Combining Brains: A Survey of Methods for Statistical Pooling of Information

Abstract

More than one subject is scanned in a typical functional brain imaging experiment. How can the scientist make best use of the acquired data to map the specific areas of the brain that become active during the performance of different tasks? It is clear that we can gain both scientific and statistical power by pooling the images from multiple subjects; furthermore, for the comparison of groups of subjects (clinical patients vs healthy controls, children of different ages, left-handed people vs right-handed people, as just some examples), it is essential to have a “group map” to represent each population and to form the basis of a statistical test. While the importance of combining images for these purposes has been recognized, there has not been an organized attempt on the part of neuroscientists to understand the different statistical approaches to this problem, which have various strengths and weaknesses. In this paper we review some popular methods for combining information, and demonstrate the surveyed techniques on a sample data set. Given a combination of brain images, the researcher needs to interpret the result and decide on areas of activation; the question of thresholding is critical here and is also explored.