Evaluation of a comprehensive multidimensional model of bipolar spectrum psychopathology through statistical and machine learning methods

Researchers have proposed that bipolar disorders are better understood as a continuum, ranging from subclinical through clinical severity and impairment. Furthermore, there is compelling evidence to reconceptualize bipolar spectrum psychopathology as multidimensional. However, researchers have struggled to capture a consistent multidimensional structure. Based on an extensive literature review, a nine-dimensional model was proposed consisting of elation-euphoria, irritability, mood lability, energy-activation, impulsivity-disrupted reward sensitivity, unstable self-esteem, unstable sociability, decreased need for sleep, and lack of insight. This study evaluated the proposed multidimensional model in a multisite, non-clinically ascertained sample of young adults (n = 1264). Participants completed self-report assessments evaluating each domain, as well as a questionnaire assessing bipolar spectrum psychopathology broadly. The proposed model was evaluated using both traditional statistical techniques, such as confirmatory factor analysis and multiple linear regression, as well as machine learning techniques, such as parallel coordinates, elastic net regression, support vector regression, and artificial neural networks. Confirmatory factor analysis indicated that this model did not result in overall acceptable fit. However, both confirmatory factor analysis and regression analyses suggested that many dimensions represented relevant features of bipolar spectrum psychopathology. Both statistical and machine learning models indicated that this model accounted for approximately 60% of the variance of the HPS. Furthermore, statistical and machine learning techniques yielded comparable results. Therefore, next steps were proposed for refinement and evaluation of a comprehensive and theoretically valuable multidimensional model of bipolar spectrum psychopathology.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Psychology

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• Chia, Talia Rebecca Berson

Contributors dc:contributor

• Kwapil, Thomas R
• Hankin, Benjamin L
• Briley, D. Ava
• Tang, Yan
• Xia, Yan

• Bipolar
• Multidimensional
• Statistical Modeling
• Regression
• Machine Learning

Statement dc:rights

• Copyright 2024 Talia Chia

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/125517

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F125517