Low-level carbon monoxide exposure affects BOLD fMRI response.

Blood oxygen level dependent (BOLD) fMRI is a common technique for measuring brain activation that could be affected by low-level carbon monoxide (CO) exposure from, e.g. smoking. This study aimed to probe the vulnerability of BOLD fMRI to CO and determine whether it may constitute a significant neuroimaging confound. Low-level (6 ppm exhaled) CO effects on BOLD response were assessed in 12 healthy never-smokers on two separate experimental days (CO and air control). fMRI tasks were breath-holds (hypercapnia), visual stimulation and fingertapping. BOLD fMRI response was lower during breath holds, visual stimulation and fingertapping in the CO protocol compared to the air control protocol. Behavioural and physiological measures remained unchanged. We conclude that BOLD fMRI might be vulnerable to changes in baseline CO, and suggest exercising caution when imaging populations exposed to elevated CO levels. Further work is required to fully elucidate the impact on CO on fMRI and its underlying mechanisms.

Identification of physiological response functions to correct for fluctuations in resting-state fMRI related to heart rate and respiration.

Functional magnetic resonance imaging (fMRI) is widely viewed as the gold standard for studying brain function due to its high spatial resolution and non-invasive nature. However, it is well established that changes in breathing patterns and heart rate strongly influence the blood oxygen-level dependent (BOLD) fMRI signal and this, in turn, can have considerable effects on fMRI studies, particularly resting-state studies. The dynamic effects of physiological processes are often quantified by using convolution models along with simultaneously recorded physiological data. In this context, physiological response function (PRF) curves (cardiac and respiratory response functions), which are convolved with the corresponding physiological fluctuations, are commonly employed. While it has often been suggested that the PRF curves may be region- or subject-specific, it is still an open question whether this is the case. In the present study, we propose a novel framework for the robust estimation of PRF curves and use this framework to rigorously examine the implications of using population-, subject-, session- and scan-specific PRF curves. The proposed framework was tested on resting-state fMRI and physiological data from the Human Connectome Project. Our results suggest that PRF curves vary significantly across subjects and, to a lesser extent, across sessions from the same subject. These differences can be partly attributed to physiological variables such as the mean and variance of the heart rate during the scan. The proposed methodological framework can be used to obtain robust scan-specific PRF curves from data records with duration longer than 5 min, exhibiting significantly improved performance compared to previously defined canonical cardiac and respiration response functions. Besides removing physiological confounds from the BOLD signal, accurate modeling of subject- (or session-/scan-) specific PRF curves is of importance in studies that involve populations with altered vascular responses, such as aging subjects.