Abstract
Functional near-infrared spectroscopy (fNIRS) is a noninvasive brain imaging technique to measure evoked changes in cerebral blood oxygenation. In many evoked-task studies, the analysis of fNIRS experiments is based on a temporal linear regression model, which includes block-averaging, deconvolution, and canonical analysis models. The statistical parameters of this model are then spatially mapped across fNIRS measurement channels to infer brain activity. The trade-offs in sensitivity and specificity of using variations of canonical or deconvolution/block-average models are unclear. We quantitatively investigate how the choice of basis set for the regression linear model affects the sensitivity and specificity of fNIRS analysis in the presence of variability or systematic bias in underlying evoked response. For statistical parametric mapping of amplitude-based hypotheses, we conclude that these models are fairly insensitive to the parameters of the regression basis for task durations and we report the highest sensitivity-specificity results using a low degree-of-freedom canonical model under these conditions. For shorter duration task ( ), the signal-to-noise ratio of the data is also important in this decision and we find that deconvolution or block-averaging models outperform the canonical models at high signal-to-noise ratio but not at lower levels.
Highlights
Functional near-infrared spectroscopy is a noninvasive brain imaging technique that measures evoked hemodynamic changes in the brain using low levels of red to near-infrared light
We have explored the case in which all three coefficients are used to define the statistical test (e.g., c 1⁄4 1⁄21 1 1), which we denote as the “fixed effects” (FE) derivative model and the case in which two derivative terms are ignored after solving and only the main term is considered in the contrast (e.g., c 1⁄4 1⁄21 0 0), which we denote as the “random effects” (RE) derivative model to indicate that these additional derivative terms are used as nuisance regressors
The bias is defined relative to the canonical hemodynamic response function (HRF) model as the R-squared fit between the simulated and the canonical models, which we denote as the “percent-overlap”
Summary
Functional near-infrared spectroscopy (fNIRS) is a noninvasive brain imaging technique that measures evoked hemodynamic changes in the brain using low levels of red to near-infrared light. For these measurements, an arrangement of light sources and detectors is placed on the scalp. Two or more wavelengths of light are recorded, which provide information to spectrally distinguish both oxy-hemoglobin (HbO2) and deoxy-hemoglobin (Hb) changes via modified Beer–Lambert law.[1] Using a grid of optical light sources and detector positions, fNIRS can record the spatial distribution of changes in hemoglobin during functional tasks, providing a measurement of underlying brain activity. FNIRS has been employed in functional imaging of infants
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