PropertyValue
is nif:broaderContext of
nif:broaderContext
is schema:hasPart of
schema:isPartOf
nif:isString
  • A total of 98 patients with schizophrenia and 102 healthy controls were recruited for this study. Diagnoses for patients were confirmed using the Structured Clinical Interview for DSM-IV. Inclusion criteria were age (18–55 years) and right-handedness. Exclusion criteria were MRI contraindications, poor image quality, presence of a systemic medical illness or CNS disorder, history of head trauma, and substance abuse within the last 3 months or lifetime history of substance abuse or dependence. Additional exclusion criteria for healthy comparison subjects were history of any Axis I or II disorders and a psychotic disorder and first-degree relative with a psychotic disorder. Four schizophrenia patients were excluded because of their oversized head motion (translational or rotational motion parameters more than 2 mm or 2°). After excluding subjects with poor image quality of the FP, 91 schizophrenia patients and 100 healthy controls were finally included. Clinical symptoms of psychosis were quantified with the Positive and Negative Syndrome Scale (PANSS) [24]. This study was approved by the Medical Research Ethics Committee of Tianjin Medical University General Hospital, and all participants provided written informed consent. MRI was performed using a 3.0-Tesla MR system (Discovery MR750, General Electric, Milwaukee, WI, USA). Tight but comfortable foam padding was used to minimize head motion, and earplugs were used to reduce scanner noise. Sagittal 3D T1-weighted images were acquired by a brain volume sequence with the following scan parameters: repetition time (TR) = 8.2 ms; echo time (TE) = 3.2 ms; inversion time (TI) = 450 ms; flip angle (FA) = 12°; field of view (FOV) = 256 mm × 256 mm; matrix = 256 × 256; slice thickness = 1 mm, no gap; and 188 sagittal slices. Two sets of the resting-state fMRI data were acquired. A gradient-echo single-short EPI sequence was performed using the following parameters: TR/TE = 2000/45 ms; FOV = 220 mm × 220 mm; matrix = 64 × 64; FA = 90°; slice thickness = 4 mm; gap = 0.5 mm; 32 interleaved transverse slices; 180 volumes. A gradient-echo SENSE-SPIRAL (spiral in) sequence was performed using parameters of TR/TE = 1400/30 ms; FA = 60°, acceleration factor = 2. The FOV, matrix, slice thickness, gap, and slice number were the same as the EPI sequence. During fMRI scans, all subjects were instructed to keep their eyes closed, to relax and move as little as possible, to think of nothing in particular, and to not fall asleep. Gray matter volume (GMV) calculation: The GMV of each voxel was calculated using Statistical Parametric Mapping software (SPM8; http://www.fil.ion.ucl.ac.uk/spm/software/spm8/). The structural MR images were segmented into gray matter (GM), white matter and cerebrospinal fluid using the standard unified segmentation model. After an initial affine registration of GM concentration map into the Montreal Neurological Institute (MNI) space, GM concentration images were nonlinearly warped using diffeomorphic anatomical registration through the exponentiated Lie algebra (DARTEL) technique and were resampled to 1.5-mm cubic voxels. The GMV of each voxel was obtained by multiplying GM concentration map by the non-linear determinants derived from the spatial normalization step. Finally, GMV images were smoothed with a Gaussian kernel of 6 × 6 × 6 mm3 full-width at half maximum (FWHM). After spatial preprocessing, the normalized, modulated, and smoothed GMV maps were used for statistical analysis. Two sets of resting-state fMRI data were preprocessed using the SPM8 with the same procedures. The first 10 volumes for each subject were discarded to allow the signal to reach equilibrium and the participants to adapt to the scanning noise. The remaining volumes were then corrected for the acquisition time delay between slices. All subjects’ fMRI data were within defined motion thresholds (translational or rotational motion parameters less than 2 mm or 2°). We also calculated framewise displacement (FD), which indexes volume-to-volume changes in head position [25]. Considering recent studies reported that signal spike caused by head motion significantly contaminated the final resting-state fMRI results even after regressing out the realignment parameters [25], we removed spike volumes when the FD of specific volume exceeded 0.5. Several nuisance covariates (six motion parameters and average BOLD signals of the ventricular, white matter and whole brain) were regressed out from the data. The datasets were band-pass filtered with a frequency range of 0.01 to 0.08 Hz. Individual structural images were linearly coregistered to the mean functional image; then the transformed structural images were segmented into GM, white matter, and cerebrospinal fluid. The GM maps were linearly coregistered to the tissue probability maps in the MNI space. Finally the motion-corrected functional volumes were spatially normalized to the MNI space using the parameters estimated during linear coregistration. The functional images were resampled into 3 × 3 × 3 mm3 voxels. After normalization, all datasets were smoothed with a Gaussian kernel of 6 × 6 × 6 mm3 FWHM. Definition of the FP subregions: The bilateral FP subregions were defined according to the maximal probability maps from a previous parcellation study of the FP [2], which the FP was parcellated into the FPo, FPm and FPl subregions. Thus, we defined a total of 6 regions of interest (ROIs), including the FPo, FPm and FPl bilaterally. Conventional EPI images of the FP are apt to susceptibility-induced signal loss and distortion; however, the SENSE-SPIRAL sequence may at least partly reduce the effects. We first compared the signal intensity and distortion of the FP derived from the two resting-state fMRI sequences. The relative signal intensity (rSI) of each FP region was calculated by dividing the mean signal intensity of the whole brain gray matter and was compared between the two imaging methods. The distortion severity was assessed by observing deviations of the normalized functional images from the structural images. As expected, the SENSE-SPIRAL sequence had a better image quality, and then the fMRI data from this sequence were used to perform rsFC analysis. To exclude possible effect of signal loss on our results, we excluded subjects whose rSI of any FP subregion was lower than 0.5. For individual dataset, Pearson’s correlation coefficients between the mean time series of each ROI and time series of each voxel in other parts of the brain GM were computed, and converted to z values using Fisher’s r-to-z transformation to improve the normality. Individuals’ z values were then entered into a random-effect one-sample t-test in a voxel-wise manner to identify brain regions that showed significant positive correlations with each ROI. Multiple comparisons for this analyses were corrected for using a family wise error (FWE) method (p<0.05).Then a two-sample t-test was performed within the positive rsFC mask to quantitatively compare group differences in rsFCs of the 6 ROIs after controlling for age and gender. Multiple comparisons for this analyses were corrected for using a false positive rate (FDR) method (p<0.05). To exclude the effect of GMV on rsFC changes, we extracted the GMV of the FP subregions and re-evaluated the rsFC changes of each FP subregion after further controlling for the GMV of the FP subregion. Considering that ROIs extracted from the maximal probability maps may result in information overlap across ROIs due to the preprocessing steps of normalization and smoothing, we also defined these ROIs using an alternative method (spheres with a radius of 6 mm centered at the gravity of each FP subregion) to exclude the effect. We repeated our rsFC analyses to test whether different methods for ROI definition influence our results. Correlations between rsFCs of the FP subregions and clinical parameters: To further test whether the rsFCs of the FP subregions with significant group differences were correlated with the clinical variables, we extracted the rsFCs of the FP subregions that exhibited significant group differences, and calculated Spearman’s correlation coefficients between these rsFCs and clinical parameters (i.e., PANSS, duration of the illness, and antipsychotic dosage) (p<0.05).
rdf:type