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Cited by 466

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Time domain functional NIRS imaging for human brain mapping

Open access
Torricelli, Alessandro, Contini, Davide, Pifferi, Antonio, Caffini, Matteo, Re, Rebecca, Zucchelli, Lucia, Spinelli, Lorenzo·2014·NeuroImage

Abstract

This review is aimed at presenting the state-of-the-art of time domain (TD) functional near-infrared spectroscopy (fNIRS). We first introduce the physical principles, the basics of modeling and data analysis. Basic instrumentation components (light sources, detection techniques, and delivery and collection systems) of a TD fNIRS system are described. A survey of past, existing and next generation TD fNIRS systems used for research and clinical studies is presented. Performance assessment of TD fNIRS systems and standardization issues are also discussed. Main strengths and weakness of TD fNIRS are highlighted, also in comparison with continuous wave (CW) fNIRS. Issues like quantification of the hemodynamic response, penetration depth, depth selectivity, spatial resolution and contrast-to-noise ratio are critically examined, with the help of experimental results performed on phantoms or in vivo. Finally we give an account on the technological developments that would pave the way for a broader use of TD fNIRS in the neuroimaging community.

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Crossref · 10.1016/j.neuroimage.2013.05.106

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Metadata

Authors
Torricelli, A., Contini, D., Pifferi, A., Caffini, M., Re, R., Zucchelli, L., & Spinelli, L.
Venue
NeuroImage
Publisher
Elsevier BV
Published
2014-01
Volume
85
Pages
28-50
ISSN
1053-8119 (ISSN-L)
OpenAlex
W2001262493
Cited by
466
References
1
FWCI
23.77
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Time domain functional NIRS imaging for human brain mapping. https://app.cochat.ai/papers/10.1016/j.neuroimage.2013.05.106
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Torricelli, A., Contini, D., Pifferi, A., Caffini, M., Re, R., Zucchelli, L., & Spinelli, L. (2014). Time domain functional NIRS imaging for human brain mapping. NeuroImage. https://doi.org/10.1016/j.neuroimage.2013.05.106

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.