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Learning Patterns of the Brain: Machine Learning Challenges of fMRI Analysis
This video was recorded at Carnegie Mellon Machine Learning Lunch seminar. Functional Magnetic Resonance Imaging (fMRI) has given neuroscientists and cognitive psychologists incredible power to analyze the deep mysteries of the human brain. With this powerful imaging technology, however, many new challenges have arisen for the statistics and machine learning communities. In this talk, I will present an overview of fMRI and some of the current machine learning challenges. I will discuss recent work on hierarchical Bayesian methods for dealing with high dimensional, sparse data. I will also discuss the application of classical order statistics to the problem of feature selection. Finally, I will show some of our latest results combining a large text corpus with fMRI to produce a generative model of neuro-activation for arbitrary words in the English language.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
