Imaging Research Center, and Departments of Psychology and Neuroscience, University of Texas at Austin, Austin, TX 78712.
Proc Natl Acad Sci U S A. 2014 Feb 18;111(7):2470-5. doi: 10.1073/pnas.1321728111. Epub 2014 Feb 3.
Previous research has implicated a large network of brain regions in the processing of risk during decision making. However, it has not yet been determined if activity in these regions is predictive of choices on future risky decisions. Here, we examined functional MRI data from a large sample of healthy subjects performing a naturalistic risk-taking task and used a classification analysis approach to predict whether individuals would choose risky or safe options on upcoming trials. We were able to predict choice category successfully in 71.8% of cases. Searchlight analysis revealed a network of brain regions where activity patterns were reliably predictive of subsequent risk-taking behavior, including a number of regions known to play a role in control processes. Searchlights with significant predictive accuracy were primarily located in regions more active when preparing to avoid a risk than when preparing to engage in one, suggesting that risk taking may be due, in part, to a failure of the control systems necessary to initiate a safe choice. Additional analyses revealed that subject choice can be successfully predicted with minimal decrements in accuracy using highly condensed data, suggesting that information relevant for risky choice behavior is encoded in coarse global patterns of activation as well as within highly local activation within searchlights.
先前的研究表明，在决策过程中处理风险涉及到大脑的一个大型网络区域。然而，目前还不清楚这些区域的活动是否可以预测未来风险决策的选择。在这里，我们检查了来自大量健康受试者进行自然风险任务的功能磁共振成像数据，并使用分类分析方法来预测个体在即将到来的试验中是否会选择风险或安全的选项。我们能够以 71.8%的成功率成功预测选择类别。搜索光分析揭示了一个大脑区域网络，其中活动模式可以可靠地预测随后的冒险行为，包括一些已知在控制过程中发挥作用的区域。具有显著预测准确性的搜索光主要位于准备避免风险时比准备参与风险时更活跃的区域，这表明冒险行为部分是由于启动安全选择所需的控制系统失败所致。进一步的分析表明，使用高度浓缩的数据可以以最小的准确性降低成功预测主体选择，这表明与风险选择行为相关的信息不仅编码在搜索光内的高度局部激活中，而且还编码在粗糙的全局激活模式中。
