How to stop or change a motor response: Laplacian and independent component analysis approach

Abstract

Response inhibition is an essential control function necessary to adapt one's behavior. This key cognitive capacity is assumed to be dependent on the prefrontal cortex and basal ganglia. It is unresolved whether varying inhibitory demands engage different control mechanisms or whether a single motor inhibitory mechanism is involved in any situation. We addressed this question by comparing electrophysiological activity in conditions that require stopping a response to conditions that require switching to an alternate response. Analyses of electrophysiological data obtained from stop-signal tasks are complicated by overlapping stimulus-related activity that is distributed over frontal and parietal cortical recording sites. Here, we applied Laplacian transformation and independent component analysis (ICA) to overcome these difficulties. Participants were faster in switching compared to stopping a response, but we did not observe differences in neural activity between these conditions. Both stop- and change-trials Laplacian transformed ERPs revealed a comparable bilateral parieto-occipital negativity around 180 ms and a frontocentral negativity around 220 ms. ICA results suggested an inhibition-related frontocentral component which was characterized by a negativity around 200 ms with a likely source in anterior cingulate cortex. The data provide support for the importance of posterior mediofrontal areas in inhibitory response control and are consistent with a common neural pathway underlying stopping and changing of a motor response. The methodological approach proved useful to distinguish frontal and parietal sources despite similar timing and the ICA approach allowed assessment of single-trial data with respect to behavioral data.

Keywords: Cognitive control; Independent component analysis; Laplacian; Stop-signal task.

Figure 1. Task and behavioral results

A Schematic representation of the task, showing the three conditions, Go, Stop and Change (SSD stop-signal delay; ChSD change-signal delay). B Results for the Stop Signal Reaction Time (SSRT) and Change Signal Reaction Time (CSRT), which differed significantly (** p < 0.001). Error bars denote standard errors.

Figure 2. Surface and Laplace ERPs of go- and inhibited stop-trials

Shown are the ERPs without current source density transformation (B) and after CSD transformation (A), both for go- (black) and inhibited stop-trials (red). The time-point 0 ms refers to onset of the go-stimulus for the go-trials and to the onset of the stop-stimulus for the stop-trials. The upper row shows the data of the midfrontal electrode cluster and the middle row shows the signal of the left parietal electrode cluster (indicated on the map on the right side). The grey bar indicates the analyzed time-window for the fronto-central electrodes (200 – 250 ms) and the parietal electrodes (150 – 250 ms). The lower row depicts the topography of the difference between inhibited stop- and go-trials between 180 and 220 ms for CSD ERPs (left side) and surface ERPs (right side).

Figure 3. Stop- and change-signal-locked Laplace-ERPs

Shown are the ERPs for Change (dashed lines) and Stop trials (solid lines) for the left posterior electrode cluster, separately for failed (A) and successful (B) trials. The time-point 0 ms refers to the stop-/change-stimulus onset. The time-window used for the analysis, 150 to 200 ms, is highlighted in grey. On the right side, the location of the posterior electrode cluster is indicated and topographical map of the average amplitude between 150–200 ms is presented for successful stop. A 15 Hz low-pass filter was applied for plotting purposes, but not for the analysis.

Figure 4. A Stop-signal-locked Laplace-ERPs

Shown are ERPs for the left, frontocentral and right frontolateral electrode cluster, separately for failed (red) and successful trials (black). The analyzed time-window (200 – 250 ms) is highlighted with a grey frame. The topographical map of the mean amplitude in the same time-window for successful stop-trials is shown. B Change-signal locked ERPs for left, frontocentral and right frontolateral electrode clusters, separately for failed (red) and successful trials (black). C+D Plotted are single-trial Laplacian ERPs for all stop-failed (C) and change-succeed (D) trials across all participants (~ 1400 trials) at electrode Cz, sorted by reaction time (relative to stop-/change-stimlus; using a smoothing window of 20 trials), which is depicted with a solid black line.

Figure 5. Results of the independent component analysis

A Shown are the topographic maps for the components included in the mediofrontal cluster with the participants’ codes noted above each component. Note that the direction of the maps (positive vs. negative) is arbitrary. The directionality of the component’s activity is split into the weight matrix (spatial map) and the time-course. B Average time-course of the depicted components back-projected to electrode Cz, separately for go-trials (solid line) and successful stop-trials (SI, dashed line). C Centroid of the estimated sources of the included components shown in anterior cingulate cortex.

Figure 6. ICA results for stop- and change-trials

A Average time-course of the mediofrontal cluster’s components back-projected to electrode Cz, separately for stop-succeed (solid line) and change-succeed trials (dashed line). B Shown is the average time-course of the mediofrontal cluster’s components back-projected to electrode Cz, separately for stop-succeed (black line) and stop-failed (red line). C+D Plotted are single-trial ERPs for all stop-failed (C) and change-succeed (D) trials across all participants (~ 1400 trials) from the mediofrontal components, sorted by reaction time (relative to stop-/change-stimulus; using a smoothing window of 20 trials), which is depicted with a solid black line. The negativity at 200 ms is clearly stimulus-related and maximal shortly after or around the erroneous motor response.