Learning About Brain: Sparse Modeling and Beyond: Sparse modeling is a rapidly developing area at the intersection of statistical learning and signal processing, motivated by the age-old statistical problem of finding a relatively small subset of ”important” variables in high-dimensional datasets. Variable selection is particularly important for improving the interpretability of predictive models in scientific applications such as computational biology and neuroscience, where the main objective is to gain a better insight into functioning of a biological system, besides just learning ”black-box” predictors. Moreover, variable selection provides an effective way of avoiding the “curse of dimensionality” as it helps to prevent overfitting and reduce computational complexity in high-dimensional but relatively small-sample datasets, such as, for example, functional MRI (fMRI), where the number of variables (brain voxels) can range from 10 to 100 thousands, while the number of samples is typically limited to several hundreds.
In this talk, I will summarize our work on sparse models and other machine-learning approaches to ”brain decoding” (aka ”mind reading”), i.e. to prediction of mental states from functional MRI data, in a wide range of applications, from analyzing pain perception to discovering predictive patterns of brain activity associated with schizophrenia and cocaine addiction. I will mention several lessons learned from those applications that can hopefully generalize to other practical machine-learning problems. Finally, I will briefly discuss our recent project that focuses on inferring mental states from ”cheap” (unlike fMRI), easily collected data, such as speech and wearable sensors, with applications ranging from clinical settings (”computational psychiatry”) to everyday life (”augmented human”).