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Front Hum Neurosci. 2012 Nov 6;6:301. doi: 10.3389/fnhum.2012.00301. eCollection 2012.

Fully exploratory network independent component analysis of the 1000 functional connectomes database.

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1
MR Centre of Excellence, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna Vienna, Austria ; Department of Statistics and Probability Theory, Vienna University of Technology Vienna, Austria.

Abstract

The 1000 Functional Connectomes Project is a collection of resting-state fMRI datasets from more than 1000 subjects acquired in more than 30 independent studies from around the globe. This large, heterogeneous sample of resting-state data offers the unique opportunity to study the consistencies of resting-state networks at both subject and study level. In extension to the seminal paper by Biswal et al. (2010), where a repeated temporal concatenation group independent component analysis (ICA) approach on reduced subsets (using 20 as a pre-specified number of components) was used due to computational resource limitations, we herein apply Fully Exploratory Network ICA (FENICA) to 1000 single-subject independent component analyses. This, along with the possibility of using datasets of different lengths without truncation, enabled us to benefit from the full dataset available, thereby obtaining 16 networks consistent over the whole group of 1000 subjects. Furthermore, we demonstrated that the most consistent among these networks at both subject and study level matched networks most often reported in the literature, and found additional components emerging in prefrontal and parietal areas. Finally, we identified the influence of scan duration on the number of components as a source of heterogeneity between studies.

KEYWORDS:

ICA; default-mode network; fMRI; magnetic resonance imaging; resting-state

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