Electroencephalographic brain dynamics following manually responded visual targets

Abstract

Scalp-recorded electroencephalographic (EEG) signals produced by partial synchronization of cortical field activity mix locally synchronous electrical activities of many cortical areas. Analysis of event-related EEG signals typically assumes that poststimulus potentials emerge out of a flat baseline. Signals associated with a particular type of cognitive event are then assessed by averaging data from each scalp channel across trials, producing averaged event-related potentials (ERPs). ERP averaging, however, filters out much of the information about cortical dynamics available in the unaveraged data trials. Here, we studied the dynamics of cortical electrical activity while subjects detected and manually responded to visual targets, viewing signals retained in ERP averages not as responses of an otherwise silent system but as resulting from event-related alterations in ongoing EEG processes. We applied infomax independent component analysis to parse the dynamics of the unaveraged 31-channel EEG signals into maximally independent processes, then clustered the resulting processes across subjects by similarities in their scalp maps and activity power spectra, identifying nine classes of EEG processes with distinct spatial distributions and event-related dynamics. Coupled two-cycle postmotor theta bursts followed button presses in frontal midline and somatomotor clusters, while the broad postmotor "P300" positivity summed distinct contributions from several classes of frontal, parietal, and occipital processes. The observed event-related changes in local field activities, within and between cortical areas, may serve to modulate the strength of spike-based communication between cortical areas to update attention, expectancy, memory, and motor preparation during and after target recognition and speeded responding.

Figure 1. Task Display

Subjects fixated on a cross above which five boxes were constantly displayed. In each 76-s task block, one of these (grey box) was colored differently. The location of this covertly attended box varied pseudorandomly across blocks. A series of disks were presented briefly in any of the five boxes in a random order. Subjects were asked to respond with a thumb button press as quickly as possible whenever a disk appeared in the attended box.

Figure 2. Grand Mean and RT-Sorted Single-Trial Responses at Sites Fz and Pz

(Left) Stimulus-locked grand mean and (right) response-locked grand mean responses to target stimuli. (A and B) Grand mean responses at all 29 scalp channels (colored traces), plus scalp maps at indicated latencies. (C–F) Grand moving mean single-trial responses from all 15 subjects, at frontocentral site Fz (C and D) and at central parietal site Pz (E and F), plotted in ERP-image format and sorted by subject RT (curving dashed trace in left column; vertical solid line in right column, plotted at the mean subject-median response time of 352 ms). ERP-image units: z = microvolts divided by root-mean-square microvolts in the (−1000 ms to 0 ms) channel baseline EEG of the same subject after removal of eye and muscle artifact components from the data. Vertical smoothing window: 300 trials. Grand mean normalized responses are shown below each image.

Figure 3. Changes in Mean Scalp Spectral Power Time-Locked to the Subject Response

The solid vertical line indicates moment of the motor response (shown at the grand mean subject median RT, 352 ms). Color scale: decibel change from prestimulus baseline. Image shows signed-RMS power changes across all 29 scalp channels prior to removal of all but the largest eye artifacts. Scalp maps show the scalp topography of the spectral power change in decibels relative to baseline. Note (A and B) the broad posterior low-theta- and anterior higher-theta-band maxima at the button press, (C, D, and F) the bilateral central alpha and beta blocking, (E) the central lateral postresponse beta increase, and (G) the increase in low-frequency eye artifacts at the end of the record.

Figure 4. Independent Component Decompositions for Two Single Trials

Black traces indicate two of 561 single target-response trials from one subject at scalp site Pz (upper right scalp map). Solid vertical lines indicate stimulus onsets; dashed vertical lines indicate button presses. A prominent late positivity occurred in the upper trial. All 561 1-sec, 31-channel EEG epochs time-locked to target stimuli were concate-nated and decomposed by infomax ICA, yielding 31 maximally independent data components. Colored traces show the projections (in microvolts) to this scalp channel of the three (nonartifact) independent components contributing the largest variance to each postresponse data window, linked to (individually scaled) maps of their scalp topographies. Component numbers (IC1–IC6), ranked by total EEG variance accounted for, and cluster affiliations (P3f, P3b, FM, P3b, Rα) are indicated above the scalp maps. Note differences in the time courses of IC1.

Figure 5. Mean Component Cluster Equivalent Dipole Locations

The mean scalp map for each of the nine component clusters could be well fit by a single equivalent dipole (mean residual variance: 4.8%). The figure shows the locations and orientations of these dipoles, as determined by BESA, plotted on the spherical head model, with ellipses showing the spatial standard deviations of the locations of the equivalent dipoles for the individual components in the cluster.

Figure 6. Far-Frontal and Parietal Component Clusters Contributing to the P300

(A–D) Far-frontal component cluster accounting for the preresponse (P3f) positivity. (E–H) Broad parietal component cluster accounting for part of the postresponse (P3b) positivity. The periresponse energy increase for these processes peaks at below 5 Hz. (A and E) The mean component scalp map. (B and F) The whole-data (black traces) and cluster-accounted (red fill) ERP envelopes (minimum and maximum voltage channel values at each time point), plus (inset) the power spectrum of the whole EEG (black traces) and the whole response-locked average ERP (red fill). The lower edge of the red fill shows actual ERP power, the upper edge, the phase-random EEG spectrum required to produce the observed average ERP spectrum by phase cancellation. The difference between the upper edge of the red fill and the actual EEG spectrum (black trace) reflects phase consistencies across trials in the single trial data. (C and G) ERP-image plot of the color-coded single trials time-locked to the response (solid vertical line) and sorted by RT from stimulus onset (dashed line). Trials normalized by dividing by the standard deviation of component activity in the 1-s prestimulus baseline. (D and H) The component mean ERSP showing mean event-related changes in (log) spectral power across data trials time-locked to the response (solid line). Here, median stimulus delivery time is indicated by the dashed line.

Figure 9. Three Posterior Alpha-Rhythm Component Clusters

Panels as in Figure 6. (A–D) Left posterior alpha (Lα) component cluster. (E–H) Central posterior alpha (Cα) component cluster with characteristic trapezoidal scalp projection, consistent with a bilateral, pericalcarine equivalent dipole source model, and demonstrating prolonged phase resetting following stimulus onset (curving dashed trace). (I–L) Right posterior alpha (Rα) component cluster. Note (C) the relative absence of the alpha-ringing pattern in the Lα cluster activity and the (D, H, and L) relatively weak postresponse alpha blocking in these clusters.

Figure 7. Two Mediofrontal Independent Component Clusters Showing a Postmotor Theta Response Pattern

Panels as in Figure 6. (A–D) FM cluster of components often exhibiting a theta-band peak in their activity spectra. (E–H) CM component cluster projecting maximally to the vertex.

Figure 8. Two Mu Rhythm Component Clusters Also Showing the Postmotor Theta Response Pattern

Panels as in Figure 6. (A–D) Left mu rhythm (Lμ) component cluster with mu characteristic 10-Hz and 22-Hz peaks in the activity spectrum. (D) Following the button press, this activity is blocked. (E–H) Corresponding right mu rhythm (Rμ) component cluster.

Figure 10. Component Time Courses and Summed Scalp Projections

Summed projections (A and B) to the grand mean ERP average of all trials time-locked to stimulus onsets (left) and to subject responses (right), plus (C–H) grand mean normalized activity time courses of each of the nine independent component clusters, scaled and separated into the same cluster groupings as in Figures 6–9. For comparison with the stimulus-locked responses (left), response-locked data epochs (right) are shown aligned to the mean subject-median response time (352 ms, dashed line in left panels).

Figure 11. Cluster Projections to the Scalp ERP

Component cluster contributions (in microvolts, thin traces) to the grand mean stimulus-locked (left) and motor-response-locked (right) target ERPs at scalp sites Fz (top) and Pz (bottom), plus their summed contributions (thick traces). Although the P3b cluster makes the largest contribution to the evoked responses at both scalp sites, its contribution does not outweigh the summed contributions of the other clusters.

Figure 12. Phase Coupling of Theta Components: Time-Domain View

(A) ERP-image view of baseline-normalized, response-aligned single-trial activity time series of components in the FM cluster, sorted (top-to-bottom) by phase at 4.87 Hz in a window centered 89 ms after the button press. Vertical smoothing: 400 trials. Units: microvolts normalized by dividing by the standard deviation of component single-trial baseline activity. The curving vertical trace (left) shows a moving mean of stimulus onset times; the central vertical line, the time of the button press. Data band pass in all panels: 0.1–40 Hz. (B) Exporting the same trial sorting order from (A) to CM cluster components (from the nine subjects contributing components to both clusters) demonstrates the significant partial theta phase coherence (r is approximately 0.3) between the two clusters in the postresponse time/frequency window. Note the induced (top-down, left-to-right) slope of the latency of the two (orange) positive-going CM cluster theta wave fronts. (C) Phase-sorted ERP image, as in (A), of the normalized CM cluster trials. (D) FM cluster component trials sorted in the same trial order as (C). Again, the partial theta-band phase coherence of the two clusters in the postresponse period is reflected in the diagonal (blue) negative-going wave fronts of the FM cluster data.