Neurophysiology Laboratory, Department of Physiological Sciences, Roberto Alcantara Gomes Biology Institute, Biomedical Center, Universidade do Estado do Rio de Janeiro, Boulevard 28 de Setembro, 87 - Vila Isabel, Rio de Janeiro, RJ, 20551-030, Brazil.
Laboratory of Neurophysiology of Behavior, Department of Physiology and Pharmacology, Biomedical Institute, Universidade Federal Fluminense, R. Prof. Hernani Pires de Mello, 101, São Domingos, Niterói, RJ, 24210-130, Brazil.
BMC Psychiatry. 2023 Oct 5;23(1):719. doi: 10.1186/s12888-023-05220-x.
The present study aimed to apply multivariate pattern recognition methods to predict posttraumatic stress symptoms from whole-brain activation patterns during two contexts where the aversiveness of unpleasant pictures was manipulated by the presence or absence of safety cues.
Trauma-exposed participants were presented with neutral and mutilation pictures during functional magnetic resonance imaging (fMRI) collection. Before the presentation of pictures, a text informed the subjects that the pictures were fictitious ("safe context") or real-life scenes ("real context"). We trained machine learning regression models (Gaussian process regression (GPR)) to predict PTSD symptoms in real and safe contexts.
The GPR model could predict PTSD symptoms from brain responses to mutilation pictures in the real context but not in the safe context. The brain regions with the highest contribution to the model were the occipito-parietal regions, including the superior parietal gyrus, inferior parietal gyrus, and supramarginal gyrus. Additional analysis showed that GPR regression models accurately predicted clusters of PTSD symptoms, nominal intrusion, avoidance, and alterations in cognition. As expected, we obtained very similar results as those obtained in a model predicting PTSD total symptoms.
This study is the first to show that machine learning applied to fMRI data collected in an aversive context can predict not only PTSD total symptoms but also clusters of PTSD symptoms in a more aversive context. Furthermore, this approach was able to identify potential biomarkers for PTSD, especially in occipitoparietal regions.
本研究旨在应用多元模式识别方法，根据在两种情境下大脑的激活模式来预测创伤后应激症状，这两种情境通过有无安全线索来操纵不愉快图片的令人厌恶程度。
创伤后暴露的参与者在功能磁共振成像（fMRI）采集过程中观看中性和肢解图片。在图片呈现之前，文字告知受试者图片是虚构的（“安全情境”）还是现实生活场景（“真实情境”）。我们训练了机器学习回归模型（高斯过程回归（GPR）），以预测真实和安全情境中的 PTSD 症状。
GPR 模型可以根据真实情境中对肢解图片的大脑反应预测 PTSD 症状，但不能预测安全情境中的 PTSD 症状。对模型贡献最大的脑区是顶枕部区域，包括顶上回、顶下小叶和缘上回。进一步的分析表明，GPR 回归模型可以准确预测 PTSD 症状集群、名义入侵、回避和认知改变。正如预期的那样，我们得到的结果与预测 PTSD 总症状的模型非常相似。
这项研究首次表明，应用于在厌恶情境中采集的 fMRI 数据的机器学习不仅可以预测 PTSD 总症状，还可以预测更厌恶情境中的 PTSD 症状集群。此外，这种方法能够识别 PTSD 的潜在生物标志物，尤其是在顶枕部区域。
