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rsta.royalsocietypublishing.org Phil.Trans.R.Soc.A374:20150190
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TE = 17 ms, FA = 70
◦
, 1.5 mm isotropic voxels, 300 volumes, GRAPPA acceleration with iPAT
factor of 3) and gradient echo field map. Structural images were acquired by three-dimensional
MP2RAGE sequence with a resolution of 0.7 mm isotropic.
(a) Physiological noise models and heart rate variability
Physiological data (respiratory and cardiac traces) were simultaneously recoded for each rs-
fMRI scan. The original data in 7 T dataset (5000 Hz) were down-sampled to 100 Hz. The
data in NKI-RS dataset are recorded at a sample rate of 62.5 Hz. Two cardiac fluctuation
correction models were constructed to account for components related to (i) cardiac phases
(CP) and (ii) heart rate (HR). The respiration fluctuations are also included to account for
the physiological noise influences: (i) respiratory phases (RP) and the interaction effects
between CP and RP (InterCRP) and (ii) respiratory volume per unit time (RVT). Models
for cardiac and respiratory phases and their interaction effects were based on RETROICOR
[25] and its extension [35]. Cardiac and respiratory response functions were employed to
model HR and RVT onto physiological process of the fMRI time series [21,22,26,27]. For
each subject, a set of 20 physiological regressors (i.e. fourth-order Fourier expansion for RP,
third-order Fourier expansion for CP, second-order Fourier expansion for InterCRP, RVT and
HR) was created using the Matlab P
HYSIO toolbox (http://www.translationalneuromodeling.
org/tnu-checkphysretroicor-toolbox/) for each slice in each fMRI run. Cardiac fluctuation
correction based on different combinations of these regressors was studied to investigate the effect
of cardiac pulse, performing by a generalized linear model (GLM). The combinations are
(1) RP & RVT (RPV-model),
(2) RP & RVT & CP & InterCRP (RPVC-model),
(3) RP & RVT & HR (RPVH-model), and
(4) RP & RVT & CP & InterCRP & HR, i.e. all models (RPVCH-model).
HRV analysis was performed on the interbeat interval (IBI) time series in each resting-state session
scan, using the HRV analysis software (HRVAS, https://github.com/jramshur/HRVAS). The IBI
time series were calculated as the peak-to-peak interval of photoplethysmography signal. IBI
outliers in each session were removed. The outliers were defined as intervals deviating 20% from
the previous interval. To alleviate any non-stationarities within IBI time series, wavelet packet
detrending was used before HRV analysis. Finally, time domain and frequency-domain measures
were derived from IBI series, including: mean IBI; the standard deviation of the normal-to-normal
(NN) interval series, SDNN; the root mean square of successive differences of the IBI series,
RMSSD; spectral power of low-frequency (LF: 0.04–0.15 Hz) and high-frequency (HF: 0.15–0.4 Hz)
band power and LF/HF ratio, which represents a measure of sympathovagal balance.
(b) Magnetic resonance imaging data processing
All structural images in both datasets were manually reoriented to the anterior commissure and
segmented into grey matter, white matter and cerebrospinal fluid (CSF), using the standard
segmentation option in SPM 12 [36]. Resting-state fMRI data pre-processing was subsequently
carried out using both AFNI and SPM12 package with default parameters [36,37], including slice
timing correction (T), registration (R), physiological noise model correction (C), despiking (D) and
normalization (N). To examine the pre-processing procedure effect on point process acquisition,
three commonly used orders of pre-processing steps were applied to the dataset: (i) DCTRN,
(ii) DRCTN, and (iii) DTRCN. The raw volumes were despiked using AFNI’s 3dDespike
algorithm to mitigate the impact of outliers. In slice timing step, the EPI volumes of each run
were corrected for the temporal difference in acquisition among different slices to match the
middle time slice or half TR (for TR = 0.645 s); in the registration step, the images were realigned
to the first volume of the first run, the gradient echo field map was processed to create a voxel
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