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Deep learning-based embedding of functional connectivity profiles for precision functional mapping.

Jiaxin Cindy Tu1, Jung-Hoon Kim2, Chenyan Lu1

  • 1Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, United States.

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|September 8, 2025
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Summary
This summary is machine-generated.

This study introduces a novel variational autoencoder method for comparing functional connectivity profiles. This approach enables simultaneous analysis of multiple samples, advancing precision functional mapping and brain network research.

Keywords:
functional connectivityindividual differenceslatent spaceresting-state fMRI

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Data Science

Background:

  • Functional connectivity profiles are used to study brain networks and individual differences.
  • Current methods for comparing these profiles are limited to pairwise comparisons.
  • Evaluating developmental changes in functional networks often relies on group-average comparisons.

Purpose of the Study:

  • To develop a method for simultaneous comparison of functional connectivity profiles across multiple samples.
  • To embed functional connectivity data into a lower-dimensional latent space for efficient analysis.
  • To facilitate exploratory analyses and visualization in precision functional mapping.

Main Methods:

  • Utilized a pre-trained variational autoencoder (VAE) to embed functional connectivity profiles.
  • Projected data from vertex space to a low-dimensional latent space (as few as two dimensions).
  • Analyzed the structure and variability of functional connectivity within the latent space.

Main Results:

  • The VAE successfully retained global and local structures of functional connectivity data.
  • Distinct functional networks occupied separate regions within the latent space.
  • Variability within functional connectivity profiles from the same anatomical location was captured.

Conclusions:

  • The VAE offers a powerful tool for simultaneous comparison of functional connectivity data.
  • This method enhances visualization and exploratory analysis in precision functional mapping.
  • The approach supports nuanced understanding of brain network organization and individual variability.