Method to Quickly Map Multifocal Pupillary Response Fields (mPRF) Using Frequency Tagging.

We present a method for mapping multifocal Pupillary Response Fields in a short amount of time using a visual stimulus covering 40° of the visual angle divided into nine contiguous sectors simultaneously modulated in luminance at specific, incommensurate, temporal frequencies. We test this multifocal Pupillary Frequency Tagging (mPFT) approach with young healthy participants (N = 36) and show that the spectral power of the sustained pupillary response elicited by 45 s of fixation of this multipartite stimulus reflects the relative contribution of each sector/frequency to the overall pupillary response. We further analyze the phase lag for each temporal frequency as well as several global features related to pupil state. Test/retest performed on a subset of participants indicates good repeatability. We also investigate the existence of structural (RNFL)/functional (mPFT) relationships. We then summarize the results of clinical studies conducted with mPFT on patients with neuropathies and retinopathies and show that the features derived from pupillary signal analyses, the distribution of spectral power in particular, are homologous to disease characteristics and allow for sorting patients from healthy participants with excellent sensitivity and specificity. This method thus appears as a convenient, objective, and fast tool for assessing the integrity of retino-pupillary circuits as well as idiosyncrasies and permits to objectively assess and follow-up retinopathies or neuropathies in a short amount of time.

From monocular photograph to angle lambda: A new clinical approach for quantitative assessment.

Angle lambda assessment is essential in pediatric and strabismus practice. An abnormal angle lambda will modify the visual appearance of a strabismus or mimic one. Currently, angle lambda can be assessed by corneal topographs. Unfortunately, the use of these devices remains limited in the context of a strabismus clinic. Herein, we propose an easy, low cost, and reproducible method for angle lambda quantification, based on monocular photographs. Monocular pictures were taken by using a camera with a ring flash, centered by a fixation point. A digital evaluation analyzed the position of the corneal reflex on the pupil diameter. Using a trigonometric formula, the resulting ratio was converted into the value of angle lambda. This method was tested on 20 healthy eyes, on two successive couples of pictures, to evaluate its repeatability. Assessment using Pentacam was performed for comparison. The mean value of angle lambda was +2.61° ± 2.92° and 2.63° ± 2.85° in both picture series, respectively, and Lin's repeatability coefficient was 0.99 - with a systematic deviation of -0.071° compared to Pentacam assessment. Angle lambda distribution was in range with values from the literature. This new method allows for angle lambda assessment without requiring a specific device and can be used in strabismus and pediatric clinics.

Pseudo-Gaze Deviation Resulting From Positive Angle Kappa and Esotropia.

ABSTRACT: A 6-year-old boy was referred for constant right gaze deviation. Rather than a gaze deviation, he constantly seemed to look on the left side of any displayed target. Examination revealed the association of a highly positive angle Kappa and an esotropia of equal values. He also exhibited signs of ocular albinism with no associated infantile nystagmus syndrome. The X-linked ocular albinism was confirmed genetically, explaining the presence of a positive angle Kappa. A highly positive angle Kappa can be associated with a convergent strabismus; in case both values offset each other, this can result in a constant "sidelooking," which should not be confused with a gaze deviation.

Extraction of Nystagmus Patterns from Eye-Tracker Data with Convolutional Sparse Coding.

The analysis of the Nystagmus waveforms from eye-tracking records is crucial for the clinical interpretation of this pathological movement. A major issue to automatize this analysis is the presence of natural eye movements and eye blink artefacts that are mixed with the signal of interest. We propose a method based on Convolutional Dictionary Learning that is able to automatically highlight the Nystagmus waveforms, separating the natural motion from the pathological movements. We show on simulated signals that our method can indeed improve the pattern recovery rate and provide clinical examples to illustrate how this algorithm performs.