The Spectral Entropy of Resting State fNIRS Signals as a Biomarker for Alzheimer's Disease

Alzheimer's disease (AD) is a complex and debilitating neurodegenerative disorder that affects millions of people worldwide. In recent years, there has been growing interest in utilizing functional near-infrared spectroscopy (fNIRS) signals as potential biomarkers of AD. fNIRS is a non-invasive neuroimaging technique that provides valuable insights into brain activity and function by measuring changes in oxygenated and deoxygenated hemoglobin concentrations in the brain. In this study, we aim to explore the role of spectral entropy (SE) of resting-state fNIRS signals as a biomarker of AD. By investigating potential changes in brain complexity, we improve early detection and intervention strategies for AD. We measured 48-channel fNIRS signals in the prefrontal region at rest in groups of AD, mild cognitive impairment (MCI), and healthy control (HC). Then propose a method to characterize AD, MCI, and healthy controls at rest by applying SE analysis using oxyhemoglobin (HbO) signals for each group. To find out which bands provide the best results, we divided them into very low frequency (VLF) (0.008-0.1 Hz), respiratory (0.1-0.6 Hz), and cardiac (0.6-3 Hz) bands. Next, a t-test was performed at the 0.05 significance level to compare the SE values of each group. The results of the average VLF band in the prefrontal region showed that the SE value of AD patients was significantly higher than that of MCI and HC. In addition, the difference between AD, MCI, and HC was noticeable in the respiratory band. In the cardiac band, the SE of HC was significantly higher than that of MCI and AD. These results suggest that the SE of fNIRS signals emerges as a promising indicator of the complexity and adaptability of brain activity, with potential implications for the diagnosis and monitoring of AD and MCI.