Automated tresholding method for fNIRS-based functional connectivity analysis: validation with a case study on Alzheimer's disease

Yee Ling Chan, Wei Chun Ung, Lam Ghai Lim, Cheng-Kai Lu, Masashi Kiguchi, Tong Boon Tang

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11 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1691-1701
Number of pages11
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number8
Publication statusPublished - Aug 2020
Externally publishedYes


  • Alzheimer's disease (AD)
  • Functional connectivity (FC)
  • functional near-infrared spectroscopy (fNIRS)
  • network thresholding
  • orthogonal minimal spanning trees (OMSTs)
  • wavelet analysis

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