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Non-Metric Multidimensional Scaling in the Analysis of Neuroanatomical Connection Data and the Organization of the Primate Cortical Visual System

M. P. Young, J. W. Scannell, M. A. O'Neill, C. C. Hilgetag, G. Burns and C. Blakemore
Philosophical Transactions: Biological Sciences
Vol. 348, No. 1325 (May 30, 1995), pp. 281-308
Published by: Royal Society
Stable URL: http://www.jstor.org/stable/56164
Page Count: 28
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Non-Metric Multidimensional Scaling in the Analysis of Neuroanatomical Connection Data and the Organization of the Primate Cortical Visual System
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Abstract

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.

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