Sampling Rate Effects on Resting State fMRI Metrics

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.

Dynamic lag analysis reveals atypical brain information flow in autism spectrum disorder

“….). Moreover, MREG sampled critically at 10 Hz enables accurate removal of cardiorespiratory signals by band‐pass filtering from data [Huotari et al, ]. This is an important step for the activation peak based lag pattern estimation, since it removes variance from the peak timings that are induced by cardiac and respiratory peaks from the data.…”

“…Therefore, we wanted to ensure that the subjects stayed as vigilant as possible by keeping the resting‐state scanning time moderate and by checking the vigilance of the subjects verbally between each scan. Furthermore, MREG offers 3000 brain volumes for each 5 min scan, and this increases the power for statistical inferences in addition to permitting more accurate physiological signal analysis [Huotari et al, ]. We believe that the number of peaks (~230–330) in the analysis between each RSN pair in the low frequency band is enough for statistical lag variation analysis given the statistical power of MREG results that survived combined surrogate data and Benjamini–Hochberg procedure corrections.…”

“…Furthermore, there have been theoretical questions regarding whether the ultra‐fast technology below 500 msec repetition time (TR) can benefit the lag and connectivity estimation due to regional lag variability and slow temporal response of the hemodynamic response to neuronal activity. Recently, it has been shown that cardiac power distributions start to overlap on top of respiratory frequency maps as TR is increased >0.5 sec, indicating aliasing [Huotari et al, ].…”

“…In other words, the lag structure enables one to follow the BOLD signal propagation between networks based on the temporal order of succeeding activation peaks detected in the resting‐state networks (RSNs). With the novel ultrafast imaging techniques without cardiorespiratory aliasing, one can estimate resting‐state metrics and lag structure more precisely [Huotari et al, ; Raatikainen et al, ; Raitamaa et al, ; Rajna et al, ]. No previous investigations of autism have concentrated on dynamic lag pattern variations in individuals with the disorder compared with NT individuals.…”

Graphical model selection for a particular class of continuous-time processes

“…In such an application data are the so called "functional magnetic resonance imaging" (fMRI) data. The latter are characterized by a high sampling time, e. g. T = 2 sec, [21]. In other words, we can observe x(T ) only at time T .…”