Neural predictors of risky driving and susceptibility to peer influences in adole

描述（由申请人提供）：机动车碰撞是青少年死亡的主要原因，也是造成非致命伤害的主要原因1,2。青少年司机在有青少年乘客的情况下发生车祸的风险尤其高。同伴的影响是普遍的、强有力的，在青少年时期尤其突出，并与危害健康的行为有关，但其可变性和涉及的机制尚未得到很好的了解。可能促进青少年冒险行为的一个因素是情感控制系统和认知控制系统之间大脑发育的不平衡[8-10]，这不仅会导致总体上更冒险的选择，还会增加在同伴面前冒险时的奖励活动[11]，以及对社会拒绝的更高敏感性[12,13]。在本研究中，我们的目的是阐明神经认知的预测因素：(1)青少年危险驾驶；(2)青少年驾驶行为对同伴影响的易感性；(3)克服同伴影响风险的能力。为了实现这些目标，我们建议将神经成像（fMRI）措施整合到当前资助的一项研究中，该研究在最先进的驾驶模拟器中实验性地操纵同伴影响。主要结果包括模拟器中的行为，这些行为在真实驾驶环境中与高伤害率和死亡率相关（超速、紧跟距离、违反交通灯）。我们假设：[（1a）在冒险任务中神经奖励系统的活动增加，（1b）在反应抑制任务中认知控制系统的活动减少]，在fMRI环境中可以预测更高的倾向。模拟器中危险驾驶行为研究（2a）风险任务中奖励活动的增加，以及（2b）在神经成像环境中排斥过程中与痛苦相关区域的神经活动的增加，将预测驾驶模拟器中同伴在场时风险承担的易感性增加；(3)认知控制系统的神经活动增加将预测驾驶模拟器中同伴影响的敏感性降低，因为认知控制系统可以缓冲在社会威胁情况下的负面情感反应，也可以减少在同伴面前冒险引起的奖励反应的影响因此，我们假设奖励和社会痛苦敏感性，以及招募认知控制资源的倾向，将与社会情境相互作用，使青少年对使他们面临崩溃风险的行为具有不同的敏感性

DESCRIPTION (provided by applicant): Motor vehicle crashes are the leading cause of death and a major contributor to non-fatal injury in adolescents 1, 2. Teen drivers' crash risk is especially high in the presence of teen passengers3-6. Peer influences are pervasive, powerful, especially salient during adolescence and associated with health-risk behaviors7, but the variability and mechanisms involved are not well understood8. One factor that may promote risky behavior in adolescence is a possible imbalance in brain development between affective and cognitive control systems8-10, resulting not only in riskier choices overall, but also increased reward activity during risk taking in the presence of peers11, and heightened sensitivity to social rejection12,13. In this investigation, we aim to elucidate neurocognitive predictors of: (1) risky teen driving; (2) susceptibility to peer influence in the context of teen driving; and (3) the ability to overcome risky peer influence. To achieve these aims, we propose to integrate neuroimaging (fMRI) measures into a currently funded study that experimentally manipulates peer influences in a state-of-the-art driving simulator. Main outcomes include behaviors in the simulator that are associated with high rates of injury and fatality in real drivig contexts (a composite of speeding, close following distances, traffic light violations). We hypothesize that: [(1a) increased activity in neural reward systems during a risk- taking task14, and (1b) decreased activity in cognitive control systems during a response inhibition task15, in the fMRI environment will predict higher propensity .toward risky-driving behaviors in the simulator; (2a) increased reward activity during the risk task14, and (2b) increased neural activity in regions associated with distress during exclusion16 in the neuroimaging environment will predict increased susceptibility to risk taking in the presence of peers in the driving simulator; and (3) increased neural activity in cognitive control systems will predict decreased susceptibility to peer influence in the driving simulator], as cognitive control systems could serv to buffer negative affective responses during socially threatening situations, and could also serve to reduce the impact of reward responses induced by taking risks in the presence of peers.11 As such, we hypothesize that [reward and social distress sensitivity, as well as a tendency to recruit cognitive control resources will interact with the social situation to render adolescents differentially susceptible to behaviors that put them at risk for crash in the presence of peers]. Preliminary data collected using key elements of the proposed protocol are consistent with our hypotheses [and suggest that neural measures explain variance in key outcomes that is not explained otherwise]. The proposed research will increase understanding of the mechanisms that lead to variability in risky behavior in adolescents, as well as mechanisms of peer influence and ability to resist such influence. Individual differences in neurocognitive resources may not only interact with the social situation to promote risk, but may also serve to buffer social vulnerability to risk; this work may eventually allow us to develop more effective programs that efficiently reduce risk across multiple domains.