Data analysis was performed with custom software packages (MatLab [MathWorks Inc, Natick, MA]and STIMULATE [Center for Magnetic Resonance Research, University of Minnesota,
www.cmrr.umn.edu/stimulate/non_frame/index.html]) as described in detail elsewhere.
13,14 A semiautomated process in MatLab was used to linearize the retina, align it to correct for motion (if any) of the eye during the scan, and conduct an automated profile analysis.
10 Profiles across the retinal thickness were obtained from images by projecting lines perpendicular to the retina with profiles obtained at 4 × spatial interpolation. The BF (mL/min/g) was calculated from the signal intensities of labeled and nonlabeled images, as follows
15,16 :
In this formula, λ (0.9 mL/g)
17 is the tissue-blood partition coefficient for water and is the value [(quantity of water/g tissue)/(quantity of water/mL blood)]. T
1 is 1.8 seconds at 7 Tesla,
18 S
NL is the signal intensity (arbitrary units) of images with nonlabeled blood, S
L (arbitrary units) is the signal intensity of images with magnetically labeled blood, and α is the arterial spin-labeling efficiency (0.7)
10 for cardiac labeling in mice. BF profiles were averaged along the length of the retina, as shown in
Figure 2. Two peaks were present in the averaged BF profile, located in the inner retina and choroid. Measurements of retinal and choroidal BF were determined from the corresponding peaks from the average BF profiles for each animal.
Data are reported as mean ± SD, with statistically significant differences reported when P < 0.05. One-way ANOVA with Tukey's multiple comparisons test was used to determine differences in choroidal or retinal blood flow within strains. Two-way ANOVA with the Bonferroni posttest was used to determine differences in choroidal or retinal blood flow by age between strains. An unpaired t-test was used to compare strain means (Prism; GraphPad, La Jolla, CA).