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Updated: May 1, 2026
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Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy
Published on: May 27, 2020
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The effect of echo time and post-processing procedure on blood oxygenation level-dependent (BOLD) functional
Swati Rane1, Emily Mason2, Erin Hussey2
1Radiology and Radiological Sciences, Vanderbilt University School of Medicine, Nashville, TN, USA.
Neuroimage
|March 29, 2014
Summary
Quantitative R2* maps improve functional connectivity detection using resting-state fMRI (BOLD). R2*-derived networks show higher explained variance and are less sensitive to motion and physiological noise than BOLD data across various echo times.
Area of Science:
- Neuroimaging
- Functional Magnetic Resonance Imaging (fMRI)
Background:
- Spontaneous BOLD fMRI signal is crucial for mapping functional connectivity but suffers from inter- and intra-subject variability.
- This variability may stem from differences in echo time (TE) sensitivity, complicating data interpretation.
- Quantitative R2* mapping offers an alternative approach to assess brain networks.
Purpose of the Study:
- To investigate the impact of different echo times (TEs) and quantitative R2* maps on detecting resting-state networks (Default Mode Network and Visual Network).
- To compare the robustness of BOLD fMRI and R2* derived connectivity against motion and physiological noise.
- To evaluate the influence of post-processing techniques on network detection.
Main Methods:
- BOLD fMRI data were acquired at three TEs (15ms, 35ms, 55ms) at 3T.
- Independent Component Analysis (ICA) and seed-based correlation analysis were applied to BOLD data and quantitative R2* maps.
- Data were analyzed with and without motion correction and physiological noise removal (CompCor).
Main Results:
- ICA analysis showed significantly higher explained variance using R2*-derived maps compared to single-TE BOLD data.
- R2*-derived Default Mode Network and Visual Network detection were minimally affected by motion correction.
- R2*-derived connectivity patterns were most robust against motion and physiological noise in seed-based correlation analysis.
Conclusions:
- Quantitative R2* mapping enhances the detection and stability of resting-state networks compared to conventional BOLD fMRI.
- R2*-derived networks are less susceptible to common sources of noise, improving reliability.
- Findings highlight the importance of TE selection and post-processing in BOLD fMRI network analysis.
