An Exploration of Neuroanatomical Signatures of Pediatric Bipolar Disorder Using Structural Neuroimaging

<p>Bipolar Disorder (BD) is diagnosed using the Diagnostic and Statistical Manual (DSM) criteria, which relies heavily on symptomatology. This method, however, lends itself to error due to variance in symptom expression and is further complicated during childhood and adolescence- a period marked by major anatomical and behavioral changes. Therefore, in order to institute early and effective interventions, it is imperative that we develop more objective methods of mood disorder characterization and diagnosis. Two proposed solutions for accomplishing this task include 1) the generation of normative development models to assess BD risk and 2) data-driven clustering of patients based on specific neuroanatomical profiles, i.e. biotypes. While the normative development model was unable to quantify BD risk at an individual level, it nevertheless emphasized the heterogeneous nature of both healthy and BD development. The clustering analysis, however, was successful at parsing the variance in the BD sample which resulted in the identification of two distinct BD biotypes. Whereas the BD clusters mapped onto specific anatomical and neurocognitive patterns, symptom-based DSM subtypes were not associated with any empirical measures of mental health. Hence, these findings are testament to the potential of unsupervised and unbiased computational methods in the future of BD diagnostics.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation (PhD)

Year dc:date.available

2020

Authors dc:creator

• Tannous, Jonika
• <p>0000-0003-4022-9267</p>

Contributors dc:contributor

• Jair Soares, MD, PhD
• Benson Mwangi, PhD
• Khader Hasan, PhD

• bipolar disorder
• neuroimaging
• machine learning
• biotypes
• psychiatry
• Medicine and Health Sciences
• Mental Disorders

Repository record dc:identifier

https://digitalcommons.library.tmc.edu/utgsbs_dissertations/1013

OAI identifier oai:identifier

oai:digitalcommons.library.tmc.edu:utgsbs_dissertations-2054