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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 湯佩芳 | zh_TW |
dc.contributor.advisor | Pei-Fang Tang | en |
dc.contributor.author | 雷采霖 | zh_TW |
dc.contributor.author | Tsai-Lin Lei | en |
dc.date.accessioned | 2023-03-02T17:03:07Z | - |
dc.date.available | 2023-11-10 | - |
dc.date.copyright | 2023-06-01 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-02-16 | - |
dc.identifier.citation | American Diabetes Association (2010) Diagnosis and classification of diabetes mellitus (Vol. 33).
A Barnes, V., & W Orme-Johnson, D. (2012). Prevention and treatment of cardiovascular disease in adolescents and adults through the transcendental meditation® program: A research review update. Current Hypertension Reviews, 8(3), 227-242. Association, A. D. (2010). Diagnosis and classification of diabetes mellitus (Vol. 33). Benarroch, E. E. (1993). The central autonomic network: functional organization, dysfunction, and perspective. Paper presented at the Mayo Clinic Proceedings. Benarroch, E. E. (2012). Central autonomic control. In Primer on the autonomic nervous system (pp. 9-12): Elsevier. Berry, J. D., Dyer, A., Cai, X., Garside, D. B., Ning, H., Thomas, A., . . . Lloyd-Jones, D. M. (2012). Lifetime risks of cardiovascular disease. New England Journal of Medicine, 366(4), 321-329. Retrieved from https://www.nejm.org/doi/pdf/10.1056/NEJMoa1012848?articleTools=true Cannon, C. P. (2007). Cardiovascular disease and modifiable cardiometabolic risk factors. Clinical cornerstone, 8(3), 11-28. Cauda, F., D'agata, F., Sacco, K., Duca, S., Geminiani, G., & Vercelli, A. (2011). Functional connectivity of the insula in the resting brain. Neuroimage, 55(1), 8-23. Chang, C., Metzger, C. D., Glover, G. H., Duyn, J. H., Heinze, H.-J., & Walter, M. (2013). Association between heart rate variability and fluctuations in resting-state functional connectivity. Neuroimage, 68, 93-104. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3746190/pdf/nihms-427055.pdf Chobanian, A. V., Bakris, G. L., Black, H. R., Cushman, W. C., Green, L. A., Izzo Jr, J. L., . . . Wright Jr, J. T. (2003). Seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure. Hypertension, 42(6), 1206-1252. Christensen, J. H., Toft, E., Christensen, M. S., & Schmidt, E. B. (1999). Heart rate variability and plasma lipids in men with and without ischaemic heart disease. Atherosclerosis, 145(1), 181-186. Cygankiewicz, I., & Zareba, W. (2013). Heart rate variability. In Handbook of clinical neurology (Vol. 117, pp. 379-393): Elsevier. de la Cruz, F., Schumann, A., Köhler, S., Reichenbach, J. R., Wagner, G., & Bär, K.-J. (2019). The relationship between heart rate and functional connectivity of brain regions involved in autonomic control. Neuroimage, 196, 318-328. de La Torre, J. C. (2012). Cardiovascular risk factors promote brain hypoperfusion leading to cognitive decline and dementia. Cardiovascular psychiatry and neurology, 2012. Driscoll, D., & DiCicco, G. (2000). The effects of metronome breathing on the variability of autonomic activity measurements. Journal of manipulative and physiological therapeutics, 23(9), 610-614. Electrophysiology, T. F. o. t. E. S. o. C. t. N. A. S. o. P. (1996). Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Circulation, 93(5), 1043-1065. Forouhi, N. G., & Wareham, N. J. (2019). Epidemiology of diabetes. Medicine, 47(1), 22-27. Furlan, R., Guzzetti, S., Crivellaro, W., Dassi, S., Tinelli, M., Baselli, G., . . . Malliani, A. (1990). Continuous 24-hour assessment of the neural regulation of systemic arterial pressure and RR variabilities in ambulant subjects. Circulation, 81(2), 537-547. Gotink, R. A., Meijboom, R., Vernooij, M. W., Smits, M., & Hunink, M. M. (2016). 8-week mindfulness based stress reduction induces brain changes similar to traditional long-term meditation practice–a systematic review. Brain and cognition, 108, 32-41. Greenwood, P. M. (2007). Functional plasticity in cognitive aging: review and hypothesis. Neuropsychology, 21(6), 657. Hoshi, R. A., Santos, I. S., Dantas, E. M., Andreão, R. V., Schmidt, M. I., Duncan, B. B., . . . Bensenor, I. (2019). Decreased heart rate variability as a predictor for diabetes—A prospective study of the Brazilian longitudinal study of adult health. Diabetes/metabolism research and reviews, 35(7), e3175. Jennings, J. R., Sheu, L. K., Kuan, D. C. H., Manuck, S. B., & Gianaros, P. J. (2016). Resting state connectivity of the medial prefrontal cortex covaries with individual differences in high‐frequency heart rate variability. Psychophysiology, 53(4), 444-454. Retrieved from https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/psyp.12586?download=true Kamath, M. V., Watanabe, M. A., & Upton, A. R. M. (2013). Heart rate variability (HRV) signal analysis clinical applications. Boca Raton: Taylor & Francis. Kannel, W. B. (1996). Blood pressure as a cardiovascular risk factor: prevention and treatment. Jama, 275(20), 1571-1576. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/8622248 https://jamanetwork.com/journals/jama/articlepdf/402826/jama_275_20_036.pdf Katona, P. G., Poitras, J. W., Barnett, G. O., & Terry, B. S. (1970). Cardiac vagal efferent activity and heart period in the carotid sinus reflex. American Journal of Physiology-Legacy Content, 218(4), 1030-1037. Kleiger, R. E., Stein, P. K., & Bigger Jr, J. T. (2005). Heart rate variability: Measurement and clinical utility. Annals of Noninvasive Electrocardiology, 10(1), 88-101. doi:10.1111/j.1542-474X.2005.10101.x Kumral, D., Schaare, H. L., Beyer, F., Reinelt, J., Uhlig, M., Liem, F., . . . Erbey, M. (2019). The age-dependent relationship between resting heart rate variability and functional brain connectivity. Neuroimage, 185, 521-533. Kupari, M., Virolainen, J., Koskinen, P., & Tikkanen, M. J. (1993). Short-term heart rate variability and factors modifying the risk of coronary artery disease in a population sample. The American journal of cardiology, 72(12), 897-903. Law, M. R., Wald, N. J., & Thompson, S. G. (1994). By how much and how quickly does reduction in serum cholesterol concentration lower risk of ischaemic heart disease? Bmj, 308(6925), 367-372. doi:10.1136/bmj.308.6925.367 Lee, H.-c. B., Chiu, H. F., Kowk, W. Y., & Leung, C. M. (1993). Chinese elderly and the GDS short form: A preliminary study. Clinical Gerontologist: The Journal of Aging and Mental Health. Lehrer, P. M., & Gevirtz, R. (2014). Heart rate variability biofeedback: how and why does it work? Frontiers in Psychology, 5, 756. Liao, D., Cai, J., Barnes, R. W., Tyroler, H. A., Rautaharju, P., Holme, I., & Heiss, G. (1996). Association of cardiac automatic function and the development of hypertension: The ARIC study. American journal of hypertension, 9(12), 1147-1156. Liao, D., Cai, J., Brancati, F. L., Folsom, A., Barnes, R. W., Tyroler, H. A., & Heiss, G. (1995). Association of vagal tone with serum insulin, glucose, and diabetes mellitus—The ARIC Study. Diabetes research and clinical practice, 30(3), 211-221. Liao, D., Sloan, R. P., Cascio, W. E., Folsom, A. R., Liese, A. D., Evans, G. W., . . . Sharrett, A. R. (1998). Multiple metabolic syndrome is associated with lower heart rate variability: the Atherosclerosis Risk in Communities Study. Diabetes care, 21(12), 2116-2122. Magnus, P., & Beaglehole, R. (2001). The real contribution of the major risk factors to the coronary epidemics: time to end the only-50% myth. Archives of Internal Medicine, 161(22), 2657-2660. Marler, J. R., Price, T. R., Clark, G. L., Muller, J. E., Robertson, T., Mohr, J. P., . . . Foulkes, M. A. (1989). Morning increase in onset of ischemic stroke. Stroke, 20(4), 473-476. McCraty, R., & Shaffer, F. (2015). Heart rate variability: new perspectives on physiological mechanisms, assessment of self-regulatory capacity, and health risk. Global advances in health and medicine, 4(1), 46-61. Retrieved from https://journals.sagepub.com/doi/pdf/10.7453/gahmj.2014.073 McDonald, M., Hertz, R. P., Unger, A. N., & Lustik, M. B. (2009). Prevalence, awareness, and management of hypertension, dyslipidemia, and diabetes among United States adults aged 65 and older. Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences, 64(2), 256-263. Mills, K. T., Stefanescu, A., & He, J. (2020). The global epidemiology of hypertension. Nature Reviews Nephrology, 16(4), 223-237. Mølgaard, H., Hermansen, K., & Bjerregaard, P. (1994). Spectral components of short-term RR interval variability in healthy subjects and effects of risk factors. European heart journal, 15(9), 1174-1183. Muller, J. E., Ludmer, P. L., Willich, S. N., Tofler, G. H., Aylmer, G., Klangos, I., & Stone, P. H. (1987). Circadian variation in the frequency of sudden cardiac death. Circulation, 75(1), 131-138. Nashiro, K., Yoo, H. J., Min, J., Cho, C., Nasseri, P., Zhang, Y., . . . Mather, M. (2022). Effects of a randomised trial of 5-week heart rate variability biofeedback intervention on mind wandering and associated brain function. Cogn Affect Behav Neurosci, 22(6), 1349-1357. doi:10.3758/s13415-022-01019-7 National Institute of Health, & National Heart, L. a. B. I. (2001). Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). Nieto-Castanon, A. (2020). Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN: Hilbert Press. O'Donnell, C. J., & Elosua, R. (2008). Cardiovascular risk factors. Insights from framingham heart study. Revista Espanola de Cardiologia (English Edition), 61(3), 299-310. Olex, S., Newberg, A., & Figueredo, V. M. (2013). Meditation: should a cardiologist care? International journal of cardiology, 168(3), 1805-1810. Ostchega, Y., Dillon, C. F., Hughes, J. P., Carroll, M., & Yoon, S. (2007). Trends in hypertension prevalence, awareness, treatment, and control in older US adults: data from the National Health and Nutrition Examination Survey 1988 to 2004. Journal of the American Geriatrics Society, 55(7), 1056-1065. Payne, R. A. (2012). Cardiovascular risk. British journal of clinical pharmacology, 74(3), 396-410. Petersmann, A., Müller-Wieland, D., Müller, U. A., Landgraf, R., Nauck, M., Freckmann, G., . . . Schleicher, E. (2019). Definition, classification and diagnosis of diabetes mellitus. Experimental and Clinical Endocrinology & Diabetes, 127(S 01), S1-S7. Pires, P. W., Dams Ramos, C. M., Matin, N., & Dorrance, A. M. (2013). The effects of hypertension on the cerebral circulation. American Journal of Physiology-Heart and Circulatory Physiology, 304(12), H1598-H1614. Pirillo, A., Casula, M., Olmastroni, E., Norata, G. D., & Catapano, A. L. (2021). Global epidemiology of dyslipidaemias. Nature Reviews Cardiology, 1-12. Priya, G., & Kalra, S. (2018). Mind–body interactions and mindfulness meditation in diabetes. European Endocrinology, 14(1), 35. Pumprla, J., Howorka, K., Groves, D., Chester, M., & Nolan, J. (2002). Functional assessment of heart rate variability: physiological basis and practical applications. International journal of cardiology, 84(1), 1-14. doi:10.1016/s0167-5273(02)00057-8 Qiu, C., Winblad, B., & Fratiglioni, L. (2005). The age-dependent relation of blood pressure to cognitive function and dementia. The Lancet Neurology, 4(8), 487-499. Quintana, D., Alvares, G. A., & Heathers, J. (2016). Guidelines for Reporting Articles on Psychiatry and Heart rate variability (GRAPH): recommendations to advance research communication. Translational psychiatry, 6(5), e803-e803. Rolls, E. T. (2019). The orbitofrontal cortex and emotion in health and disease, including depression. Neuropsychologia, 128, 14-43. Rolls, E. T., Cheng, W., & Feng, J. (2020). The orbitofrontal cortex: reward, emotion and depression. Brain Communications, 2(2), fcaa196. Saetia, S., Rosas, F., Ogata, Y., Yoshimura, N., & Koike, Y. (2020). Comparison of resting-state functional and effective connectivity between default mode network and memory encoding related areas. Journal of Neuroscience and Neurological Disorders, 4(1), 029-037. Sakaki, M., Yoo, H. J., Nga, L., Lee, T.-H., Thayer, J. F., & Mather, M. (2016). Heart rate variability is associated with amygdala functional connectivity with MPFC across younger and older adults. Neuroimage, 139, 44-52. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5133191/pdf/nihms796810.pdf Sandercock, G. R., Bromley, P. D., & Brodie, D. A. (2005). The reliability of short-term measurements of heart rate variability. International journal of cardiology, 103(3), 238-247. Shaffer, F., & Ginsberg, J. (2017). An overview of heart rate variability metrics and norms. Frontiers in public health, 5, 258. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5624990/pdf/fpubh-05-00258.pdf Sheikh, J. I., & Yesavage, J. A. (1986). Geriatric Depression Scale (GDS): recent evidence and development of a shorter version. Clinical Gerontologist: The Journal of Aging and Mental Health. Singh, J. P., Larson, M. G., O’Donnell, C. J., Wilson, P. F., Tsuji, H., Lloyd-Jones, D. M., & Levy, D. (2000). Association of hyperglycemia with reduced heart rate variability (The Framingham Heart Study). The American journal of cardiology, 86(3), 309-312. Singh, J. P., Larson, M. G., Tsuji, H., Evans, J. C., O’Donnell, C. J., & Levy, D. (1998). Reduced heart rate variability and new-onset hypertension: insights into pathogenesis of hypertension: the Framingham Heart Study. Hypertension, 32(2), 293-297. Song, H.-S., & Lehrer, P. M. (2003). The effects of specific respiratory rates on heart rate and heart rate variability. Applied psychophysiology and biofeedback, 28(1), 13-23. Tang, Y.-Y., Ma, Y., Fan, Y., Feng, H., Wang, J., Feng, S., . . . Li, J. (2009). Central and autonomic nervous system interaction is altered by short-term meditation. Proceedings of the national Academy of Sciences, 106(22), 8865-8870. Thayer, J. F., & Brosschot, J. F. (2005). Psychosomatics and psychopathology: looking up and down from the brain. Psychoneuroendocrinology, 30(10), 1050-1058. doi:10.1016/j.psyneuen.2005.04.014 Thayer, J. F., Hansen, A. L., Saus-Rose, E., & Johnsen, B. H. (2009). Heart Rate Variability, Prefrontal Neural Function, and Cognitive Performance: The Neurovisceral Integration Perspective on Self-regulation, Adaptation, and Health. Annals of Behavioral Medicine, 37(2), 141-153. doi:10.1007/s12160-009-9101-z Thayer, J. F., & Lane, R. D. (2000). A model of neurovisceral integration in emotion regulation and dysregulation. Journal of affective disorders, 61(3), 201-216. Thayer, J. F., Yamamoto, S. S., & Brosschot, J. F. (2010). The relationship of autonomic imbalance, heart rate variability and cardiovascular disease risk factors. International journal of cardiology, 141(2), 122-131. Tseng, T.-H., Chen, H.-C., Wang, L.-Y., & Chien, M.-Y. (2020). Effects of exercise training on sleep quality and heart rate variability in middle-aged and older adults with poor sleep quality: a randomized controlled trial. Journal of Clinical Sleep Medicine, 16(9), 1483-1492. Tsuji, H., Venditti Jr, F. J., Manders, E. S., Evans, J. C., Larson, M. G., Feldman, C. L., & Levy, D. (1994). Reduced heart rate variability and mortality risk in an elderly cohort. The Framingham Heart Study. Circulation, 90(2), 878-883. Retrieved from https://www.ahajournals.org/doi/pdf/10.1161/01.CIR.90.2.878?download=true Udupa, K., Sathyaprabha, T., Thirthalli, J., Kishore, K., Lavekar, G., Raju, T., & Gangadhar, B. (2007). Alteration of cardiac autonomic functions in patients with major depression: a study using heart rate variability measures. Journal of affective disorders, 100(1-3), 137-141. Valenza, G., Sclocco, R., Duggento, A., Passamonti, L., Napadow, V., Barbieri, R., & Toschi, N. (2019). The central autonomic network at rest: uncovering functional MRI correlates of time-varying autonomic outflow. Neuroimage, 197, 383-390. van der Kooy, K. G., van Hout, H. P., van Marwijk, H. W., de Haan, M., Stehouwer, C. D., & Beekman, A. T. (2006). Differences in heart rate variability between depressed and non‐depressed elderly. International Journal of Geriatric Psychiatry: A journal of the psychiatry of late life and allied sciences, 21(2), 147-150. Retrieved from https://onlinelibrary.wiley.com/doi/abs/10.1002/gps.1439 Verhaaren, B. F., Vernooij, M. W., de Boer, R., Hofman, A., Niessen, W. J., van der Lugt, A., & Ikram, M. A. (2013). High blood pressure and cerebral white matter lesion progression in the general population. Hypertension, 61(6), 1354-1359. Voss, A., Schroeder, R., Heitmann, A., Peters, A., & Perz, S. (2015). Short-term heart rate variability—influence of gender and age in healthy subjects. PloS one, 10(3), e0118308. Whelton, P., Carey, R., Aronow, W., Casey Jr, D., Collins, K., Dennison Himmelfarb, C., . . . MacLaughlin, E. (2017). ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA guideline for the prevention, detection, evaluation, and management of high blood Ppressure in adults: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Hypertension, 2017. Yang, H.-J., Koh, E., & Kang, Y. (2021). Susceptibility of Women to Cardiovascular Disease and the Prevention Potential of Mind–Body Intervention by Changes in Neural Circuits and Cardiovascular Physiology. Biomolecules, 11(5), 708. Yasuma, F., & Hayano, J.-i. (2004). Respiratory sinus arrhythmia: why does the heartbeat synchronize with respiratory rhythm? Chest, 125(2), 683-690. Zhou, B., Carrillo-Larco, R. M., Danaei, G., Riley, L. M., Paciorek, C. J., Stevens, G. A., . . . Singleton, R. K. (2021). Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants. The Lancet. Health Promotion Administration. (2021, Oct 5) 慢性病盛行率 Retrieved from https://www.hpa.gov.tw/Pages/Detail.aspx?nodeid=641&pid=1231 World Health Organization. (2021, June 11) Cardiovascular diseases(CVDs). Retrieved from https://www.who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds) 蔡佳芬、傅中玲(2012, 6 月22 日)。蒙特利爾認知評估台灣版使用及計分指引。取自http://www.mocatest.org/pdf_files/test/MoCA-Test-Taiwan.pdf | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83299 | - |
dc.description.abstract | 背景與目的: 有心血管危險因子者常有自主神經系統失衡的現象,而此失衡現象常透過較小的心率變異度和抑鬱情緒顯現出來。中樞自主網路與自主神經系統和邊緣系統功能的調節有關。本研究的目的是探討在有心血管危險因子的中老年人中,中樞自主網路內的功能性連結是否會與無心血管危險因子者不同,以及此連結之強度是否與其心率變異度和情緒抑鬱程度相關。
方法: 本研究收錄104 名認知正常且無精神疾病或抑鬱症的中年人(45-64 歲)和老年人(65-80 歲)的資料。資料來源為三個臨床隨機對照試驗(NCT編號 : 02270320、03275038 或05672940)的前測資料。其中,74名(平均年齡:64.0 ± 7.2歲)屬於心血管危險因子組,並且至少有以下三種心血管危險因子中的至少一種——高血壓、糖尿病或血脂異常。其餘30名(平均年齡:64.8 ± 6.6歲)為無心血管危險因子組。所有參與者都接受了蒙特利爾認知評估、老年抑鬱量表(GDS-15)評估和腦部靜息態磁振造影掃描。心血管危險因子組還進行了5分鐘的休息時心率變異度測試。使用的心率變異度參數為正常到正常 R 波間距秒數的標準差(SDNN)、高頻功率(HF)、低頻功率(LF)和低頻對高頻功率之比值(LF/HF)。較高的SDNN和HF數值表示副交感神經控制較強;而較高的 LF和LF/HF表示交感神經控制較強。中樞自主網路中的八個腦區為本研究之興趣腦區,含右側框額皮質、左側框額皮質、右側腦島、左側腦島、內側前額葉皮質、前扣帶皮質、右側杏仁核與左側杏仁核。以 CONN toolbox分析網路內此八個區域間兩兩之連結並用Fisher’s Z 轉換其連結強度數值,連結強度閾值設置為 Family wise error (FWE), p < 0.05。使用獨立樣本 t 檢定比較兩組間中樞自主網路內功能性連結強度之差異。以淨相關分析,控制年齡和性別,分析功能性連結強度與心率變異度及GDS-15分數間的關係。 結果: 在心血管危險因子組,除了內側前額葉皮質和雙側前扣帶皮質、內側前額葉皮質和雙側腦島、以及前扣帶皮質和雙側杏仁核之間的功能性連結為負向之外,中樞自主網路內興趣腦區之間的功能性連結皆為正向。 無心血管危險因子組的功能性連結大多比有心血管危險因子組較弱,但兩組間所有連結強度均未達顯著差異。此外,在心血管危險因子組中,內側前額葉皮質和左側杏仁核間正向功能性連結較強者,其心率變異度較弱(HF較低(r = -0.268,p = 0.027)和LF/HF較高(r = 0.315,p = 0.009)); 且內側前額葉皮質和右側杏仁核間正向功能性連結較強者,其心變異度也較弱(LF/HF較高(r = 0.289,p = 0.017))。此外,在心血管危險因子組中,內側前額葉皮質和右側杏仁核間正向功能性連結較強者,及左側島葉和右側杏仁核的正向功能性連結較弱者,其 GDS-15 分數較高(分別為r = 0.283,p = 0.019;r = -0.318,p = 0.008),亦即情緒較為低落。在無心血管危險因子組中,則未發現這些功能性連結與GDS-15分數間之顯著相關性。 討論: 無論是在有或無心血管危險因子的中老年人中,內側前額葉皮質和杏仁核間的正向功能性連結顯示:隨著年齡的增長,內側前額葉皮質與杏仁核之間缺乏互相抑制性的調節。若衰老和心血管危險因子同時存在,此現象會更明顯。更重要的是,在有心血管危險因子的中老年人中,這種正向功能性連結越強者,其心率變異度愈弱、抑鬱情緒較高,顯示中樞自主網路在調節此族群的心率變異度和情緒方面可能扮演重要角色。 結論: 此研究結果支持在有心血管危險因子的中老年人中,內側前額葉皮質和杏仁核之間的功能性連結,與自主神經系統的平衡和邊緣系統的行為表現間,具有相當重要的關聯性。 | zh_TW |
dc.description.abstract | Background: People with cardiovascular risks (CVRs) often present imbalanced autonomic nervous system (ANS) function, which is manifested by smaller heart rate variability (HRV) and more depressive mood. The central autonomic network (CAN) has been implicated in the regulation of the ANS and the limbic system functions. The purposes of this study were to investigate that in middle-aged and older adults with CVRs, whether their functional connectivity (FC) within the CAN would differ from those from middle-aged and older adults without CVRs, and whether this connectivity would be associated with their HRV and depressive mood.
Method: Baseline data of 104 cognitively normal middle-aged (45-64 years old) and older (65-80 years old) adults who did not have any mental illness or depression and were enrolled in one of three previous randomized controlled clinical trials (Trial No: NCT02270320, NCT03275038, or NCT05672940) were used in this study. Among them, 74 participants (mean age: 64.0 ± 7.2 years) were in the CVR group and had at least one of the following three CVRs- hypertension, diabetes, or dyslipidemia. The remaining 30 participants (mean age: 64.8 ± 6.6 years) were in the non-CVR group. All participants were assessed with the Montreal Cognitive Assessment (MoCA), the Geriatric Depression Scale-15 (GDS-15), and brain resting-state fMRI scans. The CVR group additionally underwent a 5-minute HRV assessment at resting. The HRV parameters calculated were the standard deviation of normal to normal R wave interval (SDNN), the high frequency power (HF), the low frequency power (LF), and the ratio of the LF power over the HF power (LF/HR). Greater SDNN and HF indicated better parasympathetic control; whereas greater LF and LF/HF ratio indicated more dominant sympathetic control. Eight brain regions in the CAN were set as the regions of interest (ROIs), including the right orbitofrontal cortex (OFC), left OFC, right insula, left insula, medial prefrontal cortex (mPFC), anterior cingulate cortex (ACC), right amygdala, and left amygdala. The FC between each pair of the ROIs was calculated using the CONN toolbox and the Fisher’s Z transformation. The connectivity threshold was set as FWE, p < 0.05. Two sample t-test was used to compare the strength (z-score) of all FC within the CAN between the two groups. Partial correlation analysis was performed to analyze the relationships of the FC with the HRV measures and GDS-15 scores, controlling age and sex. Results: In the CVR group, the FC between the ROIs within the CAN was mostly positive, except for the negative FC between the mPFC and the ACC, between the mPFC and bilateral insula, and between the ACC and bilateral amygdala. Overall, the non-CVR group tended to present weaker FC than the CVR group, but the group differences in all FC values did not the reach significance. Furthermore, in the CVR group, those with stronger positive FC between the mPFC and the left amygdala presented poorer HRV (lower HF power (r = -0.268, p = 0.027) and higher LF/HF ratio (r = 0.315, p = 0.009)); and those with stronger positive FC between the mPFC and the right amygdala also presented poorer HRV (higher LF/HF ratio (r = 0.289, p = 0.017)). In addition, in the CVR group, stronger positive FC between the mPFC and the right amygdala and weaker positive FC between the insula and the right amygdala were associated with higher GDS-15 scores (r = 0.283, p = 0.019; r = -0.318, p = 0.008, respectively), that is, more depressed mood. There were no significant correlations between FC within the CAN and GDS-15 scores in the non-CVR group. Discussion: The positive FC between the mPFC and the amygdala in middle-aged and older adults with and without CVRs suggested a lack of mutual inhibitory regulation between the mPFC and the amygdala as people get older. This phenomenon became worse if aging and CVRs coexisted. More importantly, this positive FC was significantly associated with worse HRV and more depressive mood in middle-aged and older adults with CVRs, suggesting the important roles of the CAN in regulating HRV and mood in this population. Conclusion: Results of this study supported the important associations of the FC between the mPFC and amygdala with functions of the ANS and the limbic systems in middle-aged and older adults with CVRs. | en |
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dc.description.tableofcontents | Table of Contents
謝辭 i 中文摘要 ii English Abstract v Abbreviations 1 Chapter 1 Introduction 3 1.1 Background 3 1.2 Operational Definitions 5 1.2.1 People with Cardiovascular Risks (CVRs) 5 1.2.2 Heart Rate Variability (HRV) Parameters 6 1.3 Research Questions and Hypotheses 7 1.4 Importance of This Study 9 Chapter 2 Literature Review 10 2.1 Introduction of Cardiovascular Risks Factors (CVRs) 10 2.2 Introduction of Heart Rate Variability (HRV) 11 2.2.1 Definition and Physiological Mechanisms of HRV Regulation 11 2.2.2 Methods of Measuring HRV 13 2.3 Factors Influencing HRV 15 2.4 Brain Regions Involved in Autonomic Nervous System Control 17 2.5 Relationships between Heart Rate Variability and Functional Connectivity of the Central Autonomic Network 18 2.6 Relationships between GDS-15 and Functional Connectivity of the Central Autonomic Network 19 2.7 Summary of Literature 19 Chapter 3 Methods 21 3.1 Study Design and Participants 21 3.2 Procedures 22 3.3 HRV Measurements 23 3.4 Resting State Functional Magnetic Resonance Imaging 23 3.4.1 MRI Data Acquisition 23 3.4.2 Rs-fMRI Data Analysis 24 3.5 Statistical Analysis 26 Chapter 4 Results 27 4.1 Demographics and Clinical characteristics 27 4.2 Functional Connectivity within the CAN 27 4.3 GDS-15 measure and HRV 28 4.4 Relationships of Functional Connectivity within the CAN with HRV measures and GDS-15 score 29 Chapter 5 Discussion 31 5.1 The Functional Connectivity between ROIs within the CAN 31 5.2 Relationships between Functional Connectivity and GDS-15 scores 32 5.3 The Influence of CVRs on HRV and the Brain 34 5.4 Theoretical Framework and Clinical Implications 35 5.5 Limitations 36 Chapter 6 Conclusions 37 References 38 Tables 49 Table 1-1. Demographic and HRV characteristics of the CVR and non-CVR groups. 49 Table 1-2. Demographic and HRV characteristics of the HTN and non-HTN subgroups. 51 Figures 53 Figure 1. Hypothetical model of the relationships between functional connectivity within the CAN and the HRV and GDS-15 scores. Better control within the CAN that leads to greater inhibition of bilateral amygdala output was hypothsized to be related to stronger HRV and smaller GDS-15 scores. 53 Figure 2-1. (A) Heatmap of the functional connectivity (z-score) within the CAN of the CVR group. Threshold was set as p (FWE) < 0.05. The functional connectivity between the ROIs within the CAN was mostly positive, but that between the mPFC and the ACC, between the mPFC and bilateral insula, and between the ACC and bilateral amygdala was negative. (B) Heatmap of the functional connectivity (z-score) of the non-CVR group. The functional connectivity between the ROIs within the CAN was mostly positive, but that between the mPFC and the ACC, between mPFC and bilateral insula, and between the ACC and bilateral amygdala did not pass the threshold and is coded in white. (C) Projection of the heatmap onto a schematic graph of the CAN of CVR group. (D) Projection of heatmap onto a schematic graph of the CAN of the non-CVR group. 54 Figure 2-2. (A) Heatmap of the functional connectivity (z-score) within the CAN of the HTN subgroup within the CVR group. Threshold was set as p (FWE) < 0.05. The functional connectivity between the ROIs within the CAN was mostly positive, but that between the mPFC and the ACC, and between the mPFC and bilateral insula was negative, and that between the ACC and bilateral amygdala and between the left insula and the right amygdala did not pass the threshold and is coded in white. (B) Heatmap of the functional connectivity (z-score) of the non-HTN subgroup within the CVR group. The functional connectivity between the ROIs within the CAN was mostly positive, but that between the mPFC and the ACC, between the mPFC and bilateral insula, between the ACC and bilateral amygdala, and between the left OFC and the right amygdala did not pass the threshold and is coded in white. (C) Projection of heatmap onto a schematic graph of the CAN of the HTN subgroup. (D) Projection of heatmap onto a schematic graph of the CAN of the non-HTN subgroup. 56 Figure 3-1. Partial correlation plots of the functional connectivity within the CAN against the HRV parameters (A~C) and GDS-15 scores (D~E) in the CVR group. Statistics analysis was controlled for age and sex. 58 Figure 3-2. Partial correlation plots of the functional connectivity within the CAN against the HRV parameters (A~D) and the GDS-15 scores (E) in the HTN subgroup. Statistics analysis was controlled for age and sex. 60 Figure 3-3. Partial correlation plots of the functional connectivity within the CAN against the HRV parameters (A~H) and the GDS-15 scores (I) in the non-HTN subgroup. Statistics analysis was controlled for age and sex. 62 Appendixes 64 Appendix 1. Coordinates of the selected regions of interest on the Harvard-Oxford Atlas within the CAN. 64 Appendix 2-1. The functional connectivity (z-score) between the ROIs within the CAN of the CVR and non-CVR groups. 65 Appendix 2-2. The functional connectivity (z-score) between the ROIs within the CAN of the HTN and non-HTN subgroup within the CVR group. 67 Appendix 3-1. Partial correlation coefficients (rs) between functional connectivity (z-score) within the CAN and HRV parameters of the CVR group. 69 Appendix 3-2. Partial correlation coefficients (rs) between functional connectivity (z-score) within the CAN and HRV parameters of the HTN subgroup within the CVR group. 71 Appendix 3-3. Partial correlation coefficients (rs) between functional connectivity (z-score) within the CAN and HRV parameters of the non-HTN subgroup within the CVR group. 73 Appendix 4. Partial correlation coefficients (rs) between functional connectivity (z-score) within the CAN and GDS-15 scores of the CVR group, HTN and non-HTN subgroups. 75 Appendix 5. Partial correlation coefficients (rs) between the GDS-15 scores and HRV parameters of the CVR group, and the HTN and non-HTN subgroups. 77 | - |
dc.language.iso | en | - |
dc.title | 具心血管危險因子之中老年人中樞自主網路內功能性連結與心率變異度及抑鬱情緒間之關係:靜息態磁振造影研究 | zh_TW |
dc.title | Relationships of Functional Connectivity within the Central Autonomic Network with Heart Rate Variability and Depressive Mood in Middle-aged and Older Adults with Cardiovascular Risks: A Resting-state fMRI Study | en |
dc.title.alternative | Relationships of Functional Connectivity within the Central Autonomic Network with Heart Rate Variability and Depressive Mood in Middle-aged and Older Adults with Cardiovascular Risks: A Resting-state fMRI Study | - |
dc.type | Thesis | - |
dc.date.schoolyear | 111-1 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 吳恩賜;吳文超 | zh_TW |
dc.contributor.oralexamcommittee | Joshua Goh;Wen-Chau Wu | en |
dc.subject.keyword | 老化,心血管危險因子,中樞自主神經系統網路,功能性連結,靜息態磁振造影,心律變異度,情緒, | zh_TW |
dc.subject.keyword | aging,cardiovascular risks,central autonomic network,functional connectivity,rs-fMRI,heart rate variability,mood, | en |
dc.relation.page | 77 | - |
dc.identifier.doi | 10.6342/NTU202300523 | - |
dc.rights.note | 同意授權(限校園內公開) | - |
dc.date.accepted | 2023-02-16 | - |
dc.contributor.author-college | 醫學院 | - |
dc.contributor.author-dept | 腦與心智科學研究所 | - |
dc.date.embargo-lift | 2025-02-20 | - |
顯示於系所單位: | 腦與心智科學研究所 |
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