Sensor space group analysis for fNIRS data - Tak, S.; Uga, M.; Flandin, G.; Dan, I.; Penny, W.D. (Journal of neuroscience methods, 2016)
•We apply random-effects analysis using summary statistics to fNIRS data.•Individual contrast images are generated in a 2D or 3D canonical scalp surface.•Random-effects analysis then enables inference about population effects.•We show that left frontopolar area is activated in a population during Stroop effects.•Results are consistent with previous neuroimaging findings. Functional near-infrared spectroscopy (fNIRS) is a method for monitoring hemoglobin responses using optical probes placed on the scalp. fNIRS spatial resolution is limited by the distance between channels defined as a pair of source and detector, and channel positions are often inconsistent across subjects. These challenges can lead to less accurate estimate of group level effects from channel-specific measurements. This paper addresses this shortcoming by applying random-effects analysis using summary statistics to interpolated fNIRS topographic images. Specifically, we generate individual contrast images containing the experimental effects of interest in a canonical scalp surface. Random-effects analysis then allows for making inference about the regionally specific effects induced by (potentially) multiple experimental factors in a population. We illustrate the approach using experimental data acquired during a colour-word matching Stroop task, and show that left frontopolar regions are significantly activated in a population during Stroop effects. This result agrees with previous neuroimaging findings. The proposed methods (i) address potential misalignment of sensor locations between subjects using spatial interpolation; (ii) produce experimental effects of interest either on a 2D regular grid or on a 3D triangular mesh, both representations of a canonical scalp surface; and (iii) enables one to infer population effects from fNIRS data using a computationally efficient summary statistic approach (random-effects analysis). Significance of regional effects is assessed using random field theory. In this paper, we have shown how fNIRS data from multiple subjects can be analysed in sensor space using random-effects analysis.