A Comparative Assessment of Statistical Approaches for fMRI Data to Obtain Activation Maps

Functional Magnetic Resonance Imaging (fMRI) lets us peek into the human mind and try to identify which brain areas are associated with certain tasks without the need for an invasive procedure. However, the data collected during fMRI sessions is complex; this 4 dimensional sequence of 3 dimensional volumes as images of the brain does not allow for straightforward inference. Multiple models have been developed to analyze this data and each comes with its intricacies and problems. Two of the most common ones are 2-step General Linear Model (GLM) and Independent Component Analysis (ICA). We compare these approaches empirically by fitting the models to real fMRI data using packages developed and readily available in R. The real data, obtained from an open source database openneuro.org, is named BOLD5000. The task of interest for this thesis is image viewing versus fixation cross (resting state). We found that both the first-level GLM and ICA revealed significant activation located in the occipital lobe which is consistent with the literature on visual tasks. The second-level GLM results were consistent with the first level and found activation located in the occipital lobe as well. The Group ICA results however found activation located mainly in the temporal lobe.No embargoAcademic Major: Statistic

Modelització de la difusió de la bacteria Candida Auris en el entorn de una Unitat de Vigilància Intensiva (UVI)

[ES] Candida Auris (CA) es un hongo multirresistente que se encontró por primera vez en un paciente japonés en 2009 [1]. CA puede causar candidiasis superficial y también infecciones invasivas como la candidiasis intraabdominal, la otitis media crónica y las infecciones del torrente sanguíneo. Estas complicaciones son más probables en pacientes gravemente enfermos e inmunodeprimidos, como los ingresados en Unidades de Cuidados Intensivos (UCI). Tras la colonización de un paciente de la UCI con CA, si no se toman medidas de prevención, se estima que toda la UCI estará colonizada en 48h. Por lo tanto, el personal de la UCI tiene que emprender costosas medidas para controlar la propagación del CA como la realización de pruebas semanales a los pacientes, el aislamiento de los pacientes colonizados detectados y la limpieza en profundidad. Para hacer frente a este problema, los modelos matemáticos nos permiten simular la propagación de la población de CA en un entorno como la UCI de una manera eficiente en cuanto a tiempo y costes. En este trabajo, proponemos modelar la difusión biológica de CA dentro de la UCI con la ecuación de reacción-difusión de Fisher Kolmogorov-Petrovsky-Piskunov (Fisher-KPP) [2]. Para calibrar los parámetros del modelo se han utilizado datos de crecimiento in vitro de CA e información del personal médico experto de la UCI. Con los parámetros obtenidos de la calibración introducimos unos factores de limpieza que reducen la cantidad de CA en diferentes porcentajes a intervalos de tiempo, y describimos su eficiencia en el control de la población. [1] Kazuo Satoh, Koichi Makimura, Yayoi Hasumi, Yayoi Nishiyama, Katsuhisa Uchida y Hideyo Yamaguchi. Candida auris sp. nov., a novel ascomycetous yeast isolated from the exter- nal ear canal of an inpatient in a japanese hospital. Microbiology and immunology, 53(1):41-44, 2009. [2] Vladimir M Tikhomirov. Selected Works of AN Kolmogorov: Volume I: Mathematics and Mechanics, volumen 25. Springer Science & Business Media, 1991.[EN] Candida Auris (CA) is a multi-drug resistant yeast that was first found in a Japanese patient in 2009 [1]. CA can cause superficial candidiasis and also invasive infections such as intra-abdominal candidiasis, chronic otitis media, and bloodstream infections. These complications are more likely to occur in severely ill and immunosuppressed patients, like those admitted to Intensive Care Units (ICU). After the colonization of an ICU patient with CA., if no prevention measures were to be taken, it is estimated that the whole ICU would be colonized within 48h. Therefore, ICU personnel have to undertake costly measures to control the spread of CA such as weekly testing of patients, isolation of detected colonized patients, and in depth cleaning. To face this problem, mathematical models allow us to simulate the CA population spread in a environment such as ICU in a time and cost efficient manner. In this work, we propose to model the biological diffusion of CA within the ICU with the Fisher Kolmogorov¿Petrovsky¿Piskunov (Fisher¿KPP) reaction-diffusion equation [2]. To calibrate the model¿s parameters, CA in vitro growth data and expert ICU medical personnel information has been used. With the parameters obtained from the calibration we introduce a cleaning factors that reduces the CA quantity by different percentages at timed intervals, and describe how effective these are at population control. [1] Kazuo Satoh, Koichi Makimura, Yayoi Hasumi, Yayoi Nishiyama, Katsuhisa Uchida, and Hideyo Yamaguchi. Candida auris sp. nov., a novel ascomycetous yeast isolated from the exter- nal ear canal of an inpatient in a japanese hospital. Microbiology and immunology, 53(1):41¿44, 2009. [2] Vladimir M Tikhomirov. Selected Works of AN Kolmogorov: Volume I: Mathematics and Mechanics, volume 25. Springer Science & Business Media, 1991.Perez Diukina, C. (2022). Modelización de la difusión de la bacteria Candida Auris en el entorno de una Unidad de Cuidados Intensivos (UCI). Universitat Politècnica de València. http://hdl.handle.net/10251/18551

A reaction-diffusion equation to model the population of Candida Auris in an Intensive Care Unit

[EN] In order to model a population of microorganisms in a given environment through time and space, ordinary differential equations (ODEs) and partial differential equations (PDEs) are, respectively, well studied and very valuable tools. In practice, exact solutions are few and so numerical solutions are often used to describe the dynamic behaviour of the population through time. In order to assert that the numerical solutions are modelling real world phenomena, it is important to calibrate these models with biological and physical data. In this work, we have applied the Fisher Kolmogorov- Petrovsky¿Piskunov (FKPP) equation to model the change in density trough time of Candida Auris (CA) inside an Intensive Care Unit (ICU). The multi-drug resistant yeast CA poses a global threat to the healthcare environment. This model allows us to evaluate the efficacy of well timed cleaning measures on CA population control in the ICU.Perez-Diukina, C.; Cortés, J.; Villanueva Micó, RJ. (2022). A reaction-diffusion equation to model the population of Candida Auris in an Intensive Care Unit. Universitat Politècnica de València. 91-96. http://hdl.handle.net/10251/192423919