Functional near-infrared spectroscopy (fNIRS) is recently utilized as a new approach to assess resting-state functional connectivity (RSFC) in the human brain. For any new technique or new methodology, it is necessary to be able to replicate similar experiments using different instruments in order to establish its liability and reproducibility. We apply two different diffuse optical tomographic (DOT) systems (i.e., DYNOT and CW5), with various probe arrangements to evaluate RSFC in the sensorimotor cortex by utilizing a previously published experimental protocol and seed-based correlation analysis. Our results exhibit similar spatial patterns and strengths in RSFC between the bilateral motor cortexes. The consistent observations are obtained from both DYNOT and CW5 systems, and are also in good agreement with the previous fNIRS study. Overall, we demonstrate that the fNIRS-based RSFC is reproducible by various DOT imaging systems among different research groups, enhancing the confidence of neuroscience researchers and clinicians to utilize fNIRS for future applications.
Stroop test is commonly used as a behavior-testing tool for psychological examinations that are related to attention and
cognitive control of the human brain. Studies have shown activations in Broadmann area 10 (BA10) of prefrontal cortex
(PFC) during attention and cognitive process. The use of diffuse optical tomography (DOT) for human brain mapping is
becoming more prevalent. In this study we expect to find neural correlates between the performed cognitive tasks and
hemodynamic signals detected by a DOT system. Our initial observation showed activation of oxy-hemoglobin
concentration in BA 10, which is consistent with some results seen by positron emission tomography (PET) and
functional magnetic resonance imaging (fMRI). Our study demonstrates the possibility of combining DOT with Stroop
test to quantitatively investigate cognitive functions of the human brain at the prefrontal cortex.
One of the major challenges in diffuse optical tomography (DOT) is attributed to the severe decay of sensitivity along
depth. In conventional reconstruction method using regularized inversion, it yields significant depth distortion in the
reconstructed image as a cortical activation is always projected into the skull. Recently we developed a depth
compensation algorithm (DCA) to minimize the depth localization error in DOT, which introduces a depth-variant
weight matrix to counterbalance the severe sensitivity decay of A-matrix. The DCA algorithm has been previously
validated in both laboratory phantom experiments and an in vivo human study. In this study, we first present a
comprehensive analysis on how DCA alters the depth localization and spatial resolution in DOT. It reveals that DCA
greatly improves the transverse resolution in sub-cortical region. Second, we present a quantification approach for DCA.
By forming a spatial prior directly from the reconstructed image, this approach greatly improves the quantification
accuracy in DOT.
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