Towards discovery and implementation of neurophysiologic biomarkers of Alzheimer's disease using entropy methods

Alzheimer's disease (AD) is a prevalent and debilitating neurodegenerative disease that leads to substantial loss of quality of life. Therapies currently available for AD do not modify the disease course and have limited efficacy in symptom control. As such, novel and precise therapies tailored to individual patients' neurophysiologic profiles are needed. Functional neuroimaging tools have demonstrated substantial potential to provide quantifiable insight into brain function in various neurologic disorders, particularly AD. Entropy, a novel analysis for better understanding the nonlinear nature of neurophysiological data, has demonstrated consistent accuracy in disease detection. This literature review characterizes the use of entropy-based analyses from functional neuroimaging tools, including electroencephalography (EEG) and magnetoencephalography (MEG), in patients with AD for disease detection, therapeutic response measurement, and providing clinical insights.