Predicting the macrovascular contribution to resting-state fMRI functional connectivity at 3 Tesla: A model-informed approach.

Macrovascular biases have been a long-standing challenge for functional magneticresonance imaging (fMRI), limiting its ability to detect spatially specificneural activity. Recent experimental studies, including our own, foundsubstantial resting-state macrovascular blood-oxygenation level-dependent (BOLD)fMRI contributions from large veins and arteries, extending into theperivascular tissue at 3 T and 7 T. The objective of this study is todemonstrate the feasibility of predicting, using a biophysical model, theexperimental resting-state BOLD fluctuation amplitude (RSFA) and associatedfunctional connectivity (FC) values at 3 Tesla. We investigated the feasibilityof both 2D and 3D infinite-cylinder Models as well as macrovascular anatomicalnetworks (macro-VANs) derived from angiograms. Our results demonstrate that (1)with the availability of macro-VANs, it is feasible to model macrovascular BOLDFC using both the macro-VAN-based model and 3D infinite-cylinder Models, thoughthe former performed better; (2) biophysical modelling can accurately predictthe BOLD pair-wise correlation near to large veins (with Rrangingfrom 0.53 to 0.93 across different subjects), but not near to large arteries;(3) compared with FC, biophysical modelling provided less accurate predictionsfor RSFA; (4) modelling of perivascular BOLD connectivity was feasible at closedistances from veins (with Rranging from 0.08 to 0.57), but notarteries, with performance deteriorating with increasing distance. While ourcurrent study demonstrates the feasibility of simulating macrovascular BOLD inthe resting state, our methodology may also apply to understanding task-basedBOLD. Furthermore, these results suggest the possibility of correcting formacrovascular bias in resting-state fMRI and other types of fMRI usingbiophysical modelling based on vascular anatomy.