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.
Highlights
Atlas-guided diffuse optical tomography has undergone rapid development in recent years
We provided a solution of voxel classification based on the automated anatomical labeling (AAL) of activations in 116 segmented structures (AAL 116).[32]
The reproducibility analysis of the method was based on the measurements from young adults, with each of the individuals having participated in the functional NIRS (fNIRS) recordings twice within a period of two weeks
Summary
Atlas-guided diffuse optical tomography (atlas-DOT) has undergone rapid development in recent years. Custo et al.[1] initially introduced the concept of atlas-DOT, after which it was further developed with high-density optodes and successfully validated using computational simulations and functional magnetic resonance imaging (fMRI) performed on human subjects.[2,3,4] Atlas-DOT utilized a finite element technique with either subject-specific models[3,4] or a standard magnetic resonance imaging (MRI) brain template (such as ICBM 256) in the forward calculation.[5,6] A significant improvement of accuracy in image reconstruction and source localization has been achieved with atlas-DOT.[7] More recently, atlas-DOT has been utilized together with the general linear model analysis, which further extended its feasibility of measuring hemodynamic changes in complex brain tasks. Several groups have reported consistent and accurate results of brain hemodynamic changes under visual stimulation,[7] speech,[8] and risk decision-making.[5]
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