Eye of origin voxel analysis

Beta weights for each voxel for four stimulation types (left eye stimulated, right eye stimulated, binocular stimulation, and fixation only) were extracted for each ROI in each participant. ROI beta weights were randomly sampled with replacement and cross-correlated between all stimulation types for each participant, and the bootstrapped cross-correlations

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were averaged at the group level yielding group-level 4 x 4 cross-correlation matrices for each ROI. Results for the visually-driven ROIs are shown in Figure 4.6 below.

Figure 4.6 Bootstrapped cross-correlation matrices in visually-driven ROIs, for left eye

stimulated (L), right eye stimulated (R), binocular stimulation (B), and fixation only EOO stimulus presentation conditions. The colour scale represents Pearson’s R. Hot colours indicate correlations greater than zero (voxels are responding similarly between conditions); cold colours indicate correlations smaller than zero (anticorrelation; voxels are responding differently between conditions). Black represents zero correlation (no relationship between voxel responses in different conditions. Low positive or negative correlations between left and right eye conditions imply EOO tuning, especially when those correlations are significantly smaller than the monocular vs. binocular correlations.

Across ROIs shown in Figure 4.6 (V1 inner, V2 inner, V3 inner, V3A/B, IPS-0, V4, LO- 1, LO-2, hMT and hMST), correlations between visual stimulation conditions (left eye, right eye or binocular stimulation) were generally positive, with variations in the magnitude of the correlation coefficient. In contrast to this, correlating visual stimulation conditions against the fixation condition resulted in negative correlation, with the largest negative correlations between binocular stimulation and fixation. During left, right and binocular stimulus

presentation conditions, most voxels were stimulated and therefore generated a pattern of mostly positive beta weights in each ROI. During fixation conditions however, many voxels are likely to be unstimulated or even suppressed, resulting in near-zero or negative beta weight patterns. This explains why cross-correlating visual stimulation conditions results in positive correlation coefficients, whereas correlating against fixation results in anticorrelation.

The overall pattern of cross-correlations is distinctive across ROIs. For example, in IPS- 0, the correlation coefficients between left, right and binocular stimulation conditions were very small and just above zero. By comparison, in V3A/B the correlation coefficients were much higher. This is despite similar univariate responses across voxels (see Figure 4.5), and

thus is unlikely to be caused by differences in the overall response magnitude of the voxels. Rather, this implies that voxels in V3A/B are responding in a more consistent manner to all types of visual stimulation, than voxels in IPS-0.

As might be expected, the magnitude of correlations between left, right and binocular stimulation increases from V1 to V3, as voxels lose EOO tuning and respond more similarly between different stimulation conditions. EOO tuning is indicated by lower correlation coefficients between left and right stimulation, in comparison to the correlation coefficients representing the similarity of voxel responses between monocular and binocular stimulation conditions.

To quantify this, we computed an EOO index where the left vs. right (LvR) correlation coefficient was subtracted from the mean monocular vs. binocular (LvB and RvB) correlation coefficient. Values above zero indicate smaller LvR correlations relative to LvB and RvB correlations. Results are shown in Figure 4.7. EOO indices for each participant and each ROI were entered into a repeated measures ANOVA (eleven ROIs – V1 inner, V2 inner, V3 inner, V3A/B, IPS-0, V4, LO-1, LO-2, hMT, hMST, FFA). Post-hoc pairwise comparisons with Bonferroni adjustment across ROIs were extracted to compare each ROI against the control FFA ROI.

Figure 4.7 EOO index across ROIs. The index was calculated by subtracting the left vs. right

(LvR) correlation coefficient from the mean correlation coefficient for left vs. binocular (LvB) and right vs. binocular (RvB) stimulation. Values above zero indicate a smaller LvR

correlation relative to monocular vs. binocular stimulation. The index in the control ROI, the FFA, is shown as a blue reference line. Indices significantly higher than the FFA index imply EOO tuning. Error bars represent ±1 SEM.

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The largest EOO index was measured in V1 Inner, followed by V4 and V3. In motion- sensitive areas V3A/B, hMT and hMST the index was between zero and that of the FFA. However the ANOVA found no significant main effect of ROI (F(10, 70) = 1.12, p = .3111, partial 𝜀2 _{= 0.15). Furthermore, none of the measured ROI indices were significantly different}

from the control FFA ROI, suggesting that we could not reliably detect EOO tuning in any of the ROIs.

Cross-correlations were analysed in the same manner in non-visually-driven ‘outer’ ROIs, as well as in the control ROI. Cross-correlation matrices for these ROIs are shown in

Figure 4.8. Generally, correlation coefficients for LvR, LvB and RvB conditions were lower in

the ‘outer’ ROIs than in their ‘inner’ counterparts, most notably in V1 where responses to left vs. right stimulation were anticorrelated. This implies that the responses to left eye stimulation were systematically different than responses to right eye stimulation, which could occur if a voxel is driven by one condition but is suppressed by the other.

Figure 4.8 Bootstrapped cross-correlation matrices in non-visually-driven and control ROIs,

for left eye stimulated (L), right eye stimulated (R), binocular stimulation (B), and fixation only EOO stimulus presentation conditions. The colour scale represents Pearson’s R. Weak positive or even negative correlations between left and right eye conditions imply EOO tuning, especially when those correlations are significantly smaller than the monocular vs. binocular correlations.

Reassuringly, the correlations between L, R and B stimulation in the FFA were weak, but positive, and were similar between all stimulation conditions. Correlations were negative when compared to fixation. This is to be expected in an ROI that is visually driven, but not selective for any particular stimulation condition – in other words, for an ROI that contains no monocular tuning. Uniform low correlations may result from higher variability in voxel

responses – in other words, from noisy response patterns. Thus, the FFA provides a good baseline for the amplitude of correlations that could be expected when voxels are driven by the stimulus but are not responding systematically.

Because EOO tuning is indicated by smaller LvR correlations compared to the

monocular vs. binocular stimulation conditions, we again computed the EOO index in ‘outer’ ROIs and analysed these using a repeated measures ANOVA (four ROIs – V1 outer, V2 outer, V3 outer, FFA), followed by post-hoc pairwise comparisons. Results are shown in

Figure 4.9.

The largest EOO index was measured in V1 Outer, but there was a high degree of variability across subjects. Again, there was no significant main effect of ROI (F(3, 21) = 1.14, p = .360, partial 𝜀2 = 0.14), and none of the indices were significantly different from the FFA.

Figure 4.9 EOO index across ‘outer’, unstimulated ROIs. The index was calculated by

subtracting the left vs. right (LvR) correlation coefficient from the mean correlation coefficient for left vs. binocular (LvB) and right vs. binocular (RvB) stimulation. Values above zero

indicate a smaller LvR correlation relative to monocular vs. binocular stimulation. The index in the control ROI, the FFA, in shown as a blue reference line. Indices significantly higher than the FFA index imply EOO tuning. Error bars represent ±1 SEM.