Morphometric Similarity Networks Detect Microscale Cortical Organization and Predict Inter-Individual Cognitive...

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Cognitive Neuroscience

Background:

• Macroscopic cortical networks are crucial for cognitive function.
• Constructing accurate individual structural connectomes from human neuroimaging is challenging.

Purpose of the Study:

• Introduce a novel technique for cortical network mapping using multimodal MRI.
• Investigate the topological organization and biological plausibility of morphometric similarity networks (MSNs).
• Explore the relationship between MSNs and individual differences in cognitive abilities.

Main Methods:

• Developed a new cortical network mapping technique based on inter-regional similarity of multiple morphometric parameters.
• Utilized multimodal MRI data from human and macaque cohorts.
• Analyzed network topology, gene co-expression, and tract-tracing data.
• Assessed the association between MSN node degree and individual IQ scores.

Main Results:

• MSNs exhibit complex topology with modules and hubs, recapitulating known cortical divisions.
• Inter-regional morphometric similarity correlates with gene co-expression and axonal connectivity.
• Variation in human MSN node degree explains approximately 40% of between-subject IQ variability.

Conclusions:

• Morphometric similarity mapping offers a robust and biologically plausible approach to understanding cortical networks.
• This method advances the study of individual differences in psychological functions.
• The findings highlight the link between brain structure, connectivity, and cognition.