Limits...
Communication and wiring in the cortical connectome.

Budd JM, Kisvárday ZF - Front Neuroanat (2012)

Bottom Line: We report three main conclusions.To avoid neglecting neuron and microcircuit levels of cortical organization, the connectome framework should incorporate more morphological description.We conclude the hypothesized trade-off between spatial and temporal costs may potentially offer a powerful explanation for cortical wiring patterns.

View Article: PubMed Central - PubMed

Affiliation: Department of Informatics, University of Sussex Falmer, East Sussex, UK.

ABSTRACT
In cerebral cortex, the huge mass of axonal wiring that carries information between near and distant neurons is thought to provide the neural substrate for cognitive and perceptual function. The goal of mapping the connectivity of cortical axons at different spatial scales, the cortical connectome, is to trace the paths of information flow in cerebral cortex. To appreciate the relationship between the connectome and cortical function, we need to discover the nature and purpose of the wiring principles underlying cortical connectivity. A popular explanation has been that axonal length is strictly minimized both within and between cortical regions. In contrast, we have hypothesized the existence of a multi-scale principle of cortical wiring where to optimize communication there is a trade-off between spatial (construction) and temporal (routing) costs. Here, using recent evidence concerning cortical spatial networks we critically evaluate this hypothesis at neuron, local circuit, and pathway scales. We report three main conclusions. First, the axonal and dendritic arbor morphology of single neocortical neurons may be governed by a similar wiring principle, one that balances the conservation of cellular material and conduction delay. Second, the same principle may be observed for fiber tracts connecting cortical regions. Third, the absence of sufficient local circuit data currently prohibits any meaningful assessment of the hypothesis at this scale of cortical organization. To avoid neglecting neuron and microcircuit levels of cortical organization, the connectome framework should incorporate more morphological description. In addition, structural analyses of temporal cost for cortical circuits should take account of both axonal conduction and neuronal integration delays, which appear mostly of the same order of magnitude. We conclude the hypothesized trade-off between spatial and temporal costs may potentially offer a powerful explanation for cortical wiring patterns.

No MeSH data available.


Related in: MedlinePlus

Communication cost trade-off at Pathway scale of cortical organization. (A,B) Macaque tracer-derived pathway connectivity. (A) Example of directed connectivity (adjacency) matrix of visual and somatomotor macaque cerebral cortex (Reprinted from Sporns et al., 2007), where black squares indicate evidence supporting a axonal pathway connection between areas (matrix rows as sources and columns as target cortical regions). (B) Macaque cerebral cortical network is suboptimal for total axonal length (left) but minimal length network increased averaged path length (right) (Reprinted from Kaiser and Hilgetag, 2006). (C,D) Human DSI-derived pathway connectivity. (C) An undirected spatial cortical network (bottom) is constructed from vertices of cortical regions (top, left) and edges determined from the probability of fiber tracts existing between corresponding pairs of cortical regions based on tractography tracing algorithms (top, right) (Reprinted from Hagmann et al., 2008). (D) Human DSI network is suboptimal for wire length (left) but minimal length network has lower topological dimension than observed cortical network (right) (Reprinted and partly redrawn from Bassett et al., 2010). Topological dimension here is a fractal measure of a network's degree of internal connectedness.
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Figure 5: Communication cost trade-off at Pathway scale of cortical organization. (A,B) Macaque tracer-derived pathway connectivity. (A) Example of directed connectivity (adjacency) matrix of visual and somatomotor macaque cerebral cortex (Reprinted from Sporns et al., 2007), where black squares indicate evidence supporting a axonal pathway connection between areas (matrix rows as sources and columns as target cortical regions). (B) Macaque cerebral cortical network is suboptimal for total axonal length (left) but minimal length network increased averaged path length (right) (Reprinted from Kaiser and Hilgetag, 2006). (C,D) Human DSI-derived pathway connectivity. (C) An undirected spatial cortical network (bottom) is constructed from vertices of cortical regions (top, left) and edges determined from the probability of fiber tracts existing between corresponding pairs of cortical regions based on tractography tracing algorithms (top, right) (Reprinted from Hagmann et al., 2008). (D) Human DSI network is suboptimal for wire length (left) but minimal length network has lower topological dimension than observed cortical network (right) (Reprinted and partly redrawn from Bassett et al., 2010). Topological dimension here is a fractal measure of a network's degree of internal connectedness.

Mentions: By injecting molecular labeling agents such as biocytin or Phaseolus vulgaris-leucoagglutinin (PHA-L) into anatomically identified cortical regions, the origin or termination sites of individual cortical axons can be traced over long-distances (Rockland, 2004). Individual axons can have quite convoluted trajectories (Rockland, 1997) and may diverge to terminate in more than one cortical area (Schwartz and Goldman-Rakic, 1982; Bullier et al., 1984). By combining results from different laboratories for a variety of tracer agents, it has been possible to construct draft qualitative adjacency matrices of inter-areal cortical connectivity (Felleman and Van Essen, 1991; Scannell et al., 1995), of which some are publicly available, e.g., CoCoMac database for macaque cerebral cortex (http://cocomac.g-node.org/drupal/, Stephan et al., 2001; Kötter, 2004). An example is shown in Figure 5A. Analyses of these draft connectivity matrices have proved useful in, for example, establishing the high incidence of reciprocal pathways in macaque cortex (Felleman and Van Essen, 1991), offering an anatomical basis for separate ventral and dorsal functional streams in visual cortex (Young, 1992), and discovering “nearest-neighbour” and “next-nearest-neighbour” connectivity rules between cortical areas (Felleman and Van Essen, 1991; Young, 1992). But in the latter case these rules only partly explain the full connectivity matrix (Scannell et al., 1995; Costa et al., 2007).


Communication and wiring in the cortical connectome.

Budd JM, Kisvárday ZF - Front Neuroanat (2012)

Communication cost trade-off at Pathway scale of cortical organization. (A,B) Macaque tracer-derived pathway connectivity. (A) Example of directed connectivity (adjacency) matrix of visual and somatomotor macaque cerebral cortex (Reprinted from Sporns et al., 2007), where black squares indicate evidence supporting a axonal pathway connection between areas (matrix rows as sources and columns as target cortical regions). (B) Macaque cerebral cortical network is suboptimal for total axonal length (left) but minimal length network increased averaged path length (right) (Reprinted from Kaiser and Hilgetag, 2006). (C,D) Human DSI-derived pathway connectivity. (C) An undirected spatial cortical network (bottom) is constructed from vertices of cortical regions (top, left) and edges determined from the probability of fiber tracts existing between corresponding pairs of cortical regions based on tractography tracing algorithms (top, right) (Reprinted from Hagmann et al., 2008). (D) Human DSI network is suboptimal for wire length (left) but minimal length network has lower topological dimension than observed cortical network (right) (Reprinted and partly redrawn from Bassett et al., 2010). Topological dimension here is a fractal measure of a network's degree of internal connectedness.
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Figure 5: Communication cost trade-off at Pathway scale of cortical organization. (A,B) Macaque tracer-derived pathway connectivity. (A) Example of directed connectivity (adjacency) matrix of visual and somatomotor macaque cerebral cortex (Reprinted from Sporns et al., 2007), where black squares indicate evidence supporting a axonal pathway connection between areas (matrix rows as sources and columns as target cortical regions). (B) Macaque cerebral cortical network is suboptimal for total axonal length (left) but minimal length network increased averaged path length (right) (Reprinted from Kaiser and Hilgetag, 2006). (C,D) Human DSI-derived pathway connectivity. (C) An undirected spatial cortical network (bottom) is constructed from vertices of cortical regions (top, left) and edges determined from the probability of fiber tracts existing between corresponding pairs of cortical regions based on tractography tracing algorithms (top, right) (Reprinted from Hagmann et al., 2008). (D) Human DSI network is suboptimal for wire length (left) but minimal length network has lower topological dimension than observed cortical network (right) (Reprinted and partly redrawn from Bassett et al., 2010). Topological dimension here is a fractal measure of a network's degree of internal connectedness.
Mentions: By injecting molecular labeling agents such as biocytin or Phaseolus vulgaris-leucoagglutinin (PHA-L) into anatomically identified cortical regions, the origin or termination sites of individual cortical axons can be traced over long-distances (Rockland, 2004). Individual axons can have quite convoluted trajectories (Rockland, 1997) and may diverge to terminate in more than one cortical area (Schwartz and Goldman-Rakic, 1982; Bullier et al., 1984). By combining results from different laboratories for a variety of tracer agents, it has been possible to construct draft qualitative adjacency matrices of inter-areal cortical connectivity (Felleman and Van Essen, 1991; Scannell et al., 1995), of which some are publicly available, e.g., CoCoMac database for macaque cerebral cortex (http://cocomac.g-node.org/drupal/, Stephan et al., 2001; Kötter, 2004). An example is shown in Figure 5A. Analyses of these draft connectivity matrices have proved useful in, for example, establishing the high incidence of reciprocal pathways in macaque cortex (Felleman and Van Essen, 1991), offering an anatomical basis for separate ventral and dorsal functional streams in visual cortex (Young, 1992), and discovering “nearest-neighbour” and “next-nearest-neighbour” connectivity rules between cortical areas (Felleman and Van Essen, 1991; Young, 1992). But in the latter case these rules only partly explain the full connectivity matrix (Scannell et al., 1995; Costa et al., 2007).

Bottom Line: We report three main conclusions.To avoid neglecting neuron and microcircuit levels of cortical organization, the connectome framework should incorporate more morphological description.We conclude the hypothesized trade-off between spatial and temporal costs may potentially offer a powerful explanation for cortical wiring patterns.

View Article: PubMed Central - PubMed

Affiliation: Department of Informatics, University of Sussex Falmer, East Sussex, UK.

ABSTRACT
In cerebral cortex, the huge mass of axonal wiring that carries information between near and distant neurons is thought to provide the neural substrate for cognitive and perceptual function. The goal of mapping the connectivity of cortical axons at different spatial scales, the cortical connectome, is to trace the paths of information flow in cerebral cortex. To appreciate the relationship between the connectome and cortical function, we need to discover the nature and purpose of the wiring principles underlying cortical connectivity. A popular explanation has been that axonal length is strictly minimized both within and between cortical regions. In contrast, we have hypothesized the existence of a multi-scale principle of cortical wiring where to optimize communication there is a trade-off between spatial (construction) and temporal (routing) costs. Here, using recent evidence concerning cortical spatial networks we critically evaluate this hypothesis at neuron, local circuit, and pathway scales. We report three main conclusions. First, the axonal and dendritic arbor morphology of single neocortical neurons may be governed by a similar wiring principle, one that balances the conservation of cellular material and conduction delay. Second, the same principle may be observed for fiber tracts connecting cortical regions. Third, the absence of sufficient local circuit data currently prohibits any meaningful assessment of the hypothesis at this scale of cortical organization. To avoid neglecting neuron and microcircuit levels of cortical organization, the connectome framework should incorporate more morphological description. In addition, structural analyses of temporal cost for cortical circuits should take account of both axonal conduction and neuronal integration delays, which appear mostly of the same order of magnitude. We conclude the hypothesized trade-off between spatial and temporal costs may potentially offer a powerful explanation for cortical wiring patterns.

No MeSH data available.


Related in: MedlinePlus