University of Delaware.
J Cogn Neurosci. 2017 Dec;29(12):2037-2053. doi: 10.1162/jocn_a_01178. Epub 2017 Aug 18.
When individuals are placed in stressful situations, they are likely to exhibit deficits in cognitive capacity over and above situational demands. Despite this, individuals may still persevere and ultimately succeed in these situations. Little is known, however, about neural network properties that instantiate success or failure in both neutral and stressful situations, particularly with respect to regions integral for problem-solving processes that are necessary for optimal performance on more complex tasks. In this study, we outline how hidden Markov modeling based on multivoxel pattern analysis can be used to quantify unique brain states underlying complex network interactions that yield either successful or unsuccessful problem solving in more neutral or stressful situations. We provide evidence that brain network stability and states underlying synchronous interactions in regions integral for problem-solving processes are key predictors of whether individuals succeed or fail in stressful situations. Findings also suggested that individuals utilize discriminate neural patterns in successfully solving problems in stressful or neutral situations. Findings overall highlight how hidden Markov modeling can provide myriad possibilities for quantifying and better understanding the role of global network interactions in the problem-solving process and how the said interactions predict success or failure in different contexts.
当个体处于压力环境中时，他们的认知能力可能会出现超出情境要求的缺陷。尽管如此，个体仍然可能坚持不懈，并最终在这些情况下取得成功。然而，人们对在中性和压力环境下导致成功或失败的神经网络特性知之甚少，特别是对于解决问题过程中对于最优表现必要的区域，这些区域对于解决更复杂任务至关重要。在这项研究中，我们概述了如何使用基于多体素模式分析的隐马尔可夫模型来量化复杂网络交互背后的独特大脑状态，这些交互在中性或压力环境下导致成功或失败的问题解决。我们提供的证据表明，大脑网络稳定性和解决问题过程中对于同步交互的基本区域的状态是个体在压力环境下成功或失败的关键预测因素。研究结果还表明，个体在成功解决压力或中性环境下的问题时会利用不同的神经模式。总的来说，这些发现强调了隐马尔可夫模型如何为量化和更好地理解全局网络交互在解决问题过程中的作用提供了多种可能性，以及这些交互如何在不同情境下预测成功或失败。
