relation: https://khub.utp.edu.my/scholars/12854/
title: Automated Thresholding Method for fNIRS-Based Functional Connectivity Analysis: Validation with a Case Study on Alzheimer's Disease
creator: Chan, Y.L.
creator: Ung, W.C.
creator: Lim, L.G.
creator: Lu, C.-K.
creator: Kiguchi, M.
creator: Tang, T.B.
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.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2020
type: Article
type: PeerReviewed
identifier:   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.  ISSN 15344320     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089204163&doi=10.1109%2fTNSRE.2020.3007589&partnerID=40&md5=5994e65315a872bd0e6c465dd46107df
relation: 10.1109/TNSRE.2020.3007589
identifier: 10.1109/TNSRE.2020.3007589