title: Depth-compensated diffuse optical tomography enhanced by general linear model analysis and an anatomical atlas of human head.
abstract: One of the main challenges in functional diffuse optical tomography (DOT) is to accurately recover the depth of brain activation, which is even more essential when differentiating true brain signals from task-evoked artifacts in the scalp. Recently, we developed a depth-compensated algorithm (DCA) to minimize the depth localization error in DOT. However, the semi-infinite model that was used in DCA deviated significantly from the realistic human head anatomy. In the present work, we incorporated depth-compensated DOT (DC-DOT) with a standard anatomical atlas of human head. Computer simulations and human measurements of sensorimotor activation were conducted to examine and prove the depth specificity and quantification accuracy of brain atlas-based DC-DOT. In addition, node-wise statistical analysis based on the general linear model (GLM) was also implemented and performed in this study, showing the robustness of DC-DOT that can accurately identify brain activation at the correct depth for functional brain imaging, even when co-existing with superficial artifacts.
authors.affiliation: Department of Bioengineering, Joint Program in Biomedical Engineering between UT Arlington and UT Southwestern Medical Center at Dallas, University of Texas at Arlington, Arlington, TX, USA.
authors.forename: Fenghua
authors.initials: F
authors.lastname: Tian
authors.forename: Hanli
authors.initials: H
authors.lastname: Liu
mesh_terms: D000328:Adult; D000465:Algorithms; D016477:Artifacts; D001271:Atlases as Topic; D001921:Brain / Q000098:blood supply / Q000502:physiology; D002560:Cerebrovascular Circulation / Q000502:physiology; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005385:Fingers / Q000502:physiology; D006257:Head / Q000033:anatomy & histology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D016014:Linear Models; D008297:Male; D008959:Models, Neurological; D009048:Motor Skills; D017711:Nonlinear Dynamics; D011597:Psychomotor Performance / Q000502:physiology; D015656:Respiratory Mechanics / Q000502:physiology; D041622:Tomography, Optical / Q000379:methods* / Q000706:statistics & numerical data; D055815:Young Adult
keywords: 3D; Anatomical atlas of human head; BOLD; Con-DOT; DC; DC-DOT; DCA; DOT; Depth compensation; Diffuse optical tomography; FEM; GLM; General linear model; HRF; Hb; HbO(2); MNI; MRI; Montreal Neurological Institute coordinates; NIRFAST; NIRS; NIRS-SPM; Near infrared spectroscopy; OD; SCF; SNR; SPM; SVR; SVR-DOT; Superficial artifacts; a FEM-based MATLAB package for modeling propagation of near infrared light in biological tissues; a SPM-based software package for functional NIRS data analysis; absorption coefficient; apply spatially variant regularization to regular DOT reconstruction; blood oxygen level dependent; conventional DOT without any spatially variant regularization or depth compensation; deoxy-hemoglobin concentration; depth compensation; depth-compensated DOT; depth-compensated algorithm; diffuse optical tomography; fMRI; finite element mesh; functional magnetic resonance imaging; general linear model; hemodynamic response function; magnetic resonance imaging; n; near infrared spectroscopy; optical density; oxy-hemoglobin concentration; reduced scattering coefficient; refractive index; sensitivity correction factor; signal-to-noise ratio; spatially variant regularization; statistical parametric mapping; three-dimensional; μ(a); μ(s)′