Modeling venous bias in resting state functional MRI metrics

Abstract

Resting‐state (rs) functional magnetic resonance imaging (fMRI) is used to detect low‐frequency fluctuations in the blood oxygen‐level dependent (BOLD) signal across brain regions. Correlations between temporal BOLD signal fluctuations are commonly used to infer functional connectivity. However, because BOLD is based on the dilution of deoxyhemoglobin, it is sensitive to veins of all sizes, and its amplitude is biased by draining veins. These biases affect local BOLD signal location and amplitude, and may also influence BOLD‐derived connectivity measures, but the magnitude of this venous bias and its relation to vein size and proximity is unknown. Here, veins were identified using high‐resolution quantitative susceptibility maps and utilized in a biophysical model to investigate systematic venous biases on common local rsfMRI‐derived measures. Specifically, we studied the impact of vein diameter and distance to veins on the amplitude of low‐frequency fluctuations (ALFF), fractional ALFF (fALFF), Hurst exponent (HE), regional homogeneity (ReHo), and eigenvector centrality values in the grey matter. Values were higher across all distances in smaller veins, and decreased with increasing vein diameter. Additionally, rsfMRI values associated with larger veins decrease with increasing distance from the veins. ALFF and ReHo were the most biased by veins, while HE and fALFF exhibited the smallest bias. Across all metrics, the amplitude of the bias was limited in voxel‐wise data, confirming that venous structure is not the dominant source of contrast in these rsfMRI metrics. Finally, the models presented can be used to correct this venous bias in rsfMRI metrics.

Assessment of the macrovascular contribution to resting-state fMRI functional connectivity at 3 Tesla

“…A notable recent study by Huck et al found that the intravascular venous contributions to the rs-fMRI signal decreased with increasing vascular size (Huck et al, 2023). While we did not explicitly examine the effect of diameter, qualitative examination of our data do not corroborate such a finding.…”

“…Furthermore, Huck et al (Huck et al, 2023) suggested using high-order polynomials to model and remove both binned intravascular and binned extravascular venous bias. However, they found their model is insufficient to remove voxel-wise venous bias in rs-fMRI.…”

“…Given that the macrovascular BOLD signal is deemed not neuronal specific, some may argue that it would be simple to identify and mask out regions with macrovasculature during rs-fcMRI analysis. However, according to the biophysical model of BOLD signal, the T2* relaxation effect caused by the susceptibility differences between blood and brain tissue originating from macrovasculature is capable of extending well beyond the voxel (Ogawa et al, 1993b), as recently shown in an in-vivo experiment (Huck et al, 2023). Moreover, previous results indicated that macrovascular signals and global BOLD signals are highly synchronous, given by their high cross-correlation coefficients (Tong et al, 2015), suggesting that macrovasculature may likely contribute significantly to the gray matter (GM) BOLD signal beyond the intravascular space.…”

“…It is possible, however, for large blood vessels in the brain to exhibit strong correlations among themselves as well as the global mean BOLD signal (Tong et al, 2019b).As the macrovascular system is responsible for supplying and draining blood from a large area of the brain, it is unlikely to be associated with local specific neural activity (Uludağ and Blinder, 2018), but rather with physiological noise and systemic processes. Thus, functional connectivity (FC) estimates may be biased by these macrovascular correlations (Huck et al, 2023;Kalcher et al, 2015). In order to understand macrovascular bias on FC estimates using rs-fMRI, it is imperative to understand the nature of signals that they carry, and the magntitude of their resultant BOLD signals.…”

“…More recently, Huck et al (Huck et al, 2023) extended the investigation into the rest of the venous vasculature and found that macrovascular contributions were significant for rs-fcMRI metrics. Although these two previous studies are informative, they either only examine the correlation between a very limited number of large blood vessels rather than the entire vasculature (Tong et al, 2015) or only examine venous vasculature without examining arterial vasculature (Huck et al, 2023). In addition, the macrovascular effect present in the perivascular space, despite its importance, has largely been overlooked.…”