Evaluating motion processing algorithms for use with functional near-infrared spectroscopy data from young children

Motion artifacts are often a significant component of the measured signal in functional near-infrared spectroscopy (fNIRS) experiments. A variety of methods have been proposed to address this issue, including principal components analysis (PCA), correlation-based signal improvement (CBSI), wavelet filtering, and spline interpolation. The efficacy of these techniques has been compared using simulated data; however, our understanding of how these techniques fare when dealing with task-based cognitive data is limited. Brigadoi et al. compared motion correction techniques in a sample of adult data measured during a simple cognitive task. Wavelet filtering showed the most promise as an optimal technique for motion correction. Given that fNIRS is often used with infants and young children, it is critical to evaluate the effectiveness of motion correction techniques directly with data from these age groups. This study addresses that problem by evaluating motion correction algorithms implemented in HomER2. The efficacy of each technique was compared quantitatively using objective metrics related to the physiological properties of the hemodynamic response. Results showed that targeted PCA (tPCA), spline, and CBSI retained a higher number of trials. These techniques also performed well in direct head-to-head comparisons with the other approaches using quantitative metrics. The CBSI method corrected many of the artifacts present in our data; however, this approach produced sometimes unstable HRFs. The targeted PCA and spline methods proved to be the most robust, performing well across all comparison metrics. When compared head to head, tPCA consistently outperformed spline. We conclude, therefore, that tPCA is an effective technique for correcting motion artifacts in fNIRS data from young children

Validating a new methodology for optical probe design and image registration in fNIRS studies

Functional near-infrared spectroscopy (fNIRS) is an imaging technique that relies on the principle of shining near-infrared light through tissue to detect changes in hemodynamic activation. An important methodological issue encountered is the creation of optimized probe geometry for fNIRS recordings. Here, across three experiments, we describe and validate a processing pipeline designed to create an optimized, yet scalable probe geometry based on selected regions of interest (ROIs) from the functional magnetic resonance imaging (fMRI) literature. In experiment 1, we created a probe geometry optimized to record changes in activation from target ROIs important for visual working memory. Positions of the sources and detectors of the probe geometry on an adult head were digitized using a motion sensor and projected onto a generic adult atlas and a segmented head obtained from the subject's MRI scan. In experiment 2, the same probe geometry was scaled down to fit a child's head and later digitized and projected onto the generic adult atlas and a segmented volume obtained from the child's MRI scan. Using visualization tools and by quantifying the amount of intersection between target ROIs and channels, we show that out of 21 ROIs, 17 and 19 ROIs intersected with fNIRS channels from the adult and child probe geometries, respectively. Further, both the adult atlas and adult subject-specific MRI approaches yielded similar results and can be used interchangeably. However, results suggest that segmented heads obtained from MRI scans be used for registering children's data. Finally, in experiment 3, we further validated our processing pipeline by creating a different probe geometry designed to record from target ROIs involved in language and motor processing