@article{scholars12854,
           title = {Automated Thresholding Method for fNIRS-Based Functional Connectivity Analysis: Validation with a Case Study on Alzheimer's Disease},
             doi = {10.1109/TNSRE.2020.3007589},
          number = {8},
            note = {cited By 11},
          volume = {28},
           pages = {1691--1701},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
         journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
            year = {2020},
            issn = {15344320},
          author = {Chan, Y. L. and Ung, W. C. and Lim, L. G. and Lu, C.-K. and Kiguchi, M. and Tang, T. B.},
        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. {\^A}{\copyright} 2001-2011 IEEE.},
        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},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089204163&doi=10.1109\%2fTNSRE.2020.3007589&partnerID=40&md5=5994e65315a872bd0e6c465dd46107df}
}