Young M P, Scannell J W, O'Neill M A, Hilgetag C C, Burns G, Blakemore C
Laboratory of Physiology, University of Oxford, U.K.
Philos Trans R Soc Lond B Biol Sci. 1995 May 30;348(1325):281-308. doi: 10.1098/rstb.1995.0069.
Neuroanatomists have established that the various gross structures of the brain are divided into a large number of different processing regions and have catalogued a large number of connections between these regions. The connectional data derived from neuroanatomical studies are complex, and reliable conclusions about the organization of brain systems cannot be drawn from considering them without some supporting analysis. Recognition of this problem has recently led to the application of a variety of techniques to the analysis of connection data. One of the techniques that we previously employed, non-metric multidimensional scaling (NMDS), appears to have revealed important aspects of the organization of the central nervous system, such as the gross organization of the whole cortical network in two species. We present here a detailed treatment of methodological aspects of the application of NMDS to connection data. We first examine in detail the particular properties of neuroanatomical connection data. Second, we consider the details of NMDS and discuss the propriety of different possible NMDS approaches. Third, we present results of the analyses of connection data from the primate visual system, and discuss their interpretation. Fourth, we study independent analyses of the organization of the visual system, and examine the relation between the results of these analyses and those from NMDS. Fifth, we investigate quantitatively the performance of a number of data transformation and conditioning procedures, as well as tied and untied NMDS analysis of untransformed low-level data, to determine how well NMDS can recover known metric parameters from artificial data. We then re-analyse real connectivity data with the most successful methods at removing the effects of sparsity, to ensure that this aspect of data structure does not obscure others. Finally, we summarize the evidence on the connectional organization of the primate visual system, and discuss the reliability of NMDS analyses of neuroanatomical connection data.
神经解剖学家已经确定,大脑的各种大体结构被划分为大量不同的处理区域,并已将这些区域之间的大量连接进行了分类。从神经解剖学研究中获得的连接数据非常复杂,如果没有一些辅助分析,仅考虑这些数据是无法得出关于脑系统组织的可靠结论的。对这一问题的认识最近促使人们将各种技术应用于连接数据的分析。我们之前使用的一种技术,即非度量多维标度法(NMDS),似乎已经揭示了中枢神经系统组织的重要方面,比如两个物种整个皮质网络的大体组织情况。我们在此详细阐述将NMDS应用于连接数据的方法学方面的内容。我们首先详细研究神经解剖学连接数据的特殊性质。其次,我们考虑NMDS的细节,并讨论不同可能的NMDS方法的适当性。第三,我们展示灵长类视觉系统连接数据的分析结果,并讨论其解释。第四,我们研究对视觉系统组织的独立分析,并考察这些分析结果与NMDS分析结果之间的关系。第五,我们定量研究一些数据转换和预处理程序的性能,以及对未转换的低级数据进行的绑定和非绑定NMDS分析,以确定NMDS从人工数据中恢复已知度量参数的能力有多强。然后,我们用去除稀疏性影响最成功的方法重新分析实际的连通性数据,以确保数据结构的这一方面不会掩盖其他方面。最后,我们总结关于灵长类视觉系统连接组织的证据,并讨论对神经解剖学连接数据进行NMDS分析的可靠性。