An analytical workflow for seed-based correlation and independent component analysis in interventional resting-state fMRI studies

• Published in: Neuroscience research 2021-04, Vol.165, p.26-37
• Main Authors: Seewoo, Bhedita J., Joos, Alexander C., Feindel, Kirk W.
• Format: Article
• Language: English
• Subjects: Brain - diagnostic imaging; Brain Mapping; denoising; FSL; functional magnetic resonance imaging; ICA; Magnetic Resonance Imaging; Reproducibility of Results; resting-state networks; SCA; Workflow
• ISSN: 0168-0102; 1872-8111
• DOI: 10.1016/j.neures.2020.05.006

•rs-fMRI is used to identify and compare resting-state networks in the brain.•Seed-based correlation and independent component analysis methods are investigated.•Baseline-only template is recommended over mixed templates in ICA.•ICA-based denoising method is recommended over nuisance signal regression in SCA.•In SCA, seed should be selected from individual functional connectivity maps. Resting-state functional MRI (rs-fMRI) is a task-free method of detecting spatially distinct brain regions with correlated activity, which form organised networks known as resting-state networks (RSNs). The two most widely used methods for analysing RSN connectivity are seed-based correlation analysis (SCA) and independent component analysis (ICA) but there is no established workflow of the optimal combination of analytical steps and how to execute them. Rodent rs-fMRI data from our previous longitudinal brain stimulation studies were used to investigate these two methods using FSL. Specifically, we examined: (1) RSN identification and group comparisons in ICA, (2) ICA-based denoising compared to nuisance signal regression in SCA, and (3) seed selection in SCA. In ICA, using a baseline-only template resulted in greater functional connectivity within RSNs and more sensitive detection of group differences than when an average pre/post stimulation template was used. In SCA, the use of an ICA-based denoising method in the preprocessing of rs-fMRI data and the use of seeds from individual functional connectivity maps in running group comparisons increased the sensitivity of detecting group differences by preventing the reduction in signals of interest. Accordingly, when analysing animal and human rs-fMRI data, we infer that the use of baseline-only templates in ICA and ICA-based denoising and individualised seeds in SCA will improve the reliability of results and comparability across rs-fMRI studies.