Translational models of fear conditioning and extinction have elucidated a core neural network involved in the learning, consolidation, and expression of conditioned fear and its extinction. Anxious or trauma-exposed brains are characterized by dysregulated neural activations within regions of this fear network. In this study, we examined how the functional MRI activations of 10 brain regions commonly activated during fear conditioning and extinction might distinguish anxious or trauma-exposed brains from controls. To achieve this, activations during four phases of a fear conditioning and extinction paradigm in 304 participants with or without a psychiatric diagnosis were studied. By training convolutional neural networks (CNNs) using task-specific brain activations, we reliably distinguished the anxious and trauma-exposed brains from controls. The performance of models decreased significantly when we trained our CNN using activations from task-irrelevant brain regions or from a brain network that is irrelevant to fear. Our results suggest that neuroimaging data analytics of task-induced brain activations within the fear network might provide novel prospects for development of brain-based psychiatric diagnosis.

恐惧调节和消退的转化模型阐明了一个核心神经网络，该神经网络参与条件性恐惧及其消退的学习、巩固和表达。焦虑或遭受创伤的大脑的特征是该恐惧网络区域内的神经激活失调。在这项研究中，我们检查了在恐惧条件反射和消退过程中通常激活的 10 个大脑区域的功能性 MRI 激活如何区分焦虑或创伤暴露的大脑与对照组。为实现这一目标，研究了 304 名有或没有精神病诊断的参与者在恐惧条件反射和消退范式的四个阶段中的激活情况。通过使用特定于任务的大脑激活来训练卷积神经网络 (CNN)，我们可靠地将焦虑和创伤暴露的大脑与对照组区分开来。当我们使用与任务无关的大脑区域或与恐惧无关的大脑网络的激活来训练我们的 CNN 时，模型的性能会显着下降。我们的结果表明，恐惧网络中任务诱导的大脑激活的神经影像学数据分析可能为基于大脑的精神病学诊断的发展提供新的前景。