Identification and treatment of the visual processing asymmetry in MS patients with optic neuritis: The Pulfrich phenomenon

Background: The Pulfrich phenomenon (PF) is the illusory perception that an object moving linearly along a 2-D plane appears to instead follow an elliptical 3-D trajectory, a consequence of inter-eye asymmetry in the timing of visual object identification in the visual cortex; with optic neuritis as a common etiology. Objective: We have designed an objective method to identify the presence and magnitude of the PF, in conjunction with a cooresponding strategy by which to abolish the effect; with monocular application of neutral density filters to the less affected fellow eye, in patients with MS and a history of optic neuropathy (e.g. related to acute optic neuritis or subclinical optic neuropathy). Methods: Twenty-three MS patients with a history of acute unilateral or bilateral optic neuritis, and ten healthy control subjects (HC) were recruited to participate in a pilot study to assess our strategy. Subjects were asked to indicate whether a linearly moving pendulum ball followed a linear 2-D path versus an illusory 3-D elliptical object-motion trajectory, by reporting the ball's approximation to one of nine horizontally-oriented colored wires that were positioned parallel to one another and horizontal to the linear pendulum path. Perceived motion of the bob that moved along wires behind or in front (along the ‘Z' plane) of the middle reference wire indicated an illusory elliptical trajectory of ball motion consistent with the PF. Results: When the neutral density filter titration was applied to the fellow eye the severity of the PF decreased, eventually being fully abolished in all but one patient. The magnitude of neutral density filtering required correlated to the severity of the patient's initial PF magnitude (p < 0.001). Conclusions: We ascertained the magnitude of the visual illusion associated with the PF, and the corresponding magnitude of neutral density filtering necessary to abolish it

The role of optical coherence tomography criteria and machine learning in multiple sclerosis and optic neuritis diagnosis

BACKGROUND AND OBJECTIVES: Recent studies have suggested that inter-eye differences (IEDs) in peripapillary retinal nerve fiber layer (pRNFL) or ganglion cell+inner plexiform (GCIPL) thickness by spectral-domain optical coherence tomography (SD-OCT) may identify people with a history of unilateral optic neuritis (ON). However, this requires further validation. Machine learning classification may be useful for validating thresholds for OCT IEDs and for examining added utility for visual function tests, such as low-contrast letter acuity (LCLA), in the diagnosis of people with multiple sclerosis (PwMS) and for unilateral ON history. METHODS: Participants were from 11 sites within the International Multiple Sclerosis Visual System (IMSVISUAL) consortium. pRNFL and GCIPL thicknesses were measured using SD-OCT. A composite score combining OCT and visual measures was compared individual measurements to determine the best model to distinguish PwMS from controls. These methods were also used to distinguish those with history of ON among PwMS. ROC curve analysis was performed on a training dataset (2/3 of cohort), then applied to a testing dataset (1/3 of cohort). Support vector machine (SVM) analysis was used to assess whether machine learning models improved diagnostic capability of OCT. RESULTS: Among 1,568 PwMS and 552 controls, variable selection models identified GCIPL IED, average GCIPL thickness (both eyes), and binocular 2.5% LCLA as most important for classifying PwMS vs. controls. This composite score performed best, with AUC=0.89 (95% CI 0.85, 0.93), sensitivity=81% and specificity=80%. The composite score ROC curve performed better than any of the individual measures from the model (p<0.0001). GCIPL IED remained the best single discriminator of unilateral ON history among PwMS (AUC=0.77, 95% CI 0.71,0.83, sensitivity=68%, specificity=77%). SVM analysis performed comparably to standard logistic regression models. CONCLUSIONS: A composite score combining visual structure and function improved the capacity of SD-OCT to distinguish PwMS from controls. GCIPL IED best distinguished those with history of unilateral ON. SVM performed as well as standard statistical models for these classifications. CLASSIFICATION OF EVIDENCE: The study provides Class III evidence that SD-OCT accurately distinguishes multiple sclerosis from normal controls as compared to clinical criteria