mICA-Based fMRI Analysis of Specific CO2-Level-Dependent BOLD Signal Changes in the Human Brainstem

Abstract

Noninvasive studies of the central respiratory control are of key importance to understanding the physiopathology of central apneas and periodic breathing. The study of the brainstem and cortical-subcortical centers may be achieved by using functional magnetic resonance imaging (fMRI) during gas challenges (hypercapnia). Nonetheless, disentangling specific from non-specific effects of hypercapnia in fMRI is a major methodological challenge, as CO2 vasodilatory effects and physiological noise do strongly impact the BOLD signal. This is particularly true in deep brainstem regions where chemoreceptors and rhythm pattern generators are located. One possibility to detect the true neural-related activation is given by the presence of a supralinear relation between CO2 changes and BOLD signal related to neurovascular coupling in overactive neural areas. Here, we test this hypothesis of a supralinear relationship between CO2 and BOLD signal, as a marker of specificity. We employed a group-masked Independent Component Analysis (mICA) approach and we compared activation levels across different mixtures of inspired CO2 using polynomial regression. Our results highlight key nodes of the central breathing control network, also including dorsal pontine and medullary regions. The suggested methodology allows a voxel-wise parametrization of the response, targeting an issue that affects many fMRI studies employing hypercapnic challenges.