Sampling Rate Effects on Resting State fMRI Metrics.

Niko Huotari,Heta Helakari,Timo Tuovinen,Aleksi Rasila,Jussi Kantola,Ville Raatikainen,Vesa O Korhonen,Viola Borchardt,Vesa J Kiviniemi,Lauri Raitamaa,Janne Kananen

doi:10.3389/fnins.2019.00279

Niko Huotari, Heta Helakari + Show 9 more

Open Access

https://doi.org/10.3389/fnins.2019.00279

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Abstract

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.

Highlights

In 1995 Biswal and co-workers discovered functional connectivity (FC) in resting state blood oxygen level dependent (BOLD) signal of motor cortices by their continuous very low frequency fluctuations (VLF < 0.1 Hz) (Biswal et al, 1995)
The spread of such avalanches can be depicted by novel techniques such as inverse imaging (INI) and magnetic resonance encephalography (MREG), that sample functional magnetic resonance imaging data with short repetition times (TR) (Lin et al, 2012, 2018; Assländer et al, 2013; Rajna et al, 2015)
We studied how the mean Fourier transformation (FFT) power of VLF (0.01–0.1 Hz), respiratory (0.12–0.35 Hz) and cardiac (0.9– 1.3 Hz) pulsations occur in different subsampled TR (sTR) settings

Summary

Introduction

In 1995 Biswal and co-workers discovered functional connectivity (FC) in resting state blood oxygen level dependent (BOLD) signal of motor cortices by their continuous very low frequency fluctuations (VLF < 0.1 Hz) (Biswal et al, 1995). The current view is that spontaneous neuronal activity avalanches synchronize brain activity in functionally connected areas and become visible in hemodynamic signals after a delay of few seconds (Liu and Duyn, 2013; Palva et al, 2013; Keilholz, 2014; Ma et al, 2016; Liu et al, 2018). The spread of such avalanches can be depicted by novel techniques such as inverse imaging (INI) and magnetic resonance encephalography (MREG), that sample functional magnetic resonance imaging (fMRI) data with short repetition times (TR) (Lin et al, 2012, 2018; Assländer et al, 2013; Rajna et al, 2015). As the BOLD signal reflects blood oxygen level, direct effects of the cardiorespiratory pulses themselves can be detected in fMRI data (Shmueli et al, 2007; Chang and Glover, 2009, 2010)

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