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Improving Functional Connectome Fingerprinting with Degree-Normalization.

Benjamin Chiêm1,2, Kausar Abbas3,4, Enrico Amico5,6

  • 1Institute of Communication Technologies, Electronics and Applied Mathematics, Université Catholique de Louvain, Louvain-la-Neuve, Belgium.

Brain Connectivity
|May 21, 2021
PubMed
Summary
This summary is machine-generated.

Degree-normalization enhances brain functional connectome (FC) fingerprints for individual identification. This method improves accuracy by reducing the influence of highly connected brain regions, aiding personalized medicine applications.

Keywords:
MRIdegree-normalizationfingerprintfunctional connectivitymatching rate

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Area of Science:

  • Neuroscience
  • Network Science
  • Medical Imaging

Background:

  • Functional connectivity (FC) measures statistical dependencies between brain regions using neuroimaging data like fMRI.
  • The functional connectome (FC) network can serve as an individual fingerprint for participant identification.
  • Extracting robust FC fingerprints is crucial for applications in personalized medicine.

Purpose of the Study:

  • To investigate the impact of degree-normalization on the extraction of functional connectome (FC) fingerprints.
  • To assess if degree-normalization improves the accuracy of identifying individuals based on their brain connectivity patterns.

Main Methods:

  • Applied degree-normalization to functional connectome (FC) data from 409 individuals in the Human Connectome Project.
  • Utilized the differential identifiability framework to evaluate both original and degree-normalized FCs.
  • Analyzed data from resting-state fMRI and 7 task-based fMRI conditions.

Main Results:

  • Degree-normalization systematically improved three key fingerprinting metrics: differential identifiability, identification rate, and matching rate.
  • Results suggest that individual brain fingerprints are embedded within a low-dimensional space.
  • The effectiveness of degree-normalization indicates that weakly connected subnetworks contribute to unique individual fingerprints.

Conclusions:

  • Degree-normalization is a valuable mathematical operation for enhancing the extraction of functional connectome (FC) fingerprints.
  • This technique can improve the accuracy and reliability of individual identification using neuroimaging data.
  • The findings support the potential of low-dimensional functional fingerprints for advancing personalized medicine and brain research.