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Mapping the white-matter functional connectome: a personal perspective.

Jiao Li1,2, Huafu Chen1,2, Wei Liao1,2

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This summary is machine-generated.

This review explores the brain's white matter (WM) functional connectome using resting-state fMRI. Findings show WM networks can distinguish diseases, predict intelligence, and reveal individual differences.

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Area of Science:

  • Neuroscience
  • Neuroimaging
  • Connectomics

Background:

  • Human brain functional organization is key to understanding cognition.
  • Previous research focused on gray matter, leaving white matter (WM) functional connectomes underexplored.
  • Resting-state functional magnetic resonance imaging (fMRI) reveals functional dynamics within WM.

Purpose of the Study:

  • To review current knowledge on WM functional connectome mapping using resting-state fMRI.
  • To present comparative findings on WM connectome mapping, physiology, cognitive links, and clinical relevance.
  • To highlight the potential of WM functional networks in understanding brain function and disease.

Main Methods:

  • Review of task-free (resting-state) fMRI neuroimaging analyses.
  • Comparative analysis of WM functional connectome mapping, physiological underpinnings, and cognitive neuroscience relationships.
  • Examination of clinical applications and topological characteristics of WM functional networks.

Main Results:

  • WM functional networks exhibit valid topological characteristics.
  • These characteristics can differentiate between individuals with brain diseases and healthy controls.
  • WM functional networks show potential in predicting general intelligence and identifying inter-subject variability.

Conclusions:

  • WM functional connectomes are crucial for a comprehensive understanding of brain organization.
  • Emerging evidence supports the clinical and cognitive relevance of WM functional networks.
  • Further research is needed to address limitations and explore future directions in WM connectomics.