Testing Independent Component Patterns by Inter-Subject or Inter-Session Consistency

• Published in: Frontiers in human neuroscience Vol. 7; p. 94
• Main Authors: Hyvärinen, Aapo, Ramkumar, Pavan
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 22.03.2013; Frontiers Media S.A
• Subjects: Activity patterns; Brain mapping; Computational neuroscience; Functional magnetic resonance imaging; Group analysis; Hypotheses; independent component analysis (ICA); inter-subject consistency; Methods; Neuroimaging; Neuroscience; Resting-state fMRI; Significance testing; Statistical analysis; Theory; Finland; independent component analysis; inter-subject consistency; significance testing; resting-state fMRI; group analysis
• ISSN: 1662-5161; 1662-5161
• DOI: 10.3389/fnhum.2013.00094

Independent component analysis (ICA) is increasingly used to analyze patterns of spontaneous activity in brain imaging. However, there are hardly any methods for answering the fundamental question: are the obtained components statistically significant? Most methods considering the significance of components either consider group-differences or use arbitrary thresholds with weak statistical justification. In previous work, we proposed a statistically principled method for testing if the coefficients in the mixing matrix are similar in different subjects or sessions. In many applications of ICA, however, we would like to test the reliability of the independent components themselves and not the mixing coefficients. Here, we develop a test for such an inter-subject consistency by extending our previous theory. The test is applicable, for example, to the spatial activity patterns obtained by spatial ICA in resting-state fMRI. We further improve both this and the previously proposed testing method by introducing a new way of correcting for multiple testing, new variants of the clustering method, and a computational approximation which greatly reduces the memory and computation required.