Sabharwal, Ashutosh2024-05-212024-052024-04-16May 2024Lamichhane, Bishal. Objective Sociability and Impulsivity Measures for Dimensional Assessment of Mental Health. (2024). PhD diss., Rice University. https://hdl.handle.net/1911/116109https://hdl.handle.net/1911/116109EMBARGO NOTE: This item is embargoed until 2025-05-01Mental health disorders have a high prevalence and are on the rise globally. Subjective self-reports used for mental health assessment, with their recall and reporting biases, pose a scalability challenge for accurate and frequent assessment. Additionally, the existing nosology of disorders has rampant comorbidity and heterogeneity, creating challenges in the diagnosis and treatment. Instead of the dichotomous diagnostic labeling of mental health based on subjective self-reports, as is currently practiced, assessing mental health along the objectively measured bio-behavioral dimensions of functioning could provide a foundation for scalable, robust, and accurate mental healthcare. Towards the goal of improved mental health assessment, this thesis investigated objective measures of sociability and impulsivity, the bio-behavioral dimensions of functioning implicated in several mental health disorders. Sociability represents an individual’s tendency to interact and socialize with others. Sociability deficits and altered sociability patterns are commonly implicated across several mental health disorders. In this thesis, we propose using free-living audio to obtain sociability measures objectively. We developed a deep learning-based audio processing pipeline, ECoNet, that addresses the challenges of free-living audio processing to infer sociability measures accurately. The inferred sociability measures were significantly correlated with dimensional mental health measures in a clinical study comprising participants with diverse mental health conditions. Free-living audio also complemented the conventional mobile sensing modalities for dimensional mental health score prediction. Our results establish the potential of free-living audio-based objective sociability measures to represent mental health state. Impulsivity represents an individual’s tendency to act on urges without sufficient forethought about one’s action. Several mental health disorders commonly implicate heightened impulsivity. In this thesis, we propose using multimodal behavioral, physiological, and neurobiological measurements to model impulsivity objectively. We conducted a clinical study with mood disorder participants and discovered the complementarity of objective modalities to model impulsivity and predict the clinical outcome of suicidality with high accuracy. The complementarity of modalities was additionally validated with a large open-source dataset. To exploit the richness of neurobiological measurements for predicting impulsivity, we developed ImpulsNet. ImpulsNet is a lightweight deep learning model based on multi-task learning that outperforms existing models in the literature to predict impulsivity. Our results establish the feasibility of objectively modeling impulsivity for mental health applications. This thesis provides a foundation for dimensional mental health assessment using objective sociability and impulsivity measures. Further validation in larger studies should be pursued in future work. Other objectively assessed dimensions of functioning should also be explored to better represent mental health in the vastness of bio-behavioral space.application/pdfengCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.Digital healthObjective mental healthfMRIphysiologyHRV, depressionbipolar disordersociabilityimpulsivitymultimodalfree-livingfree-living audioThesis2024-05-21