Brain Disorders & Therapy

Complexity measures of EEG signals for uncovering altered brain dynamics in Alzheimer's disease

Global congress on Neuroscience Psychiatry and Mental disorder

July 03, 2023 | Webinar

Sabatina Criscuolo

University of Naples, Italy

Scientific Tracks Abstracts: Brain Disord Ther

Abstract:

Alzheimer's disease (AD) is a widespread age-related neurodegenerative disorder, characterized by a gradual decline in cognitive and behavioral abilities1. AD is recognized as a significant public health issue worldwide, and research efforts to better understand its underlying mechanisms and develop effective therapeutic interventions are critical. Although biomarkers can be detected in vivo, diagnosing AD remains a challenge, particularly in early stages. In this regard, investigating brain complexity can provide insights into the underlying mechanisms of neural dynamics in AD patients2,3. Specifically, the examination of complexity in multi-channel electroencephalographic (EEG) signals through Multiscale Fuzzy Entropy (MFE) could help to explore the differences in neural dynamics between AD patients and healthy controls4,5. More in detail, MFE is calculated across various frequency bands and time scales6. EEG dynamics at each temporal scale and frequency band are known to be associated with distinct cognitive, perceptual, and memory function components7. The main objective of the study is to evaluate whether the slowing down of the dominant rhythm observed in AD patients is reflected in a reduction of complexity at high frequencies. The preliminary results highlight the potential of entropy measures as a promising tool for early diagnosis and improved comprehension of EEG dynamics’ alteration in AD. Through the identification of patterns of brain complexity, AD may be detected at an earlier stage allowing for more effective treatment. Moreover, these findings may provide insight into the pathophysiological mechanisms contributing to the cognitive and behavioral deficits in AD, facilitating the development of more precisely targeted therapeutic interventions.

Biography :

Sabatina Criscuolo received the M.S. degree in biomedical engineering from the University of Naples Federico II, Naples, in 2021. She is currently pursuing the Ph.D. degree in Information and Communication Technology for Health. She is also a member of the Augmented Reality for Health Monitoring Laboratory (ARHeMLab), DIETI Excellence Department, University of Naples Federico II. Her research interests include artificial intelligence techniques to support precision medicine, processing of biomedical signals, and artifact removal. Specifically, she works on EEG signals in neurodegenerative disease, EEG artifact removal, and artificial pancreas solution. She is IEEE Graduate Student Member.