Nystagmus and optical coherence tomography findings in CNGB3-associated achromatopsia

PURPOSE: To describe the nystagmus characteristics of subjects with molecularly confirmed CNGB3-associated achromatopsia and report the spectral domain optical coherence tomography (SD-OCT) findings in these individuals. METHODS: Adults and children with CNGB3-achromatopsia underwent visual acuity testing, ocular motility assessments, video nystagmography, and SD-OCT imaging. Qualitative assessment of foveal structure was performed by grading SD-OCT images into one of five categories. RESULTS: A total of 18 subjects (11 adults) were included. The majority demonstrated a phoria, with manifest strabismus present in only 3 subjects. The predominant nystagmus waveform within the cohort was pure pendular. Nine individuals demonstrated a mixture of waveforms. Nystagmus frequencies were 4-8 cycles/second, with no notable differences in eye movements between adults and children. SD-OCT imaging revealed a continuous ellipsoid zone (EZ) at the fovea in 2 subjects (grade 1) and EZ disruption (grade 2) in the remaining 16. Retinal structure characteristics were symmetrical in both eyes in each subject. CONCLUSIONS: In our study cohort, nystagmus in CNGB3-associated achromatopsia had distinctive features, and the majority of subjects had retinal abnormalities at the fovea on SD-OCT. Early use of SD-OCT in the clinical work-up may eliminate the need for more invasive investigations, such as neuro-imaging

The Fourier analysis of saccadic eye movements

This thesis examines saccadic eye movements in the frequency domain and develops sensitive tools for characterising their dynamics. It tests a variety of saccade models and provides the first strong empirical evidence that saccades are time-optimal. By enabling inferences on the neural command, it also allows for better clinical differentiation of abnormalities and the evaluation of putative mechanisms for the development of congenital nystagmus. Chapters 3 and 4 show how Fourier transforms reveal sharp minima in saccade frequency spectra, which are robust to instrument noise. The minima allow models based purely on the output trajectory, purely on the neural input, or both, to be directly compared and distinguished. The standard, most commonly accepted model based on bang-bang control theory is discounted. Chapter 5 provides the first empirical evidence that saccades are time-optimal by demonstrating that saccade bandwidths overlap across amplitude onto a single slope at high frequencies. In Chapter 6, the overlap also allows optimal (Wiener) filtering in the frequency domain without a priori assumptions. Deconvolution of the aggregate neural driving signal is then possible for current models of the oculomotor plant. The final two chapters apply these Fourier techniques to the quick phases of physiological (optokinetic) nystagmus and of pathological (congenital) nystagmus. These quick phases are commonly assumed to be saccadic in origin. This assumption is thoroughly tested and found to hold, but with subtle differences implying that the smooth pursuit system interacts with the saccade system during the movement. This interaction is taken into account in Chapter 8 in the assessment of congenital nystagmus quick phases, which are found to be essentially normal. Congenital nystagmus models based on saccadic abnormalities are appraised

Optimisation and Computational Methods to Model the Oculomotor System with Focus on Nystagmus

Open access. Use it freely but cite it.Infantile nystagmus is a condition that causes involuntary, bilateral and conjugate oscillations of the eyes, which are predominately restricted to the horizontal plane. In order to investigate the cause of nystagmus, computational models and nonlinear dynamics techniques have been used to model and analyse the oculomotor system. Computational models are important in making predictions and creating a quantitative framework for the analysis of the oculomotor system. Parameter estimation is a critical step in the construction and analysis of these models. A preliminary parameter estimation of a nonlinear dynamics model proposed by Broomhead et al. [1] has been shown to be able to simulate both normal rapid eye movements (i.e. saccades) and nystagmus oscillations. The application of nonlinear analysis to experimental jerk nystagmus recordings, has shown that the local dimensions number of the oscillation varies across the phase angle of the nystagmus cycle. It has been hypothesised that this is due to the impact of signal dependent noise (SDN) on the neural commands in the oculomotor system. The main aims of this study were: (i) to develop parameter estimation methods for the Broomhead et al. [1] model in order to explore its predictive capacity by fitting it to experimental recordings of nystagmus waveforms and saccades; (ii) to develop a stochastic oculomotor model and examine the hypothesis that noise on the neural commands could be the cause of the behavioural characteristics measured from experimental nystagmus time series using nonlinear analysis techniques. In this work, two parameter estimation methods were developed, one for fitting the model to the experimental nystagmus waveforms and one to saccades. By using the former method, we successfully fitted the model to experimental nystagmus waveforms. This fit allowed to find the specific parameter values that set the model to generate these waveforms. The types of the waveforms that we successfully fitted were asymmetric pseudo-cycloid, jerk and jerk with extended foveation. The fit of other types of nystagmus waveforms were not examined in this work. Moreover, the results showed which waveforms the model can generate almost perfectly and the waveform characteristics of a number of jerk waveforms which it cannot exactly generate. These characteristics were on a specific type of jerk nystagmus waveforms with a very extreme fast phase. The latter parameter estimation method allowed us to explore whether the model can generate horizontal saccades of different amplitudes with the same behaviour as observed experimentally. The results suggest that the model can generate the experimental saccadic velocity profiles of different saccadic amplitudes. However, the results show that best fittings of the model to the experimental data are when different model parameter values were used for different saccadic amplitude. Our parameter estimation methods are based on multi-objective genetic algorithms (MOGA), which have the advantage of optimising biological models with a multi-objective, high-dimensional and complex search space. However, the integration of these models, for a wide range of parameter combinations, is very computationally intensive for a single central processing unit (CPU). To overcome this obstacle, we accelerated the parameter estimation method by utilising the parallel capabilities of a graphics processing unit (GPU). Depending of the GPU model, this could provide a speedup of 30 compared to a midrange CPU. The stochastic model that we developed is based on the Broomhead et al. [1] model, with signal dependent noise (SDN) and constant noise (CN) added to the neural commands. We fitted the stochastic model to saccades and jerk nystagmus waveforms. It was found that SDN and CN can cause similar variability to the local dimensions number of the oscillation as found in the experimental jerk nystagmus waveforms and in the case of saccade generation the saccadic variability recorded experimentally. However, there are small differences in the simulated behaviour compared to the nystagmus experimental data. We hypothesise that these could be caused by the inability of the model to simulate exactly key jerk waveform characteristics. Moreover, the differences between the simulations and the experimental nystagmus waveforms indicate that the proposed model requires further expansion, and this could include other oculomotor subsystem(s).Engineering and Physical Sciences Research Council (EPSRC

Quick phases of infantile nystagmus show the saccadic inhibition effect

Purpose: Infantile nystagmus (IN) is a pathological, involuntary oscillation of the eyes consisting of slow, drifting eye movements interspersed with rapid reorienting quick phases. The extent to which quick phases of IN are programmed similarly to saccadic eye movements remains unknown. We investigated whether IN quick phases exhibit 'saccadic inhibition', a phenomenon typically related to normal targeting saccades, in which the initiation of the eye movement is systematically delayed by task-irrelevant visual distractors. Methods: We recorded eye position from 10 observers with early-onset idiopathic nystagmus while task-irrelevant distractor stimuli were flashed along the top and bottom of a large screen at ±10° eccentricity. The latency distributions of quick phases were measured with respect to these distractor flashes. Two additional participants, one with possible albinism and one with fusion maldevelopment nystagmus syndrome, were also tested. Results: All observers showed that a distractor flash delayed the execution of quick phases that would otherwise have occurred around 100 ms later, exactly as in the standard saccadic inhibition effect. The delay did not appear to differ between the two main nystagmus types under investigation (idiopathic IN with unidirectional and bidirectional jerk). Conclusions: The presence of the saccadic inhibition effect in IN quick phases is consistent with the idea that quick phases and saccades share a common programming pathway. This could allow quick phases to take on flexible, goal-directed behaviour, at odds with the view that IN quick phases are stereotyped, involuntary eye movements

Characterisation of nystagmus waveforms in eye-tracker signals

This thesis deals with the analysis of eye–tracker signals recorded from nystagmus patients. Nystagmus is an eye movement disorder caused by an underlying condition, and patients who suffer from nystagmus express involuntary oscillating eye move- ments. The oscillatory patterns expressed by these patiens are typically linked to the underlying condition, but it is usually difficult to precisely diagnose each individual. The main focus of this thesis is to develop methods for automatic and robust analysis of nystagmus eye movements. These methods are developed with the purpose of providing diagnostic support for clinicians, or for evaluation of treatment effects.This thesis comprises an introduction and four papers describing various aspects of nystagmus analysis. In all four papers, eye movement signals recorded using an eye tracker are used as input to the proposed methods. In the first paper, a method to robustly calibrate eye–tracker data recorded from nystagmus patients is proposed. Calibration of data from nystagmus patients using video–based systems is difficult since the calibration process relies on an ability to accurately and precisely fixate calibration targets, which is difficult for nystagmus patients. Due to the nystagmus oscillations, it is difficult to obtain calibration results that are acceptable in terms of accuracy. In this work, a novel approach to find outliers in the calibration data is implemented, and a linear Procrustes transformation is used as the calibration mapping function. The results show that the proposed approach leads to reduced gaze estimation variance, and a higher robustness against outliers in the calibration data.In the second paper, a method to model different nystagmus waveform morphologies is presented. This model is used to characterise the nystagmus oscillations and to assert the quality of the analysed eye–tracker signals. The modelling approach is based on a stationary harmonic series, and the signals are modeled in short seg- ments, allowing for tracking of local changes in signal characteristics. Each segment is assessed using a metric referred to as the normalised segment error, which is used to determine whether or not the segment contains measurement disturbances. The results show that the model is well suited to distinguish between nystagmus oscillations and disturbances in the signal.The harmonic model from the second paper is used in the third paper in order to analyse data acquired during both smooth pursuit and fixation eye movements. Smooth pursuit eye movements may carry valuable clinical information, and reliable modelling of smooth pursuit eye movements is therefore of interest. The harmonic model is used to parametrise the different waveforms. Based on the parametrisation, a waveform distance index is defined, which is a metric used to measure similarity between waveforms, as well as for clustering of waveforms. Eleven different clusters are defined using known reference nystagmus waveforms, and all recorded fixation and smooth pursuit waveforms are assigned to one of the eleven cluster centers. The results show that the waveform clustering is robust, is able to distinguish between recordings from different individuals, and is suitable for analysis of smooth pursuit recordings.In the fourth paper, a novel method to combine cycle analysis and morphological classification is proposed. The goal of this work is to provide a diagnostic tool to identify subtle differences between patients, and over time in longer or recurring recordings. The cycle analysis method uses adaptive thresholds in order to detect breaking saccades, fast phases, foveations and slow phases. Eighteen template waveforms are used to create a profile of identified morphologies for each recorded waveform. The method is evaluated against expert annotations from a public dataset. The results show that the method is capable of analysing nystagmus eye movement recordings from both video–based and magnetic scleral search coil techniques. The waveform classification is reliable for both recording techniques.The methods presented in this thesis are used to improve the robustness and reliability for analysis of nystagmus eye movements recorded using an eye–tracker. In total, the four proposed methods constitute a complete framework showing how analysis of nystagmus eye–tracker signals may be used to improve diagnostics in nystagmus patients

Optimisation of an exemplar oculomotor model using multi-objective genetic algorithms executed on a GPU-CPU combination.

BACKGROUND: Parameter optimisation is a critical step in the construction of computational biology models. In eye movement research, computational models are increasingly important to understanding the mechanistic basis of normal and abnormal behaviour. In this study, we considered an existing neurobiological model of fast eye movements (saccades), capable of generating realistic simulations of: (i) normal horizontal saccades; and (ii) infantile nystagmus - pathological ocular oscillations that can be subdivided into different waveform classes. By developing appropriate fitness functions, we optimised the model to existing experimental saccade and nystagmus data, using a well-established multi-objective genetic algorithm. This algorithm required the model to be numerically integrated for very large numbers of parameter combinations. To address this computational bottleneck, we implemented a master-slave parallelisation, in which the model integrations were distributed across the compute units of a GPU, under the control of a CPU. RESULTS: While previous nystagmus fitting has been based on reproducing qualitative waveform characteristics, our optimisation protocol enabled us to perform the first direct fits of a model to experimental recordings. The fits to normal eye movements showed that although saccades of different amplitudes can be accurately simulated by individual parameter sets, a single set capable of fitting all amplitudes simultaneously cannot be determined. The fits to nystagmus oscillations systematically identified the parameter regimes in which the model can reproduce a number of canonical nystagmus waveforms to a high accuracy, whilst also identifying some waveforms that the model cannot simulate. Using a GPU to perform the model integrations yielded a speedup of around 20 compared to a high-end CPU. CONCLUSIONS: The results of both optimisation problems enabled us to quantify the predictive capacity of the model, suggesting specific modifications that could expand its repertoire of simulated behaviours. In addition, the optimal parameter distributions we obtained were consistent with previous computational studies that had proposed the saccadic braking signal to be the origin of the instability preceding the development of infantile nystagmus oscillations. Finally, the master-slave parallelisation method we developed to accelerate the optimisation process can be readily adapted to fit other highly parametrised computational biology models to experimental data

Infantile nystagmus adapts to visual demand

purpose. To determine the effect of visual demand on the nystagmus waveform. Individuals with infantile nystagmus syndrome (INS) commonly report that making an effort to see can intensify their nystagmus and adversely affect vision. However, such an effect has never been confirmed experimentally. methods. The eye movement behavior of 11 subjects with INS were recorded at different gaze angles while the subjects viewed visual targets under two conditions: above and then at resolution threshold. Eye movements were recorded by infrared oculography and visual acuity (VA) was measured using Landolt C targets and a two-alternative, forced-choice (2AFC) staircase procedure. Eye movement data were analyzed at the null zone for changes in amplitude, frequency, intensity, and foveation characteristics. Waveform type was also noted under the two conditions. results. Data from 11 subjects revealed a significant reduction in nystagmus amplitude (P < 0.05), frequency (P < 0.05), and intensity (P < 0.01) when target size was at visual threshold. The percentage of time the eye spent within the low-velocity window (i.e., foveation) significantly increased when target size was at visual threshold (P < 0.05). Furthermore, a change in waveform type with increased visual demand was exhibited by two subjects. conclusions. The results indicate that increased visual demand modifies the nystagmus waveform favorably (and possibly adaptively), producing a significant reduction in nystagmus intensity and prolonged foveation. These findings contradict previous anecdotal reports that visual effort intensifies the nystagmus eye movement at the cost of visual performance. This discrepancy may be attributable to the lack of psychological stress involved in the visual task reported here. This is consistent with the suggestion that it is the visual importance of the task to the individual rather than visual demand per se which exacerbates INS. Further studies are needed to investigate quantitatively the effects of stress and psychological factors on INS waveforms