Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists

The 21st century marks the emergence of "big data" with a rapid increase in the availability of data sets with multiple measurements. In neuroscience, brain-imaging datasets are more commonly accompanied by dozens or even hundreds of phenotypic subject descriptors on the behavioral, neural, and genomic level. The complexity of such "big data" repositories offer new opportunities and pose new challenges for systems neuroscience. Canonical correlation analysis (CCA) is a prototypical family of methods that is useful in identifying the links between variable sets from different modalities. Importantly, CCA is well suited to describing relationships across multiple sets of data and so is well suited to the analysis of big neuroscience datasets. Our primer discusses the rationale, promises, and pitfalls of CCA.