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The study evaluates the efficacy of RETROICOR (Retrospective Image Correction) in mitigating physiological artifacts within multi‐echo (ME) fMRI data. Two RETROICOR implementations were compared: applying corrections to individual echoes (RTC_ind) versus composite multi‐echo data (RTC_comp). Data from 50 healthy participants were collected using diverse acquisition parameters, including multiband acceleration factors and varying flip angles, on a Siemens Prisma 3T scanner. Key metrics such as temporal signal‐to‐noise ratio (tSNR), signal fluctuation sensitivity (SFS), and variance of residuals demonstrated improved data quality in both RETROICOR models, particularly in moderately accelerated runs (multiband factors 4 and 6) with lower flip angles (45°). Differences between RTC_ind and RTC_comp were minimal, suggesting both methods are viable for practical applications. While the highest acceleration (multiband factor 8) degraded data quality, RETROICOR's compatibility with faster acquisition sequences was confirmed. These findings underscore the importance of optimizing acquisition parameters and noise correction techniques for reliable fMRI investigations. The study evaluates different implementations of the correction of physiological artifacts (RETROICOR) in multi‐echo fMRI data (before or after combination of the echoes) with several acquisition settings. Both implementations of RETROICOR can enhance signal quality, and the benefits are particularly notable in moderately accelerated acquisitions.

The blood oxygenation level-dependent (BOLD) contrast mechanism allows the noninvasive monitoring of changes in deoxyhemoglobin content. As such, it is commonly used in functional magnetic resonance imaging (fMRI) to study brain activity since levels of deoxyhemoglobin are indirectly related to local neuronal activity through neurovascular coupling mechanisms. However, the BOLD signal is severely affected by physiological processes as well as motion. Due to this, several noise correction techniques have been developed to correct for the associated confounds. The present study focuses on cardiac pulsatility fMRI confounds, aiming to refine model-based techniques that utilize the photoplethysmograph (PPG) signal. Specifically, we propose a new technique based on convolution filtering, termed cardiac pulsatility model (CPM) and compare its performance with the cardiac-related RETROICOR (Card-RETROICOR), which is a technique commonly used to model fMRI fluctuations due to cardiac pulsatility. Further, we investigate whether variations in the amplitude of the PPG pulses (PPG-Amp) covary with variations in amplitude of pulse-related fMRI fluctuations, as well as with the systemic low frequency oscillations (SLFOs) component of the fMRI global signal (GS – defined as the mean signal across all gray matter voxels). Capitalizing on 3T fMRI data from the Human Connectome Project, CPM was found to explain a significantly larger fraction of the fMRI signal variance compared to Card-RETROICOR, particularly for subjects with larger heart rate variability during the scan. The amplitude of the fMRI pulse-related fluctuations did not covary with PPG-Amp; however, PPG-Amp explained significant variance in the GS that was not attributed to variations in heart rate or breathing patterns. Our results suggest that the proposed approach can model high-frequency fluctuations due to pulsation as well as low-frequency physiological fluctuations more accurately compared to model-based techniques commonly employed in fMRI studies.

In task-based functional magnetic resonance imaging (fMRI), researchers seek to measure fMRI signals related to a given task or condition. In many circumstances, measuring this signal of interest is limited by noise. In this study, we present GLMdenoise, a technique that improves signal-to-noise ratio (SNR) by entering noise regressors into a general linear model (GLM) analysis of fMRI data. The noise regressors are derived by conducting an initial model fit to determine voxels unrelated to the experimental paradigm, performing principal components analysis (PCA) on the time-series of these voxels, and using cross-validation to select the optimal number of principal components to use as noise regressors. Due to the use of data resampling, GLMdenoise requires and is best suited for datasets involving multiple runs (where conditions repeat across runs). We show that GLMdenoise consistently improves cross-validation accuracy of GLM estimates on a variety of event-related experimental datasets and is accompanied by substantial gains in SNR. To promote practical application of methods, we provide MATLAB code implementing GLMdenoise. Furthermore, to help compare GLMdenoise to other denoising methods, we present the Denoise Benchmark (DNB), a public database and architecture for evaluating denoising methods. The DNB consists of the datasets described in this paper, a code framework that enables automatic evaluation of a denoising method, and implementations of several denoising methods, including GLMdenoise, the use of motion parameters as noise regressors, ICA-based denoising, and RETROICOR/RVHRCOR. Using the DNB, we find that GLMdenoise performs best out of all of the denoising methods we tested.

Physiological noise originating in cardiovascular and respiratory processes is a substantial confound in BOLD fMRI. When unaccounted for it reduces the temporal SNR and causes error in inferred brain activity and connectivity. Physiology correction typically relies on auxiliary measurements with peripheral devices such as ECG, pulse oximeters, and breathing belts. These require direct skin contact or at least a tight fit, impairing subject comfort and adding to the setup time. In this work, we explore a touch-free alternative for physiology recording, using magnetic detection with NMR field probes. Placed close to the chest such probes offer high sensitivity to cardiovascular and respiratory dynamics without mechanical contact. This is demonstrated by physiology regression in a typical fMRI scenario at 7T, including validation against standard devices. The study confirms essentially equivalent performance of noise models based on conventional recordings and on field probes. It is shown that the field probes may be positioned in the subject's back such that they could be readily integrated in the patient table. [Display omitted] •An alternative method for physiology recording in fMRI is proposed.•Physiology signals are acquired by magnetic field sensing using NMR field probes.•For noise modeling, magnetic sensing is proven equivalent to standard recordings.•Being touch-free, magnetic sensing improves comfort and reduces setup times.