Ultra-high-order ICA: An exploration of highly resolved data-driven representation of intrinsic connectivity networks (sparse ICNs)

Armin Iraji, Ashkan Faghiri, Noah Lewis, Zening Fu, Thomas Deramus, Shile Qi, Srinivas Rachakonda, Yuhui Du, Vince Calhoun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationWavelets and Sparsity XVIII
EditorsDimitri Van De Ville, Dimitri Van De Ville, Manos Papadakis, Yue M. Lu
ISBN (Electronic)9781510629691
StatePublished - Jan 1 2019
Externally publishedYes
EventWavelets and Sparsity XVIII 2019 - San Diego, United States
Duration: Aug 13 2019Aug 15 2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceWavelets and Sparsity XVIII 2019
Country/TerritoryUnited States
CitySan Diego


  • functional segmentation and parcellation
  • resting state fMRI (rsfMRI)
  • Ultra-high-order ICA

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


Dive into the research topics of 'Ultra-high-order ICA: An exploration of highly resolved data-driven representation of intrinsic connectivity networks (sparse ICNs)'. Together they form a unique fingerprint.

Cite this