Characterizing Fixational Eye Motion Variance Over Time as Recorded by the Tracking Scanning Laser Ophthalmoscope - PubMed (original) (raw)
Characterizing Fixational Eye Motion Variance Over Time as Recorded by the Tracking Scanning Laser Ophthalmoscope
Shivany Y Condor Montes et al. Transl Vis Sci Technol. 2022.
Abstract
Purpose: The purpose of this study was to characterize the benign biological variance of fixational microsaccades in a control population using a tracking scanning laser ophthalmoscope (TSLO), accounting for machine accuracy and precision, to determine ideal testing conditions to detect pathologic change in fixational eye motion (FEM).
Methods: We quantified the accuracy and precision of the TSLO, analyzing measurements made by three operators on a model eye. Repeated, 10-second retinal motion traces were then recorded in 17 controls, 3 times a day (morning, afternoon, and evening), on 3 separate days. Microsaccade metrics (MMs) of frequency, average amplitude, peak velocity, and peak acceleration were extracted. Trace to trace, interday, and intraday variability were calculated across all subjects.
Results: Intra-operator and machine variation contributed minimally to total variation, with only 0.007% and 0.14% contribution for frequency and amplitude respectively. Bias was detected, with lower accuracy for higher amplitudes. Participants had an average (SD) microsaccade frequency of 0.84 Hz (0.52 Hz), amplitude of 0.32 degrees (0.11 degrees), peak velocity of 43.68 degrees/s (14.02 degrees/s), and peak acceleration of 13,920.04 degrees/s2 (4,186.84 degrees/s2). The first trace recorded within a session significantly differed from the second two in both microsaccade acceleration and velocity (P < 0.05), and frequency was 0.098 Hz higher in the evenings (P < 0.05). There was no MM difference between days and no evidence of a session-level learning effect (P > 0.05).
Conclusions: The TSLO is both accurate and precise. However, biological inter- and intra-individual variance is present. Trace to trace variability and time of day should be accounted for to optimize detection of pathologic change.
Conflict of interest statement
Disclosure: S.Y. Condor Montes, None; D. Bennett, None; E. Bensinger, C. Light Technologies (I); L. Rani, None; Y. Sherkat, None; C. Zhao, None; Z. Helft, C. Light Technologies (O, P); A. Roorda, C. Light Technologies (I); University of California, Berkeley (P); A.J. Green, University of California - San Francisco (P); C.K. Sheehy, C. Light Technologies (E, O, P); University of California, Berkeley (P), University of California - San Francisco (P)
Figures
Figure 1.
Results of Gage R&R with ANOVA method on the input frequency signals. (A) Components of variation are part-to-part variability (defined as the variance between input signals), repeatability (inherent machine error), reproducibility (operator-to-operator error), and the sum of repeatability and reproducibility (gage R&R). %Contribution (dark blue): The percentage of variation due to the source compared to the total variation. %Study Variation (light blue bars): The percentage of variation due to the source compared to the total variation, with the added benefit of extrapolating beyond our specific data values. (B) Box plot of signal input measurements by operator. (C) Input signal number against the measured signal frequency for across all operators. (D) Input signal numbers against the measured signal frequency by operator. (E) Mean recorded measurement by signal number faceted by operators. The control limits (purple lines) represent the measurement system variation, and any measurements within them cannot be distinguished from random equipment error. (F) Range of recorded measurements by signal number faceted by operators. If the operators measure consistently, the points will fall within the control limits.
Figure 2.
Results of Gage R&R with ANOVA method on the input amplitude signals. (A) Components of variation are part-to-part variability (variability between input signals), repeatability (inherent machine error), reproducibility (operator-to-operator error), and the sum of repeatability and reproducibility (gage R&R). %Contribution (dark blue): The percentage of variation due to the source compared to the total variation. %Study Variation (light blue bars): The percentage of variation due to the source compared to the total variation, with the added benefit of extrapolating beyond our specific data values. (B) Box plot of amplitude signal input measurements by operator. (C) Input signal number against the measured signal amplitude for across all operators. (D) Input signal numbers against the measured signal amplitude by operator. (E) Mean recorded measurement by signal number faceted by operators. The control limits (purple lines) represent the measurement system variation, and any measurements within them cannot be distinguished from random equipment error. (F) Range of recorded measurements by signal number faceted by operators. If the operators measure consistently, the points will fall within the control limits.
Figure 3.
Linearity plots for frequency and amplitude signals. Bias is plotted against the signal input value for frequency (top three plots) and amplitude (bottom plots). Linearity was calculated through a linear regression, with equation, P value and R2 (represents the proportion of the variance for a dependent variable that is explained by an independent variable or variables in a regression model) displayed. The red diamond indicated the mean per signal input measurements.
Figure 4.
Tree map visualization of the hierarchical organization of the data per participant-eye. Three, 10-second video recordings were captured within a recording session in sequence per eye: trace 1, trace 2, and trace 3. Three recording sessions were conducted throughout the day, at three different time points: between morning (session 1), afternoon (session 2), and evening (session 3). Three recording sessions were conducted over 3 days within a single five-day work week - day 1 (light blue), day 2 (dark blue), and day 3 (green).
Figure 5.
Traces available for analysis by participant eye, with age. Left eye (light blue) and right eye (dark blue) number of traces shown.
Figure 6.
Box plots of trace-level microsaccade metrics by participant. Box length represents interquartile range. The black line across the box indicates the median, and the red dot within the box indicates the mean. Individuals with lower values in saccade frequency (S06 and S15) have larger spreads for other MMs, which may have contributed to the higher CV measures.
Figure 7.
ICCs (2,3) were calculated using log-transformed MMs. The primary analysis sample was used for trace level ICC, whereas session level analysis was used for time and day level ICC. Values less than 0.5, between 0.5 and 0.75, between 0.75 and 0.9, and greater than 0.90 are indicative of poor, moderate, good, and excellent reliability, respectively.
References
- Riggs LA, Ratliff F.. Visual acuity and the normal tremor of the eyes. Science. 1951; 114: 17–18. -PubMed
- Ditchburn RW, Ginsborg BL.. Vision with a Stabilized Retinal Image. Nature. 1952; 170(4314): 36–37. -PubMed
- Martinez-Conde S, Macknik SL, Troncoso XG, Dyar TA.. Microsaccades counteract visual fading during fixation. Neuron. 2006; 49(2): 297–305. -PubMed
- Tulunay-Keesey Ü. Fading of stabilized retinal images. J Optical Soc Am. 1982; 72(4): 440–447. -PubMed