Rokers, B. | bioRxiv Science

bioRxiv2018

Linking Retinal, Neural, and Perceptual Measures of Glaucoma with Diffusion Magnetic Resonance Imaging (dMRI)

AbstractO_ST_ABSPurposeC_ST_ABSTo link optic nerve (ON) structural integrity to clinical markers of glaucoma using advanced, semi-automated diffusion weighted imaging (DWI) tractography methods in human glaucoma patients.\n\nMethodsWe characterized optic neuropathy in patients with unilateral advanced-stage glaucoma (n = 6) using probabilistic DWI tractography and compared their results to those in healthy controls (n=6).\n\nResultsWe successfully identified the ONs of glaucoma patients based on DWI in all patients and confirmed that the degree of reduced structural integrity of the ONs determined using DWI correlated with clinical markers of glaucoma severity. Specifically, we found reduced fractional anisotropy (FA), a measure of structural integrity, in the ONs of eyes with advanced, as compared to mild, glaucoma (F(1,10) = 55.474, p < 0.0001). Furthermore, by comparing the ratios of ON FA in glaucoma patients to those of healthy controls (n = 6), we determined that this difference was beyond that expected from normal anatomical variation (F(1,9) = 20.276, p < 0. 005). Finally, we linked the DWI-measures of neural integrity to standard clinical glaucoma measures. ON vertical cup-to-disc ratio (vCD) predicted ON FA (F(1,10) = 11.061, p < 0.01, R2 = 0.66), retinal nerve fiber layer thickness (RNFL) predicted ON FA (F(1,10) = 11.477, p < 0.01, R2 = 0.63) and ON FA predicted perceptual deficits (visual field index [VFI]) (F(1,10) = 15.308, p < 0.005, R2 = 0.52).\n\nConclusionWe provide semi-automated methods to detect glaucoma-related structural changes using DWI and confirm that they correlate with clinical measures of glaucoma.

neuroscience↗

bioRxiv2017

Systematic misperceptions of 3D motion explained by Bayesian inference

People make surprising but reliable perceptual errors. Here, we provide a unified explanation for errors in the perception of three-dimensional (3D) motion. To do so, we characterized the retinal motion signals produced by objects moving with arbitrary trajectories through arbitrary locations in 3D. Next, we developed a Bayesian model, treating 3D motion perception as optimal inference given sensory noise and the geometry of 3D viewing. The model predicts a wide array of systematic perceptual errors, that depend on stimulus distance, contrast, and eccentricity. We then used a virtual reality (VR) headset as well as a standard 3D display to test these predictions in both traditional psychophysical and more naturalistic settings. We found evidence that people make many of the predicted errors, including a lateral bias in the perception of motion trajectories, a dependency of this bias on stimulus contrast, viewing distance, and eccentricity, and a surprising tendency to misreport approaching motion as receding and vice versa. In sum, we developed a quantitative model that provides a parsimonious account for a range of systematic misperceptions of motion in naturalistic environments.

neuroscience↗