Brain Myelin Covariance Networks: Gradients, Cognition, and Higher-Order Landscape.

Area of Science:

• Neuroscience
• Neuroimaging
• Computational Neuroscience

Background:

• Myelin is crucial for neural signaling and can be measured using the T1w/T2w ratio.
• Myelin covariance networks (MCNs) reveal correlated myelin patterns, but vertex-level gradients and cognitive links are underexplored.

Purpose of the Study:

• To construct and characterize vertex-level MCNs.
• To identify principal myelin gradients and their higher-order topological landscape.
• To associate these myelin features with cognitive functions and multimodal cortical data.

Main Methods:

• Secondary analysis of Human Connectome Project (HCP) T1w/T2w MRI data (N=1096).
• Vertex-wise MCN construction, gradient analysis, and connectivity strength calculation.
• Cognitive meta-analysis (Neurosynth) and graphlet analysis for higher-order topology.

Main Results:

• The primary myelin gradient (Gradient 1) spans sensory-motor to association cortices, correlating with connectivity strength and dissociating affective and sensorimotor functions.
• Gradient 2 and connectivity strength correlated with fractional anisotropy (FA), indicating white matter microstructure links.
• Negative and positive myelin covariance connections exhibited distinct topologies (star-like vs. path/triangular structures).

Conclusions:

• Vertex-level MCN analysis provides novel insights into cortical myelin organization.
• Myelin patterns link gray matter myelin to white matter integrity.
• Findings serve as a reference for neuropsychological research and potential biomarker discovery.