A working hypothesis visualization method for fNIRS measurements using Monte Carlo simulation

In neuroscience, clarifying the functional localization of the cerebrum using functional near-infrared spectroscopy (fNIRS) is one of the important works. To better understand and trust fNIRS data, neuroscientists formulate hypothesis about the underlying neural processes. However, visualizing and v...
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Autor*in:	Yota Kikuchi [verfasserIn] Yasutomo Nomura [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Optical properties Standard brain Colin27 MNI152 MNI space Finger tapping

Übergeordnetes Werk:	In: MethodsX - Elsevier, 2015, 11(2023), Seite 102357-
Übergeordnetes Werk:	volume:11 ; year:2023 ; pages:102357-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.mex.2023.102357

Katalog-ID:	DOAJ100169414

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allfields_unstemmed	10.1016/j.mex.2023.102357 doi (DE-627)DOAJ100169414 (DE-599)DOAJa290871579d343cfa4da98cdb2f9b764 DE-627 ger DE-627 rakwb eng Yota Kikuchi verfasserin aut A working hypothesis visualization method for fNIRS measurements using Monte Carlo simulation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In neuroscience, clarifying the functional localization of the cerebrum using functional near-infrared spectroscopy (fNIRS) is one of the important works. To better understand and trust fNIRS data, neuroscientists formulate hypothesis about the underlying neural processes. However, visualizing and validating these hypotheses is not easy due to the complex nature of brain activity and the limitations of fNIRS measurements. In this paper, we suggest the novel Monte Carlo tool designed to assist fNIRS study for neuroscientists and to deal with these problems. The tool provides a user-friendly interface for generating realistic virtual brain activity patterns based on a specified hypothesis. By setting up a region of interest in the standard brain based on the hypothesis, the simulation models the propagation of light through the brain accurately and mimics the hemodynamic response observed in fNIRS measurements. By visually displaying simulation data and identifying the major activation point, neuroscientists can validate and refine hypothesis and obtain a better understanding of the neural mechanisms underlying the fNIRS signals. • A Monte Carlo simulation method reflecting the functional localization of the cerebrum for fNIRS measurements. • Method for setting ROI corresponding to the functional localization of the cerebrum in the standard brain. • Visualization of Monte Carlo simulation results and anatomical evaluation method of activation points. Optical properties Standard brain Colin27 MNI152 MNI space Finger tapping Science Q Yasutomo Nomura verfasserin aut In MethodsX Elsevier, 2015 11(2023), Seite 102357- (DE-627)832786675 (DE-600)2830212-6 22150161 nnns volume:11 year:2023 pages:102357- https://doi.org/10.1016/j.mex.2023.102357 kostenfrei https://doaj.org/article/a290871579d343cfa4da98cdb2f9b764 kostenfrei http://www.sciencedirect.com/science/article/pii/S2215016123003539 kostenfrei https://doaj.org/toc/2215-0161 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 11 2023 102357-
allfieldsGer	10.1016/j.mex.2023.102357 doi (DE-627)DOAJ100169414 (DE-599)DOAJa290871579d343cfa4da98cdb2f9b764 DE-627 ger DE-627 rakwb eng Yota Kikuchi verfasserin aut A working hypothesis visualization method for fNIRS measurements using Monte Carlo simulation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In neuroscience, clarifying the functional localization of the cerebrum using functional near-infrared spectroscopy (fNIRS) is one of the important works. To better understand and trust fNIRS data, neuroscientists formulate hypothesis about the underlying neural processes. However, visualizing and validating these hypotheses is not easy due to the complex nature of brain activity and the limitations of fNIRS measurements. In this paper, we suggest the novel Monte Carlo tool designed to assist fNIRS study for neuroscientists and to deal with these problems. The tool provides a user-friendly interface for generating realistic virtual brain activity patterns based on a specified hypothesis. By setting up a region of interest in the standard brain based on the hypothesis, the simulation models the propagation of light through the brain accurately and mimics the hemodynamic response observed in fNIRS measurements. By visually displaying simulation data and identifying the major activation point, neuroscientists can validate and refine hypothesis and obtain a better understanding of the neural mechanisms underlying the fNIRS signals. • A Monte Carlo simulation method reflecting the functional localization of the cerebrum for fNIRS measurements. • Method for setting ROI corresponding to the functional localization of the cerebrum in the standard brain. • Visualization of Monte Carlo simulation results and anatomical evaluation method of activation points. Optical properties Standard brain Colin27 MNI152 MNI space Finger tapping Science Q Yasutomo Nomura verfasserin aut In MethodsX Elsevier, 2015 11(2023), Seite 102357- (DE-627)832786675 (DE-600)2830212-6 22150161 nnns volume:11 year:2023 pages:102357- https://doi.org/10.1016/j.mex.2023.102357 kostenfrei https://doaj.org/article/a290871579d343cfa4da98cdb2f9b764 kostenfrei http://www.sciencedirect.com/science/article/pii/S2215016123003539 kostenfrei https://doaj.org/toc/2215-0161 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 11 2023 102357-
allfieldsSound	10.1016/j.mex.2023.102357 doi (DE-627)DOAJ100169414 (DE-599)DOAJa290871579d343cfa4da98cdb2f9b764 DE-627 ger DE-627 rakwb eng Yota Kikuchi verfasserin aut A working hypothesis visualization method for fNIRS measurements using Monte Carlo simulation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In neuroscience, clarifying the functional localization of the cerebrum using functional near-infrared spectroscopy (fNIRS) is one of the important works. To better understand and trust fNIRS data, neuroscientists formulate hypothesis about the underlying neural processes. However, visualizing and validating these hypotheses is not easy due to the complex nature of brain activity and the limitations of fNIRS measurements. In this paper, we suggest the novel Monte Carlo tool designed to assist fNIRS study for neuroscientists and to deal with these problems. The tool provides a user-friendly interface for generating realistic virtual brain activity patterns based on a specified hypothesis. By setting up a region of interest in the standard brain based on the hypothesis, the simulation models the propagation of light through the brain accurately and mimics the hemodynamic response observed in fNIRS measurements. By visually displaying simulation data and identifying the major activation point, neuroscientists can validate and refine hypothesis and obtain a better understanding of the neural mechanisms underlying the fNIRS signals. • A Monte Carlo simulation method reflecting the functional localization of the cerebrum for fNIRS measurements. • Method for setting ROI corresponding to the functional localization of the cerebrum in the standard brain. • Visualization of Monte Carlo simulation results and anatomical evaluation method of activation points. Optical properties Standard brain Colin27 MNI152 MNI space Finger tapping Science Q Yasutomo Nomura verfasserin aut In MethodsX Elsevier, 2015 11(2023), Seite 102357- (DE-627)832786675 (DE-600)2830212-6 22150161 nnns volume:11 year:2023 pages:102357- https://doi.org/10.1016/j.mex.2023.102357 kostenfrei https://doaj.org/article/a290871579d343cfa4da98cdb2f9b764 kostenfrei http://www.sciencedirect.com/science/article/pii/S2215016123003539 kostenfrei https://doaj.org/toc/2215-0161 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 11 2023 102357-
language	English
source	In MethodsX 11(2023), Seite 102357- volume:11 year:2023 pages:102357-
sourceStr	In MethodsX 11(2023), Seite 102357- volume:11 year:2023 pages:102357-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Optical properties Standard brain Colin27 MNI152 MNI space Finger tapping Science Q
isfreeaccess_bool	true
container_title	MethodsX
authorswithroles_txt_mv	Yota Kikuchi @@aut@@ Yasutomo Nomura @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	832786675
id	DOAJ100169414
language_de	englisch
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author	Yota Kikuchi
spellingShingle	Yota Kikuchi misc Optical properties misc Standard brain misc Colin27 misc MNI152 misc MNI space misc Finger tapping misc Science misc Q A working hypothesis visualization method for fNIRS measurements using Monte Carlo simulation
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topic_title	A working hypothesis visualization method for fNIRS measurements using Monte Carlo simulation Optical properties Standard brain Colin27 MNI152 MNI space Finger tapping
topic	misc Optical properties misc Standard brain misc Colin27 misc MNI152 misc MNI space misc Finger tapping misc Science misc Q
topic_unstemmed	misc Optical properties misc Standard brain misc Colin27 misc MNI152 misc MNI space misc Finger tapping misc Science misc Q
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title	A working hypothesis visualization method for fNIRS measurements using Monte Carlo simulation
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title_full	A working hypothesis visualization method for fNIRS measurements using Monte Carlo simulation
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title_sort	working hypothesis visualization method for fnirs measurements using monte carlo simulation
title_auth	A working hypothesis visualization method for fNIRS measurements using Monte Carlo simulation
abstract	In neuroscience, clarifying the functional localization of the cerebrum using functional near-infrared spectroscopy (fNIRS) is one of the important works. To better understand and trust fNIRS data, neuroscientists formulate hypothesis about the underlying neural processes. However, visualizing and validating these hypotheses is not easy due to the complex nature of brain activity and the limitations of fNIRS measurements. In this paper, we suggest the novel Monte Carlo tool designed to assist fNIRS study for neuroscientists and to deal with these problems. The tool provides a user-friendly interface for generating realistic virtual brain activity patterns based on a specified hypothesis. By setting up a region of interest in the standard brain based on the hypothesis, the simulation models the propagation of light through the brain accurately and mimics the hemodynamic response observed in fNIRS measurements. By visually displaying simulation data and identifying the major activation point, neuroscientists can validate and refine hypothesis and obtain a better understanding of the neural mechanisms underlying the fNIRS signals. • A Monte Carlo simulation method reflecting the functional localization of the cerebrum for fNIRS measurements. • Method for setting ROI corresponding to the functional localization of the cerebrum in the standard brain. • Visualization of Monte Carlo simulation results and anatomical evaluation method of activation points.
abstractGer	In neuroscience, clarifying the functional localization of the cerebrum using functional near-infrared spectroscopy (fNIRS) is one of the important works. To better understand and trust fNIRS data, neuroscientists formulate hypothesis about the underlying neural processes. However, visualizing and validating these hypotheses is not easy due to the complex nature of brain activity and the limitations of fNIRS measurements. In this paper, we suggest the novel Monte Carlo tool designed to assist fNIRS study for neuroscientists and to deal with these problems. The tool provides a user-friendly interface for generating realistic virtual brain activity patterns based on a specified hypothesis. By setting up a region of interest in the standard brain based on the hypothesis, the simulation models the propagation of light through the brain accurately and mimics the hemodynamic response observed in fNIRS measurements. By visually displaying simulation data and identifying the major activation point, neuroscientists can validate and refine hypothesis and obtain a better understanding of the neural mechanisms underlying the fNIRS signals. • A Monte Carlo simulation method reflecting the functional localization of the cerebrum for fNIRS measurements. • Method for setting ROI corresponding to the functional localization of the cerebrum in the standard brain. • Visualization of Monte Carlo simulation results and anatomical evaluation method of activation points.
abstract_unstemmed	In neuroscience, clarifying the functional localization of the cerebrum using functional near-infrared spectroscopy (fNIRS) is one of the important works. To better understand and trust fNIRS data, neuroscientists formulate hypothesis about the underlying neural processes. However, visualizing and validating these hypotheses is not easy due to the complex nature of brain activity and the limitations of fNIRS measurements. In this paper, we suggest the novel Monte Carlo tool designed to assist fNIRS study for neuroscientists and to deal with these problems. The tool provides a user-friendly interface for generating realistic virtual brain activity patterns based on a specified hypothesis. By setting up a region of interest in the standard brain based on the hypothesis, the simulation models the propagation of light through the brain accurately and mimics the hemodynamic response observed in fNIRS measurements. By visually displaying simulation data and identifying the major activation point, neuroscientists can validate and refine hypothesis and obtain a better understanding of the neural mechanisms underlying the fNIRS signals. • A Monte Carlo simulation method reflecting the functional localization of the cerebrum for fNIRS measurements. • Method for setting ROI corresponding to the functional localization of the cerebrum in the standard brain. • Visualization of Monte Carlo simulation results and anatomical evaluation method of activation points.
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title_short	A working hypothesis visualization method for fNIRS measurements using Monte Carlo simulation
url	https://doi.org/10.1016/j.mex.2023.102357 https://doaj.org/article/a290871579d343cfa4da98cdb2f9b764 http://www.sciencedirect.com/science/article/pii/S2215016123003539 https://doaj.org/toc/2215-0161
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A working hypothesis visualization method for fNIRS measurements using Monte Carlo simulation

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