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Modelling variability in functional brain networks using embeddings.

Rukuang Huang1, Chetan Gohil1, Mark Woolrich1

  • 1Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, United Kingdom.

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Summary
This summary is machine-generated.

This study introduces HIVE, a novel method using embedding vectors to model individual functional neuroimaging sessions. HIVE effectively identifies subpopulation structures and enhances the estimation of dynamic functional networks.

Keywords:
Bayesian modellingfunctional connectivitygenerative modellingpopulation modellingunsupervised learning

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Data Science

Background:

  • Functional neuroimaging estimates cognitive networks but often at a group level.
  • Existing methods overlook subpopulation structures within recording sessions.
  • Individual session variability is crucial for understanding cognition.

Purpose of the Study:

  • To develop a method for modeling individual functional neuroimaging sessions.
  • To explicitly account for subpopulation structure in functional network estimation.
  • To create session "fingerprints" for clustering similar functional networks.

Main Methods:

  • Proposed HIVE (HMM with Integrated Variability Estimation).
  • Utilized embedding vectors, inspired by Natural Language Processing.
  • Applied a hierarchical Hidden Markov Model (HMM) to estimate dynamic functional networks.

Main Results:

  • HIVE successfully uncovered true subpopulation structures in simulated data.
  • Demonstrated improved performance compared to existing functional network estimation approaches.
  • Identified meaningful sources of variation across a population using real magnetoencephalography data.

Conclusions:

  • HIVE offers a powerful new approach for modeling individual sessions in functional neuroimaging.
  • The method effectively leverages group-level information while preserving individual session characteristics.
  • Session fingerprints derived from HIVE reflect significant population variability.