Resting-State Functional Magnetic Resonance Imaging for Language Preoperative Planning.
Resting-State Functional Magnetic Resonance Imaging for Language Preoperative Planning.
复制标题
DOI：
10.3389/fnhum.2016.00011
复制
发表时间：
2016
影响因子：
2.9
通讯作者：
Sunaert S
中科院分区：
文献类型：
--
作者：
Branco P;Seixas D;Deprez S;Kovacs S;Peeters R;Castro SL;Sunaert S
功能磁共振成像(FMRI)是一种著名的非侵入性脑功能研究技术。它最常见的临床应用之一是术前语言映射，这对神经外科患者的功能保护至关重要。通常，功能磁共振成像用于跟踪与任务相关的活动，但较差的任务性能和运动伪影可能是临床环境中的关键限制。静息状态方案的最新进展为术前语言映射打开了新的可能性，潜在地克服了这些限制。为了测试使用静息态fMRI代替传统的基于主动任务的方案的可行性，我们比较了15名脑损伤患者在执行动词到名词生成任务时和在休息时的结果。使用一般线性模型分析和独立成分分析(ICA)测量任务-活动。使用独立成分分析提取静止态网络，并以两种方式进一步分类：由专家手动分类和使用自动模板匹配程序。结果表明，与专家手动分类相比，自动分类程序正确地识别了语言网络。我们发现任务相关活动和静止状态语言地图之间有很好的重叠，特别是在感兴趣的语言区域内。此外，静态语言图与任务相关图一样敏感，具有更高的特异性。我们的发现表明，静息状态协议可能适合以一种快速和临床有效的方式映射语言网络。
Functional magnetic resonance imaging (fMRI) is a well-known non-invasive technique for the study of brain function. One of its most common clinical applications is preoperative language mapping, essential for the preservation of function in neurosurgical patients. Typically, fMRI is used to track task-related activity, but poor task performance and movement artifacts can be critical limitations in clinical settings. Recent advances in resting-state protocols open new possibilities for pre-surgical mapping of language potentially overcoming these limitations. To test the feasibility of using resting-state fMRI instead of conventional active task-based protocols, we compared results from fifteen patients with brain lesions while performing a verb-to-noun generation task and while at rest. Task-activity was measured using a general linear model analysis and independent component analysis (ICA). Resting-state networks were extracted using ICA and further classified in two ways: manually by an expert and by using an automated template matching procedure. The results revealed that the automated classification procedure correctly identified language networks as compared to the expert manual classification. We found a good overlay between task-related activity and resting-state language maps, particularly within the language regions of interest. Furthermore, resting-state language maps were as sensitive as task-related maps, and had higher specificity. Our findings suggest that resting-state protocols may be suitable to map language networks in a quick and clinically efficient way.