Dynamic Causal Modelling for Functional Near-Infrared Spectroscopy Using Spatial Priors Derived from Diffuse Optical Tomography

Abstract

Functional near-infrared spectroscopy (fNIRS) is an optical neuroimaging technique that measures brain activity by detecting changes in oxygenated and deoxygenated hemoglobin concentrations. Although methods exist for estimating causal interactions among brain regions using fNIRS data, current approaches typically rely on node locations defined at the sensor measurement level near the cortical surface. To address this limitation, this study extends dynamic causal modeling (DCM) for fNIRS by incorporating source-level locations derived from diffuse optical tomography (DOT). The proposed method was applied to an experimental dataset comprising 104 participants recorded during a Go/No-Go response inhibition task. Bayesian model selection confirmed that DCM models using DOT-informed neuronal source locations outperformed models using sensor-level locations. Furthermore, posterior means of effective connectivity parameters validated the inhibitory influence of the right inferior frontal gyrus on regions within the motor network during response inhibition. By providing precise neuronal source localization informed by statistical parametric mapping of DOT-reconstructed data, the proposed DCM approach enables more accurate inference of directed connectivity at the neuronal level from optical density measurements. Given the compact and portable nature of fNIRS systems, this method can readily be applied in realistic, naturalistic environments to investigate network-level modulation of brain responses.