Impact of thermal and physiological denoising on laminar functional connectivity.

The characterization of the direction of information flow between cortical areas via layer functional connectivity (FC) is of interest for many neuroscientific applications. Most laminar fMRI acquisitions are in the thermal-noise dominated regime due to spatial resolution requirements. However, laminar FC estimates using gradient-echo BOLD might be particularly biased by physiological sources of noise which are amplified by the draining vein effect. In this work, we aimed at assessing whether thermal and physiological denoising can be helpful in reducing biases in laminar functional connectivity studies. We tested NORDIC, RETROICOR, and aCompCor on a dataset acquired at 7T with a resolution of 0.8 × 0.8 × 1.5 mm3 and evaluated the following metrics on a laminar basis after each denoising step: temporal standard deviation (tSD), coefficient of variation (CoV), fluctuation amplitude (FA) and seed-based functional connectivity strength of the primary motor cortex (M1) with premotor (PM) and somatosensory (S1) regions. We found that NORDIC had the largest impact on the metrics considered, mostly in deeper laminae. The application of physiological denoising, especially aCompCor, following NORDIC had the largest effect on upper laminae, in line with the fact that they are more affected by physiological noise than the deeper laminae. The application of NORDIC and aCompCor resulted in laminar functional connectivity results pointing to a stronger connectivity of upper M1 laminae to S1 than lower M1 laminae to S1, and to a reduction in the upper-layer bias in the connectivity to PM. Therefore, both thermal and physiological denoising represent important steps to increase sensitivity and reduce the vascular bias of laminar fMRI data.