Affiliations: [a] School of Mechanical Engineering, Pusan National University, Guemjeong-gu, Busan, Republic of Korea
| [b] Department of Cogno-Mechatronics Engineering, Pusan National University, Guemjeong-gu, Busan, Republic of Korea
Correspondence:
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Correspondence to: Professor Keum-Shik Hong, School of Mechanical Engineering, Pusan National University, 2 Busan-daehak-ro, Guemjeong-gu, Busan 46241, Republic of Korea. Tel.: +82 51 510 2454; E-mail: [email protected].
Abstract: Background:Mild cognitive impairment (MCI) is considered a prodromal stage of Alzheimer’s disease. Early diagnosis of MCI can allow for treatment to improve cognitive function and reduce modifiable risk factors. Objective:This study aims to investigate the feasibility of individual MCI detection from healthy control (HC) using a minimum duration of resting-state functional near-infrared spectroscopy (fNIRS) signals. Methods:In this study, nine different measurement durations (i.e., 30, 60, 90, 120, 150, 180, 210, 240, and 270 s) were evaluated for MCI detection via the graph theory analysis and traditional machine learning approach, such as linear discriminant analysis, support vector machine, and K-nearest neighbor algorithms. Moreover, feature representation- and classification-based transfer learning (TL) methods were applied to identify MCI from HC through the input of connectivity maps with 30 and 90 s duration. Results:There was no significant difference among the nine various time windows in the machine learning and graph theory analysis. The feature representation-based TL showed improved accuracy in both 30 and 90 s cases (i.e., 30 s: 81.27% and 90 s: 76.73%). Notably, the classification-based TL method achieved the highest accuracy of 95.81% using the pre-trained convolutional neural network (CNN) model with the 30 s interval functional connectivity map input. Conclusion:The results indicate that a 30 s measurement of the resting-state with fNIRS could be used to detect MCI. Moreover, the combination of neuroimaging (e.g., functional connectivity maps) and deep learning methods (e.g., CNN and TL) can be considered as novel biomarkers for clinical computer-assisted MCI diagnosis.
