Jesús Ramírez Gamboa

I use Heidegger’s Being and Time to understand and critique racial discourse, but to also determine Heidegger’s reach into issues like racial identity. I start by examining how his introductory statements in Being and Time on the term “existentiell” suggest a path towards a conception of identity. I then go into how a racial identity could, through his terminology, be conceived as what I call a “fear existentiell.” I demonstrate how society assists the individual in maintaining a racialized existence that is embedded in fear. I move toward an examination of Heidegger’s three concepts of death to demonstrate how two of these death concepts (verenden and ableben) are often attached to a racial identity through racial discourse. I move into a discussion that relies on W.E.B. Du Bois’s conception of the black American as a problem for America and, using his seminal question, “How does it feel to be a problem?” I show that the individual’s existence, as discussed in Heidegger, becomes a problem for itself through the racial discourse about its identity. I call this “Being-a-problem.” At first, I discuss it as inauthentic, as it is driven by social fear and generalized discussions about racial identity; however, I later demonstrate how it can move from inauthentic Being-a-problem to authentic Being-a-problem. I end with an analysis of racial discourse, showing that it exemplifies what I call, “race reports,” which are comprised of reports about the individual’s racial identity that conceal a person’s potential for authenticity.

This paper presents a method for selecting Regions of Interest in brain Magnetic Resonance Imaging for diagnostic purposes, using statistical learning and vector quantization techniques. The proposed method models the distribution of GM and WM tissues grouping the voxels belonging to each tissue in ROIs associated to a specific neurological disorder. Tissue distribution of normal and abnormal images is modelled by a Self-Organizing map, generating a set of representative prototypes, and the receptive field of each SOM prototype defines a ROI. Moreover, the proposed method computes the relative importance of each ROI by means of its discriminative power. The devised method has been assessed using 818 images from the Alzheimer's disease Neuroimaging Initiative which were previously segmented through Statistical Parametric Mapping. The proposed algorithm was used over these images to parcel ROIs associated to the Alzheimer's Disease. Additionally, this method can be used to extract a reduced set of discriminative features for classification, since it compresses discriminative information contained in the brain. Voxels marked by ROIs which were computed using the proposed method, yield classification results up to 90% of accuracy for controls and Alzheimer's disease patients, and 84% of accuracy for Mild Cognitive Impairment and AD patients.