Buffering social influence: neural correlates of response inhibition predict driving safety in the presence of a peer

Abstract

Adolescence is a period characterized by increased sensitivity to social cues, as well as increased risk-taking in the presence of peers. For example, automobile crashes are the leading cause of death for adolescents, and driving with peers increases the risk of a fatal crash. Growing evidence points to an interaction between neural systems implicated in cognitive control and social and emotional context in predicting adolescent risk. We tested such a relationship in recently licensed teen drivers. Participants completed an fMRI session in which neural activity was measured during a response inhibition task, followed by a separate driving simulator session 1 week later. Participants drove alone and with a peer who was randomly assigned to express risk-promoting or risk-averse social norms. The experimentally manipulated social context during the simulated drive moderated the relationship between individual differences in neural activity in the hypothesized cognitive control network (right inferior frontal gyrus, BG) and risk-taking in the driving context a week later. Increased activity in the response inhibition network was not associated with risk-taking in the presence of a risky peer but was significantly predictive of safer driving in the presence of a cautious peer, above and beyond self-reported susceptibility to peer pressure. Individual differences in recruitment of the response inhibition network may allow those with stronger inhibitory control to override risky tendencies when in the presence of cautious peers. This relationship between social context and individual differences in brain function expands our understanding of neural systems involved in top-down cognitive control during adolescent development.

Figures

Figure 1

Study design: Participants initially completed an fMRI scanning session in which neural activity was recorded during a response inhibition task (go/no-go). Additionally, during the scanning session appointment participants completed both pre- and post-scan individual difference measures. One week following the scanning session, participants completed the driving simulator session measuring risk-taking while driving alone and in the presence of a peer.

Figure 2

Go/no-go task: On each trial, participants were presented with one alphabetic character at the center of the display. On go trials (letters A through F), participants were instructed to respond by pressing a button on the response box with their right index finger. On no-go trials (letter X), participants were instructed not to make any response.

Figure 3

Driving simulator: The driving simulator session consisted of participants driving in a fixed-base driving simulator.

Figure 4

Response inhibition network: ROIs comprising the response inhibition network (BG and rIFG). Primary results averaged activity in BG and rIFG. All anatomical ROIs were constructed in Wake Forest University Pickatlas toolbox within SPM (Maldjian, Laurienti, Kraft, & Burdette, 2003). ROIs combined definitions from the Automated Anatomical Labeling Atlas (AAL; Tzourio-Mazoyer et al., 2002) intersected with x,y,z bounds to restrict certain sub-regions. The anatomical response inhibition network was constructed by taking the union of the rIFG and BG ROIs outlined below. The anatomical rIFG was constructed using the right portion of inferior frontal gyrus comprising Brodmann's areas 44, 45, and 47. The anatomical BG was constructed by combining the union of the caudate, putamen, global pallidus, substantial nigra, and subthalamic nucleus. In addition, the subthalamic nucleus/global pallidus anatomical ROI was constructed using the union of the subthalamic nucleus and global pallidus, regions most robustly involved in motor response inhibition. Finally, the anatomical ventral striatum was constructed using the caudate head restricted to z > 2.

Figure 5

Passenger type by response inhibition interaction: Scatterplot showing the interaction between percent signal change in the response inhibition network (BG and rIFG) and passenger type (risky and cautious) correlating with the percentage of time a participant spent in the intersection while light was red.

Figure 6

Solo driving behavior and response inhibition: Scatterplot showing the percent signal change in the response inhibition network (BG and rIFG) correlating with solo driving behavior for the percentage of time a participant spent in the intersection while light was red.