An Introduction to the Analysis of Functional Magnetic Resonance Imaging Data

Functional magnetic resonance imaging (fMRI) is a brain imaging technology primarily used to investigate how cognitive processes affect neural activity. Due to its non-invasiveness and high spatial resolution, this technology has quickly become one of the most important research tools in cognitive neuroscience and has played a growing role in a number of clinical applications. The interpretation of the results of an fMRI experiment involves the analysis of massive amounts of noisy, complex, multivariate data, resolved both spatially and temporally. The extraction of information from this data is a difficult and articulated task, which relies on methodologies lying at the intersection between image processing, statistics, and machine learning. We here introduce the reader to the rich and diverse literature in the fascinating field of fMRI data analysis, providing an overview of its main challenges and of the most common approaches to overcome them.

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