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Sensor space group analysis for fNIRS data

Tak, S; Uga, M; Flandin, G; Dan, I; Penny, WD; (2016) Sensor space group analysis for fNIRS data. Journal of Neuroscience Methods , 264 pp. 103-112. 10.1016/j.jneumeth.2016.03.003. Green open access

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Abstract

Background 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. New method 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. Results 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. Compared with existing methods 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. Conclusions In this paper, we have shown how fNIRS data from multiple subjects can be analysed in sensor space using random-effects analysis.

Type: Article
Title: Sensor space group analysis for fNIRS data
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.jneumeth.2016.03.003
Publisher version: http://dx.doi.org/10.1016/j.jneumeth.2016.03.003
Language: English
Additional information: © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Science & Technology, Life Sciences & Biomedicine, Biochemical Research Methods, Neurosciences, Biochemistry & Molecular Biology, Neurosciences & Neurology, Sensor space group analysis, Functional near-infrared spectroscopy, Random-effects analysis, Canonical scalp surface, Random field theory, NEAR-INFRARED SPECTROSCOPY, SPATIAL REGISTRATION, STATISTICAL-ANALYSIS, FUNCTIONAL MRI, HEAD-SURFACE, MNI SPACE, FMRI, INTERFERENCE, ACTIVATION, INFERENCE
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
URI: https://discovery.ucl.ac.uk/id/eprint/1481272
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