macaque_cortex_kaiser — Macaque cortical connectivity with spatial positions (95 areas)

Description

Directed network of anatomical connections among 95 areas and sub-areas of one hemisphere of the macaque monkey cerebral cortex, with the three-dimensional positions of the areas, from M. Kaiser and C. C. Hilgetag. Nodes are cortical areas, and a directed edge represents a projection (axonal pathway) from one area to another, as reported by tract-tracing studies; following the usual convention, the entry $(i, j)$ of the source adjacency matrix is taken as an edge from area $i$ to area $j$ (the convention is not stated explicitly with the data). The connectivity data were retrieved from the CoCoMac database and are based on three extensive neuroanatomical compilations that together cover large parts of the cortex; the positions of the areas are the centres of mass of their three-dimensional extensions, estimated from a surface parcellation with the CARET software. This corrected version of the data contains 94 areas, since a duplicate area (6Vb/6vb) of the original data set was merged into a single area that inherited the connections of both.

The nodes have the following properties:

name: the label of the cortical area (e.g. "V1", "46", "10m").
position: the $(x, y, z)$ position of the area, in millimetres.¹

Description obtained in part from the ICON project. ↩

Tags

Biological Connectome Unweighted Spatial Metadata

Citation

M. Kaiser, C. C. Hilgetag, "Nonoptimal component placement, but short processing paths, due to long-distance projections in neural systems." PLoS Computational Biology 2(7), e95 (2006) 10.1371/journal.pcbi.0020095 Sci-Hub
R. Kötter, "Online retrieval, processing, and visualization of primate connectivity data from the CoCoMac database." Neuroinformatics 2, 127-144 (2004) 10.1385/NI:2:2:127 Sci-Hub

Upstream URL OK

https://web.archive.org/web/20160203050435/http://www.dynamic-connectome.org/?page_id=25

Networks

Tip: hover over a column header for a description.

Column legend

$\left<k\right>$: Mean degree
$\sigma_k$: Standard deviation of the degrees
$\lambda_h$: Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$: Random walk mixing time
$r$: Degree assortativity
$c$: Global clustering coefficient
$\oslash$: (Pseudo-) diameter
$S$: Relative size of the largest component
NPs: Node properties
EPs: Edge properties

Name	Nodes	Edges	$\left<k\right>$	$\sigma_k$	$\lambda_h$	$\tau$	$r$	$c$	$\oslash$	$S$	Kind	Mode	NPs	EPs	gt	GraphML	GML	csv
macaque_cortex_kaiser	94	2,390	25.43	25.57	38.27	6.64	-0.10	0.75	4	1.00	Directed	Unipartite	name position		5 KiB	11 KiB	11 KiB	10 KiB

Ridiculograms^*

^* These are automatically generated force-directed visualizations, and can be quite meaningless for networks both big and small. They should not be taken seriously as sources of scientific insight. See here for a discussion.