of neuroimaging in mild cognitive impairment. Acta Neurologica Scandinavica, 107, 52-76. Yeh, F.-C., Wedeen, V. J., Tseng, W.-Y. I. (2011). Estimation of fiber orientation and spin density distribution by diffusion deconvolution. Neuroimage, 55(3), 1054-1062. Yendiki, A., Koldewyn, K., Kakunoori, S., Kanwisher, N., Fischl, B. (2014). Spurious group differences due to head motion in a diffusion MRI study. Neuroimage, 88, 79-90. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/16548 | - |
dc.description.abstract | 隨著平均預期壽命的增加,失智症的發病率也逐漸升高。越來越多的研究指出,神經影像對於診斷阿茲海默症(Alzheimer’s disease, AD)前驅階段扮演重要角色。而認知訓練已被證實有助於老年人提高認知能力。然而,阿茲海默症前驅階段的神經影像學與認知訓練之間的關係尚未得到充分研究。因此,我們蒐集了主觀認知能力下降患者(subjective cognitive decline, SCD)和輕度認知功能障礙患者(mild cognitive impairment, MCI)的擴散頻譜影像,並探討與記憶功能,記憶功能改善之間的關聯性。所有受試者均於 3T 磁振造影儀上進行大腦的T1 權重結構影像(T1WI)與擴散頻譜影像(diffusion spectrum imaging, DSI)掃描。接著,我們透過全腦基於神經束之自動化分析(Tract-based automatic analysis, TBAA)獲得擴散指標,並用以反應白質結構損傷程度,包括部分非等向性指標(fractional anisotropy, FA)、軸向擴散指標(axial diffusivity, AD)、徑向擴散指標(radial diffusivity, RD)和平均擴散指標(mean diffusivity, MD)。並透過解剖計算工具(Computational Anatomy Toolbox, CAT12)且使用LONI分區模板(LONI Probabilistic Brain Atlas, LPBA40)計算大腦灰質體積(gray matter, GM)。此外,我們使用標準模型(normative model)將白質纖維束及灰質體積標準化,並與同年齡、同性別族群匹配,計算出擴散指數及灰質體積之標準分數(Z-score)。 本研究招募了兩組參與者:31名主觀認知能力下降患者與25名輕度認知功能障礙患者。研究結果顯示,主觀認知能力下降患的海馬迴相關平均擴散指標的標準分數與蒙特利爾認知評估量表(Montreal Cognitive Assessment, MoCA)及簡易心智量表(Mini-Mental State Examination, MMSE)子項目總分之間呈顯著負相關。在灰質中,與各種記憶得分無關。經過三個月的認知訓練後,在主觀認知能力下降患者中發現,較低的海馬迴相關平均z值與MMSE + MOCA子項目總分的改善相關。相比之下,海馬迴,海馬迴旁的灰質體積與各種記憶分數的改善之間沒有相關。 總結:在阿茲海默症前驅階段,記憶功能可能與白質有關,而不是灰質。此外,從研究結果可以推斷,針對失智症前驅階段較早期的認知訓練介入,對於記憶功能的改善將有較多的幫助。 | zh_TW |
dc.description.abstract | The incidence of dementia appears to be rising with a higher average life expectancy. More and more research proposed that neuroimaging can provide crucial biomarkers to detect the prodromal stage of Alzheimer’s disease (AD). The cognitive training has been validated to be helpful for the elderly to improve cognition. However, the relationship between neuroimaging and cognitive training in the prodromal stage of AD has not been fully investigated yet. Therefore, the present study collected diffusion and structural MRI data from patients with subjective cognitive decline (SCD) and those with mild cognitive impairment (MCI), and investigated the correlations of MRI metrics with memory performance, and memory performance improvement. Two groups of participants, SCD (N = 31) and MCI (N = 25), were recruited. All participants underwent diffusion spectrum imaging (DSI) and T1-weighted imaging (T1WI) of the brain on a 3T MRI scanner. We used tract-based automatic analysis (TBAA) to obtain diffusion indices to indicate white matter (WH) microstructure property; the indices included fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD). We also calculated gray matter (GM) volume by using LONI probabilistic brain atlas (LPBA40) and computational anatomy toolbox (CAT12). In addition, we used a normative model to assess the age-matched, sex-matched population, and calculate the z-score of diffusion index and gray matter volume. The results showed a significant negative correlation between the hippocampal-related average diffusion z-scores and MMSE+MOCA sub-item scores in the SCD group. In gray matter, there was no relationship with any kind of memory scores. After cognitive training, the hippocampal-related average z-scores were significantly correlated with MMSE+MOCA sub-item improvement in the SCD group. In contrast, there was no correlation between the hippocampus, para-hippocampal GM, with any kind of memory improvement. In conclusion, in the prodromal stages of AD, the memory performance is associated with white matter instead of the gray matter property metrics. Our findings also imply that the earlier cognitive training for the prodromal stage of dementia, the better improvement for the memory function. | en |
dc.description.provenance | Made available in DSpace on 2021-06-07T18:20:11Z (GMT). No. of bitstreams: 1 U0001-2807202012061300.pdf: 8262031 bytes, checksum: 1b250859bc926e9765421ca57becddd2 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | Contents 誌謝 i 摘要 ii Abstract iv List of Figures 4 List of Tables 5 Chapter 1 Introduction 7 1.1 Background 7 1.2 Subjective Cognitive Decline (SCD) 8 1.3 Mild Cognitive Impairment (MCI) 9 1.4 Brain imaging in SCD and MCI 10 1.5 Hippocampus 11 1.6 Cognitive training 12 1.7 Motivation and hypothesis 13 Chapter 2 Materials and Methods 14 2.1 Experimental design 14 2.2 Participants 15 2.3 Neuropsychological tests and Cognitive training 15 2.4 Diffusion MRI and Diffusion spectrum imaging (DSI) 16 2.5 MRI data acquisition 17 2.6 Imaging analysis 18 2.6.1 Image Quality Assurance and DSI data reconstruction 18 2.6.2 Tract-based automatic analysis (TBAA) 20 2.7 Voxel-based morphometry (VBM) 21 2.8 Normative model 22 2.8.1 Normative model of 76 tracts in healthy controls 22 2.8.2 Normative model of gray matter volume in healthy controls 22 2.9 Statistical analysis 23 Chapter 3 Results 25 3.1 Demographic characteristics 25 3.2 Brain structure difference between SCD and MCI group 26 3.3 Repeated measure ANOVA 28 3.4 Relationship between brain structures and memory performance 29 3.4.1 Relationship between white matter metrics and memory performance 29 3.4.2 Relationship between gray matter metrics and memory performance 31 3.5 Relationship between brain structures and memory performance change 34 3.5.1 Relationship between white matter metrics and memory performance change 34 3.5.2 Relationship between gray matter metrics and memory performance change 37 Chapter 4 Discussion 40 4.1 Brief summary 40 4.2 Brain structure of the hippocampus 40 4.3 Repeated measure ANOVA 42 4.4 Relationship between brain structures and memory performance 42 4.5 Relationship between brain structures and memory performance change 44 4.6 Limitations 45 4.7 Conclusion 46 Appendix 47 References 58 | |
dc.language.iso | en | |
dc.title | 海馬迴相關腦結構與主觀認知下降患者記憶力訓練改善之關係 | zh_TW |
dc.title | Better hippocampal-related brain structures are related to better training improvement in memory performance in patients with subjective cognitive decline | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 張玉玲(Yu-Ling Chang),吳文超(Wen-Chau Wu) | |
dc.subject.keyword | 擴散頻譜造影,主觀認知能力下降,輕度認知功能障礙,認知訓練,海馬迴, | zh_TW |
dc.subject.keyword | Diffusion spectrum imaging,Subjective cognitive decline,Mild cognitive impairment,Cognitive training,Hippocampus, | en |
dc.relation.page | 66 | |
dc.identifier.doi | 10.6342/NTU202001956 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2020-07-29 | |
dc.contributor.author-college | 醫學院 | zh_TW |
dc.contributor.author-dept | 醫療器材與醫學影像研究所 | zh_TW |
顯示於系所單位: | 醫療器材與醫學影像研究所 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
U0001-2807202012061300.pdf 目前未授權公開取用 | 8.07 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。