This study investigates the brain functional connectivity in the rest and sleep states. We collected EEG, EOG, and fNIRS signals simultaneously during rest and sleep phases. The rest phase was defined as a quiet wake-eyes open (w_o) state, while the sleep phase was separated into three states; quiet wake-eyes closed (w_c), non-rapid eye movement sleep stage 1 (N1), and non-rapid eye movement sleep stage 2 (N2) using the EEG and EOG signals. The fNIRS signals were used to calculate the cerebral hemodynamic responses (oxy-, deoxy-, and total hemoglobin). We grouped 133 fNIRS channels into five brain regions (frontal, motor, temporal, somatosensory, and visual areas). These five regions were then used to form fifteen brain networks. A network connectivity was computed by calculating the Pearson correlation coefficients of the hemodynamic responses between fNIRS channels belonging to the network. The fifteen networks were compared across the states using the connection ratio and connection strength calculated from the normalized correlation coefficients. Across all fifteen networks and three hemoglobin types, the connection ratio was high in the w_c and N1 states and low in the w_o and N2 states. In addition, the connection strength was similar between the w_c and N1 states and lower in the w_o and N2 states. Based on our experimental results, we believe that fNIRS has a high potential to be a main tool to study the brain connectivity in the rest and sleep states.

Abstract. Atlas-guided diffuse optical tomography (atlas-DOT) is a computational means to image changes in cortical hemodynamic signals during human brain activities. Graph theory analysis (GTA) is a network analysis tool commonly used in functional neuroimaging to study brain networks. Atlas-DOT has not been analyzed with GTA to derive large-scale brain connectivity/networks based on near-infrared spectroscopy (NIRS) measurements. We introduced an automated voxel classification (AVC) method that facilitated the use of GTA with atlas-DOT images by grouping unequal-sized finite element voxels into anatomically meaningful regions of interest within the human brain. The overall approach included volume segmentation, AVC, and cross-correlation. To demonstrate the usefulness of AVC, we applied reproducibility analysis to resting-state functional connectivity measurements conducted from 15 young adults in a two-week period. We also quantified and compared changes in several brain network metrics between young and older adults, which were in agreement with those reported by a previous positron emission tomography study. Overall, this study demonstrated that AVC is a useful means for facilitating integration or combination of atlas-DOT with GTA and thus for quantifying NIRS-based, voxel-wise resting-state functional brain networks.

A good understanding of age-dependent changes and modifications in brain networks is crucial for fully exploring the effects of aging on the human brain. Few reports have been found in studies of functional brain networks using functional near-infrared spectroscopy (fNIRS). Moreover, little is known about the feasibility of using fNIRS to assess age-related changes in brain connectomes. This study applied whole brain fNIRS measurement, combined with graph theory analysis, to assess the age-dependent changes in resting-state brain networks. Five to eight minutes of resting-state brain hemodynamic signals were recorded from 48 participants (18 young adults and 30 older adults) with 133 optical channels covering the majority of the cortical regions. Both local and global graph metrics were computed to identify the age-related changes of topographical brain networks. Older adults showed an overall decline of both global and local efficiency compared to young adults, as well as the decline of small-worldness. In addition, young adults showed the abundance of hubs in the prefrontal cortex, whereas older adults revealed the hub shifts to the sensorimotor cortex. These obvious shifts of hubs may potentially indicate decreases of the decision-making, memory, and other high-order functions as people age. Our results showed consistent findings with published literature and also demonstrated the feasibility of whole-head fNIRS measurements to assess age-dependent changes in resting-state brain networks.

We conducted wavelet coherence analysis to quantify/image the cerebral neurovascular coupling at infra-slow frequencies during four different awake-to-sleep vigilance states using whole-head, simultaneous EEG and functional Near Infrared Spectroscopy measurements from 15 healthy human brains.