Anatomical connectivity defines the organization of clusters of cortical areas in the macaque monkey and the cat.

By Claus C. Hilgetag, Gully A. Burns, M. O'Neill, J. Scannell, M. Young. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2000

570 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/d4e0885b8c2a266dcba6b7cfadc355f9c3789ace

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In Cognitive Neuroscience

Citations in the corpus

The application of graph theoretical analysis to complex networks in the brain. (2007, unknown): “elegans to primates, are to a large extent organized as small world networks, and that patterns of functional connectivity may follow the same pattern (Hilgetag et al., 2000; Stephan et al., 2000; Kotter and Sommer, 2000).”
Analyzing and modeling real-world phenomena with complex networks: a survey of applications (2007, unknown): “Similar results were obtained by Hilgetag [435], who studied compilations of corti cortical connections data from macaque – the whole cortex, the visual and the somatosensory– and cat.”
Hierarchical Organization of Human Cortical Networks in Health and Schizophrenia (2008, methodology): “Thus, it has been established that anatomical networks in both the cat and the macaque monkey have the typical “small-world” combination of high clustering of local connections with a short minimum path length between any pair of regions (Hilgetag et al., 2000; Sporns et al., 2002; Sakata et al., 20”
Connectivity and complexity: the relationship between neuroanatomy and brain dynamics (2000, methodology): “For example, cortico-cortical connection pathways have been used to delineate anatomical clusters of areas, revealing distinct subdivisions of cortex and hierarchical patterns (Hilgetag et al., 2000).”
Cognitive processing speed and the structure of white matter pathways: Convergent evidence from normal variation and lesion studies (2008, unknown): “Theoretical analyses of brain connectivity patterns indicate that distributed patterns of cortical activity can be accounted for to a large extent by the patterns of interaction afforded by the white matter fiber systems (Hilgetag et al., 2000; Kotter and Sommer, 2000).”
Brain graphs: graphical models of the human brain connectome. (2011, methodology): “For graphical analysis of tract-tracing data on large-scale axonal projections between regions of mammalian cortex, nodes have usually been defined cytoarchitectonically as Brodmann areas (Hilgetag et al. 2000; Scannell et al. 1995, 1999; Young 1992, 1993).”
A New Measure of Centrality for Brain Networks (2010, background): “Nodes identified through leverage centrality are critical for the function of the global network as well as local communities of network nodes known as modules [20,21,22,23].”
On the relationship between emotion and cognition (2008, background): “Indeed, it appears that the brain is configured according to a small-world topology in which the path length between nodes is small — typically, cortical areas are connected directly or by just one or two intermediate area”
Optimal Hierarchical Modular Topologies for Producing Limited Sustained Activation of Neural Networks (2009, unknown): “…analyses suggest that primate cortical connectivity is structured on more levels than connectivity in smaller brains, if one considers that there exist primate visual “streams” (Young, 1992; Hilgetag et al., 2000), that is, sub-divisions of the visual network module that are apparently absent”
Age-related changes in modular organization of human brain functional networks (2009, unknown): “Here we derived human brain functional networks from fMRI measurements of endogenous, low frequency, correlated oscillations in 90 cortical and subcortical regions for two groups of healthy (young and older) participants.”
Whole-brain anatomical networks: Does the choice of nodes matter? (2010, background): “The issue of nodal choice first arose when brain networks of mammalia such as the cat and macaque were analyzed (Felleman and Van Essen, 1991; Hilgetag et al., 2000a,b; Sporns et al., 2007; Young, 1993).”
Modular and Hierarchically Modular Organization of Brain Networks (2010, methodology): “Using these data on brain anatomical networks, Hilgetag et al. (2000) applied an algorithm looking at partitions, with a quality function aiming at maximizing the number of intra-modular edges and minimizing the number of inter-modular edges.”
Measuring information integration (2003, methodology): “To quantify the similarity of input patterns, we calculated the matching index [23,24], which reflects the correlation (scaled between 0 and 1) between discrete vectors of input connections to a pair of vertices, excluding self- and cross-connections.”