Novel Analysis Identifying Functional Connectivity Patterns Associated with Posttraumatic Stress Disorder.
Novel Analysis Identifying Functional Connectivity Patterns Associated with Posttraumatic Stress Disorder.
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DOI：
10.1177/24705470221092428
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发表时间：
2022-01
期刊：
影响因子：
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通讯作者：
Ko, Ji Hyun
中科院分区：
文献类型：
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作者：
Wright, Natalie;Patel, Ronak;Chaulk, Sarah J;Alcolado, Gillian;Podnar, David;Mota, Natalie;Monson, Candice M;Girard, Todd A;Ko, Ji Hyun
创伤后应激障碍（PTSD）是一种流行的精神疾病，可导致经历创伤性事件。准确的诊断和最佳的治疗策略可能很难实现，由于PTSD的病因和病理学的异质性，并与其他精神疾病重叠。因此，推进我们对PTSD病理生理学的理解至关重要。虽然功能连接改变已显示出阐明创伤后应激障碍神经生物学机制的希望，但之前的发现并不一致。在我们的第一个队列（PTSD-A）和11创伤暴露对照（TEC）的11例PTSD患者进行了功能磁共振成像。首先，我们研究了已知的静息状态网络（例如，默认模式，显着性和中央执行网络）的内在连接，以前涉及功能异常与PTSD症状。其次，利用图论方法对PTSD-A和TEC的网络结构进行了整体拓扑比较。最后，我们使用了一种新的图论分析和缩放子轮廓建模（SSM）的组合，以确定一个疾病相关的，脑网络组织的协变模式。在已知的静息状态网络和图论指标（聚类系数，特征路径长度，小世界性，全局和局部效率，度中心性）的内在连接性没有显着的组间差异。图论/SSM分析揭示了一个地形模式的改变程度的中心区分PTSD-A从TEC。在另一个由33名受试者组成的队列中，对这种PTSD相关的网络模式表达进行了额外的研究，这些受试者使用不同的MRI扫描仪进行扫描（22名PTSD或PTSD-B患者，11名健康的创伤初治对照或TNC）。在所有参与者组中，TEC组的模式表达评分显著较低，而PTSD-A，PTSD-B和TNC受试者特征彼此没有差异。在PTSD-B组中，该模式的表达水平与症状严重程度相关。该方法为开发与PTSD相关的客观生物标志物提供了可能。可能的解释和临床意义将被讨论。
Posttraumatic stress disorder (PTSD) is a prevalent psychiatric disorder that can result from experiencing traumatic events. Accurate diagnosis and optimal treatment strategies can be difficult to achieve, due to the heterogeneous etiology and symptomology of PTSD, and overlap with other psychiatric disorders. Advancing our understanding of PTSD pathophysiology is therefore critical. While functional connectivity alterations have shown promise for elucidating the neurobiological mechanisms of PTSD, previous findings have been inconsistent. Eleven patients with PTSD in our first cohort (PTSD-A) and 11 trauma-exposed controls (TEC) underwent functional magnetic resonance imaging. First, we investigated the intrinsic connectivity within known resting state networks (eg, default mode, salience, and central executive networks) previously implicated in functional abnormalities with PTSD symptoms. Second, the overall topology of network structure was compared between PTSD-A and TEC using graph theory. Finally, we used a novel combination of graph theory analysis and scaled subprofile modeling (SSM) to identify a disease-related, covarying pattern of brain network organization. No significant group differences were found in intrinsic connectivity of known resting state networks and graph theory metrics (clustering coefficients, characteristic path length, smallworldness, global and local efficiencies, and degree centrality). The graph theory/SSM analysis revealed a topographical pattern of altered degree centrality differentiating PTSD-A from TEC. This PTSD-related network pattern expression was additionally investigated in a separate cohort of 33 subjects who were scanned with a different MRI scanner (22 patients with PTSD or PTSD-B, and 11 healthy trauma-naïve controls or TNC). Across all participant groups, pattern expression scores were significantly lower in the TEC group, while PTSD-A, PTSD-B, and TNC subject profiles did not differ from each other. Expression level of the pattern was correlated with symptom severity in the PTSD-B group. This method offers potential in developing objective biomarkers associated with PTSD. Possible interpretations and clinical implications will be discussed.