Weight consistency specifies regularities of macaque cortical networks

Area of Science:

• Neuroscience
• Systems Neuroscience
• Computational Neuroscience

Background:

• Understanding the consistency of cortical pathway weights across individuals is crucial for mapping brain connectivity.
• Previous studies have not fully resolved the extent of weight differences and their consistency in cortical networks.

Purpose of the Study:

• To quantitatively analyze the weight consistency of afferents to cortical areas in the cynomolgus macaque brain.
• To determine if connectivity profiles are robust across individual animals.

Main Methods:

• Quantitative retrograde tracer analysis in 8 cortical areas of the cynomolgus macaque.
• Calculation of a weight index (fraction of labeled neurons, FLN) to assess connection strength.
• Analysis of distribution patterns (lognormal) of corticocortical connection weights.

Main Results:

• Consistent pattern observed: small subcortical input (1.3% FLN), high local connectivity (80% FLN), significant neighboring input (15% FLN), and weak long-range connections (3% FLN).
• Corticocortical projections to V1, V2, and V4 showed heavy-tailed, lognormal distributions spanning 5 orders of magnitude, indicating consistent connectivity profiles.
• Connection weight heterogeneity significantly influences cortical network specificity.

Conclusions:

• High investment in local projections underscores the critical role of local processing in cortical function.
• Information transmission across hierarchical levels primarily relies on pathways with low fraction of labeled neurons (FLN) values.
• The findings reveal robust and specific connectivity profiles in the macaque cortex, with significant implications for understanding brain network organization.