Low image sampling rates used in resting state functional magnetic resonance imaging (rs-fMRI) may cause aliasing of the cardiorespiratory pulsations over the very low frequency (VLF) BOLD signal fluctuations which reflects to functional connectivity (FC). In this study, we examine the effect of sampling rate on currently used rs-fMRI FC metrics. Ultra-fast fMRI magnetic resonance encephalography (MREG) data, sampled with TR 0.1 s, was downsampled to different subsampled repetition times (sTR, range 0.3–3 s) for comparisons. Echo planar k-space sampling (TR 2.15 s) and interleaved slice collection schemes were also compared against the 3D single shot trajectory at 2.2 s sTR. The quantified connectivity metrics included stationary spatial, time, and frequency domains, as well as dynamic analyses. Time domain methods included analyses of seed-based functional connectivity, regional homogeneity (ReHo), coefficient of variation, and spatial domain group level probabilistic independent component analysis (ICA). In frequency domain analyses, we examined fractional and amplitude of low frequency fluctuations. Aliasing effects were spatially and spectrally analyzed by comparing VLF (0.01–0.1 Hz), respiratory (0.12–0.35 Hz) and cardiac power (0.9–1.3 Hz) FFT maps at different sTRs. Quasi-periodic pattern (QPP) of VLF events were analyzed for effects on dynamic FC methods. The results in conventional time and spatial domain analyses remained virtually unchanged by the different sampling rates. In frequency domain, the aliasing occurred mainly in higher sTR (1–2 s) where cardiac power aliases over respiratory power. The VLF power maps suffered minimally from increasing sTRs. Interleaved data reconstruction induced lower ReHo compared to 3D sampling ( p < 0.001). Gradient recalled echo-planar imaging (EPI BOLD) data produced both better and worse metrics. In QPP analyses, the repeatability of the VLF pulse detection becomes linearly reduced with increasing sTR. In conclusion, the conventional resting state metrics (e.g., FC, ICA) were not markedly affected by different TRs (0.1–3 s). However, cardiorespiratory signals showed strongest aliasing in central brain regions in sTR 1–2 s. Pulsatile QPP and other dynamic analyses benefit linearly from short TR scanning.

IntroductionFunctional magnetic resonance imaging (fMRI) combined with simultaneous electroencephalography (EEG‐fMRI) has become a major tool in mapping epilepsy sources. In the absence of detectable epileptiform activity, the resting state fMRI may still detect changes in the blood oxygen level‐dependent signal, suggesting intrinsic alterations in the underlying brain physiology.MethodsIn this study, we used coefficient of variation (CV) of critically sampled 10 Hz ultra‐fast fMRI (magnetoencephalography, MREG) signal to compare physiological variance between healthy controls (n = 10) and patients (n = 10) with drug‐resistant epilepsy (DRE).ResultsWe showed highly significant voxel‐level (p < 0.01, TFCE‐corrected) increase in the physiological variance in DRE patients. At individual level, the elevations range over three standard deviations (σ) above the control mean (μ) CVMREG values solely in DRE patients, enabling patient‐specific mapping of elevated physiological variance. The most apparent differences in group‐level analysis are found on white matter, brainstem, and cerebellum. Respiratory (0.12–0.4 Hz) and very‐low‐frequency (VLF = 0.009–0.1 Hz) signal variances were most affected.ConclusionsThe CVMREG increase was not explained by head motion or physiological cardiorespiratory activity, that is, it seems to be linked to intrinsic physiological pulsations. We suggest that intrinsic brain pulsations play a role in DRE and that critically sampled fMRI may provide a powerful tool for their identification.

Oura Health Ltd.'s provided the surveillance smart rings for the study, but did not participate in the analysis. The manuscript in question is published as a preprint in bioRxiv, but we guarantee that it does not infringe any subsequent copyright or license agreement. 3We would like to thank all study subjects for their participation in the study. We also thank Tuomas Konttajärvi for assistance in measurements and preprocessing of EEG data, Jani Häkli, Annastiina Kivipää, Tarja Holtinkoski, Aleksi Rasila, Taneli Hautaniemi, Miia Lampinen and others who assisted in measurements or otherwise contributed. We are grateful for devices and data provided by Oura. We wish to acknowledge Jussi Kantola for data management and reconstruction of MREG data, the CSC -IT Center for Science Ltd.