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Statistical Approaches in the RDoC Paradigm for Post-Mortem Brain Tissue Studies

Gu, Hong (2016) Statistical Approaches in the RDoC Paradigm for Post-Mortem Brain Tissue Studies. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Mental disorders are diagnosed by the way a person perceives and behaves. No neurobiological measures are involved in current diagnostic systems such as the Diagnostic and Statistical Manual of Mental Disorder (DSM), which is the mostly widely used. Because of the lack of neurobiological etiological information, the DSM diagnosis is ambiguous in two ways: first, patients within the same diagnosis could be different in both symptoms and neurobiological measures; second, patients with different DSM diagnoses could be similar in both symptoms and neurobiological measures. As a result, treatments for mental illnesses have not been accurate and successful.
In order to better understand and treat mental illnesses, the National Institute of Mental Health has launched a Strategic Plan for Research in 2008. Part of the plan is the Research Domain Criteria (RDoC) Initiative, which is a framework to link basic neurobiology with mental functions. Under the RDoC framework, a study focuses on a particular mental function, which is called construct, and would ignore the DSM diagnosis of a patient. The RDoC intends to guide studies to find neurobiological characteristics such as genes that are significantly associated with a construct of interest and also the mechanism how defects in these neurobiological characteristics lead to illness in the construct. Although without symptom measures, existing post-mortem brain tissue databases are still useful to facilitate RDoC research.
In this dissertation, we develop statistical approaches to utilize existing post-mortem brain tissue databases following the RDoC spirit. We first propose a method to identify the neurobiological characteristics that are significantly associated with a construct of interest. This is achieved through identifying the neurobiological characteristics that are significantly associated with all the DSM diagnoses relevant to the construct. We then propose a matched subject study design to compare the distribution of the identified neurobiological characteristics in the means and the quantiles between the population with dysfunction in the construct and the healthy population. Finally we develop an algorithm to optimally determine the sample size for each DSM diagnosis in the matched subject study subject to a sample availability constraint as well as a budget constraint.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Gu, Honghog3@pitt.eduHOG3
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairSampson, Allan Rasampson@pitt.eduASAMPSON
Committee MemberBlock, Henry Whwb@pitt.eduHWB
Committee MemberChen, Kehuikhchen@pitt.eduKHCHEN
Committee MemberSweet, Robert Asweetra@upmc.eduSWEET
Date: 19 January 2016
Date Type: Publication
Defense Date: 2 December 2015
Approval Date: 19 January 2016
Submission Date: 29 November 2015
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 164
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Statistics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: RDoC, Post-Mortem Brain Tissue Database, FDR Control, Quantile Regression, Optimal Design, Mixture Population
Date Deposited: 19 Jan 2016 18:49
Last Modified: 19 Jan 2017 06:15


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