Dark Adaptation and Its Role in Age-Related Macular Degeneration.

Dark adaptation (DA) refers to the slow recovery of visual sensitivity in darkness following exposure to intense or prolonged illumination, which bleaches a significant amount of the rhodopsin. This natural process also offers an opportunity to understand cellular function in the outer retina and evaluate for presence of disease. How our eyes adapt to darkness can be a key indicator of retinal health, which can be altered in the presence of certain diseases, such as age-related macular degeneration (AMD). A specific focus on clinical aspects of DA measurement and its significance to furthering our understanding of AMD has revealed essential findings underlying the pathobiology of the disease. The process of dark adaptation involves phototransduction taking place mainly between the photoreceptor outer segments and the retinal pigment epithelial (RPE) layer. DA occurs over a large range of luminance and is modulated by both cone and rod photoreceptors. In the photopic ranges, rods are saturated and cone cells adapt to the high luminance levels. However, under scotopic ranges, cones are unable to respond to the dim luminance and rods modulate the responses to lower levels of light as they can respond to even a single photon. Since the cone visual cycle is also based on the Muller cells, measuring the impairment in rod-based dark adaptation is thought to be particularly relevant to diseases such as AMD, which involves both photoreceptors and RPE. Dark adaptation parameters are metrics derived from curve-fitting dark adaptation sensitivities over time and can represent specific cellular function. Parameters such as the cone-rod break (CRB) and rod intercept time (RIT) are particularly sensitive to changes in the outer retina. There is some structural and functional continuum between normal aging and the AMD pathology. Many studies have shown an increase of the rod intercept time (RIT), i.e., delays in rod-mediated DA in AMD patients with increasing disease severity determined by increased drusen grade, pigment changes and the presence of subretinal drusenoid deposits (SDD) and association with certain morphological features in the peripheral retina. Specifications of spatial testing location, repeatability of the testing, ease and availability of the testing device in clinical settings, and test duration in elderly population are also important. We provide a detailed overview in light of all these factors.

BASELINE PREDICTORS ASSOCIATED WITH 3-YEAR CHANGES IN DARK ADAPTATION IN AGE-RELATED MACULAR DEGENERATION.

PURPOSE: To assess the relationship between baseline age-related macular degeneration (AMD) and disease stage, as well as optical coherence tomography features seen in AMD, with 3-year changes in dark adaptation (DA). METHODS: Prospective longitudinal study including patients with AMD and a comparison group (n = 42 eyes, 27 patients). At baseline and 3 years, we obtained color fundus photographs, spectral-domain optical coherence tomography, and rod-mediated DA (20 minutes protocol). Multilevel mixed-effect models were used for analyses, with changes in rod intercept time at 3 years as the primary outcome. As some eyes (n = 11) reached the DA testing ceiling value at baseline, we used 3-year changes in area under the DA curve as an additional outcome. RESULTS: Baseline AMD, AMD stage, and hyperreflective foci on optical coherence tomography were associated with larger changes in rod intercept time at 3 years. When change in area under the DA curve was used as an outcome, in addition to these features, the presence of retinal atrophy and drusenoid pigment epithelial detachment had significant associations. New subretinal drusenoid deposits at 3 years were also associated with more pronounced changes in rod intercept time and area under the DA curve. CONCLUSION: Specific optical coherence tomography features are associated with DA impairments over time, which supports that structural changes predict functional loss over 3 years.

Real-time motion segmentation of sparse feature points at any speed.

We present a real-time incremental approach to motion segmentation operating on sparse feature points. In contrast to previous work, the algorithm allows for a variable number of image frames to affect the segmentation process, thus enabling an arbitrary number of objects traveling at different relative speeds to be detected. Feature points are detected and tracked throughout an image sequence, and the features are grouped using a spatially constrained expectation-maximization (EM) algorithm that models the interactions between neighboring features using the Markov assumption. The primary parameter used by the algorithm is the amount of evidence that must accumulate before features are grouped. A statistical goodness-of-fit test monitors the change in the motion parameters of a group over time in order to automatically update the reference frame. Experimental results on a number of challenging image sequences demonstrate the effectiveness and computational efficiency of the technique.