Template Independent Component Analysis: Targeted and Reliable Estimation of Subject-level Brain Networks Using Big Data Population Priors

• Published in: Journal of the American Statistical Association Vol. 115; no. 531; pp. 1151 - 1177
• Main Authors: Mejia, Amanda F., Nebel, Mary Beth, Wang, Yikai, Caffo, Brian S., Guo, Ying
• Format: Journal Article
• Language: English
• Published: United States Taylor & Francis 02.07.2020; Taylor & Francis Ltd
• Subjects: Algorithms; Applications and case studies; Bayesian analysis; Bayesian methods; Bayesian theory; Big Data; Brain; Brain size; Computationally intensive methods; Computer simulation; Empirical analysis; Expectation-maximization; Functional magnetic resonance imaging; humans; Independent component analysis; Magnetic resonance imaging; magnetism; Networks; Neuroimaging; Populations; Regression analysis; Reliability; Reliability analysis; Simulation; Statistical methods; Statistics; Wealth

Large brain imaging databases contain a wealth of information on brain organization in the populations they target, and on individual variability. While such databases have been used to study group-level features of populations directly, they are currently underutilized as a resource to inform singl...