Determining cortical landscapes.
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Biomedical subjects
J W Scannell
Publications and source records attributed to J W Scannell.
Visual motion processing in the anterior ectosylvian sulcus of the cat.
1. Neurons that are selectively sensitive to the direction of motion of elongated contours have been found in several cortical areas in many species. However, in the striate cortex of the cat and monkey, and the extrastriate posteromedial lateral suprasylvian visual area of the cat, such cells are generally component motion selective, signaling only the direction of movement orthogonal to the preferred orientation; a direction that is not necessarily the same as the motion of the entire pattern or texture of which the cell's preferred contour is part. The primate extrastriate middle temporal area is the only cortical region currently known to contain a substantial population of pattern-motion-selective cells that respond to the shared vector of motion of mixtures of contours. 2. From analyzing published data on the connectivity of the cat's cortex, we predicted that the anterior ectosylvian visual area (AEV), situated within the anterior ectosylvian sulcus, might be a higher-order motion processing area and thus likely to contain pattern-motion-selective neurons. This paper presents the results of a study on neuronal responses in AEV. 3. Ninety percent of AEV cells that responded strongly to drifting grating and/or plaid stimuli were directionally selective (directionality index > 0.5). For this group, the mean directionality index was 0.75. Moreover, 55% of these cells were unequivocally classified as pattern motion selective and only one neuron was classified as definitely component motion selective. Thus high-level pattern motion coding occurs in the cat extrastriate cortex and is not limited to the primate middle temporal area. 4. AEV contains a heterogeneous population of directionally selective cells. There was no clear relation between the degree of directional selectivity for plaids or gratings and the degree of selectivity for pattern motion or component motion. Nevertheless, 28% of the highly responsive cells were both more strongly modulated by plaids than gratings and more pattern motion selective than component motion selective. Such cells could correspond to a population of "selection units" signaling the salience of local motion information. 5. AEV lacks global retinotopic order but the preferred direction of motion of neurons (rather than axis of motion, as in the middle temporal area and the posteromedial lateral suprasylvian visual area) is mapped systematically across the cortex. Our data are compatible with AEV being a nonretinotopic, feature-mapped area in which cells representing similar parts of "motion space" are brought together on the cortical sheet.
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Component-placement optimization in the brain.
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Non-metric multidimensional scaling in the analysis of neuroanatomical connection data and the organization of the primate cortical visual system.
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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Analysis of connectivity in the cat cerebral cortex.
The mammalian cerebral cortex is innervated by a large number of corticocortical connections. The number of connections makes it difficult to understand the organization of the cortical network. Nonetheless, conclusions about the organization of cortical systems drawn from examining connectional data have often been made in a speculative and informal manner, unsupported by any analytic treatment. Recently, progress has been made toward more systematic ways of extracting organizing principles from data on the network of connections between cortical areas of the monkey. In this article, we extend these approaches to the cortical systems of the cat. We collated information from the neuroanatomical literature about the corticocortical connections of the cat. This collation incorporated 1139 reported corticocortical connections between 65 cortical areas. We have previously used an optimization technique (Scannell and Young, 1993) to analyze this database in order to represent the connectional organization of cortical systems in the cat. Here, we report the connectional database and analyze it in a number of further ways. First, we employed rules from Felleman and Van Essen (1991) to investigate hierarchical relations among the areas. Second, we compared quantitatively the results of the optimization method with the results of the hierarchical method. Third, we examined quantitatively whether simple connection rules, which may reflect the development and evolution of the cortex, can account for the experimentally identified corticocortical connections in the database. The results showed, first, that hierarchical rules, when applied to the cat visual system, define a largely consistent hierarchy. Second, in both auditory and visual systems, the ordering of areas by hierarchical analysis and by optimization analysis was statistically significantly related. Hence, independent analyzes concur broadly in their ordering of areas in the cortical hierarchies. Third, the majority of corticocortical connections, and much of the pattern of connectivity, were accounted for by a simple "nearest-neighbor-or-next-door-but-one" connection rule, which may suggest one of the mechanisms by which the development of cortical connectivity is controlled.
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Analysis of connectivity: neural systems in the cerebral cortex.
The mammalian cerebral cortex is composed of many distinct areas, which are very richly interconnected. The very large number of connections between cortical areas require analysis to be undertaken before reliable conclusions about the organization of neural systems in the cortex can be drawn. We review the methodology and results of two means of analysing central nervous connectivity, hierarchical analysis and optimization analysis. We conclude that these methods are reliable methods for analysing neural connectivity data, and that their results concur. The analyses indicate that all major cortical sensory systems are organized hierarchically, some central sensory systems are divided structurally into several "streams" of processing, the cortical motor system is embedded in the cortical somatosensory system, the frontal and limbic structures are connectionally associated, and that these frontal and limbic areas are invariably associated with the least peripheral sensory processing regions, and are therefore connectionally central. Finally, we discuss the differences on this common plan between the organizations of the cat and primate that these analyses reveal.
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