Ranking and averaging independent component analysis by reproducibility (RAICAR)

• Authors: Zhi Yang, Emory University; Stephen LaConte, Emory University; Xuchu Weng, Chinese Academy of Sciences; Xiaoping Hu, Emory University
• Journal Title: Human Brain Mapping
• Volume: Volume 29, Number 6
• Publisher: Wiley | 2008-06-01, Pages 711-725
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1002/hbm.20432
• Subjects: Health Sciences, Radiology; Engineering, Biomedical; Biology, Neuroscience
• Keywords: Science & Technology; Life Sciences & Biomedicine; Neurosciences; Neuroimaging; Radiology, Nuclear Medicine & Medical Imaging; Neurosciences & Neurology; fMRI; independent component analysis; data analysis; Functional connectivity; Motor cortex; Time series; FMRI data; Algorithms

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.