Why data coherence and quality is critical for understanding interareal cortical networks

Area of Science:

• Neuroscience
• Computational Neuroscience
• Connectomics

Background:

• Investigating inter-areal cortical networks is crucial for understanding brain function.
• Existing methods like diffusion MRI and axonal tract-tracing have limitations in human and non-human primate studies, respectively.
• Previous collated data from tract-tracing studies lack coherence and completeness for mapping large-scale brain networks.

Purpose of the Study:

• To develop and analyze a consistent database of macaque cortex connectivity using standardized procedures.
• To characterize the density, specificity, and directed nature of inter-areal pathways.
• To identify unexpected properties of the cortical graph, such as high density and unidirectional connections.

Main Methods:

• Developed a consistent database using standardized experimental and parcellation procedures across macaque brains.
• Utilized retrograde tracer injections in 29 cortical areas within a 91-area parcellation of the macaque cortex.
• Applied graph theory measures, including density and minimum dominating set analysis, to characterize network properties.

Main Results:

• The consistent database reveals a significantly denser cortical graph (66% connection existence) compared to collated data (7-32%).
• Identified 37% more pathways than previously reported, including a substantial number of unidirectional connections.
• Observed high binary specificity, attributed to the decline in connection probability with distance, and identified small groups of areas with high input (high domination).

Conclusions:

• The macaque cortex exhibits a highly dense and directed network with numerous previously unreported long-distance connections.
• These newly discovered pathways are highly specific and play a critical role in integrating information across large brain regions.
• The findings provide a more accurate and complex map of the cortical connectome, paving the way for future detailed network construction.