Classification of Mild Cognitive Impairment Using Functional Near-Infrared Spectroscopy-Derived Biomarkers With Convolutional Neural Networks

• 발행기관: 대한신경정신의학회
• 저자명: Jin-Hyuck Park
• 간행물 정보: 『Psychiatry Investigation』제21권 제3호, 294~299쪽, 전체 6쪽
• 주제분류: 의약학 > 정신과학
• 파일형태: PDF
• 발행일자: 2024.03.31

국문 초록

Objective To date, early detection of mild cognitive impairment (MCI) has mainly depended on paper-based neuropsychological assessments. Recently, biomarkers for MCI detection have gained a lot of attention because of the low sensitivity of neuropsychological assessments. This study proposed the functional near-infrared spectroscopy (fNIRS)-derived data with convolutional neural networks (CNNs) to identify MCI.Methods Eighty-two subjects with MCI and 148 healthy controls (HC) performed the 2-back task, and their oxygenated hemoglobin (HbO2) changes in the prefrontal cortex (PFC) were recorded during the task. The CNN model based on fNIRS-derived spatial features with HbO2 slope within time windows was trained to classify MCI. Thereafter, the 5-fold cross-validation approach was used to evaluate the performance of the CNN model.Results Significant differences in averaged HbO2 values between MCI and HC groups were found, and the CNN model could better discriminate MCI with over 89.57% accuracy than the Korean version of the Montreal Cognitive Assessment (MoCA) (89.57%). Specifically, the CNN model based on HbO2 slope within the time window of 20–60 seconds from the left PFC (96.09%) achieved the highest accuracy.Conclusion These findings suggest that the fNIRS-derived spatial features with CNNs could be a promising way for early detection of MCI as a surrogate for a conventional screening tool and demonstrate the superiority of the fNIRS-derived spatial features with CNNs to the MoCA.

목차

INTRODUCTION

METHODS

RESULTS

DISCUSSION

REFERENCES

키워드

Functional neuroimaging Mild cognitive impairment Machine learning Brain biomarkers