Bayesian nonparametric modeling of psychiatric disorders

Mención Internacional en el título de doctorMental health care has become one of the major priorities in developed countries, where the annual budgets assigned to mental health care reach hundreds of billion of dollars. Due to lack of laboratory tests as objective diagnostic criteria, there is not consensus among the psychiatrists either on the diagnostic criteria or the treatments. As a consequence, there exists an increasing interest in improving both the detection and treatment of mental disorders. This thesis is an interdisciplinary work, in which we study the causes behind suicide attempts and provide thorough analysis of pathological and comorbidity patterns of mental disorders. The final goal of this study is to help psychiatrists detect people with higher risk and guide them to improve treatments. To this end, we apply latent feature modeling to the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), which collects information about the mental health of the U.S. population. In order to avoid the model selection step needed to infer the number of variables in the latent feature model, we make use of the Indian Buffet Process (IBP) [27]. However, the discrete nature of the database does not allow us to use the standard Gaussian observation model, and therefore, we need to adapt the observation model to discrete random variables. In a first step, we propose an IBP model for categorical observations, which are the most common in the NESARC. We consider two likelihood observation models: a multinomial-logit and a multinomial-probit model. We derive efficient Monte-Carlo Markov chain (MCMC) inference algorithms that resort to either the Laplace approximation or the expectation propagation (EP) algorithm to compute the marginal likelihood. We also derive a variational inference algorithm that provides a less expensive, in terms of computational complexity, alternative to the samplers. Afterwards, to account for all the available information about the subjects (that includes also non categorical observations, such as age, incomes or education level), we extend the IBP observation model to handle mixed continuous (real-valued and positive real-valued) and discrete (categorical, ordinal and count) observations. This model keeps the properties of conjugate models and allows us to derive an inference algorithm that scales linearly with the number of observations. Finally, we present the experimental results obtained after applying the proposed models to the NESARC database, studying both the hidden causes behind suicide attempts and the pathological and comobidity patterns of mental disorders.La salud mental se ha convertido en una de las principales prioridades de los países desarrollados, los cuales dedican anualmente cientos de miles de millones de dólares al cuidado de la misma. Debido a la falta de pruebas de laboratorio como criterios objetivos para el diagnóstico de los desórdenes mentales, existe una falta de consenso tanto en los criterios de diagnóstico como en los tratamiento. Esta tesis es un trabajo interdisciplinario que tiene como propósito encontrar las causas latentes detrás de los intentos de suicidio y proveer de un profundo análisis sobre los patrones, tanto patológicos como de comorbilidad, de los desórdenes psiquiátricos. Como objetivo final de este trabajo, pretendemos ayudar a los psiquiatras a detectar aquellas personas con mayor riesgo de sufrir de desórdenes mentales, y guiarlos en la categorización y los tratamientos para dichos desórdenes. Para ello, aplicamos modelado de características latentes a la base de datos NESARC (National Epidemiologic Survey on Alcohol and Related Conditions), la cual contiene información sobre la salud mental de una muestra representativa de la población estadounidense. Con el fin de evitar fijar la complejidad del modelo a priori, recurrimos al Indian Buffet Process (IBP) [27]. Sin embargo, debido a la naturaleza discreta de la base de datos, debemos adaptar a observaciones discretas el modelo de observación del IBP, que normalmente asume verosimilitudes Gaussianas. Inicialmente, adaptamos el modelo de observación del IBP a datos categóricos, los más comunes en la NESARC. Para ello, consideramos dos funciones de verosimilitud (la multinomial-logit y la multinomial-probit) y desarrollamos algoritmos de inferencia basados en muestreo (Monte-Carlo Markov chain) los cuales recurren a la aproximación de Laplace o al algoritmo Expectation Propagation para calcular la verosimilitud marginal. Adicionalmente, derivamos un algoritmo variacional que presenta menor complejidad que los algoritmos de muestreo. Después, con el fin de tener en cuenta en nuestro análisis toda la información disponible en la base de datos (que incluye otras variables no categóricas como la edad, los ingresos anuales o el nivel de estudios), proponemos un modelo de observación para el IBP que permite manejar bases de datos heterogéneas. Este modelo mantiene las propiedades de los modelos conjugados y permite derivar un algoritmo de inferencia de complejidad lineal con el número de observaciones. Finalmente, analizamos los resultados obtenidos al aplicar los modelos propuestos a la base de datos NESARC, estudiando tanto las causas latentes del suicidio como los patrones patológicos y de comorbilidad de los desórdenes mentales.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Antonio Artés Rodríguez.- Secretario: Juan José Murillo Fuentes.- Vocal: Sinead Williamso

Complexity Variability Assessment of Nonlinear Time-Varying Cardiovascular Control

The application of complex systems theory to physiology and medicine has provided meaningful information about the nonlinear aspects underlying the dynamics of a wide range of biological processes and their disease-related aberrations. However, no studies have investigated whether meaningful information can be extracted by quantifying second-order moments of time-varying cardiovascular complexity. To this extent, we introduce a novel mathematical framework termed complexity variability, in which the variance of instantaneous Lyapunov spectra estimated over time serves as a reference quantifier. We apply the proposed methodology to four exemplary studies involving disorders which stem from cardiology, neurology and psychiatry: Congestive Heart Failure (CHF), Major Depression Disorder (MDD), Parkinson?s Disease (PD), and Post-Traumatic Stress Disorder (PTSD) patients with insomnia under a yoga training regime. We show that complexity assessments derived from simple time-averaging are not able to discern pathology-related changes in autonomic control, and we demonstrate that between-group differences in measures of complexity variability are consistent across pathologies. Pathological states such as CHF, MDD, and PD are associated with an increased complexity variability when compared to healthy controls, whereas wellbeing derived from yoga in PTSD is associated with lower time-variance of complexity

Folk Classification and Factor Rotations:Whales, Sharks, and the Problems With the Hierarchical Taxonomy of Psychopathology (HiTOP)

The Hierarchical Taxonomy of Psychopathology (HiTOP) uses factor analysis to group self-reported symptoms of mental illness (i.e., like goes with like). It is hailed as a significant improvement over other diagnostic taxonomies. However, the purported advantages and fundamental assumptions of HiTOP have received little, if any, scientific scrutiny. We critically evaluated five fundamental claims about HiTOP. We conclude that HiTOP does not demonstrate a high degree of verisimilitude and has the potential to hinder progress on understanding the etiology of psychopathology. It does not lend itself to theory building or taxonomic evolution, and it cannot account for multifinality, equifinality, or developmental and etiological processes. In its current form, HiTOP is not ready to use in clinical settings and may result in algorithmic bias against underrepresented groups. We recommend a bifurcation strategy moving forward in which the Diagnostic and Statistical Manual of Mental Disorders is used in clinical settings while researchers focus on developing a falsifiable theory-based classification system

Out of Joint: The Nexus of Madness and Time-Consciousness

According to Edmund Husserl, time is one of the most difficult phenomenological problems, and difficulties arise as soon as we attempt to reach an understanding of how temporal objectivity can become constituted in the subjective consciousness of time. I am focusing on one aspect of time-consciousness - the role it plays in studies of mental illness - in order to expose the difficulties, and ultimately the social problems, that arise when Husserlian structures of time-consciousness are taken as a medically normal foundation. In the first part of this project, I argue that Husserlian structures of time-consciousness are uncritically reliant upon a linear flow of time itself and a linear flow of consciousness. In addition, the structures of time-consciousness must implicitly belong to a psychologically normal consciousness, because Husserl expressly excludes the insane from empathetic activities like world-time constitution due to the insane\u27s lack of rational capacity. To support my claims, I turn to phenomenological psychiatry and cognitive science that study mental illness. Husserlian structures of time-consciousness have been used to explain the way abnormal experiences of time occur in certain patients diagnosed with mental illness. Historically speaking, this justifies my claim that Husserlian structures of time-consciousness can be taken as normal. In the second part of this project, I frame my critique of these difficulties in terms of Michel Foucault\u27s historical epistemic conditions and in terms of power relations. Using the epistemic conditions Foucault claims frame the modern era (starting from the end of the 18th century), I construct an explanation for the employment of Husserlian phenomenology in the psychiatric field, based on a reliance upon the linear flow of time and linear flow of consciousness. Nevertheless, I argue that we must be cautious about viewing time-consciousness as a normal feature of human consciousness. This type of application is used in the service of what Foucault calls demonstrative truth that has dominated thought for centuries. This ultimately leads me to a critique of both truth and the social effects of using Husserlian time-consciousness as a tool in normalization practices, specifically medicalization, that can marginalize individuals unfairly