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

• Published in: NeuroImage (Orlando, Fla.) Vol. 216; p. 116745
• Main Authors: Wang, Hao-Ting, Smallwood, Jonathan, Mourao-Miranda, Janaina, Xia, Cedric Huchuan, Satterthwaite, Theodore D., Bassett, Danielle S., Bzdok, Danilo
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.08.2020; Elsevier Limited; Elsevier
• Subjects: Behavior; Big Data; Brain research; Cognition & reasoning; Correlation analysis; Data science; Datasets; Decomposition; Deep phenotyping; Disease; Genetics; Humans; Machine Learning; Mathematics; Modality fusion; Models, Statistical; Nervous system; Neuroimaging; Neuroimaging - methods; Neurosciences; Neurosciences - methods; Personality; Personality traits; Principal components analysis; Statistical analysis; Variables; Data science; Deep phenotyping; Big data; Modality fusion; Machine learning; Data Science; Big Data; Neuroscience; Machine Learning
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2020.116745

The 21st century marks the emergence of “big data” with a rapid increase in the availability of datasets with multiple measurements. In neuroscience, brain-imaging datasets are more commonly accompanied by dozens or 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, such as in recently available big biomedical datasets. Our primer discusses the rationale, promises, and pitfalls of CCA. •Introduction to the feature of canonical correlation analysis and its applications in combining two or more domains of data, such as behavioural and neuroimaging measures.•The utility of different variations the pros/cons of CCA.•Tips on application of CCA on rich phenotype datasets such as UK Biobank and HCP.