Acute stress induces large‐scale neural reorganization with relevance to stress‐related psychopathology. Here, we applied a novel supervised machine learning method, combining the strengths of a priori theoretical insights with a data‐driven approach, to identify which connectivity changes are most prominently associated with a state of acute stress and individual differences therein. Resting‐state functional magnetic resonance imaging scans were taken from 334 healthy participants (79 females) before and after a formal stress induction. For each individual scan, mean time‐series were extracted from 46 functional parcels of three major brain networks previously shown to be potentially sensitive to stress effects (default mode network (DMN), salience network (SN), and executive control networks). A data‐driven approach was then used to obtain discriminative spatial linear filters that classified the pre‐ and post‐stress scans. To assess potential relevance for understanding individual differences, probability of classification using the most discriminative filters was linked to individual cortisol stress responses. Our model correctly classified pre‐ versus post‐stress states with highly significant accuracy (above 75%; leave‐one‐out validation relative to chance performance). Discrimination between pre‐ and post‐stress states was mainly based on connectivity changes in regions from the SN and DMN, including the dorsal anterior cingulate cortex, amygdala, posterior cingulate cortex, and precuneus. Interestingly, the probability of classification using these connectivity changes were associated with individual cortisol increases. Our results confirm the involvement of DMN and SN using a data‐driven approach, and specifically single out key regions that might receive additional attention in future studies for their relevance also for individual differences.

中文翻译：

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基于人脑的静息状态连接区分压力和休息：一项有监督的机器学习研究。

急性压力诱导与压力相关的精神病理学相关的大规模神经重组。在这里，我们应用了一种新颖的监督机器学习方法，将先验理论见解的优势与数据驱动方法相结合，以确定哪些连接变化与急性压力状态和其中的个体差异最显着相关。在正式压力诱导前后，对 334 名健康参与者（79 名女性）进行静息态功能磁共振成像扫描。对于每个单独的扫描，从先前显示对压力效应潜在敏感的三个主要大脑网络的 46 个功能包中提取平均时间序列（默认模式网络 (DMN)、显着性网络 (SN) 和执行控制网络）。然后使用数据驱动的方法获得区分性空间线性滤波器，对预应力和后应力扫描进行分类。为了评估理解个体差异的潜在相关性，使用最具辨别力的过滤器进行分类的概率与个体皮质醇应激反应相关联。我们的模型以非常显着的准确度（高于 75%；相对于机会表现的留一法验证）正确地对压力前状态和压力后状态进行了分类。区分压力前和压力后状态主要基于 SN 和 DMN 区域的连接变化，包括背侧前扣带回皮层、杏仁核、后扣带回皮层和楔前叶。有趣的是，使用这些连接变化进行分类的概率与个体皮质醇的增加有关。