Investigation of the sensitivity of functional near-infrared spectroscopy brain imaging to anatomical variations in 5- to 11-year-old children

Abstract

"Investigation of the sensitivity of functional near-infrared spectroscopy brain imaging to anatomical variations in 5-to 11-year-old children," Neurophoton. 5(1), 011009 (2017), doi: 10.1117/1.NPh.5.1.011009. Abstract. Functional near-infrared spectroscopy (fNIRS) is a noninvasive brain imaging technique that uses scalp-placed light sensors to measure evoked changes in cerebral blood oxygenation. The portability, low overhead cost, and ability to use this technology under a wide range of experimental environments make fNIRS wellsuited for studies involving infants and children. However, since fNIRS does not directly provide anatomical or structural information, these measurements may be sensitive to individual or group level differences associated with variations in head size, depth of the brain from the scalp, or other anatomical factors affecting the penetration of light into the head. This information is generally not available in pediatric populations, which are often the target of study for fNIRS. Anatomical magnetic resonance imaging information from 90 school-age children (5 to 11 years old) was used to quantify the expected effect on fNIRS measures of variations in cerebral and extracerebral structure. Monte Carlo simulations of light transport in tissue were used to estimate differential and partial optical pathlengths at 690, 780, 808, 830, and 850 nm and their variations with age, sex, and head size. This work provides look-up tables of these values and general guidance for future investigations using fNIRS sans anatomical information in this child population.

Investigating Developmental Changes in Scalp-Cortex Correspondence Using Diffuse Optical Tomography Sensitivity in Infancy

“…The pattern was not observed in adult head models (Okamoto et al, 2004). Studies adopting the scalp projection method indicate that the scalp-to-cortex distance is smaller in infants (Emberson, Crosswhite, Richards, & Aslin, 2017;Kabdebon et al, 2014) and children (Whiteman, Santosa, Chen, Perlman, & Huppert, 2017) than in adults (Okamoto et al, 2004). Existing infant studies sampled a single age, or a narrow age range.…”

“…Age differences in DOT sensitivity may be attributed to anatomical variations across ages. Increased scalp-to-brain distance was related to reduced path length through the brain regions in children (Whiteman et al, 2017). Moreover, greater scalp and skull thickness was associated with reduced sensitivity to the gray matter across source-detector separations from 20 mm to 50 mm in adults (Strangman et al, 2014).…”

“…For example, Haeussinger et al (2011) showed that the average distance from scalp channel locations to the cortical surface that could be sampled by the channel sensitivity distribution was 14.5±1.4 mm in adults. Studies have estimated DOT sensitivity in head models with atlas parcellations in infants (Bulgarelli et al, 2019;Bulgarelli et al, 2020;Perdue et al, 2019), children (Whiteman et al, 2017) and adults (Zimeo Morais, Balardin, & Sato, 2018). These studies computed the channel-ROI correspondence and generated look-up tables to show channel-ROI probabilities.…”

“…There are developmental effects in DOT sensitivity. Age was positively associated with the depth sensitivity in the frontal and occipital regions in head models of children between 5 to 11 years old (Whiteman et al, 2017). Fukui et al (2003) compared DOT sensitivity by separation distances in neonates (20-24 weeks) and adults.…”

A Paradigm for Measuring Resting State Functional Connectivity in Young Children Using fNIRS and Freeplay

“…Any detector saturation was also adjusted for in this step. fNIRS data was recorded 266 for each participant using custom data collection software that interfaced with the fNIRS system, described Raw light attenuation measurements were converted to oxy-hemoglobin and deoxy-hemoglobin 278 concentration changes using the modified Beer-Lambert law [44,45]. We removed long-term drifts in the 279 data by subtracting a least-squares linear fit.…”