Analysis of connectivity in the cat cerebral cortex

Area of Science:

• Neuroscience
• Computational Neuroscience

Background:

• Mammalian cerebral cortex has numerous corticocortical connections, making network organization complex.
• Previous studies on cortical network organization relied on speculative, informal analyses.
• Systematic methods are emerging for analyzing connectional data, particularly in primate models.

Purpose of the Study:

• To extend systematic analysis of corticocortical connections to the cat brain.
• To report a comprehensive database of cat corticocortical connections.
• To investigate hierarchical organization and developmental rules governing cortical connectivity.

Main Methods:

• Collated neuroanatomical literature for 1139 cat corticocortical connections across 65 areas.
• Applied optimization techniques (Scannell and Young, 1993) to analyze the connectional database.
• Utilized hierarchical rules (Felleman and Van Essen, 1991) and compared with optimization results.
• Examined simple connection rules for explaining observed connectivity patterns.

Main Results:

• Hierarchical rules defined a consistent hierarchy within the cat visual system.
• Hierarchical and optimization analyses showed statistically significant agreement in ordering auditory and visual areas.
• A "nearest-neighbor-or-next-door-but-one" rule accounted for the majority of corticocortical connections and their pattern.

Conclusions:

• Independent analyses (hierarchical and optimization) broadly concur on cat cortical hierarchies.
• Simple connection rules likely play a role in controlling the development of cortical connectivity.
• This study provides a systematic analysis of cat corticocortical connectivity, offering insights into brain network organization.