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Ranking and averaging independent component analysis by reproducibility (RAICAR) by Zhi Yang; Stephen LaConte; Xuchu Weng; Xiaoping Hu is a Neuroscience article available to read on EtoBox.

What is Ranking and averaging independent component analysis by reproducibility (RAICAR) about?

## Abstract Independent component analysis (ICA) is a data‐driven approach that has exhibited great utility for functional magnetic resonance imaging (fMRI). Standard ICA implementations, however, do not provide the number and relative importance of the resulting components. In addition, ICA algorithms utilizing gradient‐based optimization give decompositions that are dependent on initialization values, which can lead to dramatically different results. In this work, a new method, RAICAR (Ranking and Averaging Independent Component Analysis by Reproducibility), is introduced to address these issues for spatial ICA applied to fMRI. RAICAR utilizes repeated ICA realizations and relies on the reproducibility between them to rank and select components. Different realizations are aligned based on correlations, leading to aligned components. Each component is ranked and thresholded based on between‐realization correlations. Furthermore, different realizations of each aligned component are selectively averaged to generate the final estimate of the given component. Reliability and accuracy of this method are demonstrated with both simulated and experimental fMRI data. Hum Brain Mapp, 2008.

Who reads Ranking and averaging independent component analysis by reproducibility (RAICAR)?

Author

Zhi Yang; Stephen LaConte; Xuchu Weng; Xiaoping Hu

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Inc.; Wiley; Research Square (ISSN 1065-9471)

Published

2007

Language

EN

Field

Neuroscience (Life Sciences)