Surprisingly little is known about the statistics of cortical networks due to an absence of investigation of their weighted and spatial properties. Using brain-wide retrograde tracing experiments in macaque, we are generating a consistent database of between area connections with projection densities, and distances. The network is neither a sparse small-world graph nor scale-free (Markov et al., 2013). Local connectivity accounts for 80% of labeled neurons, meaning that cortex is heavily involved in local function (Markov et al., 2011). Importantly link weights, are highly characteristic across animals, follow a heavy-tailed lognormal distribution over 6 orders of magnitude, and decay exponentially with distance (Markov et al., 2014a).
The statistical properties of the cortex will give insight into the nature of the processing mode of the cortex (Markov and Kennedy, 2013). We are making a weighted network analysis, which reveals a tradeoff between local and global efficiencies. An important finding is that a distance rule predicts the binary features, the global and local communication efficiencies as well as the clustered topography of the graph (Ercsey-Ravasz et al., 2013). These findings underline the importance of weight-based hierarchical layering in cortical architecture and hierarchical processing, and point to the need to consider the embedded properties of the cortcx (Markov and Kennedy, 2013, Markov et al., 2014b).
References:
(1) Ercsey-Ravasz M, Markov NT, Lamy C, Van Essen DC, Knoblauch K, Toroczkai Z, Kennedy H (2013) A predictive network model of cerebral cortical connectivity based on a distance rule. Neuron 80:184-197.
(2) Markov NT, Ercsey-Ravasz M, Van Essen DC, Knoblauch K, Toroczkai Z, Kennedy H (2013) Cortical high-density counter-stream architectures. Science 342:1238406.
(3) Markov NT, Ercsey-Ravasz MM, Ribeiro Gomes AR, Lamy C, Magrou L, Vezoli J, Misery P, Falchier A, Quilodran R, Gariel MA, Sallet J, Gamanut R, Huissoud C, Clavagnier S, Giroud P, Sappey-Marinier D, Barone P, Dehay C, Toroczkai Z, Knoblauch K, Van Essen DC, Kennedy H (2014a) A weighted and directed interareal connectivity matrix for macaque cerebral cortex. Cerebral Cortex 24:17-36.
(4) Markov NT, Kennedy H (2013) The importance of being hierarchical. Curr Opin Neurobiol 23:187-194.
(5) Markov NT, Misery P, Falchier A, Lamy C, Vezoli J, Quilodran R, Gariel MA, Giroud P, Ercsey-Ravasz M, Pilaz LJ, Huissoud C, Barone P, Dehay C, Toroczkai Z, Van Essen DC, Kennedy H, Knoblauch K (2011) Weight Consistency Specifies Regularities of Macaque Cortical Networks. Cerebral Cortex 21:1254-1272.
(6) Markov NT, Vezoli J, Chameau P, Falchier A, Quilodran R, Huissoud C, Lamy C, Misery P, Giroud P, Barone P, Dehay C, Ullman S, Knoblauch K, Kennedy H (2014b) Anatomy of Hierarchy: Feedforward and feedback pathways in macaque visual cortex. Journal of Comparative Neurology 522:225-259.
BIOGRAPHY:
Professor Henry Kennedy is the director of research at Stem-Cell and Brain Research Institute (SBRI). SBRI investigates the development, function and repair of neuronal circuits involved in cognition, motor control and biological rhythms and researches the structural foundations of computation in the cortex. SBRI seeks to develop embryonic stem cell based therapies so as to reverse the effects of brain lesions leading to the motor and cognitive deficits found in neurological disease including Parkinson's.
More information about his research, please visit: http://www.sbri.fr/members/henry-kennedy.html
For more information：http://www.brainnetome.org/en/news/437-henry-20140603
