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The quest for identifiability in human functional connectomes.

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Maximizing the individual "fingerprint" of the human functional connectome (FC) enhances subject identifiability. This method improves brain connectivity analysis for individualized connectomics, regardless of the task.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Brain Imaging

Background:

  • Individual functional connectome (FC) fingerprinting offers potential for single-subject inferences.
  • Current methods may not fully optimize the extraction of unique FC patterns.

Purpose of the Study:

  • To develop and validate a method for maximizing the individual fingerprint of the human functional connectome.
  • To enhance subject identifiability and robustness of FC analysis.

Main Methods:

  • Group-wise decomposition of functional connectivity data into a finite number of brain connectivity modes (eigenmodes).
  • Reconstruction of individual FCs using these eigenmodes.
  • Evaluation using Human Connectome Project data across resting-state and task conditions.

Main Results:

  • Optimal reconstruction via connectivity eigenmodes significantly maximizes subject identifiability.
  • Increased identifiability observed at both global and edgewise levels.
  • Reconstructed FC data show more robust associations with task-behavioral measurements.

Conclusions:

  • A novel reconstruction procedure based on group-wise decomposition effectively maximizes individual functional connectome fingerprinting.
  • This approach enhances subject identifiability and data robustness, advancing individualized connectomics.
  • The method is applicable across different tasks and functional data, identifying task-sensitive connections.