Abstract
Consistent, high-quality data is key to the success of fMRI studies given the many confounding factors and undesired signals that contaminate these data. Several quality assurance (QA) metrics exist for fMRI (e.g., temporal signal-to-noise ratio (TSNR), percent ghosting, motion estimates), but none of them leverage relationships between echoes that are part of multi-echo (ME) fMRI acquisitions. Here, we fill this gap by proposing a new QA metric for for ME-fMRI that quantifies the likelihood a given ME scan is dominated by BOLD (Blood Oxygenation Level-Dependent) fluctuations. We refer to this metric as p(BOLD) ; the probability of the signal change being primarily BOLD contrast-dominated. Having an estimate of overall BOLD weighting - both before and after preprocessing - is meaningful because BOLD is the intrinsic contrast mechanism used in fMRI to infer neural activity. We introduce p(BOLD) to the neuroimaging community by first describing the theoretical principles supporting the metric. Next, we validate p(BOLD) efficacy using a small dataset (N=7 scans) of constant- and cardiac-gated scans that have distinct levels of contributing BOLD fluctuations. Third, we apply p(BOLD) to a larger publicly available ME dataset (N=439 scans), to evaluate six different preprocessing pipelines, and show how p(BOLD) provides complementary information to TSNR. Our results show that ME-based denoising increases both p(BOLD) and TSNR relative to basic denoising; however, including the global signal (GS) as a regressor only improves TSNR, but worsens p(BOLD) . Further analyses looking at the BOLD-like characteristics of the GS and its relationship to cardiac and respiratory traces suggest that the observed decrease in p(BOLD) is likely due to a decrease in BOLD fluctuations of neural origin contributing to the GS, and not due to contributions from other physiological BOLD fluctuations (i.e., respiratory and cardiac function). Finally, we also demonstrate how p(BOLD) can be applied as a data quality metric, by showing how higher p(BOLD) results in better ability to predict phenotypes based on whole-brain functional connectivity matrices.