Related Experiment Video
Updated: Jul 2, 2025
Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
Published on: August 5, 2014
Predicting the macrovascular contribution to resting-state fMRI functional connectivity at 3 Tesla: A model-informed
Abstract:
Macrovascular biases have been a long-standing challenge for fMRI, limiting its ability to detect spatially specific neural activity. Recent experimental studies, including our own (Huck et al., 2023; Zhong et al., 2023), found substantial resting-state macrovascular BOLD fMRI contributions from large veins and arteries, extending into the perivascular tissue at 3 T and 7 T. The objective of this study is to demonstrate the feasibility of predicting, using a biophysical model, the experimental resting-state BOLD fluctuation amplitude (RSFA) and associated functional connectivity (FC) values at 3 Tesla. We investigated the feasibility of both 2D and 3D infinite-cylinder models as well as macrovascular anatomical networks (mVANs) derived from angiograms. Our results demonstrate that: 1) with the availability of mVANs, it is feasible to model macrovascular BOLD FC using both the mVAN-based model and 3D infinite-cylinder models, though the former performed better; 2) biophysical modelling can accurately predict the BOLD pairwise correlation near to large veins (with R 2 ranging from 0.53 to 0.93 across different subjects), but not near to large arteries; 3) compared with FC, biophysical modelling provided less accurate predictions for RSFA; 4) modelling of perivascular BOLD connectivity was feasible at close distances from veins (with R 2 ranging from 0.08 to 0.57), but not arteries, with performance deteriorating with increasing distance. While our current study demonstrates the feasibility of simulating macrovascular BOLD in the resting state, our methodology may also apply to understanding task-based BOLD. Furthermore, these results suggest the possibility of correcting for macrovascular bias in resting-state fMRI and other types of fMRI using biophysical modelling based on vascular anatomy.
Insights
Biophysical modeling can predict macrovascular contributions to resting-state fMRI functional connectivity near veins. This approach may help correct for macrovascular bias in fMRI studies.
Area of Science:
- Neuroimaging
- Biophysics
- Vascular Biology
Background:
- Macrovascular signals in BOLD fMRI pose challenges for detecting specific neural activity.
- Large veins and arteries contribute significantly to resting-state BOLD signals, even in perivascular tissue.
Approach:
- Developed and tested biophysical models (2D/3D cylinder, macrovascular anatomical networks) to predict resting-state BOLD fluctuation amplitude (RSFA) and functional connectivity (FC) at 3 Tesla.
- Investigated the feasibility of modeling macrovascular BOLD FC and RSFA using anatomical data from angiograms.
Key Points:
- Macrovascular anatomical networks enable feasible modeling of BOLD FC, outperforming simpler cylinder models.
- Biophysical models accurately predict BOLD pairwise correlation near large veins (R² 0.53–0.93) but not arteries.
- Modeling perivascular BOLD connectivity is feasible near veins (R² 0.08–0.57) but limited by distance and not effective for arteries.
Conclusions:
- Biophysical modeling shows feasibility for simulating resting-state macrovascular BOLD signals and functional connectivity.
- The methodology holds potential for correcting macrovascular bias in resting-state and task-based fMRI.
- This work advances understanding of macrovascular influences on BOLD signals and offers a path toward improved fMRI accuracy.
