fNIRS-based functional connectivity estimation using semi-metric analysis to study decision making by nursing students and registered nurses

Abstract This study aims to investigate the generalizability of the semi-metric analysis of the functional connectivity (FC) for functional near-infrared spectroscopy (fNIRS) by applying it to detect the dichotomy in differential FC under affective and neutral emotional states in nursing students an...

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Autores principales:	Jie Sheng Chong, Yee Ling Chan, Esther G. M. Ebenezer, Hoi Yen Chen, Masashi Kiguchi, Cheng-Kai Lu, Tong Boon Tang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fcdc883a6b9e4f20aa9c90a9c5a7e439
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spelling	oai:doaj.org-article:fcdc883a6b9e4f20aa9c90a9c5a7e4392021-12-02T12:42:28ZfNIRS-based functional connectivity estimation using semi-metric analysis to study decision making by nursing students and registered nurses10.1038/s41598-020-79053-z2045-2322https://doaj.org/article/fcdc883a6b9e4f20aa9c90a9c5a7e4392020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79053-zhttps://doaj.org/toc/2045-2322Abstract This study aims to investigate the generalizability of the semi-metric analysis of the functional connectivity (FC) for functional near-infrared spectroscopy (fNIRS) by applying it to detect the dichotomy in differential FC under affective and neutral emotional states in nursing students and registered nurses during decision making. The proposed method employs wavelet transform coherence to construct FC networks and explores semi-metric analysis to extract network redundancy features, which has not been considered in conventional fNIRS-based FC analyses. The trials of the proposed method were performed on 19 nursing students and 19 registered nurses via a decision-making task under different emotional states induced by affective and neutral emotional stimuli. The cognitive activities were recorded using fNIRS, and the emotional stimuli were adopted from the International Affective Digitized Sound System (IADS). The induction of emotional effects was validated by heart rate variability (HRV) analysis. The experimental results by the proposed method showed significant difference (FDR-adjusted p = 0.004) in the nursing students’ cognitive FC network under the two different emotional conditions, and the semi-metric percentage (SMP) of the right prefrontal cortex (PFC) was found to be significantly higher than the left PFC (FDR-adjusted p = 0.036). The benchmark method (a typical weighted graph theory analysis) gave no significant results. In essence, the results support that the semi-metric analysis can be generalized and extended to fNIRS-based functional connectivity estimation.Jie Sheng ChongYee Ling ChanEsther G. M. EbenezerHoi Yen ChenMasashi KiguchiCheng-Kai LuTong Boon TangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-12 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jie Sheng Chong Yee Ling Chan Esther G. M. Ebenezer Hoi Yen Chen Masashi Kiguchi Cheng-Kai Lu Tong Boon Tang fNIRS-based functional connectivity estimation using semi-metric analysis to study decision making by nursing students and registered nurses
description	Abstract This study aims to investigate the generalizability of the semi-metric analysis of the functional connectivity (FC) for functional near-infrared spectroscopy (fNIRS) by applying it to detect the dichotomy in differential FC under affective and neutral emotional states in nursing students and registered nurses during decision making. The proposed method employs wavelet transform coherence to construct FC networks and explores semi-metric analysis to extract network redundancy features, which has not been considered in conventional fNIRS-based FC analyses. The trials of the proposed method were performed on 19 nursing students and 19 registered nurses via a decision-making task under different emotional states induced by affective and neutral emotional stimuli. The cognitive activities were recorded using fNIRS, and the emotional stimuli were adopted from the International Affective Digitized Sound System (IADS). The induction of emotional effects was validated by heart rate variability (HRV) analysis. The experimental results by the proposed method showed significant difference (FDR-adjusted p = 0.004) in the nursing students’ cognitive FC network under the two different emotional conditions, and the semi-metric percentage (SMP) of the right prefrontal cortex (PFC) was found to be significantly higher than the left PFC (FDR-adjusted p = 0.036). The benchmark method (a typical weighted graph theory analysis) gave no significant results. In essence, the results support that the semi-metric analysis can be generalized and extended to fNIRS-based functional connectivity estimation.
format	article
author	Jie Sheng Chong Yee Ling Chan Esther G. M. Ebenezer Hoi Yen Chen Masashi Kiguchi Cheng-Kai Lu Tong Boon Tang
author_facet	Jie Sheng Chong Yee Ling Chan Esther G. M. Ebenezer Hoi Yen Chen Masashi Kiguchi Cheng-Kai Lu Tong Boon Tang
author_sort	Jie Sheng Chong
title	fNIRS-based functional connectivity estimation using semi-metric analysis to study decision making by nursing students and registered nurses
title_short	fNIRS-based functional connectivity estimation using semi-metric analysis to study decision making by nursing students and registered nurses
title_full	fNIRS-based functional connectivity estimation using semi-metric analysis to study decision making by nursing students and registered nurses
title_fullStr	fNIRS-based functional connectivity estimation using semi-metric analysis to study decision making by nursing students and registered nurses
title_full_unstemmed	fNIRS-based functional connectivity estimation using semi-metric analysis to study decision making by nursing students and registered nurses
title_sort	fnirs-based functional connectivity estimation using semi-metric analysis to study decision making by nursing students and registered nurses
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/fcdc883a6b9e4f20aa9c90a9c5a7e439
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fNIRS-based functional connectivity estimation using semi-metric analysis to study decision making by nursing students and registered nurses

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