Resting-State Functional Connectivity Estimated With Hierarchical Bayesian Diffuse Optical Tomography

Abstract

Resting-state functional connectivity (RSFC) has been generally assessed with functional magnetic resonance imaging (fMRI) thanks to its high spatial resolution. However, fMRI has several disadvantages such as high cost and low portability. In addition, fMRI may not be appropriate for people with metal or electronic implants in their bodies, with claustrophobia and who are pregnant. Diffuse optical tomography (DOT), a method of neuroimaging using functional near-infrared spectroscopy (fNIRS) to reconstruct three-dimensional brain activity images, offers a non-invasive alternative, because fNIRS as well as fMRI measures changes in deoxygenated hemoglobin concentrations and, in addition, fNIRS is free of above disadvantages. We recently proposed a hierarchical Bayesian (HB) DOT algorithm and verified its performance in terms of task-related brain responses. In this study, we attempted to evaluate the HB DOT in terms of estimating RSFC. In 20 healthy males (21–38 years old), 10 min of resting-state data was acquired with 3T MRI scanner or high-density NIRS on different days. The NIRS channels consisted of 96 long (29-mm) source-detector (SD) channels and 56 short (13-mm) SD channels, which covered bilateral frontal and parietal areas. There were one and two resting-state runs in the fMRI and fNIRS experiments, respectively. The reconstruction performances of our algorithm and the two currently prevailing algorithms for DOT were evaluated using fMRI signals as a reference. Compared with the currently prevailing algorithms, our HB algorithm showed better performances in both the similarity to fMRI data and inter-run reproducibility, in terms of estimating the RSFC.