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Related Experiment Video

Updated: Mar 8, 2026

Author Spotlight: Advancing Upper Limb Rehabilitation in Patients with Right Hemisphere Damage Using Assisted Active Exercise

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Dynamic causal modelling for functional near-infrared spectroscopy using spatial priors derived from diffuse optical

Truc Chu¹, Kiyomitstu Niioka², Ippeita Dan²

¹Center for Bio-Imaging and Translational Research, Korea Basic Science Institute, Cheongju 28119, Republic of Korea; Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon 34134, Republic of Korea.

|March 6, 2026

Summary

This summary is machine-generated.

This study enhances brain connectivity analysis using functional near-infrared spectroscopy (fNIRS) by integrating diffuse optical tomography (DOT) data. This improves the accuracy of dynamic causal modeling (DCM) for understanding brain networks during cognitive tasks.

Keywords:

Diffuse optical tomography Dynamic causal modelling Effective connectivity Functional near-infrared spectroscopy Source localization

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Area of Science:

Neuroscience
Biomedical Engineering
Cognitive Science

Background:

Functional near-infrared spectroscopy (fNIRS) measures brain activity via hemodynamic changes.
Existing methods for fNIRS causal interaction analysis often use sensor-level locations, limiting accuracy.
Dynamic causal modeling (DCM) is a powerful tool for inferring directed brain connectivity.

Purpose of the Study:

To extend DCM for fNIRS by incorporating source-level brain activity localization from diffuse optical tomography (DOT).
To improve the accuracy of causal connectivity inference from fNIRS data.
To investigate neural network modulation during response inhibition tasks.

Main Methods:

Developed a novel DCM approach for fNIRS integrating DOT-derived source-level locations.
Applied the method to fNIRS data from 104 participants during a Go/No-Go task.
Utilized Bayesian model selection to compare sensor-level vs. DOT-informed source-level models.

Main Results:

DCM models using DOT-informed source locations showed superior evidence compared to sensor-level models.
Effective connectivity analysis revealed inhibitory influence from the right inferior frontal gyrus (rIFG) on motor network regions.
The findings highlight the rIFG's role in response inhibition.

Conclusions:

The proposed DCM approach enhances causal connectivity inference from fNIRS data by providing depth-dependent source localization.
This method offers more accurate network-level brain activity analysis, applicable in naturalistic settings.
Improved localization accuracy facilitates a deeper understanding of neural mechanisms underlying cognitive functions.