The vasodilation and vasoconstriction properties of cerebral arterioles (arteriolar vasomotor function) would be a biomarker of early diagnosis of dementia. Although the vasodilation ability has been studied by using vasodilators such as Diamox, these vasodilators cause non-natural extreme vasodilation. Focusing on the natural arteriolar vasomotion induced by respiratory variation of blood CO2, we have reported a method to evaluate cerebral arteriolar vasomotor function by spectral analysis of fluctuation of venous MRI signal. In this study, we improved our method and applied it to young smokers, and demonstrated the degeneration of arteriolar vasomotor function after a few years of chronic smoking.
A single slice perpendicular to the superior sagittal sinus was imaged for 45 s by using spin-echo echo-planar imaging (SE-EPI, TR = 250 ms, TE = 30 ms) at 3 T for 7 non-smokers (male, aged 22.6 ± 1.1 years) and 6 smokers (male, aged 24.7 ± 2.2 years, smoking history 4.3 ± 2.1 years). This time series of imaging was repeated for 5 slice thicknesses (7-15 mm). Respiratory rate during scan was fixed at 15 times per minute by a metronome. To investigate the chronic and acute influences of smoking, smokers were imaged after abstaining from smoking for more than 8 hours (pre-smoking) and immediately after smoking (post-smoking) respectively. The time courses of MR signal of superior sagittal sinus were Fourier-transformed for each slice thickness. Because the spectrum at respiratory frequencies was contaminated by line broadening of low frequency component which represents the default mode network, a hamming window was used to reduce this contamination (Fig.1). The spectral fluctuation intensity (SFI) at respiratory frequencies (0.2–0.3 Hz) was obtained for each slice thickness. As aliasing of harmonics of the cardiac pulsation frequency component contaminates the respiratory frequency component, the SFIs which suffers from this contamination due to aliasing were excluded from further analysis by observing the cardiac frequency which can be aliased in respiratory frequencies. SFI versus average signal intensity of superior sagittal sinus was plotted and regression line was drawn for each volunteer. The MR signal of venous blood is written by
$$$S=C\cdot{e^{-R_{2}\cdot{TE}}}\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space\space$$$(1),
where C is the proportional constant, R2 is a relaxation rate as given by
$$$R_{2}=C_{1}\cdot\left(1-Y\right)^{2}+C_{2}\space\space\space\space\space\space\space\space\space\space\space\space\space\space$$$(2),
where C1、C2 are the constants and Y is the venous blood oxygenation. Using Eqs.1 and 2, the following equation is obtained.
$$$\frac{ΔS}{S}=2C_{1}\cdot\left(1-Y\right)\cdot{TE}\cdot{ΔY_{r}}\space\space\space\space$$$(3),
The right side of Eq. 3 (ΔS/S) is the slope of the regression line of the plot of SFI versus average signal intensity. The respiratory fluctuation of blood oxygenation (ΔYr), which reflects the cerebral arteriolar vasomotor function, can be calculated by substituting the values of C1 (59)4 and Y (0.66).5 Then the values of ΔYr of non-smokers and smokers were compared.
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