Altered Dynamic Functional Network Connectivity in Healthy Adults with Acute Pain: Findings from the Human Connectome Project.
Altered Dynamic Functional Network Connectivity in Healthy Adults with Acute Pain: Findings from the Human Connectome Project.
复制标题
患有急性疼痛的健康成年人动态功能网络连接的改变：人类连接组项目的发现。
DOI：
10.1109/embc40787.2023.10339952
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Jarrahi,Behnaz
中科院分区：
文献类型：
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作者：
Jarrahi,Behnaz
表征疼痛的神经特征及其调节对于评估治疗效果和进行转化性临床研究至关重要。然而，大脑中疼痛处理的动力学在很大程度上仍然是未知的。在这项研究中，我们在静息状态功能磁共振成像(FMRI)上使用独立成分分析(ICA)作为数据驱动的聚类方法，以获得来自人类连接组计划(HCP)的一组健康成年人的内在连接网络(ICN)，这些人被确认为患有急性疼痛。我们使用滑动时间窗关联和k-均值聚类来检验时间动态功能网络连通性(DFNC)，并比较组之间的dFNC状态属性和元状态度量。结果表明，急性疼痛对dFNC在几个ICN对之间的共同连接状态(动态5)有显著影响，这些ICN对包括突出网络、默认模式网络、中央执行、背侧注意网络和基底节(错误发现率[FDR]-校正p=0.05)。此外，有急性疼痛的健康成年人和没有急性疼痛的健康成年人在平均停留时间(动态状态3)上显示出差异，这表明有急性疼痛的人比没有疼痛的人在特定状态下花费的时间更多。元状态动态分析进一步表明，在状态数量(即，每个对象的唯一时间窗口)、状态之间的变化(即，每个对象从一个元状态改变到另一个元状态的次数)和总行驶距离方面存在显著的组差异。这些初步结果提供了与急性疼痛相关的疼痛状态随时间变化的新信息，并为未来疼痛生物标记物的发现和开发提供了进一步的基于状态的疼痛分析。
Characterizing the neural signature of pain and its modulation is critical for assessing treatment efficacy and conducting translational clinical research. However, the dynamics of pain processing in the brain have remained largely unknown. In this study, we employed independent component analysis (ICA) as a data-driven clustering method on resting-state functional magnetic resonance imaging (fMRI) to obtain intrinsic connectivity networks (ICNs) in a cohort of healthy adults from the Human Connectome Project (HCP) who were identified as having acute pain. We examined the temporal dynamic functional network connectivity (dFNC) with sliding time window correlation and k-means clustering, and compared dFNC state properties and meta-state metrics between groups. Results showed that acute pain had a significant impact on dFNC in a common connectivity state (dynamic state 5) among several ICN pairs, including the salience network, default mode network, central executive, dorsal attention networks, and basal ganglia (false discovery rate [FDR]-corrected p of 0.05). Furthermore, healthy adults with and without acute pain exhibited differences in mean dwell time (dynamic state 3), which indicated that individuals with acute pain spent more time in particular states than those without pain. Meta-state dynamic analysis further indicated significant group differences in the number of states (i.e., unique time windows for each subject), changes between states (i.e., number of times each subject changes from one meta-state to other), and total travelled distances. These preliminary results provide new information about time-varying properties of pain states related to acute pain and advocate for further state-based analyses of pain for future pain biomarker discovery and development.