Effective connectivity in subjects with mild cognitive impairment as assessed using functional near-infrared spectroscopy

Objective This study aimed to reveal the physiological mechanism in subjects with mild cognitive impairment (MCI) based on effective connectivity (EC) method. Methods EC was assessed by dynamic Bayesian inference (DBI) of the oxygenated hemoglobin concentration (Δ[HbO2]) signals measured through functional near-infrared spectroscopy (fNIRS). The Δ[HbO2] signals were recorded from the left prefrontal cortex (LPFC), right prefrontal cortex (RPFC), left motor cortex (LMC), right motor cortex (RMC), left occipital lobe (LOL) and right occipital lobe (ROL) of 26 subjects with MCI (MCI group) and 28 healthy elderly subjects (control group) at resting state. Results The coupling strength (CS) of RPFC to LPFC (F = 7.964, p = 0.007) and LPFC to ROL (F = 4.278, p = 0.044) in interval III, and LPFC to LOL (F = 5.637, p = 0.021), ROL to LPFC (F = 4.762, p = 0.034) and RPFC to LOL (F = 4.06, p = 0.049) in interval IV in the MCI group were significantly lower than those in the control group. Conclusions The decreased EC levels among brain regions may be a marker of impaired cognitive function in the MCI group. The constructed EC network based on fNIRS provide a non-invasive method to assess MCI. Corresponding author: Yonghui Wang, Department of physical medicine and rehabilitation, Qilu hospital, Shandong University, Jinan, 250061. Tel:86-18560083581. Email: yonghuiw6606@126.com; Zengyong Li, National Research Center for Rehabilitation Technical Aids, Beijing, 100176, P. R. China. Tel: +86-10- 58122815. Email: lizengyong@nrcrta.cn. these authors contributed equally to this work. Financial disclosure statements have been obtained, and no conflicts of interest have been reported by the authors or by any individuals in control of the content of this article. The project was supported by the National Natural Science Foundation of China (NSFC No. 31771071, 61761166007, 11732015 and 81672249) and Fundamental Research Funds for Central Public Welfare Research Institutes (118009001000160001). Copyright © 2018 Wolters Kluwer Health, Inc. All rights reserved.

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