Participants
Twenty PTSD patients with motor vehicle accidents and twenty age-, sex-, and education-matched healthy controls (HCs) were recruited (age 32.92 ± 8.48 years and 31.53 ± 7.43 years, respectively; gender 13 male/7 female and 14 male/6 female, respectively; education 11.20 ± 3.80 years and 13.00 ± 2.20 years, respectively). PTSD diagnosis was made using the Clinician-Administered PTSD Scale for DSM-IV (CAPS-DX). All participants had no history of psychiatric, neurological disorders and head injury.
MRI data acquisition
All images were obtained by a 3.0 T Siemens MRI scanner (Trio; Siemens Medical, Erlangen, Germany). Resting-state fMRI data were acquired using the echo-planar imaging (EPI) sequence with the following protocols: repetition time (TR) = 2000 ms, echo time (TE) = 30 ms, flip angle (FA) = 90°, matrix = 64 × 64, slice thickness = 3 mm, transverse slices = 36, and field of view (FOV) = 220 mm × 220 mm.
Data preprocessing
All fMRI data were preprocessed using Data Processing Assistant for Resting-State fMRI (DPARSF) (Yan and Zang 2010). The first ten volumes were discarded for equilibrium. Then slice-timing correction and realignment for head motion correction were performed. No translation or rotation parameters in any participants exceeded 3 mm or 3°. In addition, the imagings were further spatially normalized to the Montreal Neurological Institute EPI template image, and each voxel was resampled to 3 × 3 × 3 mm3. Then, the data were spatially smoothed using Gaussian kernel of 6 mm FWHW and detrended to abandon linear trend. After this, several sources of spurious variance were then removed from the data using linear regression, including Friston-24 head motion parameters, white matter signal, and cerebrospinal fluid. Finally, the data were temporally band-pass filtered (0.01–0.08 Hz) to reduce the effects of low-frequency drift and high-frequency noise.
Granger causality method
The bilateral thalamus of the automated anatomical labeling template was selected as the region of interest for the effective connectivity analysis. GC was used to describe the effective connectivity analysis between the seed regions and all other brain regions. The averaged time series of the seed region was defined as the seed time series X, and the time series Y represents the time series of voxels within the whole brain. The linear direct effect of X on Y (F
x→y
) and the linear direct effect of Y on X (F
y→x
) were calculated voxel by voxel within the whole brain. Therefore, two GC maps for each participant were obtained.
The calculation of GC method value was based on the Geweke’s feedback model (Geweke 1982).
The autoregressive representation:
$$Y_{t} = \mathop \sum \limits_{k = 1}^{p} b_{k} Y_{(t - k)} + cZ_{t} + \varepsilon_{t}$$
(1)
$$X_{t} = \mathop \sum \limits_{k = 1}^{p} b_{k}^{{\prime }} X_{(t - k)} + c^{{\prime }} Z_{t} + \varepsilon_{t}^{{\prime }} .$$
(2)
The joint regressive representation:
$$Y_{t} = \mathop \sum \limits_{k = 1}^{p} A_{k} X_{(t - k)} + \mathop \sum \limits_{k = 1}^{p} B_{k} Y_{{\left( {t - k} \right)}} + CZ_{t} + \mu_{t}$$
(3)
$$X_{t} = \mathop \sum \limits_{k = 1}^{p} A_{k}^{{\prime }} Y_{(t - k)} + \mathop \sum \limits_{k = 1}^{p} B_{k}^{{\prime }} X_{{\left( {t - k} \right)}} + C^{{\prime }} Z_{t} + \mu_{t}^{{\prime }} .$$
(4)
Finally,
$$F_{x \to y} = { \ln }\frac{{{\text{var}}(\varepsilon_{t} )}}{{{\text{var}}(\mu_{t} )}}$$
(5)
$$F_{y \to x} = { \ln }\frac{{{\text{var}}(\varepsilon_{t}^{{\prime }} )}}{{{\text{var}}(\mu_{t}^{{\prime }} )}} ,$$
(6)
where X
t
is the seed region signal and Y
t
is the other voxels signal, ɛ
t and \(\varepsilon_{t}^{{\prime }}\) are the residuals of autoregression, \(\mu_{\text{t}}\) and \(\mu_{t}^{{\prime }}\) are residuals, and Z
t is the covariate. F
x→y
represents directional influence from the time series X to Y. F
y→x
represents directional influence from the time series from Y to X.
Statistical analysis
Mean values of F
x→y
and F
y→x
maps were calculated. The two-sample t test was conducted on the GC method data in SPM8 to test the group differences between the PTSD patients and HCs. The multiple comparison correction was conducted using the AlphaSim program in the REST software (http://resting-fmri.sourceforge.net). The significance levels were set at p < 0.05.
The pattern classification
Pattern classification was included to address the potential effects related to group difference in more detail. The support vector machine (SVM) was applied to the GC, which found significant difference through statistical analysis. Therefore, the SVM, based on the LIBSVM implementation with linear kernel and default parameter, was applied using a leave-one-out cross-validation procedure. Furthermore, the statistical significance of pattern classification was assessed using permutation testing.