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Related Experiment Video

Updated: Jan 13, 2026

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
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Sex Classification Based on the Functional Connectivity Patterns of the Language Network: A Resting State fMRI Study.

X Lajoie1,2, C DeRoy2,3, C Bedetti1,2

  • 1Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Québec, Canada.

Human Brain Mapping
|January 10, 2026
PubMed
Summary
This summary is machine-generated.

Sex differences exist in the brain's language network functional connectivity. Machine learning accurately identified sex based on resting-state patterns, revealing distinct connectivity in men and women.

Keywords:
language networkmachine learningresting‐state functional connectivitysex classification

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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.