A Predictive Network Model of Cerebral Cortical Connectivity Based on a Distance Rule

By Mária Ercsey-Ravasz, Nikola T. Markov, C. Lamy, D. V. Van Essen, Kenneth Knoblauch, Zoltan Toroczkai, Henry Kennedy. Neuron, 2013

417 citations recorded by the source corpus.

SUMMARY Recent advances in neuroscience have engendered interest in large-scale brain networks. Using a consistent database of corticocortical connectivity, generated from hemisphere-wide, retrograde tracing experiments in the macaque, we analyzed interareal weights and distances to reveal an important organizational principle of brain connectivity. Using appropriate graph theoretical measures, we show that although very dense (66%), the interareal network has strong structural specificity. Connection weights exhibit a heavy-tailed lognormal distribution spanning five orders of magnitude and conform to a distance rule reflecting exponential decay with interareal separation. A single-parameter random graph model based on this rule predicts numerous features of the cortical network: (1) the existence of a network core and the distribution of cliques, (2) global and local binary properties, (3) global and local weight-based communication efficiencies modeled as network conductance, and (4) overall wire-length minimization. These findings underscore the importance of distance and weight-based heterogeneity in cortical architecture and processing.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/47c313fd624257911f8c35f78b32376a28ef0183

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In Cognitive Neuroscience

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Citations in the corpus

Anatomy of hierarchy: Feedforward and feedback pathways in macaque visual cortex (2013, positive): “The exponential decline of connection weight with distance (Ercsey-Ravasz et al., 2013) leads to prediction of a relationship between SLN and FLN, which was indeed the case, as shown in Figure 6A.”
Cortical High-Density Counterstream Architectures (2013, neutral): “To what extent is the cortical network determined by this principle? To address this question, we defined two random network models [see (13) for details], one based on the EDR and the other on a constant distance rule (CDR, where projection length bears uniform cost).”
Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework (2016, neutral): “Moreover, connections from excitatory and inhibitory neurons exhibit different levels of sparseness and specificity, with non-random features in the distribution of connection patterns among neurons both within local circuits [23 – 27] and among cortical areas [28 – 30].”