Reduced blood flow by laser speckle flowgraphy after 125I-plaque brachytherapy for uveal melanoma.

BACKGROUND: To determine whether reductions in retinal and choroidal blood flow measured by laser speckle flowgraphy are detected after 125I-plaque brachytherapy for uveal melanoma. METHODS: In a cross-sectional study, retinal and choroidal blood flow were measured using laser speckle flowgraphy in 25 patients after treatment with 125I-plaque brachytherapy for uveal melanoma. Flow was analyzed in the peripapillary region by mean blur rate as well as in the entire image area with a novel superpixel-based method. Relationships between measures were determined by Spearman correlation. RESULTS: Significant decreases in laser speckle blood flow were observed in both the retinal and choroidal vascular beds of irradiated, but not fellow, eyes. Overall, 24 of 25 patients had decreased blood flow compared to their fellow eye, including 5 of the 6 patients imaged within the first 6 months following brachytherapy. A significant negative correlation between blood flow and time from therapy was present. CONCLUSIONS: Decreases in retinal and choroidal blood flow by laser speckle flowgraphy were detected within the first 6 months following brachytherapy. Reduced retinal and choroidal blood flow may be an early indicator of microangiographic response to radiation therapy.

Utility of Spectral-Domain Optical Coherence Tomography in Differentiating Papilledema From Pseudopapilledema: A Prospective Longitudinal Study.

BACKGROUND: Prospective and longitudinal studies assessing the utility of spectral-domain optical coherence tomography (SD-OCT) to differentiate papilledema from pseudopapilledema are lacking. We studied the sensitivity and specificity of baseline and longitudinal changes in SD-OCT parameters with 3D segmentation software to distinguish between papilledema and pseudopapilledema in a cohort of patients referred for evaluation of undiagnosed optic disc elevation. METHODS: Fifty-two adult patients with optic disc elevation were enrolled in a prospective longitudinal study. A diagnosis of papilledema was made when there was a change in the appearance of the optic disc elevation on fundus photographs as noted by an independent observer at or before 6 months. The degree of optic disc elevation was graded using the Frisen scale and patients with mild optic disc elevation (Frisen grades 1 and 2) were separately analyzed. SD-OCT parameters including peripapillary retinal nerve fiber layer (pRNFL), total retinal thickness (TRT), paracentral ganglion cell layer-inner plexiform layer (GCL-IPL) thickness, and optic nerve head volume (ONHV) at baseline and within 6 months of follow-up were measured. RESULTS: Twenty-seven (52%) patients were diagnosed with papilledema and 25 (48%) with pseudopapilledema. Among patients with mild optic disc elevation (Frisen grades 1 and 2), baseline pRNFL (110.1 µm vs 151.3 µm) and change in pRNFL (ΔpRNFL) (7.3 µm vs 52.3 µm) were greater among those with papilledema. Baseline and absolute changes in TRT and ONHV were also significantly higher among patients with papilledema. The mean GCL-IPL thickness was similar at baseline, but there was a small reduction in GCL-IPL thickness among patients with papilledema. Receiver operator curves (ROCs) were generated; ΔpRNFL (0.93), ΔTRT (0.94), and ΔONHV (0.95) had the highest area under the curve (AUC). CONCLUSIONS: The mean baseline and absolute changes in SD-OCT measurements (pRFNL, TRT, and ONHV) were significantly greater among patients with papilledema, and remained significantly greater when patients with mild optic disc elevation were separately analyzed. ROCs demonstrated that ΔpRNFL, ΔTRT, and ΔONHV have the highest AUC and are best able to differentiate between papilledema and pseudopapilledema.

RetFM-J, an ImageJ-based module for automated counting and quantifying features of nuclei in retinal whole-mounts.

The present article introduces RetFM-J, a semi-automated ImageJ-based module that detects, counts, and collects quantitative data on nuclei of the inner retina from H&E-stained whole-mounted retinas. To illustrate performance, computer-derived outputs were analyzed in inbred C57BL/6J mice. Automated characterization yielded computer-derived outputs that closely matched manual counts. As a method using open-source software that is freely available, inexpensive staining reagents that are robust, and imaging equipment that is routine to most laboratories, RetFM-J could be utilized in a wide variety of experiments benefiting from high-throughput, quantitative, uniform analyses of total cellularity in the inner retina.

Quantitative measurement of retinal ganglion cell populations via histology-based random forest classification.

The inner surface of the retina contains a complex mixture of neurons, glia, and vasculature, including retinal ganglion cells (RGCs), the final output neurons of the retina and primary neurons that are damaged in several blinding diseases. The goal of the current work was two-fold: to assess the feasibility of using computer-assisted detection of nuclei and random forest classification to automate the quantification of RGCs in hematoxylin/eosin (H&E)-stained retinal whole-mounts; and if possible, to use the approach to examine how nuclear size influences disease susceptibility among RGC populations. To achieve this, data from RetFM-J, a semi-automated ImageJ-based module that detects, counts, and collects quantitative data on nuclei of H&E-stained whole-mounted retinas, were used in conjunction with a manually curated set of images to train a random forest classifier. To test performance, computer-derived outputs were compared to previously published features of several well-characterized mouse models of ophthalmic disease and their controls: normal C57BL/6J mice; Jun-sufficient and Jun-deficient mice subjected to controlled optic nerve crush (CONC); and DBA/2J mice with naturally occurring glaucoma. The result of these efforts was development of RetFM-Class, a command-line-based tool that uses data output from RetFM-J to perform random forest classification of cell type. Comparative testing revealed that manual and automated classifications by RetFM-Class correlated well, with 83.2% classification accuracy for RGCs. Automated characterization of C57BL/6J retinas predicted 54,642 RGCs per normal retina, and identified a 48.3% Jun-dependent loss of cells at 35 days post CONC and a 71.2% loss of RGCs among 16-month-old DBA/2J mice with glaucoma. Output from automated analyses was used to compare nuclear area among large numbers of RGCs from DBA/2J mice (n = 127,361). In aged DBA/2J mice with glaucoma, RetFM-Class detected a decrease in median and mean nucleus size of cells classified into the RGC category, as did an independent confirmation study using manual measurements of nuclear area demarcated by BRN3A-immunoreactivity. In conclusion, we have demonstrated that histology-based random forest classification is feasible and can be utilized to study RGCs in a high-throughput fashion. Despite having some limitations, this approach demonstrated a significant association between the size of the RGC nucleus and the DBA/2J form of glaucoma.

Retinal ganglion cell layer thinning within one month of presentation for optic neuritis.

BACKGROUND: Spectral domain optical coherence tomography (SD-OCT) reveals retinal ganglion cell layer plus inner plexiform layer (GCL+IPL) and peripapillary retinal nerve fiber layer (pRNFL) thinning in chronic optic nerve injury. At presentation, swelling of the pRNFL confounds evaluation of early axon loss. OBJECTIVE: We studied whether the GCL+IPL thins before the pRNFL, the trajectory of GCL+IPL loss and relationship to vision. METHODS: We prospectively evaluated 33 eyes (study) with new optic neuritis, using perimetry and SD-OCT with investigative three-dimensional layer segmentation and commercial two-dimensional segmentation to compute the GCL+IPL and pRNFL thickness. RESULTS: At presentation, GCL+IPL thickness (82.4±8.8 µm) did not differ from unaffected fellow eyes (81.2±6.7 µm), via the three-dimensional method, while the two-dimensional method failed in 9% of study eyes. At 1-2 months, there was thinning of the pRNFL in 10% and of the GCL+IPL in 93% of study eyes. GCL+IPL reduction was greatest during the first 2 months. GCL+IPL thinning at 1-2 months correlated with GCL+IPL thinning at 6 months (r=0.84, P=0.01) and presentation visual acuity (r=0.48, P=0.006) and perimetric mean deviation (r=0.52, P=0.003). CONCLUSION: GGL+IPL is an early biomarker of structural injury in optic neuritis as thinning develops within 1-2 months of onset, prior to pRNFL thinning.

Visualization of Optic Nerve Structural Patterns in Papilledema Using Deep Learning Variational Autoencoders.

Purpose: To visualize and quantify structural patterns of optic nerve edema encountered in papilledema during treatment. Methods: A novel bi-channel deep-learning variational autoencoder (biVAE) model was trained using 1498 optical coherence tomography (OCT) scans of 125 subjects over time from the Idiopathic Intracranial Hypertension Treatment Trial (IIHTT) and 791 OCT scans of 96 control subjects from the University of Iowa. An independent test dataset of 70 eyes from 70 papilledema subjects was used to evaluate the ability of the biVAE model to quantify and reconstruct the papilledema spatial patterns from input OCT scans using only two variables. Results: The montage color maps of the retinal nerve fiber layer (RNFL) and total retinal thickness (TRT) produced by the biVAE model provided an organized visualization of the variety of morphological patterns of optic disc edema (including differing patterns at similar thickness levels). Treatment effects of acetazolamide versus placebo in the IIHTT were also demonstrated in the latent space. In image reconstruction, the mean signed peripapillary retinal nerve fiber layer thickness (pRNFLT) difference ± SD was -0.12 ± 17.34 µm, the absolute pRNFLT difference was 13.68 ± 10.65 µm, and the RNFL structural similarity index reached 0.91 ± 0.05. Conclusions: A wide array of structural patterns of papilledema, integrating the magnitude of disc edema with underlying disc and retinal morphology, can be quantified by just two latent variables. Translational Relevance: A biVAE model encodes structural patterns, as well as the correlation between channels, and may be applied to visualize individuals or populations with papilledema throughout treatment.

Classifying and quantifying changes in papilloedema using machine learning.

Background: Machine learning (ML) can differentiate papilloedema from normal optic discs using fundus photos. Currently, papilloedema severity is assessed using the descriptive, ordinal Frisén scale. We hypothesise that ML can quantify papilloedema and detect a treatment effect on papilloedema due to idiopathic intracranial hypertension. Methods: We trained a convolutional neural network to assign a Frisén grade to fundus photos taken from the Idiopathic Intracranial Hypertension Treatment Trial (IIHTT). We applied modified subject-based fivefold cross-validation to grade 2979 longitudinal images from 158 participants' study eyes (ie, the eye with the worst mean deviation) in the IIHTT. Compared with the human expert-determined grades, we hypothesise that ML-estimated grades can also demonstrate differential changes over time in the IIHTT study eyes between the treatment (acetazolamide (ACZ) plus diet) and placebo (diet only) groups. Findings: The average ML-determined grade correlated strongly with the reference standard (r=0.76, p<0.001; mean absolute error=0.54). At the presentation, treatment groups had similar expert-determined and ML-determined Frisén grades. The average ML-determined grade for the ACZ group (1.7, 95% CI 1.5 to 1.8) was significantly lower (p=0.0003) than for the placebo group (2.3, 95% CI 2.0 to 2.5) at the 6-month trial outcome. Interpretation: Supervised ML of fundus photos quantified the degree of papilloedema and changes over time reflecting the effects of ACZ. Given the increasing availability of fundus photography, neurologists will be able to use ML to quantify papilloedema on a continuous scale that incorporates the features of the Frisén grade to monitor interventions.

Hybrid deep learning and optimal graph search method for optical coherence tomography layer segmentation in diseases affecting the optic nerve.

Accurate segmentation of retinal layers in optical coherence tomography (OCT) images is critical for assessing diseases that affect the optic nerve, but existing automated algorithms often fail when pathology causes irregular layer topology, such as extreme thinning of the ganglion cell-inner plexiform layer (GCIPL). Deep LOGISMOS, a hybrid approach that combines the strengths of deep learning and 3D graph search to overcome their limitations, was developed to improve the accuracy, robustness and generalizability of retinal layer segmentation. The method was trained on 124 OCT volumes from both eyes of 31 non-arteritic anterior ischemic optic neuropathy (NAION) patients and tested on three cross-sectional datasets with available reference tracings: Test-NAION (40 volumes from both eyes of 20 NAION subjects), Test-G (29 volumes from 29 glaucoma subjects/eyes), and Test-JHU (35 volumes from 21 multiple sclerosis and 14 control subjects/eyes) and one longitudinal dataset without reference tracings: Test-G-L (155 volumes from 15 glaucoma patients/eyes). In the three test datasets with reference tracings (Test-NAION, Test-G, and Test-JHU), Deep LOGISMOS achieved very high Dice similarity coefficients (%) on GCIPL: 89.97±3.59, 90.63±2.56, and 94.06±1.76, respectively. In the same context, Deep LOGISMOS outperformed the Iowa reference algorithms by improving the Dice score by 17.5, 5.4, and 7.5, and also surpassed the deep learning framework nnU-Net with improvements of 4.4, 3.7, and 1.0. For the 15 severe glaucoma eyes with marked GCIPL thinning (Test-G-L), it demonstrated reliable regional GCIPL thickness measurement over five years. The proposed Deep LOGISMOS approach has potential to enhance precise quantification of retinal structures, aiding diagnosis and treatment management of optic nerve diseases.

Radiation effects on retinal layers revealed by OCT, OCT-A, and perimetry as a function of dose and time from treatment.

Optical coherence tomography (OCT) has become a key method for diagnosing and staging radiation retinopathy, based mainly on the presence of fluid in the central macula. A robust retinal layer segmentation method is required for identification of the specific layers involved in radiation-induced pathology in individual eyes over time, in order to determine damage driven by radiation injury to the microvessels and to the inner retinal neurons. Here, we utilized OCT, OCT-angiography, visual field testing, and patient-specific dosimetry models to analyze abnormal retinal layer thickening and thinning relative to microvessel density, visual function, radiation dose, and time from radiotherapy in a cross-sectional cohort of uveal melanoma patients treated with 125I-plaque brachytherapy. Within the first 24 months of radiotherapy, we show differential thickening and thinning of the two inner retinal layers, suggestive of microvessel leakage and neurodegeneration, mostly favoring thickening. Four out of 13 eyes showed decreased inner retinal capillary density associated with a corresponding normal inner retinal thickness, indicating early microvascular pathology. Two eyes showed the opposite: significant inner retinal layer thinning and normal capillary density, indicating early neuronal damage preceding a decrease in capillary density. At later time points, inner retinal thinning becomes the dominant pathology and correlates significantly with decreased vascularity, vision loss, and dose to the optic nerve. Stable multiple retinal layer segmentation provided by 3D graph-based methods aids in assessing the microvascular and neuronal response to radiation, information needed to target therapeutics for radiation retinopathy and vision loss.

Archetypal analysis of longitudinal visual fields for idiopathic intracranial hypertension patients presenting in a clinic setting.

We previously applied archetypal analysis (AA) using visual fields (VF) from the Idiopathic Intracranial Hypertension Treatment Trial (IIHTT) to derive a model, which quantified patterns (or archetypes [ATs] of VF loss), anticipated recovery, and identified residual VF deficits. We hypothesized that AA could produce similar results using IIH VFs collected in clinical practice. We applied AA to 803 VFs from 235 eyes with IIH from an outpatient neuro-ophthalmology clinic and created a clinic-derived model of ATs, with the relative weight (RW) and average total deviation (TD) for each AT. We also created a combined-derived model from an input dataset containing the clinic VFs and 2862 VFs from the IIHTT. We used both models to decompose clinic VF into ATs of varying percent weight (PW), correlated presentation AT PW with mean deviation (MD), and evaluated final visit VFs considered "normal" by MD ≥ -2.00 dB for residual abnormal ATs. The 14-AT clinic-derived and combined-derived models revealed similar patterns of VF loss previously identified in the IIHTT model. AT1 (a normal pattern) was most prevalent in both models (RW = 51.8% for clinic-derived; 35.4% for combined-derived). Presentation AT1 PW correlated with final visit MD (r = 0.82, p < 0.001 for the clinic-derived model; r = 0.59, p < 0.001 for the combined-derived model). Both models showed ATs with similar patterns of regional VF loss. The most common patterns of VF loss in "normal" final visit VFs using each model were clinic-derived AT2 (mild global depression with enlarged blind spot; 44/125 VFs; 34%) and combined-derived AT2 (near-normal; 93/149 VFs; 62%). AA provides quantitative values for IIH-related patterns of VF loss that can be used to monitor VF changes in a clinic setting. Presentation AT1 PW is associated with the degree of VF recovery. AA identifies residual VF deficits not otherwise indicated by MD.

AxoNet 2.0: A Deep Learning-Based Tool for Morphometric Analysis of Retinal Ganglion Cell Axons.

Purpose: Assessment of glaucomatous damage in animal models is facilitated by rapid and accurate quantification of retinal ganglion cell (RGC) axonal loss and morphologic change. However, manual assessment is extremely time- and labor-intensive. Here, we developed AxoNet 2.0, an automated deep learning (DL) tool that (i) counts normal-appearing RGC axons and (ii) quantifies their morphometry from light micrographs. Methods: A DL algorithm was trained to segment the axoplasm and myelin sheath of normal-appearing axons using manually-annotated rat optic nerve (ON) cross-sectional micrographs. Performance was quantified by various metrics (e.g., soft-Dice coefficient between predicted and ground-truth segmentations). We also quantified axon counts, axon density, and axon size distributions between hypertensive and control eyes and compared to literature reports. Results: AxoNet 2.0 performed very well when compared to manual annotations of rat ON (R2 = 0.92 for automated vs. manual counts, soft-Dice coefficient = 0.81 ± 0.02, mean absolute percentage error in axonal morphometric outcomes < 15%). AxoNet 2.0 also showed promise for generalization, performing well on other animal models (R2 = 0.97 between automated versus manual counts for mice and 0.98 for non-human primates). As expected, the algorithm detected decreased in axon density in hypertensive rat eyes (P ≪ 0.001) with preferential loss of large axons (P < 0.001). Conclusions: AxoNet 2.0 provides a fast and nonsubjective tool to quantify both RGC axon counts and morphological features, thus assisting with assessing axonal damage in animal models of glaucomatous optic neuropathy. Translational Relevance: This deep learning approach will increase rigor of basic science studies designed to investigate RGC axon protection and regeneration.

Biological Correlations and Confounders for Quantification of Retinal Ganglion Cells by Optical Coherence Tomography Based on Studies of Outbred Mice.

Purpose: Despite popularity of optical coherence tomography (OCT) in glaucoma studies, it's unclear how well OCT-derived metrics compare to traditional measures of retinal ganglion cell (RGC) abundance. Here, Diversity Outbred (J:DO) mice are used to directly compare ganglion cell complex (GCC) thickness measured by OCT to metrics of retinal anatomy measured ex vivo with retinal wholemounts and optic nerve histology. Methods: J:DO mice (n = 48) underwent fundoscopic and OCT examinations, with automated segmentation of GCC thickness. RGC axons were quantified from para-phenylenediamine-stained optic nerve cross-sections and somas from BRN3A-immunolabeled retinal wholemounts, with total inner retinal cellularity assessed by TO-PRO and subsequent hematoxylin staining. Results: J:DO tissues lacked overt disease. GCC thickness, RGC abundance, and total cell abundance varied broadly across individuals. GCC thickness correlated significantly to RGC somal density (r = 0.58) and axon number (r = 0.44), but not total cell density. Retinal area and nerve cross-sectional area varied widely. No metrics were significantly influenced by sex. In bilateral comparisons, GCC thickness (r = 0.95), axon (r = 0.72), and total cell density (r = 0.47) correlated significantly within individuals. Conclusions: Amongst outbred mice, OCT-derived measurements of GCC thickness correlate significantly to RGC somal and axon abundance. Factors limiting correlation are likely both biological and methodological, including differences in retinal area that distort sampling-based estimates of RGC abundance. Translational Relevance: There are significant-but imperfect-correlations between GCC thickness and RGC abundance across genetic contexts in mice, highlighting valid uses and ongoing challenges for meaningful use of OCT-derived metrics.

AxonDeep: Automated Optic Nerve Axon Segmentation in Mice With Deep Learning.

Purpose: Optic nerve damage is the principal feature of glaucoma and contributes to vision loss in many diseases. In animal models, nerve health has traditionally been assessed by human experts that grade damage qualitatively or manually quantify axons from sampling limited areas from histologic cross sections of nerve. Both approaches are prone to variability and are time consuming. First-generation automated approaches have begun to emerge, but all have significant shortcomings. Here, we seek improvements through use of deep-learning approaches for segmenting and quantifying axons from cross-sections of mouse optic nerve. Methods: Two deep-learning approaches were developed and evaluated: (1) a traditional supervised approach using a fully convolutional network trained with only labeled data and (2) a semisupervised approach trained with both labeled and unlabeled data using a generative-adversarial-network framework. Results: From comparisons with an independent test set of images with manually marked axon centers and boundaries, both deep-learning approaches outperformed an existing baseline automated approach and similarly to two independent experts. Performance of the semisupervised approach was superior and implemented into AxonDeep. Conclusions: AxonDeep performs automated quantification and segmentation of axons from healthy-appearing nerves and those with mild to moderate degrees of damage, similar to that of experts without the variability and constraints associated with manual performance. Translational Relevance: Use of deep learning for axon quantification provides rapid, objective, and higher throughput analysis of optic nerve that would otherwise not be possible.

Temporal Relationship Between Visual Field, Retinal and Microvascular Pathology Following 125I-Plaque Brachytherapy for Uveal Melanoma.

Purpose: To define the temporal relationship of vascular versus neuronal abnormalities in radiation retinopathy. Methods: Twenty-five patients with uveal melanoma treated with brachytherapy and sixteen controls were tested. Functional outcome measures included visual acuity and threshold perimetry (HVF 10-2), while structural outcomes included retinal thickness by OCT and vascular measures by OCT angiography and digital fundus photography. The degree of structural abnormality was determined by intereye asymmetry compared with normal subject asymmetry. Diagnostic sensitivity and specificity of each measure were determined using receiver operating characteristic curves. The relationships between the outcome measures were quantified by Spearman correlation. The effect of time from brachytherapy on visual function, retinal layer thickness, and capillary density was also determined. Results: Within the first 2 years of brachytherapy, outcome measures revealed visual field loss and microvascular abnormalities in 38% and 31% of subjects, respectively. After 2 years, they became more prevalent, increasing to 67% and 67%, respectively, as did retinal thinning (50%). Visual field loss, loss of capillary density, and inner retinal thickness were highly correlated with one another. Diagnostic sensitivity and specificity were highest for abnormalities in digital fundus photography, visual field loss within the central 10°, and decrease in vessel density. Conclusions: Using quantitative approaches, radiation microvasculopathy and visual field defects were detected earlier than loss of inner retinal structure after brachytherapy. Strong correlations eventually developed between vascular pathology, change in retinal thickness, neuronal dysfunction, and radiation dose. Radiation-induced ischemia seems to be a primary early manifestation of radiation retinopathy preceding visual loss.

A Deep-Learning Approach for Automated OCT En-Face Retinal Vessel Segmentation in Cases of Optic Disc Swelling Using Multiple En-Face Images as Input.

Purpose: In cases of optic disc swelling, segmentation of projected retinal blood vessels from optical coherence tomography (OCT) volumes is challenging due to swelling-based shadowing artifacts. Based on our hypothesis that simultaneously considering vessel information from multiple projected retinal layers can substantially increase vessel visibility, in this work, we propose a deep-learning-based approach to segment vessels involving the simultaneous use of three OCT en-face images as input. Methods: A human expert vessel tracing combining information from OCT en-face images of the retinal pigment epithelium (RPE), inner retina, and total retina as well as a registered fundus image served as the reference standard. The deep neural network was trained from the imaging data from 18 patients with optic disc swelling to output a vessel probability map from three OCT en-face input images. The vessels from the OCT en-face images were also manually traced in three separate stages to compare with the performance of the proposed approach. Results: On an independent volume-matched test set of 18 patients, the proposed deep-learning-based approach outperformed the three OCT-based manual tracing stages. The manual tracing based on three OCT en-face images also outperformed the manual tracing using only the traditional RPE en-face image. Conclusions: In cases of optic disc swelling, use of multiple en-face images enables better vessel segmentation when compared with the traditional use of a single en-face image. Translational Relevance: Improved vessel segmentation approaches in cases of optic disc swelling can be used as features for an improved assessment of the severity and cause of the swelling.

Association of Optical Coherence Tomography With Longitudinal Neurodegeneration in Veterans With Chronic Mild Traumatic Brain Injury.

Importance: Mild traumatic brain injury (TBI) may predispose individuals to progressive neurodegeneration. Objective: To identify evidence of neurodegeneration through longitudinal evaluation of changes in retinal layer thickness using optical coherence tomography in veterans with a history of mild TBI. Design, Setting, and Participants: This longitudinal cohort study evaluated veterans who were receiving services at the Minneapolis Veterans Affairs Health Care System. Symptomatic or mild TBI was diagnosed according to the Mayo TBI Severity Classification System. Participants in the age-matched control group had no history of TBI. Participants with any history or evidence of retinal or optic nerve disease that could affect retinal thickness were excluded. Data analysis was performed from July 2019 to February 2020. Exposures: The presence and severity of mild TBI were determined through consensus review of self-report responses during the Minnesota Blast Exposure Screening Tool semistructured interview. Main Outcomes and Measures: Change over time of retinal nerve fiber layer (RNFL) thickness. Results: A total of 139 veterans (117 men [84%]; mean [SD] age, 49.9 [11.1] years) were included in the study, 69 in the TBI group and 70 in the control group. Veterans with mild TBI showed significantly greater RNFL thinning compared with controls (mean [SE] RNFL slope, -1.47 [0.24] µm/y vs -0.31 [0.32] µm/y; F1,122 = 8.42; P = .004; Cohen d = 0.52). Functionally, veterans with mild TBI showed greater declines in visual field mean deviation (mean [SE] slope, -0.09 [0.14] dB/y vs 0.46 [0.23] dB/y; F1,122 = 4.08; P = .046; Cohen d = 0.36) and pattern standard deviation (mean [SE] slope, 0.09 [0.06] dB/y vs -0.10 [0.07] dB/y; F1,122 = 4.78; P = .03; Cohen d = 0.39) and high spatial frequency (12 cycles/degree) contrast sensitivity compared with controls. Cognitively, there was a significantly greater decrease in the number of errors over time during the Groton Maze Learning Test (GMLT) in controls compared with veterans with mild TBI (mean [SE] slope, -9.30 [1.48] errors/y vs -5.23 [1.24] errors/y; F1,127 = 4.43; P = .04; Cohen d = 0.37). RNFL tissue loss was significantly correlated with both worsening performance on the GMLT over time (Spearman ρ = -0.20; P = .03) and mild TBI severity (Spearman ρ = -0.25; P = .006). The more severe the mild TBI (larger Minnesota Blast Exposure Screening Tool severity score), the faster the reduction in RNFL thickness (ie, the more negative the slope) across time. Conclusions and Relevance: This cohort study found longitudinal evidence for significant, progressive neural degeneration over time in veterans with mild TBI, as indicated by greater RNFL tissue loss in patients with mild TBI vs controls, as well as measures of function. These results suggest that these longitudinal measures may be useful biomarkers of neurodegeneration. Changes in this biomarker may provide early detection of subsequent cognitive and functional deficits that may impact veterans' independence and need for care.

The Effect of Acetazolamide and Weight Loss on Intraocular Pressure in Idiopathic Intracranial Hypertension Patients.

PURPOSE: Acetazolamide (ACZ) lowers intraocular pressure (IOP), acutely in normal eyes and both acutely and chronically in eyes with glaucoma, and cerebrospinal fluid pressure (CSFp), chronically in patients with idiopathic intracranial hypertension (IIH). We hypothesize chronic daily ACZ would significantly reduce IOP and contribute to a translaminar pressure gradient change reflected by alteration in the CSFp-IOP difference and the deformation of the neural canal in patients with IIH and no glaucoma. PATIENTS AND METHODS: Before randomization to ACZ or placebo treatment for 6 months, 165 participants in the IIH Treatment Trial had evaluations that included Goldmann applanation, CSFp measurement, and optical coherence tomography determination of the neural canal deformation. These measures were repeated at the 6-month outcome. RESULTS: The IOP was not significantly decreased from baseline at 1, 3, or 6 months in eyes in both treatment groups. At month 6, the amount of ACZ or weight modification did not correlate with any IOP change. The 6-month mean change in neural canal deformation was 0.96 and -0.04 (P=0.001) and in CSFp was -128 and -38 mm H2O (P=0.001), but CSFp-IOP difference change was not significant, in the ACZ and placebo groups, respectively. CONCLUSIONS: ACZ does not reduce the IOP in eyes without glaucoma but does decrease the pathologic elevated CSFp, providing evidence that normal systems can compensate for chronic medication effects. The CSFp-IOP is not a direct marker of translaminar pressure gradient and the ACZ normalization of the neural canal deformation appears due to CSFp reduction alone.

Effects of sustained daily latanoprost application on anterior chamber anatomy and physiology in mice.

Latanoprost is a common glaucoma medication. Here, we study longitudinal effects of sustained latanoprost treatment on intraocular pressure (IOP) in C57BL/6J mice, as well as two potential side-effects, changes in iris pigmentation and central corneal thickness (CCT). Male C57BL/6J mice were treated daily for 16 weeks with latanoprost. Control mice were treated on the same schedule with the preservative used with latanoprost, benzalkonium chloride (BAK), or handled, without ocular treatments. IOP and CCT were studied at pre-treatment, 2 "early" time points, and 2 "late" time points; slit-lamp analysis performed at a late time point; and expression of corneal and iridial candidate genes analyzed at the end of the experiment. Latanoprost lowered IOP short, but not long-term. Sustained application of BAK consistently resulted in significant corneal thinning, whereas sustained treatment with latanoprost resulted in smaller and less consistent changes. Neither treatment affected iris pigmentation, corneal matrix metalloprotease expression or iridial pigment-related genes expression. In summary, latanoprost initially lowered IOP in C57BL/6J mice, but became less effective with sustained treatment, likely due to physiological adaptation. These results identify a new resource for studying changes in responsiveness associated with long-term treatment with latanoprost and highlight detrimental effects of commonly used preservative BAK.

Peripapillary Retinal Pigment Epithelium Layer Shape Changes From Acetazolamide Treatment in the Idiopathic Intracranial Hypertension Treatment Trial.

Purpose: Recent studies indicate that the amount of deformation of the peripapillary retinal pigment epithelium and Bruch's membrane (pRPE/BM) toward or away from the vitreous may reflect acute changes in cerebrospinal fluid pressure. The study purpose is to determine if changes in optic-nerve-head (ONH) shape reflect a treatment effect (acetazolamide/placebo + weight management) using the optical coherence tomography (OCT) substudy of the Idiopathic Intracranial Hypertension Treatment Trial (IIHTT) at baseline, 3, and 6 months. Methods: The pRPE/BM shape deformation was quantified and compared with ONH volume, peripapillary retinal nerve fiber layer (pRNFL), and total retinal (pTR) thicknesses in the acetazolamide group (39 subjects) and placebo group (31 subjects) at baseline, 3, and 6 months. Results: Mean changes of the pRPE/BM shape measure were significant and in the positive direction (away from the vitreous) for the acetazolamide group (P < 0.01), but not for the placebo group. The three OCT measures reflecting the reduction of optic disc swelling were significant in both treatment groups but greater in the acetazolamide group (P < 0.01). Conclusions: Change in the pRPE/BM shape away from the vitreous reflects the effect of acetazolamide + weight management in reducing the pressure differential between the intraocular and retrobulbar arachnoid space. Weight management alone was also associated with a decrease in optic nerve volume/edema but without a significant change in the pRPE/BM shape, implying an alternative mechanism for improvement in papilledema and axoplasmic flow, independent of a reduction in the pressure differential. (ClinicalTrials.gov number, NCT01003639.).