ORIGINAL RESEARCH article
Front. Neurol.
Sec. Cognitive and Behavioral Neurology
fNIRS-Based Early Identification of Mild Cognitive Impairment: Large-Scale Multi-Paradigm Study with Ensemble Machine Learning Models
Yufei Chong 1,2
Can Duan 3
Xinzi Xu 1,2
Zhengliang Li 3
Heling Zhang 2,3
Jingyi Gong 1,2
Qingqing Wu 3
Lirong Xia 2
Pei-Wen Zhang 2
Wenguang Xia 1,2,3
1. Hubei Rehabilitation Hospital, wuhan, China
2. Hubei Engineering Research Center of Neuromodulation technology, wuhan, China
3. Hubei University of Chinese Medicine, Wuhan, China
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Abstract
Early and accurate identification of Mild Cognitive Impairment (MCI) is crucial for timely intervention and preventing further cognitive decline. Functional near-infrared spectroscopy (fNIRS) is a non-invasive, portable tool for clinical screening, but its diagnostic accuracy is often constrained by single-paradigm approaches and small sample sizes. To address this limitation, this study aimed to develop and validate an efficient early MCI screening model by integrating large-sample fNIRS data from resting-state and 1-back task paradigms using ensemble machine learning, thereby enhancing the accuracy and reliability of early MCI diagnosis. A total of 462 right-handed participants (185 MCI patients and 277 healthy controls, aged 58–87 years) were included in the final analysis after screening, with MCI diagnosis jointly determined by two experienced neurologists based on Petersen's criteria. fNIRS signals were collected during resting-state and 1-back task sessions; after preprocessing in MATLAB 2014a, features were extracted from oxygenated hemoglobin (HbO) signals of both paradigms. Feature selection was performed via a Gradient Boosting Classifier based on feature importance scores, resulting in 108 selected features. Five classifiers were trained and evaluated using 10-fold cross-validation. The integrated dataset combining resting-state and 1-back task features outperformed the single-paradigm datasets: the Neural Network model on this integrated dataset achieved an accuracy of 86.49%, sensitivity of 94.74%, specificity of 77.78%, and Area Under the Curve (AUC) of 93.49%. In contrast, the Nearest Neighbor model on the resting-state dataset and the Decision Tree model on the 1-back task dataset yielded accuracies of 70.27% and 75.68%, respectively. Group classification using MoCA scores achieved an accuracy of 86.55%, which was comparable to single-paradigm machine learning models but inferior to the integrated model. This study demonstrates the value of a large-sample, data-driven approach and multi-paradigm feature integration in fNIRS-based MCI screening, providing an efficient diagnostic model for clinical application.
Summary
Keywords
1-back task, ADRD, Alzheimer's disease, bagging, boosting, CNN, Convolutional Neural Networks, deoxyhemoglobin
Received
03 November 2025
Accepted
03 February 2026
Copyright
© 2026 Chong, Duan, Xu, Li, Zhang, Gong, Wu, Xia, Zhang and Xia. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Wenguang Xia
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