Department of Clinical Neuroscience, Imperial College London London, UK.
Front Syst Neurosci. 2010 Apr 6;4:8. doi: 10.3389/fnsys.2010.00008. eCollection 2010.
The last 15 years have witnessed a steady increase in the number of resting-state functional neuroimaging studies. The connectivity patterns of multiple functional, distributed, large-scale networks of brain dynamics have been recognised for their potential as useful tools in the domain of systems and other neurosciences. The application of functional connectivity methods to areas such as cognitive psychology, clinical diagnosis and treatment progression has yielded promising preliminary results, but is yet to be fully realised. This is due, in part, to an array of methodological and interpretative issues that remain to be resolved. We here present a review of the methods most commonly applied in this rapidly advancing field, such as seed-based correlation analysis and independent component analysis, along with examples of their use at the individual subject and group analysis levels and a discussion of practical and theoretical issues arising from this data 'explosion'. We describe the similarities and differences across these varied statistical approaches to processing resting-state functional magnetic resonance imaging signals, and conclude that further technical optimisation and experimental refinement is required in order to fully delineate and characterise the gross complexity of the human neural functional architecture.
过去 15 年见证了静息态功能神经影像学研究数量的稳步增加。多个功能、分布式、大规模脑动力学网络的连接模式已被认为是系统和其他神经科学领域的有用工具。功能连接方法在认知心理学、临床诊断和治疗进展等领域的应用已经取得了有希望的初步结果，但尚未完全实现。这部分是由于仍有待解决的一系列方法学和解释性问题。在这里，我们回顾了该领域中最常用的方法，如基于种子的相关分析和独立成分分析，并举例说明了它们在个体和组分析水平上的应用，以及讨论了从这些数据“爆炸”中产生的实际和理论问题。我们描述了这些处理静息态功能磁共振成像信号的不同统计方法之间的相似性和差异，并得出结论，需要进一步进行技术优化和实验改进，以充分描绘和描述人类神经功能结构的总体复杂性。
