Functional magnetic resonance imaging
Functional magnetic resonance imaging
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DOI：
10.1016/b978-0-444-53485-9.00004-0
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发表时间：
2016-01-01
期刊：
影响因子：
--
通讯作者：
Buchbinder, Bradley R.
中科院分区：
文献类型：
--
作者：
Buchbinder, Bradley R.
功能性磁共振成像（fMRI）映射在不同的认知条件下大脑中神经活动的时空分布。自1991年问世以来，血氧水平依赖（BOLD）功能磁共振成像已迅速成为基础和应用神经科学研究的重要方法。在临床领域，它已成为一个既定的工具，术前功能脑映射。本章有三个主要目标。首先，我们回顾关键的生理，生物物理，方法学的原则，BOLD功能磁共振成像的基础，无论其特定的应用领域。这些原则为BOLD fMRI信号的细微解释提供了信息，沿着其神经生理学意义和缺陷。第二，我们说明了临床应用任务为基础的功能磁共振成像术前运动，语言和记忆映射的患者附近的功能脑区的病变。BOLD fMRI和弥散张量白质纤维束成像的结合为术前计划和术中导航提供了路线图，有助于最大限度地扩大病变切除范围，同时最大限度地降低术后神经功能缺损的风险。最后，我们强调了静息态功能磁共振成像的几个基本原则及其新兴的翻译临床应用。静息态功能磁共振成像代表了一个重要的范式转变，关注内在认知网络内的功能连接。
Functional magnetic resonance imaging (fMRI) maps the spatiotemporal distribution of neural activity in the brain under varying cognitive conditions. Since its inception in 1991, blood oxygen level-dependent (BOLD) fMRI has rapidly become a vital methodology in basic and applied neuroscience research. In the clinical realm, it has become an established tool for presurgical functional brain mapping. This chapter has three principal aims. First, we review key physiologic, biophysical, and methodologic principles that underlie BOLD fMRI, regardless of its particular area of application. These principles inform a nuanced interpretation of the BOLD fMRI signal, along with its neurophysiologic significance and pitfalls. Second, we illustrate the clinical application of task-based fMRI to presurgical motor, language, and memory mapping in patients with lesions near eloquent brain areas. Integration of BOLD fMRI and diffusion tensor white-matter tractography provides a road map for presurgical planning and intraoperative navigation that helps to maximize the extent of lesion resection while minimizing the risk of postoperative neurologic deficits. Finally, we highlight several basic principles of resting-state fMRI and its emerging translational clinical applications. Resting-state fMRI represents an important paradigm shift, focusing attention on functional connectivity within intrinsic cognitive networks.