Department of Neuroscience, Western University, London, Ontario, Canada.
Department of Psychiatry, Western University, London, Ontario, Canada.
Psychophysiology. 2020 Jan;57(1):e13472. doi: 10.1111/psyp.13472. Epub 2019 Sep 10.
Individuals with post-traumatic stress disorder (PTSD) typically experience states of reliving and hypervigilance; however, the dissociative subtype of PTSD (PTSD+DS) presents with additional symptoms of depersonalization and derealization. Although the insula is critical to emotion processing, its association with these contrasting symptom profiles is yet to be fully delineated. Accordingly, we investigated insula subregion resting-state functional connectivity patterns among individuals with PTSD, PTSD+DS, and healthy controls. Using SPM12 and PRONTO software, we implemented a seed-based resting-state functional connectivity approach, along with multiclass Gaussian process classification machine learning, respectively, in order to evaluate unique patterns and the predictive validity of insula subregion connectivity among individuals with PTSD (n = 84), PTSD+DS (n = 49), and age-matched healthy controls (n = 51). As compared to PTSD and PTSD+DS, healthy controls showed increased right anterior and posterior insula connectivity with frontal lobe structures. By contrast, PTSD showed increased bilateral posterior insula connectivity with subcortical structures, including the periaqueductal gray. Strikingly, as compared to PTSD and controls, PTSD+DS showed increased bilateral anterior and posterior insula connectivity with posterior cortices, including the left lingual gyrus and the left precuneus. Moreover, machine learning analyses were able to classify PTSD, PTSD+DS, and controls using insula subregion connectivity patterns with 80.4% balanced accuracy (p < .01). These findings suggest a neurobiological distinction between PTSD and its dissociative subtype with regard to insula subregion functional connectivity patterns. Furthermore, machine learning algorithms were able to utilize insula resting-state connectivity patterns to discriminate between participant groups with high predictive accuracy.
个体患有创伤后应激障碍（PTSD）通常会经历再体验和过度警惕的状态；然而，创伤后应激障碍的分离亚型（PTSD+DS）则表现出人格解体和现实解体的附加症状。尽管脑岛对于情绪处理至关重要，但它与这些对比症状特征的关联尚未完全描绘。因此，我们研究了 PTSD、PTSD+DS 和健康对照组个体的脑岛亚区静息状态功能连接模式。使用 SPM12 和 PRONTO 软件，我们分别实施了基于种子的静息状态功能连接方法，以及多类高斯过程分类机器学习，以评估 PTSD（n=84）、PTSD+DS（n=49）和年龄匹配的健康对照组个体（n=51）中脑岛亚区连接的独特模式和预测有效性。与 PTSD 和 PTSD+DS 相比，健康对照组表现出与额叶结构的右侧前、后脑岛连接增加。相比之下，PTSD 表现出双侧后脑岛与皮质下结构，包括导水管周围灰质的连接增加。引人注目的是，与 PTSD 和对照组相比，PTSD+DS 表现出双侧前、后脑岛与包括左侧舌回和左侧楔前叶在内的后皮质的连接增加。此外，使用脑岛亚区连接模式的机器学习分析能够以 80.4%的平衡准确率（p<0.01）对 PTSD、PTSD+DS 和对照组进行分类。这些发现表明，在脑岛亚区功能连接模式方面，PTSD 与其分离亚型之间存在神经生物学差异。此外，机器学习算法能够利用脑岛静息状态连接模式来区分具有高预测准确性的参与者组。
