Branco Paulo, Seixas Daniela, Deprez Sabine, Kovacs Silvia, Peeters Ronald, Castro São L, Sunaert Stefan
Center for Psychology and Faculty of Psychology and Educational Sciences, University of Porto Porto, Portugal.
Department of Experimental Biology, Faculty of Medicine of Porto UniversityPorto, Portugal; Department of Imaging, Centro Hospitalar de Vila Nova de Gaia/EspinhoVila Nova de Gaia, Portugal.
Front Hum Neurosci. 2016 Feb 1;10:11. doi: 10.3389/fnhum.2016.00011. eCollection 2016.
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.
功能磁共振成像（fMRI）是一种用于研究脑功能的著名非侵入性技术。其最常见的临床应用之一是术前语言图谱绘制，这对于神经外科手术患者的功能保留至关重要。通常，fMRI用于追踪与任务相关的活动，但在临床环境中，任务表现不佳和运动伪影可能是关键限制因素。静息态协议的最新进展为术前语言图谱绘制开辟了新的可能性，有可能克服这些限制。为了测试使用静息态fMRI而非传统的基于主动任务的协议的可行性，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静息时的结果。任务活动通过一般线性模型分析和独立成分分析（ICA）进行测量。静息态网络通过ICA提取，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。结果显示，与专家手动分类相比，自动分类程序正确识别了语言网络。我们发现与任务相关的活动和静息态语言图谱之间有很好的重叠，特别是在感兴趣的语言区域内。此外，静息态语言图谱与任务相关图谱一样敏感，且具有更高的特异性。我们的研究结果表明，静息态协议可能适合以快速且临床高效的方式绘制语言网络。
