TY - GEN
T1 - Ultra-high-order ICA
T2 - Wavelets and Sparsity XVIII 2019
AU - Iraji, Armin
AU - Faghiri, Ashkan
AU - Lewis, Noah
AU - Fu, Zening
AU - Deramus, Thomas
AU - Qi, Shile
AU - Rachakonda, Srinivas
AU - Du, Yuhui
AU - Calhoun, Vince
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
KW - functional segmentation and parcellation
KW - resting state fMRI (rsfMRI)
KW - Ultra-high-order ICA
UR - http://www.scopus.com/inward/record.url?scp=85077131075&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85077131075&partnerID=8YFLogxK
U2 - 10.1117/12.2530106
DO - 10.1117/12.2530106
M3 - Conference contribution
AN - SCOPUS:85077131075
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Wavelets and Sparsity XVIII
A2 - Van De Ville, Dimitri
A2 - Van De Ville, Dimitri
A2 - Papadakis, Manos
A2 - Lu, Yue M.
PB - SPIE
Y2 - 13 August 2019 through 15 August 2019
ER -