eprintid: 12854
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/28/54
datestamp: 2023-11-10 03:27:25
lastmod: 2023-11-10 03:27:25
status_changed: 2023-11-10 01:49:42
type: article
metadata_visibility: show
creators_name: Chan, Y.L.
creators_name: Ung, W.C.
creators_name: Lim, L.G.
creators_name: Lu, C.-K.
creators_name: Kiguchi, M.
creators_name: Tang, T.B.
title: Automated Thresholding Method for fNIRS-Based Functional Connectivity Analysis: Validation with a Case Study on Alzheimer's Disease
ispublished: pub
keywords: Cost effectiveness; Diagnosis; Infrared devices; Near infrared spectroscopy, Automated thresholding; Cost-effective network; Functional connectivity; Functional integration; Functional near infrared spectroscopy; Minimal spanning tree; Thresholding methods; Thresholding techniques, Neurodegenerative diseases, aged; algorithm; Alzheimer disease; Article; assortativity; case study; clinical article; Clinical Dementia Rating; controlled study; cost effectiveness analysis; female; functional connectivity; functional near-infrared spectroscopy; human; male; mathematical model; mathematical parameters; Mini Mental State Examination; prefrontal cortex; validation process; brain; brain mapping; near infrared spectroscopy; nuclear magnetic resonance imaging; wavelet analysis, Alzheimer Disease; Brain; Brain Mapping; Humans; Magnetic Resonance Imaging; Spectroscopy, Near-Infrared; Wavelet Analysis
note: cited By 11
abstract: 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.
date: 2020
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089204163&doi=10.1109%2fTNSRE.2020.3007589&partnerID=40&md5=5994e65315a872bd0e6c465dd46107df
id_number: 10.1109/TNSRE.2020.3007589
full_text_status: none
publication: IEEE Transactions on Neural Systems and Rehabilitation Engineering
volume: 28
number: 8
pagerange: 1691-1701
refereed: TRUE
issn: 15344320
citation:   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