Investigations into resting-state connectivity using independent component analysis
Investigations into resting-state connectivity using independent component analysis
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DOI：
10.1098/rstb.2005.1634
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发表时间：
2005-05-29
影响因子：
6.3
通讯作者：
Smith, SM
中科院分区：
文献类型：
--
作者：
Beckmann, CF;DeLuca, M;Smith, SM
从功能性磁共振成像（fMRI）数据推断静息态连接模式对于任何分析技术来说都是一项具有挑战性的任务。在本文中，我们回顾了概率独立成分分析（PICA）的方法，优化功能磁共振成像数据的分析，并讨论了这种探索性的技术可以在科学调查的结构，这些影响的作用。我们将PICA应用于在休息时获得的fMRI数据，以表征这种数据的时空结构，并证明这是一种有效和强大的工具，用于识别低频静息状态模式从各种不同的空间和时间分辨率的数据采集。我们发现，这些网络表现出高度的空间一致性，在受试者和离散的皮质功能网络，如视觉皮质区或感觉运动皮质非常相似。
Inferring resting-state connectivity patterns from functional magnetic resonance imaging (fMRI) data is a challenging task for any analytical technique. In this paper, we review a probabilistic independent component analysis (PICA) approach, optimized for the analysis of fMRI data, and discuss the role which this exploratory technique can take in scientific investigations into the structure of these effects. We apply PICA to fMRI data acquired at rest, in order to characterize the spatio-temporal structure of such data, and demonstrate that this is an effective and robust tool for the identification of low-frequency resting-state patterns from data acquired at various different spatial and temporal resolutions. We show that these networks exhibit high spatial consistency across subjects and closely resemble discrete cortical functional networks such as visual cortical areas or sensory-motor cortex.