The primate connectome in context: Principles of connections of the cortical visual system
Introduction
Macroscopic connections among cortical areas form intricate networks for neural communication (Van Essen et al., 2013). These networks are neither completely nor randomly wired, and their organization has been the subject of extensive investigations (reviewed in Sporns et al., 2004, Bullmore and Sporns, 2009, Sporns, 2010).
An essential question is what determines the existence or absence of connections, since not all possible connections exist. A starting point is the observation that neighboring areas or neighbors-one-over are frequently connected (Barbas and Pandya, 1989), accounting for about half of the connections in primate visual cortex (Young, 1992). While proximity may contribute to the formation of connections (Ercsey-Ravasz et al., 2013), it cannot be the only factor, because some connections extend across considerable distances (e.g., Barbas and Mesulam, 1981, Kaiser and Hilgetag, 2006, Markov et al., 2013). Another model suggests that connections are formed between areas of similar thickness (He et al., 2007, Bassett et al., 2008, He et al., 2008, Liu et al., 2008, Bassett and Bullmore, 2009, He et al., 2009). Alternatively, a structural model posits that similarities in overall laminar organization (Barbas, 1986, Barbas and Rempel-Clower, 1997, Medalla and Barbas, 2006) help explain the presence and patterns of connections (Barbas and Rempel-Clower, 1997, Barbas et al., 2005a).
An important feature of connections is their strength – the number of neurons in a pathway – that may help estimate functional impact (Vanduffel et al., 1997). Strength varies considerably across pathways (MacNeil et al., 1997, Scannell et al., 2000, Hilgetag and Grant, 2000) and possesses a logarithmic (Scannell et al., 2000) or lognormal (Markov et al., 2011) global distribution, where a few pathways are very dense and most others are either sparse or absent. The factors that underlie this distribution are still incompletely understood (Kaiser et al., 2009).
A further question concerns the laminar patterns of connections, reviewed in Felleman and Van Essen (1991), which emanate and terminate in functionally distinct laminar micro-environments (Barbas et al., 2005b). Laminar patterns of connections are diverse but strikingly repetitive. For example, in the cortical visual system, connections can be arranged into sequences or hierarchies. In such sequences, projections that mostly originate from upper cortical layers and terminate in the middle-to-deep layers (‘feedforward’) point in one direction, while the reciprocal (‘feedback’) projections that mostly originate from deep layers and terminate in upper cortical layers point in the opposite direction of the sequence. Most visual projections fit such a scheme, with only 6 violations out of 318 patterns (Hilgetag et al., 1996). By contrast, if such arrangements are attempted for randomly assigned projection patterns, a large number of disagreements (> 100) remain (Hilgetag et al., 2000b). Thus, laminar projection patterns demonstrate a remarkably regular motif in cortical organization. It has been suggested that the regularity depends on cortical structure (Barbas, 1986, Barbas and Rempel-Clower, 1997), physical distance (Bullier and Nowak, 1995), or hierarchical rank (Barone et al., 2000).
Here we systematically investigated factors that contribute to the organization of cortical connections, by quantitative hypothesis testing based on distinct proposed models. To test these models, we studied extensive data from various sources to increase the reliability and generality of the findings: a collation of qualitatively described primate visual connections (Felleman and Van Essen, 1991); quantitative datasets from the laboratory of Kennedy (Barone et al., 2000, Markov et al., 2014); and newly compiled quantitative data on connections and cortical structure from our laboratory (Barbas, 1986, Barbas and Rempel-Clower, 1997). The primate visual cortical system is ideal for this study, because extensive available data make it possible to test to what extent alternative models, based on differences in distance, cortical thickness, or cortical structure between connected areas, best account for the existence, absence, density and laminar distribution of connections. Preliminary results from this study were presented in abstract form (Hilgetag et al., 2008).
Section snippets
Connection datasets
We analyzed the following four datasets which provide qualitative and quantitative information on the existence or absence, strength, as well as laminar patterns of projection neurons in the primate visual cortex in macaque monkeys. These data include the landmark compilation of Felleman and Van Essen of visual connections (Felleman and Van Essen, 1991), two sets of quantitative laminar projection patterns for visual projections (Barone et al., 2000, Markov et al., 2014), as well as a partly
Overview
We first investigated the relationship between different structural features of the cortex and the existence or absence of connections, as well as relative projection strength. We then related the structural cortical features to laminar origin and, where available, laminar termination patterns of corticocortical pathways. Both aspects of the analyses were considered in turn for the global visual dataset, as well as for the quantitative database of visual-prefrontal projections (Barbas, 1986,
Overview
Using four extensive datasets of visual connections, we demonstrate that cortical structure and connections are closely linked. Structure is captured by a few salient parameters, including neuronal density and laminar structure distilled into a few cortical types seen throughout the cortical mantle (Barbas, 2015). Differences in these parameters between areas successfully predict essential connection features, including their presence or absence, density and laminar distribution of connection
Conflict of interest
The authors declare no competing financial interests.
Acknowledgments
CCH and SB were supported by DFG Collaborative Research Center Grant SFB 936/A1. CCH was also supported by DFG Collaborative Research Center Grant TRR 169/A2. MM was supported by NIMH (National Institute of Mental Health) grant K99 MH101234. HB was supported by grants from NIH (NINDS R01NS024760; and NIMH, R01MH057414) and CELEST, an NSF Science of Learning Center (NSF OMA-0835976).
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