The data of this study were obtained from the Monash rsPET-MR dataset[21–24] in the OpenNeuro database (https://doi.org/10.18112/openneuro.ds002898.v1.1.1). It is a simultaneous fMRI-fPET dataset acquired from young, healthy individuals at rest. Here is a brief description of this dataset: Participants (n=27, 21 female) were all right-handed, 18 to 23 years in age (mean 19 years), and 13 to 18 years of education (mean 14 years), and no history of diabetes, diagnosed Axis-1 mental illness or cardiovascular illness. Participants underwent a 95-minute simultaneous MR-PET scan in a Siemens (Erlangen) 3 Tesla Biograph molecular MR scanner (Syngo VB20P). 18F-fluorodeoxyglucose (FDG; average dose 233MBq) was infused over the course of the scan. Over the first 30-mins following infusion onset, T1 3D MPRAGE (TA = 7.01 mins, TR = 1640ms, TE = 2.34 ms, flip angle = 8°, FOV = 256 × 256mm2, voxel size = 1 × 1 × 1mm3, 176 slices; sagittal acquisition); For the remainder of the scan, six consecutive 10 min blocks of T2*-weighted echo-planar images (EPIs) were acquired (TR = 2.45s, TE = 30ms, FOV = 190mm2, 3 × 3 × 3mm3 voxels, 44 slices, ascending axial acquisition). More details could be reviewed in previous paper[21,23].
2.2 Image Preprocessing
The first 10 min block BOLD data and the corresponding 10 min FDG PET data were used in this study, as well as the T1 3D MPRAGE data for structural segmentation and registration. According to the scan protocol, the PET data were reconstructed every 16 seconds to a volume, and the corresponding 10 min data should be volume number starting from 129 to 165 for each participant. These PET volumes were extracted and averaged to get the static FDG PET image.
CONN toolbox (version 20.b) was used for BOLD data preprocessing. It is a functional connectivity toolbox based on the Statistical Parameter Mapping 12 toolbox (SPM, http:// www.fil.ion.ucl.ac.uk/spm). The default preprocessing pipeline for volume-based analyses (direct normalization to MNI-space) was utilized, including the following steps: Realignment and unwarp, slice timing correction for inter-slice differences in acquisition time, ART-based outlier detection (https://www.nitrc.org/projects/artifact_detect) to identify outlier scans for scrubbing, segmentation functional image and anatomical images to gray/white matter and cerebrospinal fluid (CSF) tissue classes using SPM unified segmentation and normalization procedure, and normalize to 2mm (functional) or 1mm (anatomical) isotropic voxel size in MNI space. Finally, the functional images were smoothed using spatial convolution with Gaussian kernel of 4mm full width half maximum (FWHM). After preprocessing, functional data were further denoised using CONN’s denoising pipeline, removing potential confounding effects in the BOLD signal such as noise components from white matter and CSF areas, estimated subject-motion parameters, scrubbing and session effects. Temporal frequencies below 0.008 Hz or above 0.09 Hz are removed from the BOLD signal in order to focus on slow-frequency fluctuations while minimizing the influence of physiological, head-motion and other noise sources.
2.3 ROI Definition and FC Analysis
FSL built-in atlases were used to generate 10 ROIs in dopamine system. Eight bilateral ROIs were extracted from Harvard-Oxford probability atlas, thresholded at 50% probability, and then binarized into masks. These ROIs were thalamus (THA), nucleus accumbens (NAc), caudate (CAU) and orbital frontal cortex (OFC), bilaterally. Talairach Daemon atlas was used to generate bilateral substantia nigra (SN) ROI masks directly. For each participant, we calculated the ROI-wise and voxel-wise functional connectivity of every ROI, forming one FC matrix and 10 FC maps per participant. The element value in FC matrices and voxel value in FC maps were further transformed to z-value utilizing Fisher’s r to z transformation equation, thus the distribution of z-value would approximately be normal. For ROI-wise FC matrices, one sample t-test was utilized to determine significant FC pathways within participant group. The significance level was defined by p<0.05, after false discover rate (FDR) multiple comparison correction.
2.4 Granger Causality Analysis
The granger causality indices (GCI) of each ROI pairs were calculated along each significant FC pathways. This GCI was calculated using autoregressive model:
And the joint regressive representation was:
Here, Yt and Xt are the preprocessed ROI signals, εt and μt represents the residuals of autoregression and joint regression, p means the lag of autoregression model. Fx→y is the GCI value defined as the granger causality effect from ROIx to ROIy, so each ROI pair has two GCI value, representing GC effect of two directions. According to Bayesian information criterion, the lag of vector auto-regression model was determined to be 2.
While the distribution of GCI was not clear, the one sample t-test of group GCI statistical which needs the normal distribution assumption may not be appropriate. To get the real distribution of GCI, permutation-based technology was adopted. We randomly switched ROI signals among participants preprocessed BOLD data, then calculated the GCI values of each ROI pairs, and repeat this procedure for 100,000 times. Finally, this generated a simulated GCI null distribution for real datasets. To test the significance of group GCI, the non-parametric one-sample Wilcoxon signed rank test was utilized. If the GCI value of one directional connection was significantly higher than the median value of GCI null distribution, this directional connection should be considered significant (p<0.05, FDR corrected).
2.5 Metabolic Connectivity Map (MCM) Analysis
Static FDG-PET images were preprocessed using FSL, including registration and normalization to 2mm voxel size MNI space. Standard uptake value ratio (SUVr) maps were converted by dividing the mean value of reference region. According to previous research[30,31], cerebellum gray matter was chosen as the reference region. With regard to high-resolution PET data acquisition, there was less need to perform partial volume effect correction. The normalized SUVr maps and BOLD data of each participants were used to calculate MCM. The MCM value of seed ROI X to target ROI Y was calculated as follows, the same method in the research of Riedl et.al [3,10]:
In the formula, FDGY is the voxel-wise profiles in ROI Y, representing the neuronal activity in ROI Y; FCY|X is the voxel-wise FC in ROI Y while ROI X as the seed ROI, representing the correlation between each voxel time series in ROI Y and the cluster time series of ROI X; The spatial correlation between FDG and FC voxel-wise profiles is the MCM value which represents the metabolic effective connectivity from ROI X (seed) to ROI Y (target).
Based on the cellular model of neuro-energetics, a positive MCMx→y value identifies the signaling input along the FC pathway from ROI X to ROI Y. To identify the significant MCM connections among participant group, one-sample t-test were conducted for MCM values along each direction of FC pathways. The significance level was set at p<0.05, FDR corrected.