u_macaque: Connection of macaque brain cortical regions

In mixer: Random Graph Clustering

Description

The dataset of consists in 47 brain cortical regions connected by 505 inter-regional pathways in the Macaque Cortex.

Usage

data(macaque)

Format

A data frame desribing the adjacency matrix of the connection of the 47 brain cortical regions of the Macaque Cortex

Details

As brain function is based on inter-regional connections, studying the way cortical regions interact may offer new perspectives in the comprehension of information flows within the brain. It appears that particular brain regions may play different roles: some regions can be at the ”center” of a particular part of the network, meaning that a lot of information will pass through them, whereas other parts of the network may be more ”peripherica”. Consequently, identifying central zones would be important, as their lesion may compromise the integrity of the whole network. From a topological view, finding those ”hubs” as focused much attention, with a definition based on degree only. However, there exists many ways for a node to be a hub, and degree is one criteria. As there is no definition of what a hub is, there are many different hubs (provincial and central). This is why [1] developed a multi-criteria strategy to find nodes that can be called ”hubs”. From a methodological point of view, this approach seems to be limited as the resuting hubs will be criteria-dependent. The gain of Mixer is that the model can be used to find those hubs. Indeed, using the underlying missing data framework, MixNet will find nodes that connect heavily to other nodes in the network, and that share this connectivity pattern (a class of hubs for instance).

References

[1] Sporns O., Honey C., and Kotter R. Identification and classification of hubs in brain networks. PLOS one, 10:1-14, 2007.

Examples

data(macaque)
mixer(macaque,qmin=8)->xout
## Not run: plot(xout)

mixer documentation built on Feb. 21, 2018, 1:02 a.m.