Abstract
Mild cognitive impairment (MCI), a condition characterizing poor cognition, is associated with aging and depicts early symptoms of severe cognitive impairment, known as Alzheimer’s disease (AD). Meanwhile, early detection of MCI can prevent progression to AD. A great deal of research has been performed in the past decade on MCI detection. However, availability of biomarkers for MCI detection requires greater attention. In our study, we evaluated putative and reliable biomarkers for diagnosing MCI by performing different mental tasks (i.e., N-back task, Stroop task, and verbal fluency task) using functional near-infrared spectroscopy (fNIRS) signals on a group of 15 MCI patients and 9 healthy control (HC). The 15 digital biomarkers (i.e., five means, seven slopes, peak, skewness, and kurtosis) and two image biomarkers (t-map, correlation map) in the prefrontal cortex (PFC) (i.e., left PFC, middle PFC, and right PFC) between the MCI and HC groups were investigated by the statistical analysis, linear discriminant analysis (LDA), and convolutional neural network (CNN) individually. The results reveal that the statistical analysis using digital biomarkers (with a p-value < 0.05) could not distinguish the MCI patients from the HC over 60% accuracy. Therefore, the current statistical analysis needs to be improved to be used for diagnosing the MCI patients. The best accuracy with LDA was 76.67% with the N-back and Stroop tasks. However, the CNN classification results trained by image biomarkers showed a high accuracy. In particular, the CNN results trained via t-maps revealed the best accuracy (90.62%) with the N-back task, whereas the CNN result trained by the correlation maps was 85.58% with the N-back task. Also, the results illustrated that investigating the sub-regions (i.e., right, middle, left) of the PFC for detecting MCI would be better than examining the whole PFC. The t-map (or/and the correlation map) is conclusively recommended as an image biomarker for early detection of AD. The combination of CNN and image biomarkers can provide a reliable clinical tool for diagnosing MCI patients.
Highlights
Alzheimer’s disease (AD) is a degenerative brain disorder of unknown etiology, a common form of dementia, which begins in middle-aged or older adults (Ieracitano et al, 2018)
We investigate 15 digital biomarkers and 2 image biomarkers generated from the functional near-infrared spectroscopy (fNIRS) hemodynamic responses for 15 Mild cognitive impairment (MCI) patients and 9 healthy control (HC)
The purpose behind this strategy is to observe any visual differences between the MCI patients and HC
Summary
Alzheimer’s disease (AD) is a degenerative brain disorder of unknown etiology, a common form of dementia, which begins in middle-aged or older adults (Ieracitano et al, 2018). In the final stages of AD, people lose the ability to communicate or respond to their environment. They need assistance in all their activities of daily living, and they may even lose their ability to swallow. In 2017, more than 16 million family members and other unpaid caregivers, a contribution valued at more than $232 billion, were devoting toward the care of Alzheimer’s patients. Such findings highlight the requirement for solutions to prevent dementiarelated costs from jeopardizing the health and financial security of the families of people with Alzheimer’s related diseases
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