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Cited by 83Optical Imaging and Spectroscopy TechniquesFunctional Brain Connectivity StudiesSpectroscopy and Chemometric Analyses

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Sensor space group analysis for fNIRS data

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Tak, S., Uga, M., Flandin, G., Dan, I., Penny, W.D.·2016·Journal of Neuroscience Methods
DOI 10.1016/j.jneumeth.2016.03.003PMC PMC4840017
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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.

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Metadata

Authors
Tak, S., Uga, M., Flandin, G., Dan, I., & Penny, W. D.
Affiliations
  • Wellcome Centre for Human Neuroimaging (GB)
  • Jichi Medical University (JP)
Corresponding author
  • Tak, S.
  • Penny, W.D.
ORCID
Venue
Journal of Neuroscience Methods
Publisher
Elsevier BV
Published
2016-05
Volume
264
Pages
103-112
ISSN
0165-0270 (ISSN-L)
DOI
10.1016/j.jneumeth.2016.03.003
PMC
PMC4840017
OpenAlex
W2289562584
Cited by
83
References
4
Citations by year
2026: 9 · 2025: 7 · 2024: 8 · 2023: 9 · 2022: 15 · 2021: 11 · 2020: 10 · 2019: 4 · 2018: 4 · 2017: 5 · 2016: 1
FWCI
4.05
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Neuroscience Methods (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • UCL Discovery (University College London) (submittedVersion)
  • UEA Digital Repository (University of East Anglia) (submittedVersion)
Funders
  • Newton International Fellowship
  • Japan Society for the Promotion of Science
  • Wellcome Trust (091593/Z/10/Z)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Optical Imaging and Spectroscopy Techniques
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
Keywords
functional near-infrared spectroscopy, spatial interpolation, Stroop task, frontopolar cortex, hemoglobin response, random field theory
MeSH terms
Brain Mapping, Humans, Image Processing, Computer-Assisted, Prefrontal Cortex, Spectroscopy, Near-Infrared, Executive Function, Stroop Test

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