Top-Down Control of Human Visual Cortex by Frontal and Parietal Cortex in Anticipatory Visual Spatial Attention

Participants and task.

Six right-handed subjects (three male, three female), aged 26–30, performed a demanding visual-spatial attention task (Sylvester et al., 2007) (Fig. 1). Subjects had no history of neurological illness, and had normal or corrected-to-normal vision. Informed consent was obtained as per human studies committee guidelines at Washington University School of Medicine. Each subject performed 1400–1600 trials over 16–24 scanning sessions. Eye position was monitored to ensure fixation on a central crosshair. Trials began with a 500 ms preparatory cue (the spoken word “left” or “right”), which directed attention to one of two locations at 5° eccentricity in the upper hemifield, 45° clockwise or counterclockwise from the vertical meridian (Fig. 1). Right- and left-cued trials were randomly intermixed with equal probability. After a stimulus-onset asynchrony (SOA) of 6.192 (25%), 8.256 (25%), or 10.32 s (50%), visual target stimuli appeared for 100 ms centered at both locations, concurrent with an auditory report cue (“left” or “right”). Targets were 3.5 cycle per degree Gabor patches (0.3° Gaussian envelope SD). On valid trials (75%), the report cue matched the preparatory cue. Subjects indicated the orientation (left tilt, vertical, right tilt) of the report-cued patch by pressing one of three buttons. High (50%) and low (5–12%) contrast targets were presented in separate scans. Stimulus parameters were adjusted based on in-scanner practice sessions to yield approximately 70% correct valid trial performance. Valid trials having the 10.32 s SOA, with either target contrast level and cue direction, were included in the analysis. The selective analysis of trials with 10.32 s SOA was aimed at maximizing anticipatory effects, which were shown by Sylvester et al. (2007) to increase with time after the cue. Mean task performance was 70.6% correct for valid and 61.5% correct for invalid trials (chance = 33.3%), indicating that subjects used the preparatory cue to discriminate the target.

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Figure 1.

Visual spatial attention behavioral paradigm. An auditory preparatory cue (“Left” or “Right”) began each trial, instructing subjects to attend a location left or right of the vertical meridian. After a stimulus-onset asynchrony (10.32 s), visual targets appeared for 100 ms centered at both locations, concurrent with an auditory report cue (“Left” or “Right”). BOLD data were acquired with TR = 2.064 s. The sample times used for analysis are marked by triangles. The figure is not drawn to scale.

Data acquisition.

BOLD data were acquired with a Siemens Allegra 3T scanner using an asymmetric spin-echo echoplanar sequence [retention time (TR) = 2.064 s, echo time = 25 ms, flip angle = 90°, 32 contiguous 4 mm axial slices, 4 × 4 mm in-plane resolution). BOLD images were motion-corrected within and between runs, corrected for across-slice timing differences, resampled into 3 mm isotropic voxels, and warped into a standardized atlas space.

Region of interest creation.

For each subject, BOLD data at each voxel were subjected to a general linear model using in-house software. Constant and linear terms over BOLD runs modeled baseline and linear drift. Sine waves modeled low-frequency noise (<0.009 Hz). Separate δ function regressors coded each time point after the preparatory cue. To generate individual-trial time series, modeled responses of the appropriate event type were summed with the residuals from the linear model at each time point.

Regions of interest (ROIs) outside retinotopic cortex were created by voxel-wise ANOVA in each subject over the first six trial time points using the residuals dataset. An in-house clustering algorithm defined ROIs from the map of the main effect of cue direction. ROIs were 8 mm spheres centered on map peaks with z-scores > 3; spheres within 12 mm of each other were consolidated into a single ROI. ROIs were retained for subsequent analysis if they were present in at least 6 of the 12 subject hemispheres so as to reduce intersubject variability, which could come from variability in BOLD signal strength, task strategy (causing the relative involvement of different functional areas to vary), or localization of functional areas.

If a subject lacked a particular ROI, the z threshold was lowered to 2; if the subject still lacked the ROI, it was not included in subsequent analyses. This procedure yielded ROIs in FEF (at the junction of the superior frontal sulcus and the precentral sulcus), anterior IPS (aIPS), and posterior IPS (pIPS).

To create ROIs inside retinotopic cortical areas, subjects first passively viewed contrast-reversing checkerboard stimuli extending along the horizontal and vertical meridians. From a contrast of responses to the horizontal and vertical meridians, early visual region borders were hand-drawn on flattened representations of each subject's anatomy using the Caret software suite (Van Essen et al., 2001). The V3A ROI consisted of V3A voxels with responses that varied with the direction of the preparatory cue. In separate localizer scans, subjects passively viewed high-contrast (∼50%) Gabor patches flickering at 4 Hz. In each 12 s block, a patch randomly appeared at one of five locations (two target, two mirrored across the horizontal meridian, and one central). Voxels representing each of these locations were localized by t tests: voxel responses to each non-central stimulus were significantly larger than those to its mirror stimulus across the vertical meridian; responses to the central location (1° width) were significantly larger than the summed responses to all other locations. Subdivisions of early visual cortex (V1v, V2v, VP, V4) were made from the conjunction of voxels with a stimulus preference in the localizer scans and the retinotopic regions for upper hemifield locations.

BOLD time series preprocessing.

Outlier voxels were rejected, based on their BOLD amplitude variability over trials, using Tukey's boxplot technique. BOLD time series were z-normalized to have zero mean and unit variance.

Granger causality method.

Granger causality testing followed the method of Greene (2002). For any two voxels, X and Y, Granger causality is tested from X to Y, and from Y to X. For X to Y, two different linear regression models are considered. The restricted model is: Y(t) = α₁Y(t − m) + ε₁(t), where Y(t) is the Y time series at time t, Y(t − m) is the m-lagged Y time series, α₁ is the regression coefficient, and ε₁(t) is the restricted model residual at time t.

The unrestricted model is: Y(t) = α₂Y(t − m) + βX(t − m) + ε₂(t), where Y(t) is the Y time series at time t, Y(t − m) and X(t − m) are the m-lagged time series of Y and X, α₂ and β are regression coefficients, and ε₂(t) is the unrestricted model residual at time t.

Granger causality significance testing.

If the variability of the residual of the unrestricted model is significantly reduced compared with that of the restricted model, then there is an improvement in the prediction of Y due to X. The amount of reduction is measured as an F statistic: F = [(RSS_r − RSS_ur)/m]/[RSS_ur/(T − 2m − 1)], where RSS_r is the restricted residual sum of squares, RSS_ur is the unrestricted residual sum of squares, m is the lag value, and T is the total number of observations used to estimate the unrestricted model. The F statistic approximately follows an F distribution with degrees of freedom m and (T − 2m − 1). If the F statistic from X to Y is significant (i.e., greater than the critical value at p < 0.05 in the standard F_{m,T − 2m − 1} distribution), then the unrestricted model yields a better explanation of Y(t) than does the restricted model, and X is said to Granger cause Y.

Granger causality BOLD data analysis.

For a given ROI pair, F statistics were computed in both directions between BOLD time series of every voxel pair. The portion of voxel pairs carrying Granger causality was quantified for each ROI pair in both directions as the fraction of F statistics that were significant (p < 0.05). To compare significant fractions of F distributions having equal numbers of voxel pairs, the McNemar (1947) statistic was calculated. Significance of this statistic came from comparison with the standard χ² distribution with one degree of freedom. F-statistic distributions were compared by the Mann–Whitney U test (Mann and Whitney, 1947) when the distributions had unequal numbers. Correction for multiple comparisons was by Dunn's procedure (Kirk, 1982).

Granger causality was tested on F statistics derived from linear regression models using the last two BOLD measurements of the anticipatory period (marked by triangles in Fig. 1). Balanced numbers of correct and incorrect validly cued trials were used to maximize the number of trials while avoiding biases that might otherwise result. Frontal and parietal ROIs consisted of spatially selective voxels in right and left FEF, aIPS, and pIPS. Visual occipital ROIs consisted of voxels in right and left V1v, V2v, V3A, VP, and V4. With the exception of two subjects, who lacked significantly identifiable ROIs in left FEF and aIPS, 60 pairwise frontoparietal-occipital relationships (six frontal and parietal with 10 visual occipital ROIs) were examined, as were 4 pairwise frontal-parietal relationships (two FEF with two IPS, anterior and posterior combined). Each ROI pair was tested independently of all others.

F-statistic distributions were derived for individual subjects. These distributions were either analyzed separately for each subject or were combined across subjects for group analysis. Analyses of the veridical data were compared with analyses of randomized data sets created in two ways. First, trial-randomized data consisted of ROIs having the same voxels as the veridical data, and with F-statistic distributions computed with randomized trial order for each voxel. Trial-randomized data provided an estimate of expected Granger causality with physiological influences removed. Second, voxel-randomized data consisted of ROIs having voxels randomly selected from the entire cortex, and with F-statistic distributions computed with the same trial order for each voxel as the veridical data. Voxel-randomized data provided an estimate of Granger causality expected for any two randomly selected cortical locations.