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. 2013 Mar 26;110(13):5187-92.
doi: 10.1073/pnas.1218972110. Epub 2013 Mar 11.

The Role of Long-Range Connections on the Specificity of the Macaque Interareal Cortical Network

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Free PMC article

The Role of Long-Range Connections on the Specificity of the Macaque Interareal Cortical Network

Nikola T Markov et al. Proc Natl Acad Sci U S A. .
Free PMC article

Erratum in

  • Proc Natl Acad Sci U S A. 2013 Oct 15;110(42):1761

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.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Surface maps of cortical connectivity for an exemplar injected area (area F2, in black). (Upper) Known connections; (Lower) NFPs. (Left) Flat maps; (Center) lateral inflated maps; (Right) medial inflated maps. Connection strengths are color coded as values of log10(FLNe), varying from 0 (red) to −6 (yellow). Area injected is in black. See Fig. S1 for surface connectivity maps of the remaining 28 injected areas.
Fig. 2.
Fig. 2.
Regional analysis of the similarity of binary connectivity patterns. Positive values indicate correlation; negative values indicate anticorrelation between the binary connectivity profiles of the pairs of areas located in cortical regions (color bar). (A) Out-link similarity. (B) In-link similarity. In both cases, the diagonal encodes the average intraregion similarity of area pairs, and the rest of the matrix the average interregion similarity of area pairs. Front, frontal region; Occ, occipital region; Par, parietal region; Pref, prefrontal region; Temp, temporal region.
Fig. 3.
Fig. 3.
Weight and distance characteristics of NFPs. (A) Distribution of known projections and NFPs as a function of projection strength (FLNe) at intervals of 0.5 log10(FLNe), generated after injection of the 29 target areas. Blue line, percentages of NFPs. (B) Influence of distance on proportions of known projections and NFPs. (C) Surface maps showing spatial relationships of the common-source area projections to target areas in the occipital region (shaded in dark gray). Areas projecting to all injected areas of a region via known connections are in blue and via known + NFPs in red. (D) Box plot analysis of projection distances of known projections and NFPs. (E) Intra- and interregion frequency of NFPs, one-sided t test; error bars represent SD. (F) Contribution of NFPs to regional connectivity signature. Numbers of common-source areas to target areas within a given region; see color code and text for details. Error bars in F indicate 95% confidence intervals. (D and E) Red bars, NFPs; white bars, known. Abbreviations are the same as in Fig. 2.
Fig. 4.
Fig. 4.
Influence of distance on connectivity. (A) Number of intraregion (Left) and interregion (Right) common-source areas and effects of randomization of connections with preservation of target in-degree. Error bars, 5–95% quantiles after 2 × 104 permutation tests. (B) Density of the edge-complete graphs for intra- and interregions. (C) Histogram showing the number of connected and nonconnected areas at given distance intervals from injected target areas. Black bars, connected source areas; white bars, nonconnected areas. In red, connection density percentage (proportion of connected with respect to unconnected areas) of connectivity with distance. (D) Binary similarity index as a function of distance between target pairs. Abbreviations are the same as in Fig. 2.
Fig. 5.
Fig. 5.
Prefrontal cortex is characterized by a high in-degree distribution and long-distance interregion connectivity. (A) In-degree of prefrontal areas compared with all other regions. Dots indicate individual areas; one-sided t test with P value < 0.05. (B) Frequency of interregional connections in the prefrontal cortex, showing a preponderance of long-distance connections. Blue dots, difference between prefrontal long-distance connections and all other regions; yellow transparent lines, permuted values (for further details, see text).

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