Sinus, Superior Sagittal

Data were processed using in-house MATLAB (Mathworks, Natick, MA) scripts. After motion correction, pair-wise subtraction was performed for the control and label images to obtain the difference images for each eTE (e.g. Fig. 3a). For this study, we were primarily interested in venous oxygenation in the sagittal sinus. Therefore, an ROI covering the sagittal sinus region was manually drawn and, within this ROI, four voxels containing the largest difference signals in the eTE=0 image were selected as the mask for spatial averaging. The voxel number, 4, is chosen because we found that all subjects have at least 4 sagittal sinus voxels (at the resolution of 3.6×3.6×5 mm³) as visible in the difference image. Some subjects had greater sagittal sinus, but as long as we can accurately estimate the signal decay constant with 4 voxels, the other voxels do not have to be included. The effect of subjective ROI drawing and the number of voxels used are further discussed in the Results section.
The ΔS was fitted to Eqn. 4 (e.g. Fig. 3b) to obtain the exponent C, from which T_2b was calculated by assuming a blood T1 of 1624ms (22 (link)). The T_2b was then converted to blood oxygenation using the calibration plot shown in Fig. 3c. In addition, from the fitting procedure, the 95% confidence interval for the estimated parameters was also calculated (using Matlab routine nlparci.m). This gave an assessment of the uncertainty of the reported parameter values.

Data processing of TRUST and PC MRI followed methods used previously (21 (link),34 (link),40 (link)). Briefly, for TRUST MRI data, after motion correction and pair-wise subtraction between control and tag images, a preliminary region-of-interest (ROI) was manually drawn to include the superior sagittal sinus. To further define the venous voxels, four voxels with the highest signals in the difference images in the ROI were chosen as the final mask for spatial averaging. The venous blood signals were then fitted to a monoexponential function to obtain T₂. The T₂ was in turn converted to Y_v via a calibration plot obtained by in vitro bovine blood experiments under controlled oxygenation, temperature, and Hct conditions (40 (link)). For PC MRI data, a ROI was manually drawn on the targeted artery of each PC MRI scans based on the magnitude image. The operator was instructed to trace the boundary of the targeted artery without including adjacent vessels. The phase signals, i.e. velocity values, within the mask were summed to yield the blood flow of each artery. To account for brain size differences, the unit volume CBF (in ml/100 g/min) was obtained by normalizing the total CBF (in ml/min) of all four arteries to the intracranial mass (in gram), which was estimated from the high resolution T₁-MPRAGE image using the software FSL (FMRIB Software Library, Oxford University). OEF was calculated from Y_a and Y_v.
Several reproducibility indices were calculated for each of the physiologic parameters evaluated. Intra-session Coefficient of Variation (CoV) was calculated as:
$$Co{V}_{\text{intra–session}}=\frac{1}{I\cdot J}\sum _i\sum _j\frac{\mid M_{ij1}-M_{ij2}\mid }{\sqrt{2}\cdot Mean(M_{ij1},M_{ij2})}$$
where M_ij1 and M_ij2 represent measurement #1 and #2, respectively of Subject #i (i = 1, 2, …, I) in Session #j (j = 1, 2, …, J). Inter-session CoV was calculated as:
$$Co{V}_{\text{inter–session}}=\frac{1}{I\cdot K}\sum _i\sum _k\frac{S{D}_j\left(M_{ijk}\right)}{Mea{n}_j\left(M_{ijk}\right)}$$
where SD stands for standard deviation.
Inter-subject CoV was calculated as:
$$Co{V}_{\text{inter–subject}}=\frac{1}{I\cdot K}\sum _j\sum _k\frac{S{D}_i\left(M_{ijk}\right)}{Mea{n}_i\left(M_{ijk}\right)}$$
Compared to intra-session CoV, the value of inter-session is expected to contain additional variance due to subject repositioning and day-to-day differences in physiologic states. These contributions can be calculated as $\sqrt{Co{V}_{i\text{nter–session}}^2-Co{V}_{\text{intra–session}}^2}$ . Similarly, compared to inter-session CoV, inter-subject CoV contains additional inter-subject physiologic differences. These contributions can be calculated as $\sqrt{Co{V}_{i\text{nter–subject}}^2-Co{V}_{\text{inter–session}}^2}$ .
Additionally, since CBF quantification involves manual ROI selection, inter-rater reliability of CBF measurement was evaluated by having two raters (PL and FX) analyze the same datasets independently and calculating the correlation of the CBF values.
Relationships between physiologic parameters were evaluated with Pearson correlation and mixed effect model. In all analyses, a p < 0.05 is considered statistically significant.