Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China.
Huaxi Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
Acta Psychiatr Scand. 2020 Mar;141(3):285-298. doi: 10.1111/acps.13150. Epub 2020 Feb 7.
The identification of post-traumatic stress disorder (PTSD) among natural disaster survivors is remarkably challenging, and there are no reliable objective signatures that can be used to assist clinical diagnosis and optimize treatment. The current study aimed to establish a neurobiological signature of PTSD from the connectivity of large-scale brain networks and clarify the brain network mechanisms of PTSD.
We examined fifty-seven unmedicated survivors with chronic PTSD and 59 matched trauma-exposed healthy controls (TEHCs) using resting-state functional magnetic resonance imaging (rs-fMRI). We extracted the node-to-network connectivity and obtained a feature vector with a dimensionality of 864 (108 nodes × 8 networks) to represent each subject's functional connectivity (FC) profile. Multivariate pattern analysis with a relevance vector machine was then used to distinguish PTSD patients from TEHCs.
We achieved a promising diagnostic accuracy of 89.2% in distinguishing PTSD patients from TEHCs. The most heavily weighted connections for PTSD classification were among the default mode network (DMN), visual network (VIS), somatomotor network, limbic network, and dorsal attention network (DAN). The strength of the anticorrelation of FC between the ventral medial prefrontal cortex (vMPFC) in DMN and the VIS and DAN was associated with the severity of PTSD.
This study achieved relatively high accuracy in classifying PTSD patients vs. TEHCs at the individual level. This performance demonstrates that rs-fMRI-derived multivariate classification based on large-scale brain networks can provide potential signatures both to facilitate clinical diagnosis and to clarify the underlying brain network mechanisms of PTSD caused by natural disasters.
在自然灾害幸存者中识别创伤后应激障碍（PTSD）极具挑战性，目前尚无可靠的客观特征可用于协助临床诊断和优化治疗。本研究旨在从大脑网络的连通性中建立 PTSD 的神经生物学特征，并阐明 PTSD 的大脑网络机制。
我们使用静息态功能磁共振成像（rs-fMRI）检查了 57 名未经治疗的慢性 PTSD 幸存者和 59 名匹配的创伤暴露健康对照者（TEHC）。我们提取了节点到网络的连接，并获得了一个具有 864 个维度的特征向量（108 个节点×8 个网络）来表示每个受试者的功能连接（FC）谱。然后，使用支持向量机的多变量模式分析来区分 PTSD 患者和 TEHC。
我们在区分 PTSD 患者和 TEHC 方面取得了高达 89.2%的有希望的诊断准确性。用于 PTSD 分类的最重要的加权连接是默认模式网络（DMN）、视觉网络（VIS）、躯体运动网络、边缘网络和背侧注意网络（DAN）。DMN 中的腹内侧前额叶皮质（vMPFC）与 VIS 和 DAN 之间 FC 的反相关强度与 PTSD 的严重程度相关。
这项研究在个体水平上实现了相对较高的 PTSD 患者与 TEHCs 分类准确性。该性能表明，基于大脑网络的 rs-fMRI 衍生的多变量分类可以提供潜在的特征，既有助于临床诊断，又有助于阐明自然灾害引起的 PTSD 的潜在大脑网络机制。
