Psychiatric Phenotyping Using Symptom Profiles: Can Self-Report Symptoms Inform a New Psychiatric Taxonomy?

by

Jessica Ross

The Diagnostic and Statistical Manual (DSM) has served as the gold standard for psychiatric diagnosis for the past several decades in the United States, and it mirrors mental health and substance abuse diagnoses in the ICD-9 and ICD-10, which are used in numerous other countries. However, DSM diagnoses have severe limitations when used as phenotypes for studies of the pathophysiology underlying mental disorders, as well as for clinical treatment and research. This dissertation proposes a novel approach for deconstructing DSM diagnostic criteria using expert knowledge to inform feature selection for unsupervised machine learning. A multimodal dataset comprised of combat veterans, approximately one-third of whom had received a DSM-IV diagnosis of post-traumatic stress disorder (PTSD), is used in these analyses. Unsupervised learning methods are employed to identify robust groups of patients who clustered together with respect to clinical symptoms. Symptom profiles are used to stratify subjects into cohorts who have clinical and biological homogeneity, irrespective of their DSM diagnoses. Clusters identified suggest that prior contrasting biomarker findings in patients with PTSD may be due to heterogeneity that is reduced when using phenotypes derived from self-report psychiatric symptoms. Results of these analyses can be represented in rich clinical phenotypes that relay both clinical and biological markers of interest. These findings suggest that itemized self-report symptom data may be useful to inform a new taxonomy for psychiatry, enhancing the bidirectional translation of knowledge from the bench to the clinic through a common terminology.

Action and perception are codependent processes with continuously updating bidirectional interaction, resulting in a sum that is greater than it’s parts. Here, I focus on the auditory and motor systems, and use musical timing to explore topics including entrainment, simulation, prediction and correction, and motor inhibition. In collaboration with numerous co-authors, I show evidence for a strong relationship between sound perception and body movements. Our auditory environment has clear and measurable impacts on subcortical and cortical movement control and planning (Chapters 2-4), and on neural signatures of movement control (Chapter 5). I show that music can be used to explore predictive and feedback-based (Chapters 2-4, 6,7), as well as inhibitory aspects of movement control (Chapter 5). I discuss claims that neural activity used for movement control and planning can causally contribute to auditory perception (Chapters 6, 7, Discussion), and I present evidence in support of these views (Chapters 6, 7). The robust synergy between human audition and action has enormous potential for clinical applications for people recovering from stroke and with movement disorders, but also for typically aging adults (Chapter 3) and healthy young adults (Chapters 2-4). However, the majority of the work presented here is intended to add insights to the fundamental understanding of the brain as a predicting and adapting organ that guides human action through a fast-paced and multisensory world. This dissertation, Sound Guides Action and Action Scaffolds Sound Perception, is submitted by Jessica M. Ross in the summer of 2018 in partial fulfillment of the degree Doctor of Philosophy in Cognitive and Information Sciences at the University of California, Merced, under the guidance of Ramesh Balasubramaniam.