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Interareal pathways in the primate cortex

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Surprisingly little is known about the statistics of cortical networks due to an absence of investigation at the single-cell level of their weighted and spatial properties. Using brain-wide retrograde tracing experiments in macaque we have generated a weighted and directed inter-areal matrix (1). This constitutes a consistent data base that reveals 36% more connections than in collated data (2). Local connectivity accounts for 80% of labeled neurons, meaning that cortex is heavily involved in local function (3). The inter-areal cortical graph has a density of 66%, meaning that over two thirds of the connections that can exist, do exist. At such densities the network is neither a sparse small world nor scale-free as claimed by studies using collated data. At high graph densities binary connectivity is expected to show little specificity. However, we have shown that probability of connectivity falls of with distance conferring a high-specificity for long-distance connections (4) Importantly connection weights are highly characteristic across animals, follow a heavy-tailed lognormal distribution over 6 orders of magnitude, and decay exponentially with distance. This has enabled us to extract an exponential distance rule (EDR). By building random networks employing the EDR we show that there is a trade-off between local and global efficiency. An important finding is that the EDR predicts the binary features, the communication efficiency and the structural heterogeneity of the graph exhibiting a highly efficient dense core as found in other information processing self-organizing networks eg WWW ). Further, the EDR explains the economy of connectivity, which was previously understood in terms of Small World architectures. Integration of the inter-areal network into the laminar structure of the local connectivity reveals hierarchical areal relations, which are believed to be important for understanding computational processes in the cortex (5). Presently we are investigating how to combine the large-scale inter-areal network with the cortical hierarchy.

Markov NT et al, 2013, Cereb Cortex DOI : 10.1093/cercor/bhs1270 Kennedy H, Knoblauch K, Toroczkai Z2013 , NeuroImage. DOI : 10.1016/j.neuroimage.2013.1004.1031. Markov NT et al, 2011, Cereb Cortex 21:1254-1272. Markov NT, Ercsey-Ravasz MM, Lamy C, Ribeiro Gomes AR, Magrou L, Misery P, Giroud P, Barone P, Dehay C, Toroczkai Z, Knoblauch K, Van Essen DC, Kennedy H, 2013, Proc Nat Acad Sci USA 110 :5187-5192. Markov and Kennedy, 2013, Curr Opin Neurobiol 23:187-194.

This talk is part of the Brain Mapping Unit Networks Meeting and the Cambridge Connectome Consortium series.

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