Robust and Nonparametric Statistical Tools for Big Data in Neuroscience. Henry Laniado Rodas
Escuela de Ciencias Universidad EAFIT. Agosto 4 de 2017
Seminario de Doctorado en Ingeniería Matemática Universidad EAFIT.
Seminar of the PhD in Mathematical Engineering EAFIT University.
Abstract: How the human brain works is one of the most beautiful questions we have been asking our whole life, and it is amazing how the statistical field can help to answer this question. Functional Magnetic Resonance Imaging (fMRI) is one of the top techniques within the neuroimaging field that relates with this topic. The aim of fMRI data analysis is to determine which regions of the brain are either activated or inactivated with respect to an experimental design. In order to do this, one must consider a large partition of the whole brain, consisting of a set of very small cuboid elements called voxels, each of one representing a million of brain cells. After the patient is subjected to some type of stimulus (auditory, visual, mechanical), the result of the entire procedure is an image of the brain, showing some zones that were positively related to the experiment and the rest of the area, represents the non-activated zones, i.e. the areas that did not have relation at all with the experiment. Note that they are actually clusters of voxels—perhaps hundreds of them. This leads to the statistical problem of how to manage this dataset to obtain an image as the explained previously. Complexity and massive amount of this kind of data, and the presence of different types of noises, makes the fMRI data analysis a challenging one; that demands robust and computationally efficient statistical analysis methods for high Dimensional data. The classical approach is to consider in each voxel of the brain a General Linear Model to estimate if the observed signal is significantly similar to the expected signal, in order to decide activation or not activation for each voxel. However, we need to be aware of the assumptions of the models, in order to consider the results as valid and then obtaining correct statistical inference, but with this kind of data, these assumptions do not always hold. So, the adopted methodology to address fMRI statistical analysis lacks of robustness, although it is computationally efficient. We propose here a non-parametric a robust statistical techniques to face this problem, while maintaining efficient computational time, comparable with the classic method. In other wor
Herramientas estadísticas robustas y no paramétricas para grandes datos en neurociencia. Resumen: Cómo funciona el cerebro humano es una de las preguntas más bellas que hemos estado preguntando toda nuestra vida, y es sorprendente cómo el campo estadístico puede ayudar a responder a esta pregunta.