Interpreting brain image experiments requires analysis of complex, multivariate data. In recent years, one analysis approach that has grown in popularity is the use of machine learning algorithms to train classifiers to decode stimuli, mental states, behaviors and other variables of interest from fMRI data and thereby show the data contain enough information about them. In this tutorial overview we review some of the key choices faced in using this approach as well as how to derive statistically significant results, illustrating each point from a case study. Furthermore, we show how, in addition to answering the question of ‘is there information about a variable of interest ’ (pattern discrimination), classifiers can be used to tackle other classes of question, namely ‘where is the information ’ (pattern localization) and ‘how is that information encoded ’ (pattern characterization). 1

The task of learning models for many real-world problems requires incorporating domain knowledge into learning algorithms, to enable accurate learning from a realistic volume of training data.

Abstract. Modern classification techniques perform well when the number of training examples exceed the number of features. If, however, the number of features greatly exceed the number of training examples, then these same techniques can fail. To address this problem, we present a hierarchical Bayesian framework that shares information between features by modeling similarities between their parameters. We believe this approach is applicable to many sparse, high dimensional problems and especially relevant to those with both spatial and temporal components. One such problem is fMRI time series, and we present a case study that shows how we can successfully classify in this domain with 80,000 original features and only 2 training examples per class. 1

Over the last two decades, Functional Magnetic Resonance Imaging (fMRI) has revolutionized the study of the brain. This non-invasive technique produces snapshots of brain activity over time, allowing researchers to literally peer into the mind as it performs everyday tasks like reading or viewing images. Gradually the need has emerged for fMRI analysis techniques that model activity occurring at numerous locations throughout the brain simultaneously, and make predictions about what a person is doing or thinking solely from his or her brain activity, or ”mind read. ” Machine learning techniques can accomplish both these goals, and thus have become a popular modeling choice; however, most standard machine learning algorithms were designed for problems in which there are relatively few candidate predictor variables, and the modeling objective is to make accurate predictions. In fMRI data, the number of predictor variables can be very large, while the likely number of relevant predictors may be quite small. Furthermore, machine learning algorithms are increasingly being employed in the natural sciences, and while accurate predictions can serve to validate scientific models, the end goal of such modeling is

Machine learning algorithms have been successfully applied to learning classifiers in many domains such as computer vision, fraud detection, and brain image analysis. Typically, classifiers are trained to predict a class value given a set of labeled training data that includes all possible class values, and sometimes additional unlabeled training data. Little research has been performed where the possible values for the class variable include values that have been omitted from the training examples. This is an important problem setting, especially in domains where the class value can take on many values, and the cost of obtaining labeled examples for all values is high. We show that the key to addressing this problem is not predicting the held-out classes directly, but rather by recognizing the semantic properties of the classes such as their physical or functional attributes. We formalize this method as zero-shot learning and show that by utilizing semantic knowledge mined from large text corpora