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Related Experiment Video

Updated: Jul 9, 2026

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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Published on: July 1, 2014

Light on broken networks: Resting-state fNIRS as a tool for connectivity mapping.

Foivos Kotsogiannis1, Michael Lührs2, Geert-Jan M Rutten3

  • 1Department of Cognitive Neuropsychology, Tilburg University, the Netherlands; Department of Cognitive Neuroscience, Maastricht University, the Netherlands.

Neuroimage
|July 7, 2026
PubMed
Summary

Functional near-infrared spectroscopy (fNIRS) shows promise for mapping brain networks, aligning well with functional MRI (fMRI) findings. This supports fNIRS

Keywords:
Clinical neuroimagingResting-state functional connectivityResting-state networksfMRIfNIRS

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Resting-State Connectivity and Neuroimaging of Prefrontal Cortex Activity During a Block-Design Yoga Asana Practice Using fNIRS

Published on: June 24, 2025

Area of Science:

  • Neuroscience
  • Brain Imaging
  • Systems Neuroscience

Background:

  • Resting-state functional connectivity (RSFC) and networks (RSNs) are crucial for understanding brain organization and neurological disease.
  • Functional MRI (fMRI) is standard for RSN assessment but limited by cost, motion sensitivity, and feasibility for repeated measures.
  • Functional near-infrared spectroscopy (fNIRS) offers a portable alternative, but its reliability for RSFC and RSN mapping requires further validation.

Purpose of the Study:

  • To compare the reliability and cross-modal correspondence of RSNs derived from fNIRS and fMRI.
  • To assess the utility of fNIRS for capturing large-scale brain organization comparable to fMRI.
  • To evaluate the impact of correlation methods (bivariate vs. partial) on cross-modal agreement at different analytical levels.

Main Methods:

  • Acquired near whole-head fNIRS data and fMRI-BOLD signals from corresponding cortical regions in two independent cohorts (n=31 each).
  • Compared RSN organization using bivariate and partial correlations across edge, nodal, and network levels.
  • Assessed cross-modal convergence and divergence using graph theory metrics and module overlap analysis (Jaccard index).

Main Results:

  • Substantial modality differences in edge-wise connectivity (50-61%) reduced significantly with partial correlations (<3%).
  • Moderate cross-modal similarity in group-averaged connectivity patterns (r ≈ 0.37) and broadly similar graph-metric distributions.
  • fNIRS-derived modules significantly overlapped with fMRI modules, identifying major RSNs (Jaccard ≈ 0.27-0.5).

Conclusions:

  • fNIRS effectively captures key features of large-scale RSFC and RSN organization comparable to fMRI.
  • Cross-modal correspondence supports the translational utility of fNIRS for brain network research.
  • Partial correlations improve edge-level agreement but may attenuate nodal/modular recovery, favoring targeted analyses over whole-network characterization.