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

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 le...