Eye movement data were collected as the strabismic animals performed a saccade task where the target appeared at random horizontal or vertical locations within a ±15° grid (5° increments). Data were collected during both monocular and binocular viewing in separate experimental sessions. Binocular eye and target position feedback signals were digitized at 1 kHz with 16-bit precision (Labview software and DAQ boards from National Instruments, Austin, TX). The analyses of the saccade data were partially automated and performed with custom software (MatLab; The MathWorks, Natick, MA).
21 The computer displayed the target position, binocular eye position, eye velocity, and eye acceleration traces of a single-saccade trial on the screen. Velocity and acceleration signals were generated by digital differentiation of the position signal using a central difference algorithm. Position, velocity, and acceleration signals were filtered using software FIR filters (80 points; 0–80 Hz band-pass) also designed in the custom software. The investigator viewed the traces and decided whether the saccade trial was to be accepted or rejected. Trials that were rejected were usually those in which the animal was not fixating before the target step, the saccade did not appear to be directed toward the target, or the saccade did not occur within 500 ms of the target step. Once a decision to accept the trial was made, the mean ± SD control eye acceleration before the saccade was calculated over a 100-ms fixation period selected by the user. Saccade onset was automatically determined by the software as the first time point at which eye acceleration was greater than 3 SD away from the control eye acceleration and saccade offset was determined as the last time point at which eye deceleration was less than 3 SD away from the same mean eye acceleration. Although detection of saccade onset and offset was automated, the investigator visually examined the velocity and acceleration traces of every saccade and had the option of either accepting or changing the computer’s selection. Typically, only a small percentage of the computer’s choices were changed by the investigator. For the binocular viewing data, the investigator also made the determination if the saccade was of the alternating/nonalternating variety, and this information was recorded in the computer along with the saccade parameters.
After data collection and initial analysis of saccade onset and offset, the data were parsed into the following six bins depending on viewing condition and saccade type: (1) saccades during monocular right eye viewing (MR), (2) saccades during monocular left eye viewing (ML), (3) binocular viewing nonalternating saccades with the right eye fixating (BR), (4) binocular viewing nonalternating saccades with the left eye fixating (BL), (5) binocular viewing alternating saccades where the fixating eye was switched from right eye to left eye (BRL), and (6) binocular viewing alternating saccades where the fixating eye was switched from left eye to right eye (BLR).
Once the data were parsed, saccade metric parameters (amplitude, latency, peak velocity, and duration) for each eye were calculated. Since saccades included both horizontal and vertical components, vectorial values were used for amplitude and peak velocity, duration was the maximum of the duration of the horizontal and vertical components, and latency was the minimum of the latency of the horizontal and vertical components. Amplitude–peak velocity and amplitude–duration relationships were plotted, and data were fit according to the following equations commonly used to describe main-sequence data.
6 8 22 23 \[\mathrm{Amplitude}{\mbox{--}}\mathrm{peak\ velocity\ relationship:\ peak\ velocity}\ (\mathrm{PV}){=}PV_{\mathrm{max}}{[}1{-}e^{({-}\mathrm{amplitude}\ {\cdot}\ C)}{]}\]
\[\mathrm{Amplitude}{\mbox{--}}\mathrm{duration\ relationship:\ duration}{=}D_{0}{+}D_{1}\ {\cdot}\ \mathrm{amplitude}\]
In these equations, the parameters
PV max,
C,
D 0, and
D 1 characterize the main-sequence relationships and can therefore be used to identify certain abnormalities in generation of saccadic eye movements. For example, slow saccades would result in a lower
PV max.
PV max,
C,
D 0, and
D 1 were estimated from the right eye and left eye saccade data separately. Fitting was performed in a commercial program (SigmaPlot, ver. 10.0; Systat Software, San Jose, CA). One-way ANOVA at a significance value of 0.05 (SigmaStat ver. 3.5; SPSS, Chicago, IL) was used to compare each estimated parameter across the six saccade conditions (MR, ML, BR, BL, BRL, and BLR).
For saccade latency data, histograms of the inverse of saccade latency were developed and a Gaussian was fitted to the data. The inverse of latency was used for developing the Gaussian fit because it has been shown that this parameter is more representative of a Gaussian process than is saccade latency directly.
24 25 26 The mean and SD of this Gaussian fit was compared across the six saccade conditions using ANOVA.