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Ultra-high-order ICA: an exploration of highly resolved data-driven representation of intrinsic connectivity networks (sparse ICNs)
Author(s): Armin Iraji; Ashkan Faghiri; Noah Lewis; Zening Fu; Thomas DeRamus; Shile Qi; Srinivas Rachakonda; Yuhui Du; Vince Calhoun
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Paper Abstract

Spatial independent component analysis (sICA) has become an integral part of functional MRI (fMRI) studies, particularly with resting-state fMRI. Early work used low-order ICA with between 20 and 45 components, which has led to the identification of around a dozen reproducible, distributed, large-scale brain networks. While regions within each largescale network are fairly temporally coherent, later studies have shown that each distributed network can be split into a group of spatially granular, and temporally covarying functional parcels. Thus, higher model order ICAs (75~150 components) have been employed to identify functional units known as intrinsic connectivity networks (ICNs). Our recent work suggests that an ICA framework can identify even more granular and functionally homogeneous brain functional units, and has the potential to provide more precise estimates of ICNs. In this study, we adopted an ICA with 1000 components (1k-ICA) to parcellate the brain into fine-grain sparse but overlapping ICNs and evaluated their properties and reliability in various ways. Our findings show that ultra-high-order ICA approaches like 1k-ICA can provide reliable, spatially-sparse ICNs.

Paper Details

Date Published: 9 September 2019
PDF: 9 pages
Proc. SPIE 11138, Wavelets and Sparsity XVIII, 111380I (9 September 2019); doi: 10.1117/12.2530106
Show Author Affiliations
Armin Iraji, Tri-Institutional Ctr. for Translational Research in Neuroimaging and Data Science (TReNDS) (United States)
Ashkan Faghiri, Tri-Institutional Ctr. for Translational Research in Neuroimaging and Data Science (TReNDS) (United States)
Noah Lewis, Tri-Institutional Ctr. for Translational Research in Neuroimaging and Data Science (TReNDS) (United States)
Zening Fu, Tri-Institutional Ctr. for Translational Research in Neuroimaging and Data Science (TReNDS) (United States)
Thomas DeRamus, Tri-Institutional Ctr. for Translational Research in Neuroimaging and Data Science (TReNDS) (United States)
Shile Qi, Tri-Institutional Ctr. for Translational Research in Neuroimaging and Data Science (TReNDS) (United States)
Srinivas Rachakonda, Tri-Institutional Ctr. for Translational Research in Neuroimaging and Data Science (TReNDS) (United States)
Yuhui Du, Shanxi Univ. (China)
Vince Calhoun, Tri-Institutional Ctr. for Translational Research in Neuroimaging and Data Science (TReNDS) (United States)


Published in SPIE Proceedings Vol. 11138:
Wavelets and Sparsity XVIII
Dimitri Van De Ville; Manos Papadakis; Yue M. Lu, Editor(s)

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