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Heterogeneity in brain aging.

Nettiksimmons, Jasmine Alexandra.

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Heterogeneity in brain aging.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Heterogeneity in brain aging./
作者:	Nettiksimmons, Jasmine Alexandra.
面頁冊數:	142 p.
附註:	Source: Dissertation Abstracts International, Volume: 74-02(E), Section: B.
Contained By:	Dissertation Abstracts International74-02B(E).
標題:	Health Sciences, Epidemiology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3540711
ISBN:	9781267662637

Heterogeneity in brain aging.
Nettiksimmons, Jasmine Alexandra.

Heterogeneity in brain aging. - 142 p.

Source: Dissertation Abstracts International, Volume: 74-02(E), Section: B.

Thesis (Ph.D.)--University of California, Davis, 2012.

Alzheimer's disease (AD) is a neurodegenerative disease that appears in the elderly and is characterized by cognitive impairment, specifically in memory. Due to shifting demographics and improving medical technology resulting in longer lifespans, the prevalence of AD and other types of dementia is on the rise. While AD has been recognized for over a century, the scientific understanding of the biological pathways involved is still fragmentary. Definitive diagnosis of AD requires examination of the brain in an autopsy, which complicates studying the disease in living patients. A wide variety of cognitive tests exist, but intra- and inter-person variability in test-taking ability along with overlapping symptomology between different types of dementia makes precise characterization of disease more difficult.

ISBN: 9781267662637Subjects--Topical Terms:

1019544
Health Sciences, Epidemiology.

Heterogeneity in brain aging.
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245 1 0 $a Heterogeneity in brain aging.
300 $a 142 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-02(E), Section: B.
500 $a Adviser: Laurel Beckett.
502 $a Thesis (Ph.D.)--University of California, Davis, 2012.
520 $a Alzheimer's disease (AD) is a neurodegenerative disease that appears in the elderly and is characterized by cognitive impairment, specifically in memory. Due to shifting demographics and improving medical technology resulting in longer lifespans, the prevalence of AD and other types of dementia is on the rise. While AD has been recognized for over a century, the scientific understanding of the biological pathways involved is still fragmentary. Definitive diagnosis of AD requires examination of the brain in an autopsy, which complicates studying the disease in living patients. A wide variety of cognitive tests exist, but intra- and inter-person variability in test-taking ability along with overlapping symptomology between different types of dementia makes precise characterization of disease more difficult.
520 $a In recent years, there has been substantial development in the use of a variety of different types of biomarkers to understand AD. AD biomarkers include regional brain volume measures from MRI, measures of regional metabolism with FDG-PET, protein levels in cerebrospinal fluid (CSF), along with others. The Alzheimer's Disease Neuroimaging Initiative (ADNI) was a large, prospective study of AD biomarkers funded by a public/private partnership. Starting in 2003, ADNI enrolled approximately 800 subjects, 200 with AD, 400 with mild cognitive impairment (MCI), and 200 who were cognitively normal, and followed each group longitudinally over 2-3 years collecting repeated clinical and biological data. Although there are many biomarkers that are associated with AD, none of them is sufficiently sensitive or specific enough to be used as a diagnostic test; for most biomarkers, there is substantial overlap in distribution between the three diagnostic groups.
520 $a This research focused on examining multivariate structure in AD biomarkers using unsupervised cluster analysis in an effort to understand whether subgroups exist which have clinical implications. The biomarker profiles found in both the cognitively normal controls and MCI subjects were highly informative regarding the degree and type of biological heterogeneity accompanying the relative clinical homogeneity in each diagnostic group. A small subgroup of the ADNI controls had a biomarker profile clearly characteristic of the early stages of AD at baseline, even though they were still cognitively normal. Over time, this group experienced more rapid cognitive decline than the rest of the controls. Another large subgroup of the normal controls demonstrated marked atrophy in multiple brain regions, but lacked the CSF profile characteristic of AD. Vascular damage was examined as a possible explanation, and in fact, this group was consistently associated with a variety of vascular risk factors and also exhibited significantly more cognitive decline than the typical/healthy normal cluster. There was a great deal of biological heterogeneity in the MCI group, with two of the four clusters exhibiting biomarker patterns that are not well described by the prevailing theory of AD.
520 $a Finally, structurally missing data in ADNI provoked an exploration into the possibilities of using imputation techniques for clustering. Although imputation is very commonly used in regression, there is virtually no literature on its use with other statistical techniques. The simulation performed here indicated that both single and multiple imputation methods have the capacity to perform very well in a clustering context, even when the clusters are not compact and well-separated.
590 $a School code: 0029.
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