Early screening model for mild cognitive impairment based on resting-state functional connectivity: a functional near-infrared spectroscopy study

Shen Zhang; Ting Zhu; Yizhu Tian; Wenyu Jiang; Deyu Li; Daifa Wang

doi:10.1117/1.NPh.9.4.045010

Early screening model for mild cognitive impairment based on resting-state functional connectivity: a functional near-infrared spectroscopy study

Neurophotonics. 2022 Oct;9(4):045010. doi: 10.1117/1.NPh.9.4.045010. Epub 2022 Dec 6.

Authors

Shen Zhang¹, Ting Zhu¹, Yizhu Tian¹, Wenyu Jiang², Deyu Li^{1

3

4}, Daifa Wang¹

Affiliations

¹ Beihang University, School of Biological Science and Medical Engineering, Beijing Advanced Innovation Centre for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing, China.
² Guangxi Jiangbin Hospital, Department of Neurological Rehabilitation, Nanning, China.
³ Beihang University, State Key Laboratory of Software Development Environment, Beijing, China.
⁴ Beihang University, State Key Laboratory of Virtual Reality Technology and System, Beijing, China.

Abstract

Significance: As an early stage of Alzheimer's disease (AD), the diagnosis of amnestic mild cognitive impairment (aMCI) has important clinical value for timely intervention of AD. Functional near-infrared spectroscopy (fNIRS)-based resting-state brain connectivity analysis, which could provide an economic and quick screening strategy for aMCI, remains to be extensively investigated.

Aim: This study aimed to verify the feasibility of fNIRS-based resting-state brain connectivity for evaluating brain function in patients with aMCI, and to determine an early screening model for auxiliary diagnosis.

Approach: The resting-state fNIRS was utilized for exploring the changes in functional connectivity of 64 patients with aMCI. The region of interest (ROI)-based and channel-based connections with significant inter-group differences have been extracted through the two-sample $t$ -tests and the receiver operating characteristic (ROC). These connections with specificity and sensitivity were then taken as features for classification.

Results: Compared with healthy controls, connections of the MCI group were significantly reduced between the bilateral prefrontal, parietal, occipital, and right temporal lobes. Specifically, the long-range connections from prefrontal to occipital lobe, and from prefrontal to parietal lobe, exhibited stronger identifiability (area under the ROC curve $> 0.65$ , ** $p < 0.01$ ). Subsequently, the optimal classification accuracy of ROI-based connections was 71.59%. Furthermore, the most responsive connections were located between the right dorsolateral prefrontal lobe and the left occipital lobe, concomitant with the highest classification accuracy of 73.86%.

Conclusion: Our findings indicate that fNIRS-based resting-state functional connectivity analysis could support MCI diagnosis. Notably, long-range connections involving the prefrontal and occipital lobes have the potential to be efficient biomarkers.

Keywords: amnesic mild cognitive impairment; functional connectivity; functional near-infrared spectroscopy; resting state.