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Abstract #3017

Gaussian process classification of Alzheimer's disease and mild cognitive impairment from resting state fMRI

Edward Challis 1,2 , Barbara Spano 3 , Laura Serra 3 , Marco Bozzali 3 , Seb Oliver 1 , and Mara Cercignani 2

1 Physics and Astronomy, University of Sussex, Brighton, Sussex, United Kingdom, 2 Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Brighton, Sussex, United Kingdom, 3 Neuroimaging Laboratory, IRCSS Santa Lucia, Rome, Italy

Statistical machine learning techniques are seeing increased interest by the neuroimaging community. Simultaneously clinicians and researchers are also studying the functional connectivity patterns of brains and how these relations might change in conditions like Alzheimers disease or clinical depression. In this study we investigate the performance of Gaussian process classifiers to perform patient stratification from functional connectivity patterns of brains at rest. The majority of previous approaches to such problems have focused on using support vector machines to perform classification in this setting. Our results confirm that Gaussian process classifiers form a promising direction for future research.

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