Gender differences in cortical morphological networks

Area of Science:

• Neuroscience
• Neuroimaging
• Brain Morphology

Background:

• Cortical morphological networks (CMN) analyze inter-regional brain morphology relationships.
• Previous research has not explored gender differences in CMNs.
• Univariate methods may miss complex, multivariate gender markers in brain structure.

Purpose of the Study:

• To investigate gender differences in human brain cortical morphological networks.
• To identify discriminative CMN connections between males and females using machine learning.
• To explore potential links between CMN differences and behavioral variations, such as learning styles.

Main Methods:

• Utilized machine learning to analyze cortical morphological networks (CMN).
• Examined relationships between different cortical brain regions using measurements like cortical thickness and sulcal depth.
• Focused on identifying multivariate interacting effects within CMNs to detect gender-specific patterns.

Main Results:

• The most discriminative CMN connections between males and females were found in the left hemisphere, specifically using mean sulcal depth.
• Across both hemispheres, the primary discriminative morphological connection involved the entorhinal cortex paired with either the caudal anterior cingulate cortex (left hemisphere) or the transverse temporal cortex (right hemisphere).

Conclusions:

• Cortical morphological networks reveal significant gender-based differences in brain structure.
• Specific CMN connections, notably those involving the entorhinal cortex, are key indicators of these differences.
• These findings provide an omics perspective on behavioral gender differences and may explain variations in learning between sexes.