Light-dependent changes in outer retinal water diffusion in rats in vivo

Animals

Male Sprague-Dawley rats (n=8; age: 5.8±0.2 months (mean±standard error of the mean [SEM]); wt: 544±15 g; Hilltop Labs Animals, Scottdale, PA) were housed and maintained in normal 12 h:12 h light-dark cycling before experimentation, and were treated in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals and The Association for Research in Vision and Ophthalmology Statement for the Use of Animals in Ophthalmic and Vision Research. Aside from the light exposure used during the MRI examination, all procedures took place in darkness or dim red light.

Magnetic resonance imaging acquisition

After overnight dark adaptation, rats were anesthetized with urethane (5.35±0.36 ml/kg bodyweight, of a 36% w/v solution in 0.9% saline; Sigma-Aldrich, Milwaukee, WI). Within 5 min of the urethane injection, atropine (2–3 drops 1% atropine sulfate ophthalmic solution; Falcon Pharmaceuticals, Fort Worth, TX) was applied to the left eye to dilate the pupil and ensure maximal dark-light differences during imaging. Full dilation throughout scanning was confirmed in each animal by observing the iris in MRI images (data not shown). A similar amount (2–3 drops) of lidocaine solution (0.6% dissolved in saline, Sigma Aldrich) was also applied to ensure corneal anesthesia. Scanning started after the pupil was fully dilated (1.1±0.1 h post-application). MRI data were acquired on a 7 T system (Clinscan; Bruker ClinScan, Billeric, MA) using a receive-only surface coil (1.0 cm diameter) centered on the left eye. The eye was stimulated with continuous (i.e., nonflickering) white light during half of the scans: one end of a fiber-optic line was attached to a light source (150 W EJA halogen bulb, Mark II Light Source; Prescott’s Inc., Monument, CO), and the other end was placed caudal to the eye to project light at a white piece of paper placed approximately 1 cm from the eye. When the light was on during MRI scans, the eye was exposed to approximately 600 lx (measured outside the magnet with a Traceable Dual-Range Light Meter [Control Company, Friendswood, TX] placed against a 1 cm diameter aperture—measured this way, normal laboratory lighting is approximately 150–300 lx). Aside from the fiber optic light source, all lights in the MRI room were off.

All images were collected as single slices perpendicular to the vitreoretinal border, passing through the optic nerve head and the center of the lens. Collection of structural images (two per lighting condition; spin-echo; repetition time 1,000 ms; echo time 13 ms; acquisition time 3 min 47 s; matrix size 160×320; slice thickness 600 µm; field of view 8 mm×8 mm; for an in-plane resolution of 25 µm [axial; up/down in sample images on the left side of Figure 1] ×50 µm [superior/inferior]) was intermixed with collection of diffusion-weighted images. Note that these relatively high-resolution acquisition parameters limit comparisons with previous studies of retinal ADC in rat [13], mouse [14], and cat [15], which were collected at lower in-plane resolution—resulting in higher apparent signal-to-noise on raw images, but also more partial volume averaging of adjacent retinal layers. Twelve images per lighting condition (24 total) were collected with diffusion weighting: Weighting was applied parallel to the optic nerve (to calculate the ADC parallel to the optic nerve [ADC_║]; Figure 1) in four images—one per b value (250 s/mm², 500 s/mm², 750 s/mm², and 990 s/mm²)—for each lighting condition. Weighting was applied in the two mutually perpendicular directions to the optic nerve (which were later combined to measure the ADC perpendicular to the optic nerve [ADC_┴]) at four b values per direction (250 s/mm², 500 s/mm², 750 s/mm², and 990 s/mm²). These measurements were collected using a diffusion-weighted spin-echo sequence, in which a twice-refocused spin-echo module [16] was inserted to introduce diffusion weightings (repetition time 1,000 ms; echo time 33 ms; acquisition time 3 min 25 s; slice thickness 600 µm; matrix size 288×144; field of view 8 mm×8 mm; resolution 27.8 µm×55.6 µm). In this sequence, each diffusion gradient lobe had the same duration of 4,000 μs, and three refocusing pulses per excitation: two for the diffusion module (0.52 ms in duration) and one for the readout. This produced three echoes: the first in the middle of the diffusion module (13.03 ms from the center of the excitation pulse); the second after the diffusion module but before the third refocusing pulse (at 26.06 ms); and the third at the echo time (33 ms). The same sequence was used to collect four images per lighting condition without diffusion-weighting gradients (b=0). The reproducibility and sensitivity of these imaging parameters was validated in our previous work using an osmotic challenge, and in diabetic rats [4].

To minimize the potential contribution of light-dark order or stimulus duration, the experiments were performed in the following way. For five rats, we alternated between light and darkness while the order of scans was randomized (e.g., b250_┴,Light, b990_║,Dark, b0_Light, b500_║,Dark, b750_║,Light, b0_Dark, structural_Light, b990_┴,Dark, …). For these rats, uninterrupted exposure to light (or darkness) lasted no more than 3 min 47 s (the acquisition time of a single structural image). For the other three rats, the light and dark of each scan type was paired, but the order was randomized in all other regards (e.g., b250_┴,Dark, b250_┴,Light, b750_║,Light, b750_║,Dark, b0_Dark, b0_Light, b500_║,Dark, b500_║,Light, …). The multiple acquisitions of structural and b0 images were split respectively into two and four light-dark pairs. Thus, for those rats, the sequence of scans contained no more than two light (or two dark) scans in a row, and uninterrupted exposure to light was always less than 7 min 12 s (e.g., structural_Dark, structural_Light, b0_Light, b0_Dark, …; although a 7 min 34 s exposure could have occurred if the two pairs of structural scans were collected one after the other, but, by chance, that specific order of scans did not arise from randomization). No differences were noted between these two acquisition strategies (not shown), and data were combined for further analysis. At the end of scanning, each rat had been exposed to the 600 lx stimulus for a total of 70 min, including a brief period of light exposure (<4 min) during animal setup and coil placement, a single approximately 4 min:4 min light-dark cycle performed immediately before the start of image acquisition, and all periods of light exposure during image acquisition. Light-induced retinal damage—a phenomenon that typically takes several hours of continuous exposure to much more substantial illumination (>1,600 lx [17])—is unlikely with the present stimulus parameters. In a study of intermittent exposure to a 2,000 lx stimulus [18], a cumulative ≥3 h of exposure was necessary to produce even mild retinal damage in Sprague-Dawley male rats raised in cyclical lighting (as in the present study).

Image processing

Because the possibility of light-dependent gross structural changes (e.g., whole-retinal thickness) could not be excluded a priori, each complete set of images per animal per lighting condition was processed separately until statistical comparisons were performed, as will be explained shortly.

Images were exported from Siemens’s Syngo software (after reconstruction to an isotropic matrix: 320×320 for structural images, 288×288 for diffusion images) for additional processing. To facilitate registration, all images were resampled in ImageJ to a 576×576 matrix. Structural images were resampled using bilinear signal interpolation. Diffusion images were resampled without signal interpolation (i.e., each voxel was converted to a 2×2 matrix of voxels with the same signal intensity value as the original). The two structural images (per subject per lighting condition) were registered and averaged to produce a single structural image, and the four b0 images were registered and averaged to produce a single b0 image (per rat per lighting condition).

After image resizing, averaging, and registration steps were complete, the structural image was used to measure gross retinal morphology, as previously detailed [4,19]: the hemiretinal extent, which is the distance between the optic nerve head and ciliary body, was measured along the vitreoretinal border in each hemiretina, then averaged to yield a single measure of retinal extent per animal per lighting condition. The contrast between the retina and vitreous makes this border straightforward to identify in the present T₁-weighted structural images: vitreous T₁ is approximately 3.5 s, and retinal T₁ is approximately 2.0 s (similar to brain gray matter) at this field strength. Polynomial functions fit to the vitreoretinal border were integrated about the central axis of the eye to calculate surface area of the vitreoretinal border, and were also used to linearize the retina (Figure 1). Data from the linearized central retina (between 10% and 30% of hemiretinal extent; abbreviated henceforth as '10%–30%extent') were binned together and used to calculate average profiles of signal intensity as a function of depth into the retina (Figure 1). Profiles from the structural image were used to calculate retinal thickness with the half-height approach, wherein a border is demarked where the profile crosses the midpoint between the local minimum and maximum [19,20]. Thickness was calculated as the distance between the vitreoretinal and retina-choroid borders as defined on the structural image. The choroid, which contains very fast-flowing blood, appears black in the present scans and was readily distinguished from the retina (Figure 1); its location was validated by our previous work using gadolinium-diethylenetriaminepentaacetic acid (Gd-DTPA) [21]. Knowing these two boundaries (vitreoretinal and retina-choroid), and confirming that the whole retinal thickness was normal (relative to histology and optical coherence tomography), allowed us to take advantage of the highly ordered and well described layering of the retina to approximate the location of different cell layers. We previously verified that the border between the inner and outer retina can be accurately localized in this way, allowing us to monitor inner versus outer retinal responses to light and darkness using other MRI techniques [22,23].

Thickness data and polynomial functions were combined, as previously described [19], to calculate retinal volume. A second, finer, automated registration step was then applied to the data using variants of the half-height approach. Briefly, the inner half of the retina is easily identified in structural and diffusion-weighted images due to its proximity to the vitreous. It maintains relatively high signal intensities at all diffusion directions for the range of b values used presently, forming a broad peak in signal intensity profiles (Figure 1). Each profile’s inner retinal profile peak is centered on the same location to complete registration, which is checked with visual inspection and some quantitative techniques (see section below on verification of image registration). This registration step was adopted without modification from a previous study [4], and is detailed extensively therein. As with the registration steps used to generate the averaged structural and b0 images, this automated registration was required to compensate for minor involuntary eye movement observed between the start and end of the approximately 2 h scanning period. We note that movement from one image to the next was negligible, consistent with previous measurements under urethane anesthesia [24]. This, combined with interspersed acquisition of dark and light stimulation, precluded full analyses of factors that could conceivably influence involuntary eye movement (e.g., light versus dark exposure, or the history thereof). Finally, all profiles (structural, b0, and diffusion-weighted) were expressed with signal intensity as a percentage of the whole retinal thickness ([%thick]—hereafter, whole-retinal-thickness percentages are thus abbreviated; see, e.g., Figure 1). To avoid partial-volume averaging with nonretinal tissue, areas within 12%thick of the structurally defined vitreoretinal and retina-choroid borders are routinely excluded from statistical comparisons [4,19].

Dark-light comparisons of structural and data collected when the diffusion weighting gradient was set to 0

Paired two-tailed t tests (α=0.05) were used to compare gross retinal morphology (thickness, extent, surface area, volume) in light versus darkness. Structural signal intensity profiles were similarly compared, as were b0 signal intensity profiles, but only results falling below a standard false discovery-rate threshold (q=0.05; 20 tests in each; from 12%–88%thick) were considered significant [25].

Analysis of apparent diffusion coefficient

The tissue apparent diffusion coefficient (ADC) was calculated based on the relationship between signal intensity and b value. Log-transformed signal intensity (symbolized below as some variant of natural logarithm of the signal intensity [ln(S)]) decreases with increasing diffusion gradient strength defined as a “b” value” in an approximately linear fashion. As in previous studies, the slope of the function relating ln(S) and b is the ADC [4,13,14], as shown in Equation 1:

ln(S_b)=−b*ADC+ln(S₀)

where S_b is the signal intensity when diffusion weighting is applied with a given b value, and S₀ is the signal intensity when b=0.

Multiple data points relating ln(S) to b are collected from each subject and used to calculate the ADC using stimulus- and direction-specific versions of Equation 1. For example Equation 2 and Equation 3:

ln(S_{b, ||,  Light})=−b∗ADC_||,  Light+ln(S_0,  Light)

ln(S_{b, ||,  Dark})=−b∗ADC_||,  Dark+ln(S_0,  Dark)

To test for light-dependent changes, Equation 2 and Equation 3 are subtracted and simplified to Equation 4:

ln(S_{b, ||,  Dark}/S_{b, ||,  Light})=−b∗(ΔADC_{||,  Dark - Light})+ln(S_0,  Dark/S_0,  Light)

In an ordinary least-squares (OLS) analysis, linear regression is used to calculate the slope relating the b value to the log-transformed ratio of dark-to-light signal intensity for each subject. A one-sample t test is then used to test whether those slopes—as per Equation 4, the difference between the ADC in dark versus light (i.e., ΔADC)—are, on average, significantly different from zero.

ΔADC is convenient for estimating effect sizes, since Cohen’s d (for ΔADC_{║,Dark-Light}≠0) is given as the mean of subjects’ calculated ΔADC_{║,Dark-Light}s divided by the standard deviation (SD). As part of our analyses of intraretinal ADCs, we offer a brief comparison of these effect sizes (reported as an absolute value of Cohen’s d) when data from all five b values are used, versus using data from only b0 and one other b value. Effect sizes (absolute value of Cohen’s d; |d|) for dark-light differences in ADC_║ and ADC_┴ were calculated for regions of the retina showing significant results. For a region with multiple adjacent positions reaching significance (e.g., at 80%–88%thick), we report |d| as reaching the highest value calculated in that range of positions (e.g., if |d|=1.2, 1.4, and 1.1 at, respectively, 80%thick, 84%thick, and 88%thick, then |d| reached 1.4 in 80%thick–88%thick). We note that OLS-based p values are typically higher than the p values obtained using the generalized estimating equation (GEE) analyses described next. Using these |d| values in power calculations (for instance) may therefore produce conservative results. OLS-based p values can be derived from the |d| values reported in the results section, such that |d|>0.89 demonstrates a two-tailed p<0.05 from an OLS analysis. |d| is calculated using data from all five b values (0, 250, 500, 750, 990)—as used in the analyses of the retinal ADC and shown in Figure 2—and using data from only b0 and another b value (e.g., b0 and b990 only). These effect-size calculations were performed, in part, to provide guidance for future studies, in which total scan time could be reduced by collecting data at fewer b values than used presently.

As we have previously discussed in some detail [4] the OLS procedure described above has some shortcomings for calculating retinal ADC, related, for instance, to its inability to use within-subject variability to refine group average estimates and standard errors. Where possible, we therefore use an extension of the OLS approach—a GEE analysis—which can appropriately account for the repeated measures composing each within-individual relationship into calculations of the across-subjects ADC means and standard errors [26]. We calculated the ADC at different depths into the retina as previously detailed [4], using autoregressive [AR (1)] GEE fits for Equations 1–4. For instance, a GEE fit based on Equation 2 is used to calculate the group mean and standard error values for ADC_║,Light shown in Figure 2, while a GEE fit to Equation 4 is used to calculate the dark-light difference in ADC_║ and to test the null hypothesis that ΔADC_{║,Dark - Light}=(ADC_║,Dark - ADC_║,Light)=0. A similar approach is used to calculate anisotropy of diffusion, which is characterized within the central retina (Figure 1) as the difference in water diffusion perpendicular versus parallel to the direction of the optic nerve (i.e., ADC_┴ - ADC_║). We have previously published detailed procedures for performing these ADC calculations, including samples of relevant R code and illustrations of best-fit lines for the ln(S_b) versus b relationship described in Equation 1 [4].

We used GEE fits of the monoexponential model to test for dark-light differences from 12%thick to 88%thick (at 20 points per profile in 4% increments) in three profiles (ADC_║, ADC_┴, and ADC_┴ - ADC_║). Findings below a standard false discovery-rate threshold (q=0.05; 60 tests) were considered significant.

Verification of image registration

The second registration step, mentioned in the section above on image processing, is critical to properly localizing ADC data within the structurally defined vitreoretinal and retina-choroid borders. That fully automated approach—which yields reproducible outcomes [4]—was applied to the present data. Though visual inspection suggested good postregistration alignment in all cases, we sought to confirm this with the following additional quantitative checks.

As previously described for the present imaging parameters, rapid diffusion in the vitreous causes large signal loss with even modest b values [4]. This produces artifactually low vitreous ADC estimates when all b values are used. However, vitreous signal intensity is above background levels when b=250 s/mm². This allows for a calculation of vitreous ADCs using the OLS-based procedure with only the 0 s/mm² and 250 s/mm² b values (henceforth, ADC_║,0,250 and ADC_┴,0,250). The resulting profile shows high (>2.0 μm²/ms) ADC_0,250s in the vitreous, with an abrupt change to low (typically ≤0.5 μm²/ms) ADC_0,250s in the inner half of the retina (see [4]). The ADC_0,250-defined vitreoretinal border is then determined by the half-height approach described above. Based on our previous use of this method [4], the local minimum in each subject’s four ADC_0,250 profiles (light_║, light_┴, dark_║, and dark_┴) was calculated between 4%thick and 44%thick, and the local maximum between −24%thick and 0%thick. The vitreoretinal borders defined by each ADC_0,250 profile (which are expected to fall near the vitreoretinal border defined by structural images, at 0%thick) were compared across diffusion directions and stimulus conditions using a two-way repeated measures ANOVA (ANOVA; α=0.05) to verify good alignment before the comparisons of light versus dark intraretinal ADCs.

A related approach is used to estimate the location of the retina-choroid border: water movement within the choroid is predominantly in the direction of blood flow, which is parallel to the surface of the eye (i.e., ┴ for the central retinal data considered here). Due to the high water mobility of fast-flowing choroidal blood, diffusion weighting will greatly diminish signal, even at low b values, analogous to the vitreous. Although this produces artifactually low ADCs when all b values are used, high ADC_0,250 values with high anisotropy (i.e., ADC_┴,0,250>ADC_║,0,250) are expected of the choroid. We have previously used the measurement ADC_┴,0,250 - ADC_║,0,250 to localize the choroid in diffusion images, and found reasonable agreement with previous work that used intravascular contrast (gadolinium-based) to highlight the choroid [4,21]. On each subject’s light and dark profiles of ADC_┴,0,250 - ADC_║,0,250, we calculated the local minimum from 66%thick to 96%thick, the local maximum from 64%thick to 128%thick, and the borders of the choroid with the retina and sclera using the half-height approach. To verify good alignment of light and dark ADC data, the location of these borders was compared with a paired two-tailed t test (α=0.05). Based on previous work, the apparent borders of the choroid are expected to fall at approximately 85% and approximately 115%thick [4]. We note that the slight choroidal overlap with the structurally defined retinal borders (i.e., the appearance of choroid at <100%thick) herein is consistent with gadolinium-based measurements [21], and is attributable to several factors, including partial volume averaging.

Although the above analyses confirmed good alignment of ADC data, we also evaluated their robustness to alignment errors: The GEE-based dark-light comparisons described in the section above on analysis of the ADC were repeated after purposely misaligning light and dark ADC data: All dark data were fixed in place, while all light data were shifted by the same amount to either the left or right. Since profiles are reported in 4%thick increments, tests were repeated after shifts of 4%thick, 8%thick, and 12%thick from the original position. Of the retinal regions with significant dark-light differences in the first pass analysis, those that retained differences of p<0.05 after each of the six deliberate misalignments were considered highly robust to alignment errors.

Assessing signal-to-noise relevant to ADC calculations

Accurate ADC measurements require high signal-to-noise ratios (SNRs). At the present high spatial image resolution, SNR is at a premium. To avoid increased intra-individual variability (e.g., pixel-to-pixel), spatial averaging was performed on linearized images—to preserve retinal layer-specific information—before calculating the ADC as described above. Following image processing (Figure 1), results from central retinas (10%–30%extent) were averaged to produce a single profile of signal intensity as a function of depth into the retina (%thick) for each image. Each rat contributed two structural profiles (one for light, one for dark), two b0 profiles (light and dark), and 24 diffusion-weighted profiles (three diffusion directions, four nonzero b values, light and dark) to subsequent analyses. With the present imaging parameters, vitreous signal is indistinguishable from noise for b values of ≥500 s/mm² (present observations; also see [4]). Thus, noise was measured at b values of >500 s/mm² in the profile region occupied by the vitreous. Profile signal intensities (S) at −24%thick were collected from each rat’s b750 and b990 images, and the standard deviation of those values (S_noise,SD) calculated. Next, profile intensities were collected from three positions in the retina—selected because they yielded significant dark-light differences—at 56%–60%thick, 68%thick, and 80%–88%thick. Since the lowest signal intensities (S_tissue,mean) used to calculate retinal ADCs are measured at b990, we use the spatially averaged tissue signal at that b value to calculate signal-to-noise ratios [27]: SNR_tissue=0.655×(S_tissue,mean/S_noise,SD). Some ADC calculations are also performed on the vitreous and choroid (see section on verification of image registration), but only using b0 and b250. For those tissues, SNR_tissue was calculated from the S_tissue,mean of b250 profiles at −24%thick (vitreous) and 104%thick (choroid).

In addition, we checked to see if low SNR resulted in underestimation of ADCs (i.e., the rectified noise floor effect) [27]. To test for this effect, we calculated retinal ADCs using the GEE analysis of all five b values described above, then reran those ADC calculations, omitting the highest b value (990 s/mm²). Similar results obtained from both analyses would indicate that signal at the highest b value was far enough from the noise levels to preserve the linear relationship described by Equation 1—suggesting sufficient SNR for accurate ADC calculation [27]. In cases where retinal ADCs differed when using 0≤b≤990 s/mm² versus 0≤b≤750 s/mm², we retested for dark-light differences using the 0≤b≤750 s/mm² data.

Dark-light comparisons of structural and data collected when the diffusion weighting gradient was set to 0

There were no differences on any measure of gross retinal morphology between light and darkness (p>0.05; Table 1). Central retinal signal intensities were not affected by lighting conditions at any retinal depth in structural images (the lowest p value, P_min, was 0.213 in the inner half of the retina [12%–48%thick] and 0.176 in the outer half of the retina [52%–88%thick]). Similarly, there was no effect of lighting condition on signal intensities in b0 images (Figure 2; P_min was 0.059 in the inner half and 0.141 in the outer half).

Analysis of ADC

Regardless of lighting conditions or gradient directions, the intraretinal ADC profiles had relatively lower values in the inner half of the retina (approximately 0.5 μm²/ms) compared to those in the outer half (approximately 1.0 μm²/ms; Figure 1). This pattern reasonably replicates previous observations for retinal layer-specific measurements of the ADC in the rat and reflects the robustness of our registration protocol [4,13]. With light and dark exposure, three distinct patterns in the intraretinal ADC profiles were apparent:

(1) Near the retina-choroid border, the ADC was significantly greater in the light than in the dark, but only in the direction parallel to the optic nerve and long axis of photoreceptors (i.e., ADC_║,Dark<ADC_║,Light; q<0.05 for 80%–88%thick, where P_min=6.13 e-5). This pattern was not seen for diffusion perpendicular to the optic nerve (ADC_┴,Dark≈ADC_┴,Light; P_min>0.05 for 76%–88%thick). Since ADC_║ generally appears lower than ADC_┴ in this part of the retina (Figure 2), ADC_║ and ADC_┴ are more similar in light than darkness. Put differently, anisotropy of diffusion (ADC_┴-ADC_║>0) near the retina-choroid border is greater in darkness than in light (q<0.05 for 72%–84%thick, P_min=1.24 e-3).

(2) Anterior to that, but still in the photoreceptor-dominated outer half of the retina, we found that water diffusion was significantly lower in the light than in the dark, both for parallel (ADC_║,Dark> ADC_║,Light; q<0.05 at 56 and 60%thick, P_min=1.00 e-4) and perpendicular (ADC_┴,Dark> ADC_┴,Light; q<0.05 at 68%thick, P_min=7.76 e-3) directions.

(3) In the inner half of the retina, we found that diffusion was similar in light and darkness, both for parallel (ADC_║,Dark≈ADC_║,Light; P_min>0.05 for 12%–36%thick) and perpendicular (ADC_┴,Dark≈ADC_┴,Light; P_min>0.05 for 12%–48%thick) directions.

When data from all five b values were used in OLS analyses of ADC_║ at 56%–60%thick (based on Equation 4), |d| reached 0.91. Using data from b0 and only one other b value, |d| reached 0.47 (using b0 and b250), 0.91 (b0 and b500), 0.25 (b0 and b750), and 1.39 (b0 and b990) for 56%–60%thick. When data from all five b values were used in OLS analyses of ADC_║ at 80%–88%thick, |d| reached 1.42. Using data from b0 and only one other b value, |d| reached 0.50 (using b0 and b250), 0.31 (b0 and b500), 1.11 (b0 and b750), and 1.04 (b0 and b990) for 80%–88%thick. When data from all five b values were used in OLS analyses of ADC_┴ at 68%thick, |d|=0.92. Using data from b0 and only one other b value, |d|=0.23 (using b0 and b250), 0.57 (b0 and b500), 0.74 (b0 and b750), and 0.78 (b0 and b990) at 68%thick. In short, effect sizes calculated using data from all five b values were usually larger than effect sizes calculated using data from only two b values.

Verification of image registration

Results of the ADC_0,250-based localization of vitreoretinal and retina-choroid borders, which are presented in Table 2, were in good agreement with previous work [4], and suggested reasonable alignment of structural data with diffusion data. The consistency of vitreous ADC_0,250 values with previous work [4,13], as well as a confirmation the significant anisotropy (ADC_┴,0,250>ADC_║,0,250) used to localize the choroid, are also noted in Table 2. Calculated choroidal thicknesses were in reasonable agreement with previous in vivo and ex vivo studies [4,21], and were similar in darkness and light (mean±SEM of 50±6 μm and 46±5 μm, respectively). Importantly, locations of ADC_0,250-based borders were similar (p>0.05) across lighting conditions and diffusion directions, suggesting that the first-pass analysis was free of alignment errors.

Following each of the six intentional misalignments of light and dark data—shifting light data anterior or posterior (left or right in Figure 2 plots) by 4%thick, 8%thick, and 12%thick—dark-light differences in ADC_║ were still found in the posterior outer retina (i.e., at least one point between 80%thick and 88%thick reached p<0.05). Dark-light differences in anisotropy (ADC_┴-ADC_║) were similarly robust to misalignment, being present in the outer half of the retina following each of the six intentional misalignments (always at least two points within 52%–88%thick at p<0.05). However, those findings fell outside the 72%–84%thick range (selected based on the significant findings noted in the section on analysis of ADC) when light data were shifted left by 8%thick or 12%thick. For five of the six intentional misalignments (excepting a 4%thick leftward shift of light data) dark-light differences were still found for ADC_║ in the anterior outer retina (at both 56%thick and 60%thick), and for ADC_┴ in the outer retina (at least one point within 52%–88%thick). Taken together, these data strongly support the good alignment of the diffusion data, and that the significant findings in the previous section—particularly for ADC_║ in the posterior outer retina—were robust to some misalignment.

Assessing signal-to-noise relevant to ADC calculations

SNR_tissues were calculated for each subject, and are presented here as the across-subject mean±SEM: In the retina, when b=990 s/mm², SNR_tissues at 56%–60%thick, 68%thick, and 80%–88%thick, respectively, were 13.4±2.2, 10.5±2.0, and 8.4±0.8. In the vitreous (−24%thick) and choroid (104%thick), when b=250 s/mm², SNR_tissues were, respectively, 8.1±1.3 and 9.1±1.3.

We also compared ADCs calculated using 0≤b≤750 s/mm² (i.e., omitting the highest b value data that had the lowest SNR) to the ADCs shown in Figure 2 (calculated with 0≤b≤990 s/mm²). For the anterior two-thirds of the retina, omitting the b=990 s/mm² data had little effect: from 12%thick to 64%thick, the mean ADCs (for ADC_║,Light, ADC_║,Dark, ADC_┴,Light, and ADC_┴,Dark) calculated with 0≤b≤990 s/mm² fell within the 95% confidence interval of those ADCs calculated with 0≤b≤750 s/mm². In this section of the retina, ADCs calculated with 0≤b≤750 s/mm² data were on average 4% higher than those shown in Figure 2—well within the present experimental error—indicating that the SNR was adequate at all five b values. For the posterior one-third of the retina, omitting the b=990 s/mm² data produced an increase in ADC: From 68%thick to 88%thick, at least one of mean ADCs (for ADC_║,Light, ADC_║,Dark, ADC_┴,Light, and ADC_┴,Dark) calculated with 0≤b≤990 s/mm² fell below the 95% confidence interval of those ADCs calculated with 0≤b≤750 s/mm². In this section of the retina, ADCs calculated with 0≤b≤750 s/mm² were on average 17% higher than those shown in Figure 2. Importantly, the apparent underestimation of ADCs in this region (Figure 2) was similar across all four curves (ADC_║ and ADC_┴, in light and dark), meaning that the dark-light differences—the central focus of the present work—remained when analysis was restricted to the 0≤b≤750 s/mm² data: reanalyzing this 68%–88%thick span using only 0≤b≤750 s/mm² data, we again found that ADC_┴,Dark>ADC_┴,Light in the anterior outer retina (though strongest result shifted from 68%thick [see above] to 64%thick, where p=3.05 e-5), and that ADC_║,Dark<ADC_║,Light from 80%–88%thick (P_min=2.72 e-4; data not shown).