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在意识障碍患者的空间 IC 分析中识别默认模式成分。

Identifying the default-mode component in spatial IC analyses of patients with disorders of consciousness.

机构信息

Coma Science Group, Cyclotron Research Centre, University of Liège, Liège, Belgium.

出版信息

Hum Brain Mapp. 2012 Apr;33(4):778-96. doi: 10.1002/hbm.21249. Epub 2011 Apr 11.

Abstract

OBJECTIVES

Recent fMRI studies have shown that it is possible to reliably identify the default-mode network (DMN) in the absence of any task, by resting-state connectivity analyses in healthy volunteers. We here aimed to identify the DMN in the challenging patient population of disorders of consciousness encountered following coma.

EXPERIMENTAL DESIGN

A spatial independent component analysis-based methodology permitted DMN assessment, decomposing connectivity in all its different sources either neuronal or artifactual. Three different selection criteria were introduced assessing anticorrelation-corrected connectivity with or without an automatic masking procedure and calculating connectivity scores encompassing both spatial and temporal properties. These three methods were validated on 10 healthy controls and applied to an independent group of 8 healthy controls and 11 severely brain-damaged patients [locked-in syndrome (n = 2), minimally conscious (n = 1), and vegetative state (n = 8)].

PRINCIPAL OBSERVATIONS

All vegetative patients showed fewer connections in the default-mode areas, when compared with controls, contrary to locked-in patients who showed near-normal connectivity. In the minimally conscious-state patient, only the two selection criteria considering both spatial and temporal properties were able to identify an intact right lateralized BOLD connectivity pattern, and metabolic PET data suggested its neuronal origin.

CONCLUSIONS

When assessing resting-state connectivity in patients with disorders of consciousness, it is important to use a methodology excluding non-neuronal contributions caused by head motion, respiration, and heart rate artifacts encountered in all studied patients.

摘要

目的

最近的 fMRI 研究表明,通过对健康志愿者的静息状态连接分析,在没有任何任务的情况下,有可能可靠地识别默认模式网络(DMN)。我们旨在识别昏迷后意识障碍患者这一极具挑战性的患者群体中的 DMN。

实验设计

基于空间独立成分分析的方法允许评估 DMN,将神经元或人为因素的连接分解为不同的来源。引入了三种不同的选择标准,评估校正相关后的连接,有无自动掩蔽过程,并计算同时包含空间和时间特性的连接分数。这三种方法在 10 名健康对照者上进行了验证,并应用于一个独立的 8 名健康对照者和 11 名严重脑损伤患者组(闭锁综合征 2 名,最小意识状态 1 名,植物状态 8 名)。

主要观察结果

与对照组相比,所有植物状态患者在默认模式区域的连接较少,而闭锁状态患者的连接几乎正常。在最小意识状态患者中,只有考虑空间和时间特性的两种选择标准能够识别出完整的右侧 BOLD 连接模式,代谢 PET 数据表明其具有神经元起源。

结论

在评估意识障碍患者的静息状态连接时,重要的是使用一种排除所有研究患者中因头部运动、呼吸和心率伪影引起的非神经元贡献的方法。

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