Article Figures & Data
Figures
- Figure 1.
Topographic mapping of human SC. A, To model voxel pRFs, subjects viewed bars of contrast reversing checkerboards that swept across the visual field. Bar positions over time converted into binary apertures were projected onto a 2D Gaussian model of a receptive field (RF) and convolved with an HRF. Right, A single-sample voxel in SC is plotted for one run. B, Enlarged coronal slices through the human SC in an example subject (red box inset). R, Right; V, ventral. Left to right, Columns represent the T1 anatomy, polar angle, eccentricity, and size parameter maps of an example subject (S5; thresholded at r2 ≥ 0.1). Colored circles represent visual field keys. C, Topography of SC is consistent across other subjects.
- Figure 2.
A, Radial histograms of pRF polar angle in SC demonstrate strong contralateral coverage of the visual field. Based on pRFs from all subjects, the lines are the mean fractional volume representing each polar angle (±SEM). B, Aggregate FOV when pRF location and size parameters are combined. Each gray dot represents the center of single-voxel pRFs. Color represents the maximum pRF value across the population of voxels in the SC and reflects the relative effectiveness of visual stimulation in evoking a response in the SC. Black dots (n = 14) represent pRFs from the left SC with a center in the ipsilateral left visual field; no such ipsilateral centers were found in the right SC. C, Size of voxel pRFs in the SC increased linearly with eccentricity. Red squares represent SC voxels from all subjects. Black dots represent binned means (±SEM). Gray line indicates linear fit.
- Figure 3.
Task schematic, behavioral data, and delay-period activity in human SC. A, Schematic of four types of MGS trials. In each condition, trials began with a brief visual target located in the periphery (colored dots; left column). Following a delay, subjects made a VGS to a target whose location was unpredictable. Then, subjects immediately made an MGS to a location based on the initial visual target. In one condition, the MGS was directed to the visual target. In the other conditions, the MGS was made to simple geometric transformations of the visual target (dashed circles, left column; for reference here but not displayed). These included mirror transformations across each meridian and both meridians. The color of the visually cued target represented the type of transformation. Feedback was provided after the MGS with a visual stimulus at the correct location. Because of the VGS, the metrics of the MGS could not be predicted. The transformations dissociated the goal of the MGS from the visually stimulated retinal position. B, Locations of VGS and MGS targets were distributed 9°-11° in the periphery. C, Median saccade accuracy, precision, and response time for VGS (top) and MGS (bottom) plotted separately for the trials in which there was no transformation of the visual target (same; white) and the trials in which the visual target required a mirrored transformation (i.e., trials with transformations across horizontal, vertical, and both meridians are collapsed). There were no statistically significant differences between the transformed and untransformed trials for any of the saccade metrics. Error bars indicate SEM across subjects. D, Example eye-tracking traces (gray lines) from a subject during one scanner session. All trials are rotated such that, despite the various transformations of the visual target location, all MGS targets are rotated to a location 10° to the right. Red trace represents an example trial. Inset, We replot only the MGS trajectories (white lines), which start from a wide variety of peripheral locations following the VGS but converge and end (black circles) near the aligned MGS target location. Red trace represents an example trial. E, Group-averaged (±SEM) BOLD signal in SC voxels persisted significantly above pretrial baseline (gray dashed line) during the delay period for trials when the MGS target was in the contralateral and ipsilateral hemifield. The delay was defined as the average of the last four TRs in the delay period, identified by the bracket above the time courses.
- Figure 4.
IEM. Using a standard IEM, we calculated regression weights (W) from a training set of BOLD data (Btrain; orange box) and corresponding hypothetical channel coefficients (Ctrain) represented by nine evenly spaced radial basis functions, each tuned to a specific angle. We calculated the contribution of each basis function in the final reconstruction (Ctest) by linearly combining a new set of BOLD data (Btest; blue box) and the inverse of the regression weights. To reconstruct a representation of visual space, we used a linear combination of all basis functions, each weighted by its corresponding contribution in Ctest. Right, We unwrap the curve to show a sample sensitivity profile across angles in visual space. We calculated representational fidelity, a metric for the goodness of reconstructions, as the vector mean of a set of unit vectors around different angles, each weighted by the reconstructed sensitivity at that position. Displayed are examples of poor/untuned and good/tuned representations. Conceptually, our model provides a means to map a multivoxel population response into the coordinates of visual space.
- Figure 5.
Modeling WM representations in human SC. A, We used delay period activity in human SC to reconstruct visual space. From left to right: the average reconstructed sensitivity (±SEM) in visual space aligned to the visually cued target, VGS target, and MGS target locations, respectively. In each panel, all trials are aligned to the corresponding reference location centered at 0°. Dashed white lines indicate reconstructions from BOLD data with the trial labels permuted. B, Representational fidelity (±SEM) corresponding to three reference locations, compared with shuffled data (white lines) computed at the group level. The SC population activity during the delay is largely tuned for the visual-spatial location of the MGS (p < 10−18), not the visual target or VGS. C, Even at the individual subject level, we find greater fidelity for the MGS location for all subjects, except the 1 subject whose pRF model failed (S4). In 3 subjects, there was smaller tuning for the visually cued target, but this small effect was not significant at the group level. Asterisk indicate p < 0.01.
