Multimodal optical imaging of the oculofacial region using a solid tissue-simulating facial phantom.

Significance: Spatial frequency domain imaging (SFDI) applies patterned near-infrared illumination to quantify the optical properties of subsurface tissue. The periocular region is unique due to its complex ocular adnexal anatomy. Although SFDI has been successfully applied to relatively flat in vivo tissues, regions that have significant height variations and curvature may result in optical property inaccuracies. Aim: We characterize the geometric impact of the periocular region on SFDI imaging reliability. Approach: SFDI was employed to measure the reduced scattering coefficient ( µ s ' ) and absorption coefficient ( µ a ) of the periocular region in a cast facial tissue-simulating phantom by capturing images along regions of interest (ROIs): inferior temporal quadrant (ITQ), inferior nasal quadrant (INQ), superior temporal quadrant (STQ), central eyelid margin (CEM), rostral lateral nasal bridge (RLNB), and forehead (FH). The phantom was placed on a chin rest and imaged nine times from an "en face" or "side profile" position, and the flat back of the phantom was measured 15 times. Results: The measured µ a and µ s ' of a cast facial phantom are accurate when comparing the ITQ, INQ, STQ, and FH to its flat posterior surface. Paired t tests of ITQ, INQ, STQ, and FH µ a and µ s ' concluded that there is not enough evidence to suggest that imaging orientation impacted the measurement accuracy. Regions of extreme topographical variation, i.e., CEM and RLNB, did exhibit differences in measured optical properties. Conclusions: We are the first to evaluate the geometric implications of wide-field imaging along the periocular region using a solid tissue-simulating facial phantom. Results suggest that the ITQ, INQ, STQ, and FH of a generalized face have minimal impact on the SFDI measurement accuracy. Areas with heightened topographic variation exhibit measurement variability. Device and facial positioning do not appear to bias measurements. These findings confirm the need to carefully select ROIs when measuring optical properties along the periocular region.

Optical coherence tomography-measured blood vessel characteristics of port-wine birthmarks by depth: A cross-sectional study.

BACKGROUND: Port-Wine Birthmarks (PWB) are congenital capillary malformations requiring multiple treatments. Optical coherence tomography (OCT), a noninvasive imaging technique, characterizes vessels in cutaneous vascular lesions, including PWBs. OBJECTIVE: To assess variability in blood vessel characteristics within and between individual PWBs. METHODS: OCT was used to measure blood vessel density (%) and modal vessel diameter (micrometers) at increments of 0.05 mm from the skin surface to a depth of 0.50 mm at several adjacent spots of single PWBs in this cross-sectional study. Average ratios of vessel density and diameter in affected to control skin were obtained for each PWB by averaging data for all spots within a lesion. Statistical analysis was performed with a linear mixed effects model using SPSS software (IBM Corporation). RESULTS: There was great variability in vessel density and diameter within and between PWBs. Depths where average ratios of vessel density were consistently greater in affected to control skin were shallow, between 0.15 mm and 0.2 mm deep from the skin surface. LIMITATIONS: Small sample size and device's inability to measure diameters smaller than 20 micrometers. CONCLUSION: There is variability in vessel density and diameter within and between PWBs. Individualized treatment planning guided by OCT mapping should be studied further.

A predictor-informed multi-subject bayesian approach for dynamic functional connectivity.

Dynamic functional connectivity investigates how the interactions among brain regions vary over the course of an fMRI experiment. Such transitions between different individual connectivity states can be modulated by changes in underlying physiological mechanisms that drive functional network dynamics, e.g., changes in attention or cognitive effort. In this paper, we develop a multi-subject Bayesian framework where the estimation of dynamic functional networks is informed by time-varying exogenous physiological covariates that are simultaneously recorded in each subject during the fMRI experiment. More specifically, we consider a dynamic Gaussian graphical model approach where a non-homogeneous hidden Markov model is employed to classify the fMRI time series into latent neurological states. We assume the state-transition probabilities to vary over time and across subjects as a function of the underlying covariates, allowing for the estimation of recurrent connectivity patterns and the sharing of networks among the subjects. We further assume sparsity in the network structures via shrinkage priors, and achieve edge selection in the estimated graph structures by introducing a multi-comparison procedure for shrinkage-based inferences with Bayesian false discovery rate control. We evaluate the performances of our method vs alternative approaches on synthetic data. We apply our modeling framework on a resting-state experiment where fMRI data have been collected concurrently with pupillometry measurements, as a proxy of cognitive processing, and assess the heterogeneity of the effects of changes in pupil dilation on the subjects' propensity to change connectivity states. The heterogeneity of state occupancy across subjects provides an understanding of the relationship between increased pupil dilation and transitions toward different cognitive states.