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Updated: May 12, 2026

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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Multiscale modes of functional brain connectivity.

S Rezvan Farahibozorg1, Samuel J Harrison1, Janine D Bijsterbosch2

  • 1FMRIB, Oxford Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neuroscience, Oxford University, Oxford, United Kingdom.

Imaging Neuroscience (Cambridge, Mass.)
|December 15, 2025
PubMed
Summary
This summary is machine-generated.

We introduce Multiscale Probabilistic Functional Modes (mPFMs), a novel brain mapping technique. mPFMs enable better estimation of functional brain connectivity across multiple scales, improving predictions of personalized traits from fMRI data.

Keywords:
PROFUMObig dataindividual-specific modellingmultiscale modesresting-state fMRItrait prediction

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Medical Imaging

Background:

  • Brain information processing involves localized and distributed systems operating at multiple scales.
  • Current functional brain connectivity methods often miss cross-scale interactions.
  • Existing approaches use limited modes or parcellations, failing to capture multiscale dynamics.

Purpose of the Study:

  • To introduce Multiscale Probabilistic Functional Modes (mPFMs) for comprehensive functional brain connectivity analysis.
  • To enable direct estimation of functional connectivity within and across different scales of brain organization.
  • To develop enhanced functional MRI (fMRI) biomarkers for personalized traits and diseases.

Main Methods:

  • Data-driven multilevel Bayesian modeling applied to large functional MRI (fMRI) population and individual data.
  • Development of a novel mapping (mPFMs) comprising modes at various scales of granularity.
  • Validation using simulations and real UK Biobank data.

Main Results:

  • mPFMs emerged from data, capturing both distributed brain modes and their subcomponents.
  • The new mapping enables direct estimation of within- and across-scale functional connectivity.
  • mPFMs achieved higher accuracy in predicting ~900 personalized traits from UK Biobank data compared to standard techniques.

Conclusions:

  • mPFMs provide a new framework for functional connectivity modeling, integrating information across multiple brain scales.
  • This approach offers a more complete understanding of brain function and its relation to individual traits.
  • mPFMs can yield enhanced fMRI biomarkers for predicting traits and potentially identifying diseases.