Modeling venous bias in resting state functional MRI metrics

Area of Science:

• Neuroimaging
• Biophysics
• Functional Connectivity Analysis

Background:

• Resting-state functional magnetic resonance imaging (fMRI) infers brain connectivity from blood oxygen-level dependent (BOLD) signal fluctuations.
• The BOLD signal is sensitive to venous blood, potentially introducing biases in connectivity measures.
• The extent and nature of this venous bias on common resting-state fMRI metrics remain largely unknown.

Purpose of the Study:

• To investigate and quantify the impact of venous structure on common local resting-state fMRI metrics.
• To determine how vein size and proximity influence BOLD signal fluctuations and derived connectivity measures.
• To develop models for correcting systematic venous biases in rsfMRI data.

Main Methods:

• Identification of cerebral veins using high-resolution quantitative susceptibility mapping.
• Application of a biophysical model to simulate and assess venous bias effects.
• Evaluation of the impact of vein diameter and distance on amplitude of low-frequency fluctuations (ALFF), fractional ALFF (fALFF), Hurst exponent (HE), regional homogeneity (ReHo), and eigenvector centrality.

Main Results:

• rsfMRI metrics showed higher values near smaller veins and decreased with increasing vein diameter.
• Metrics associated with larger veins demonstrated decreased values with increasing distance.
• ALFF and ReHo were most susceptible to venous bias, while HE and fALFF showed minimal bias. Overall bias was limited in voxel-wise data.

Conclusions:

• Venous structure introduces systematic biases in rsfMRI metrics, varying with vein size and proximity.
• While present, the venous bias is not the dominant source of signal contrast in voxel-wise rsfMRI data.
• The developed biophysical models offer a method for correcting venous bias, improving the accuracy of resting-state fMRI analyses.