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Decomposition of individual-specific and individual-shared components from resting-state functional connectivity

Xuetong Wang1, Qiongling Li1, Yan Zhao1

  • 1School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China.

Neuroimage
|June 11, 2021
PubMed
Summary

We developed a new method to separate unique brain connections from shared ones. This helps identify individuals and predict behavior using brain scans.

Keywords:
Fingerprint analysisIndividual differenceIndividual-specific connectivityPredict behavioral scoresRSFC

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Brain Imaging

Background:

  • Resting-state functional connectivity (RSFC) maps brain networks during rest.
  • Distinguishing individual-specific from shared connectivity is crucial for identifying individuals and predicting behavior.

Purpose of the Study:

  • To propose a novel multi-task learning based sparse convex alternating structure optimization (MTL-sCASO) method.
  • To decompose RSFC into individual-specific and individual-shared connectivity components.

Main Methods:

  • Developed and validated the MTL-sCASO method using synthetic data.
  • Applied the method to 886 individuals from the Human Connectome Project (HCP).
  • Compared individual-specific connectivity with the Pearson correlation (PC) method for identification rates.

Main Results:

  • MTL-sCASO successfully decomposed RSFC into distinct individual-specific and shared components.
  • Individual-specific connectivity demonstrated higher identification rates than PC, serving as potential "fingerprints".
  • Individual-specific connectivity showed low inter-subject similarity ( -0.005±0.023) versus high for shared connectivity (0.822±0.061).
  • Individual-specific connectivity predicted cognitive behavioral scores with 9.4% improvement over PC.
  • Anatomical analysis revealed distinct network properties for individual-specific (e.g., high centrality in control systems) and shared connectivity.

Conclusions:

  • The MTL-sCASO method effectively decomposes RSFC into individual-specific and shared components.
  • Individual-specific connectivity offers a promising biomarker for individual identification and behavioral prediction.
  • This decomposition provides a novel framework for analyzing individual traits and group patterns in functional brain networks.