The role of long-range connections on the specificity of the macaque interareal cortical network
Cerebral Cortex
Brain Mapping
anatomy
Databases, Factual
03 medical and health sciences
0302 clinical medicine
neocortex
Animals
Macaca
[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]
[SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]
monkey
Nerve Net
DOI:
10.1073/pnas.1218972110
Publication Date:
2013-03-12T01:09:22Z
AUTHORS (13)
ABSTRACT
We investigated the influence of interareal distance on connectivity patterns in a database obtained from the injection of retrograde tracers in 29 areas distributed over six regions (occipital, temporal, parietal, frontal, prefrontal, and limbic). One-third of the 1,615 pathways projecting to the 29 target areas were reported only recently and deemed new-found projections (NFPs). NFPs are predominantly long-range, low-weight connections. A minimum dominating set analysis (a graph theoretic measure) shows that NFPs play a major role in globalizing input to small groups of areas. Randomization tests show that (
i
) NFPs make important contributions to the specificity of the connectivity profile of individual cortical areas, and (
ii
) NFPs share key properties with known connections at the same distance. We developed a similarity index, which shows that intraregion similarity is high, whereas the interregion similarity declines with distance. For area pairs, there is a steep decline with distance in the similarity and probability of being connected. Nevertheless, the present findings reveal an unexpected binary specificity despite the high density (66%) of the cortical graph. This specificity is made possible because connections are largely concentrated over short distances. These findings emphasize the importance of long-distance connections in the connectivity profile of an area. We demonstrate that long-distance connections are particularly prevalent for prefrontal areas, where they may play a prominent role in large-scale communication and information integration.
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