國立虎尾科技大學 |

Quantifying default mode network connectivity for biomarker generation using structural equation modeling.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Quantifying default mode network connectivity for biomarker generation using structural equation modeling./
作者:	Li, Karl.
面頁冊數:	1 online resource (134 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-01(E), Section: B.
標題:	Neurosciences. -
電子資源:	click for full text (PQDT)
ISBN:	9781339933689

Quantifying default mode network connectivity for biomarker generation using structural equation modeling.
Li, Karl.

Quantifying default mode network connectivity for biomarker generation using structural equation modeling. - 1 online resource (134 pages)

Source: Dissertation Abstracts International, Volume: 78-01(E), Section: B.

Thesis (Ph.D.)--The University of Texas Health Science Center at San Antonio, 2016.

Includes bibliographical references

The default mode network is a set of regions that is tonically engaged during the resting state and exhibits task-related deactivation that is readily reproducible across a wide range of paradigms and modalities. The default mode network has been implicated in numerous cognitive and psychiatric disorders, and also demonstrates significant changes in healthy aging. While the connectivity of the default mode network has been well investigated, understanding of the underlying factors that contribute to default mode network connectivity is relatively poor. This dissertation is focused on establishing a robust default mode network model using structural equation modeling that is viable for hunting for biomarkers, and quantifying factors that have a strong influence on default mode network connectivity in an effort to better refine the search for biomarkers. Aim 1: Quantify age-related changes in default mode network connectivity using resting state functional magnetic resonance imaging scans and establish a model that fits well for all age groups. Aim 2: Test the viability of the model in post-traumatic stress disorder to determine if the model is suitable in probing for biomarkers of disease. Aim 3: Quantify changes in default mode network connectivity strength associated with metabolic syndrome disorders (hypertension, diabetes, and hypercholesterolemia) that have been shown to alter connectivity in the brain to further refine the model. A default mode network model was successfully fit to cohorts across six decades of age, and a progressive trend in connectivity strength was found in 4 pairs of paths that demonstrate declines in connectivity strength with matching compensatory effects. Using the established default mode network model to investigate changes in connectivity in combat veterans with post-traumatic stress disorder three specific hyperconnected pathways were discovered. Applying logistic regression to the hyperconnected paths on a per-subject level, a diagnostic accuracy of over 85% was established. Applying the model to subjects with metabolic syndrome disorders revealed that three of the four paths previously shown to decline in connectivity strength with age are further aged by the presence of the disorders. Taken together, the findings of this dissertation establish a refined model of the default mode network that can account for aging and common systemic disorders while producing viable biomarkers for investigating disease.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781339933689Subjects--Topical Terms:

593561
Neurosciences.
Index Terms--Genre/Form:

554714
Electronic books.

Quantifying default mode network connectivity for biomarker generation using structural equation modeling.
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520 $a The default mode network is a set of regions that is tonically engaged during the resting state and exhibits task-related deactivation that is readily reproducible across a wide range of paradigms and modalities. The default mode network has been implicated in numerous cognitive and psychiatric disorders, and also demonstrates significant changes in healthy aging. While the connectivity of the default mode network has been well investigated, understanding of the underlying factors that contribute to default mode network connectivity is relatively poor. This dissertation is focused on establishing a robust default mode network model using structural equation modeling that is viable for hunting for biomarkers, and quantifying factors that have a strong influence on default mode network connectivity in an effort to better refine the search for biomarkers. Aim 1: Quantify age-related changes in default mode network connectivity using resting state functional magnetic resonance imaging scans and establish a model that fits well for all age groups. Aim 2: Test the viability of the model in post-traumatic stress disorder to determine if the model is suitable in probing for biomarkers of disease. Aim 3: Quantify changes in default mode network connectivity strength associated with metabolic syndrome disorders (hypertension, diabetes, and hypercholesterolemia) that have been shown to alter connectivity in the brain to further refine the model. A default mode network model was successfully fit to cohorts across six decades of age, and a progressive trend in connectivity strength was found in 4 pairs of paths that demonstrate declines in connectivity strength with matching compensatory effects. Using the established default mode network model to investigate changes in connectivity in combat veterans with post-traumatic stress disorder three specific hyperconnected pathways were discovered. Applying logistic regression to the hyperconnected paths on a per-subject level, a diagnostic accuracy of over 85% was established. Applying the model to subjects with metabolic syndrome disorders revealed that three of the four paths previously shown to decline in connectivity strength with age are further aged by the presence of the disorders. Taken together, the findings of this dissertation establish a refined model of the default mode network that can account for aging and common systemic disorders while producing viable biomarkers for investigating disease.
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