Improving laminar fMRI specificity by reducing macrovascular bias revealed by respiration effects

Abstract

Abstract Functional MRI (fMRI) time series are inherently susceptible to the influence of respiratory variations. While many studies treat respiration as a source of noise in fMRI, this study employs natural respiratory variations during high resolution (0.8 mm) fMRI at 7T to formulate a respiration effect related map and then use this map to reduce macrovascular bias for a more laminar-specific fMRI measurement. Our results indicate that respiratory-related signal changes are modulated by breath phase (breathing in/out or in the transition between breath in and out) during fMRI acquisition, with distinct patterns across various brain regions. We demonstrate that respiration maps generated from normal fMRI runs, such as task-oriented sessions, closely resemble those from deep-breath and breath-hold experiments. These maps show a significant correlation with the macro-vasculature automatically segmented based on susceptibility weighted imaging (SWI) and quantitative susceptibility mapping (QSM) images. Most crucially, by removing voxels most responsive to respiratory variations, we can refine high-resolution fMRI measurements to be more layer-specific, improving the accuracy of laminar fMRI analysis.