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Automated Thresholding Method for fNIRS-Based Functional Connectivity Analysis: Validation with a Case Study on Alzheimer's Disease

While functional integration has been suggested to reflect brain health, non-standardized network thresholding methods complicate network interpretation. We propose a new method to analyze functional near-infrared spectroscopy-based functional connectivity (fNIRS-FC). In this study, we employed wave...

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Main Authors: Chan, Y.L., Ung, W.C., Lim, L.G., Lu, C.-K., Kiguchi, M., Tang, T.B.
Format: Article
Published: Institute of Electrical and Electronics Engineers Inc. 2020
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089204163&doi=10.1109%2fTNSRE.2020.3007589&partnerID=40&md5=5994e65315a872bd0e6c465dd46107df
http://eprints.utp.edu.my/23199/
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spelling my.utp.eprints.231992021-08-19T06:09:33Z Automated Thresholding Method for fNIRS-Based Functional Connectivity Analysis: Validation with a Case Study on Alzheimer's Disease Chan, Y.L. Ung, W.C. Lim, L.G. Lu, C.-K. Kiguchi, M. Tang, T.B. While functional integration has been suggested to reflect brain health, non-standardized network thresholding methods complicate network interpretation. We propose a new method to analyze functional near-infrared spectroscopy-based functional connectivity (fNIRS-FC). In this study, we employed wavelet analysis for motion correction and orthogonal minimal spanning trees (OMSTs) to derive the brain connectivity. The proposed method was applied to an Alzheimer's disease (AD) dataset and was compared with a number of well-known thresholding techniques. The results demonstrated that the proposed method outperformed the benchmarks in filtering cost-effective networks and in differentiation between patients with mild AD and healthy controls. The results also supported the proposed method as a feasible technique to analyze fNIRS-FC, especially with cost-efficiency, assortativity and laterality as a set of effective features for the diagnosis of AD. © 2001-2011 IEEE. Institute of Electrical and Electronics Engineers Inc. 2020 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089204163&doi=10.1109%2fTNSRE.2020.3007589&partnerID=40&md5=5994e65315a872bd0e6c465dd46107df Chan, Y.L. and Ung, W.C. and Lim, L.G. and Lu, C.-K. and Kiguchi, M. and Tang, T.B. (2020) Automated Thresholding Method for fNIRS-Based Functional Connectivity Analysis: Validation with a Case Study on Alzheimer's Disease. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28 (8). pp. 1691-1701. http://eprints.utp.edu.my/23199/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description While functional integration has been suggested to reflect brain health, non-standardized network thresholding methods complicate network interpretation. We propose a new method to analyze functional near-infrared spectroscopy-based functional connectivity (fNIRS-FC). In this study, we employed wavelet analysis for motion correction and orthogonal minimal spanning trees (OMSTs) to derive the brain connectivity. The proposed method was applied to an Alzheimer's disease (AD) dataset and was compared with a number of well-known thresholding techniques. The results demonstrated that the proposed method outperformed the benchmarks in filtering cost-effective networks and in differentiation between patients with mild AD and healthy controls. The results also supported the proposed method as a feasible technique to analyze fNIRS-FC, especially with cost-efficiency, assortativity and laterality as a set of effective features for the diagnosis of AD. © 2001-2011 IEEE.
format Article
author Chan, Y.L.
Ung, W.C.
Lim, L.G.
Lu, C.-K.
Kiguchi, M.
Tang, T.B.
spellingShingle Chan, Y.L.
Ung, W.C.
Lim, L.G.
Lu, C.-K.
Kiguchi, M.
Tang, T.B.
Automated Thresholding Method for fNIRS-Based Functional Connectivity Analysis: Validation with a Case Study on Alzheimer's Disease
author_facet Chan, Y.L.
Ung, W.C.
Lim, L.G.
Lu, C.-K.
Kiguchi, M.
Tang, T.B.
author_sort Chan, Y.L.
title Automated Thresholding Method for fNIRS-Based Functional Connectivity Analysis: Validation with a Case Study on Alzheimer's Disease
title_short Automated Thresholding Method for fNIRS-Based Functional Connectivity Analysis: Validation with a Case Study on Alzheimer's Disease
title_full Automated Thresholding Method for fNIRS-Based Functional Connectivity Analysis: Validation with a Case Study on Alzheimer's Disease
title_fullStr Automated Thresholding Method for fNIRS-Based Functional Connectivity Analysis: Validation with a Case Study on Alzheimer's Disease
title_full_unstemmed Automated Thresholding Method for fNIRS-Based Functional Connectivity Analysis: Validation with a Case Study on Alzheimer's Disease
title_sort automated thresholding method for fnirs-based functional connectivity analysis: validation with a case study on alzheimer's disease
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2020
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089204163&doi=10.1109%2fTNSRE.2020.3007589&partnerID=40&md5=5994e65315a872bd0e6c465dd46107df
http://eprints.utp.edu.my/23199/
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score 13.211869

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