Sex Classification Based on the Functional Connectivity Patterns of the Language Network: A Resting State fMRI Study

Area of Science:

• Neuroscience
• Cognitive Neuroscience
• Sex Differences Research

Background:

• Understanding sex differences in brain development and aging is crucial for neurological and psychiatric conditions.
• Few studies have explored sex-based variations in functional brain networks, especially the language network.
• Previous research often used task-based fMRI, potentially missing subtle resting-state functional differences.

Purpose of the Study:

• To investigate sex-related differences in the resting-state functional connectivity of the language network.
• To apply machine learning to classify sex based on language network functional connectivity patterns.
• To identify specific functional connectivity features that distinguish between sexes in the language network.

Main Methods:

• Utilized a machine learning classifier to analyze resting-state functional connectivity data.
• Included 270 men and 288 women (ages 22-36) as participants.
• Identified key discriminant features within the language network's functional connectivity.

Main Results:

• Achieved 91.3% accuracy in classifying participants' sex based on language network connectivity.
• Key discriminant features involved the left inferior frontal gyrus, left planum temporale, and left anterior middle temporal gyrus.
• Men showed stronger functional connectivity in these regions compared to women, despite some overlap.

Conclusions:

• Sex-related differences are present in the resting-state functional connectivity of the language network.
• These findings emphasize the importance of considering sex as a biological variable in brain and language research.
• Distinct connectivity patterns highlight potential sex-specific neural underpinnings of language processing.