A Weighted and Directed Interareal Connectivity Matrix for Macaque Cerebral Cortex. (25th September 2012)

• Record Type: Journal Article
• Authors: Markov, N. T.; Ercsey-Ravasz, M. M.; Ribeiro Gomes, A. R.; Lamy, C.; Magrou, L.; Vezoli, J.; Misery, P.; Falchier, A.; Quilodran, R.; Gariel, M. A.; Sallet, J.; Gamanut, R.; Huissoud, C.; Clavagnier, S.; Giroud, P.; Sappey-Marinier, D.; Barone, P.; Dehay, C.; Toroczkai, Z.; Knoblauch, K.; Van Essen, D. C.; Kennedy, H.
• Is Part Of: Cerebral cortex. Volume 24:Number 1(2014:Jan.)
• Page Start: 17
• Page End: 36
• Publication Date: 2012-09-25
• Subjects: connection -- cortex -- graph -- monkey -- network; Cerebral cortex -- Periodicals; Brain -- Periodicals; 612.825
• DOI: 10.1093/cercor/bhs270
• Languages: English
• ISSNs: 1047-3211

Abstract

Abstract : Retrograde tracer injections in 29 of the 91 areas of the macaque cerebral cortex revealed 1, 615 interareal pathways, a third of which have not previously been reported. A weight index (extrinsic fraction of labeled neurons [FLNe]) was determined for each area-to-area pathway. Newly found projections were weaker on average compared with the known projections; nevertheless, the 2 sets of pathways had extensively overlapping weight distributions. Repeat injections across individuals revealed modest FLNe variability given the range of FLNe values (standard deviation <1 log unit, range 5 log units). The connectivity profile for each area conformed to a lognormal distribution, where a majority of projections are moderate or weak in strength. In the G 29 × 29 interareal subgraph, two-thirds of the connections that can exist do exist. Analysis of the smallest set of areas that collects links from all 91 nodes of the G 29 × 91 subgraph (dominating set analysis) confirms the dense (66%) structure of the cortical matrix. The G 29 × 29 subgraph suggests an unexpectedly high incidence of unidirectional links. The directed and weighted G 29 × 91 connectivity matrix for the macaque will be valuable for comparison with connectivity analyses in other species, including humans. It will also inform future modeling studies that explore the regularities of cortical networks.