Being "slow to see" is a dynamic visual function consequence of infantile nystagmus syndrome: model predictions and...

The objective of this study was to investigate the dynamic properties of infantile nystagmus syndrome (INS) that affect visual function; i.e., which factors influence latency of the initial reflexive saccade (Ls) and latency to target acquisition (Lt). We used our behavioral ocular motor system (OMS) model to simulate saccadic responses (in the presence of INS) to target jumps at different times within a single INS cycle and at random times during multiple cycles. We then studied the responses of 4 INS subjects with different waveforms to test the model's predictions. Infrared reflection was used for 1 INS subject, high-speed digital video for 3. We recorded and analyzed human responses to large and small target-step stimuli. We evaluated the following factors: stimulus time within the cycle (Tc), normalized Tc (Tc%), initial orbital position (Po), saccade amplitude, initial retinal error (e(i)), and final retinal error (e(f)). The ocular motor simulations were performed in MATLAB Simulink environment and the analysis was performed in MATLAB environment using OMLAB software. Both the OMS model and OMtools software are available from http://http:www.omlab.org. Our data analysis showed that for each subject, Ls was a fixed value that is typically higher than the normal saccadic latency. Although saccadic latency appears somewhat lengthened in INS, the amount is insufficient to cause the "slow-to-see" impression. For Lt, Tc% was the most influential factor for each waveform type. The main refixation strategies employed by INS subjects made use of slow and fast phases and catch-up saccades, or combinations of them. These strategies helped the subjects to foveate effectively after target movement, sometimes at the cost of increased target acquisition time. Foveating or braking saccades intrinsic to the nystagmus waveforms seemed to disrupt the OMS' ability to accurately calculate reflexive saccades' amplitude and refoveate. Our OMS model simulations demonstrated this emergent behavior and predicted the lengthy target acquisition times found in the patient data.