Share Email Print
cover

Proceedings Paper • new

Improved estimation of dynamic connectivity from resting-state fMRI data
Author(s): Biao Cai; Julia M. Stephen; Tony W. Wilson; Vince D. Calhoun; Yu-Ping Wang
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Functional magnetic resonance imaging (fMRI) has been widely used for neuronal connectivity analysis. As a datadriven technique, independent component analysis (ICA) has become a valuable tool for fMRI studies. Recently, due to the dynamic nature of the human brain, time-varying connectivity analysis is regarded as an important measure to reveal essential information within the network. The sliding window approach has been commonly used to extract dynamic information from fMRI time series. However, it has some limitations due to the assumption that connectivity at a given time can be estimated from all the samples of the input time series data spanned by the selected window. To address this issue, we apply a time-varying graphical lasso model (TVGL) proposed by Hallac et al., which can infer the network even when the observation interval is at only one time point. On the other hand, recent results have shown that the individual’s connectivity profiles can be used as “fingerprint” to identify subjects from a large group. We hypothesize that the subject-specific FC profiles may have the critical effect on analyzing FC dynamics at a group level. In this work, we apply a group ICA (GICA) based data-driven framework to assess dynamic functional network connectivity (dFNC), based on the combination of GICA and TVGL. Also, we use the regression model to remove the subject-specific individuality in detecting functional dynamics. The results prove our hypothesis and suggest that removing the individual effect may benefit us to assess the connectivity dynamics within the human brain.

Paper Details

Date Published: 15 March 2019
PDF: 6 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109490P (15 March 2019); doi: 10.1117/12.2512976
Show Author Affiliations
Biao Cai, Tulane Univ. (United States)
Julia M. Stephen, The Mind Research Network (United States)
Tony W. Wilson, Univ. of Nebraska Medical Ctr. (United States)
Vince D. Calhoun, The Mind Research Network (United States)
The Univ. of New Mexico (United States)
Yu-Ping Wang, Tulane Univ. (United States)


Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

© SPIE. Terms of Use
Back to Top