EEG-Based Biomarkers on Working Memory Tasks for Early Diagnosis of Alzheimer's Disease and Mild Cognitive Impairment

Author(s)

Mamani, Godofredo Quispe ; Fraga, Francisco J. ; Tavares, Guilherme ; Johns, Erin ; Phillips, Natalie D. ; IEEE

• Document type: Journal article
• Source: 2017 IEEE-NIH HEALTHCARE INNOVATIONS AND POINT OF CARE TECHNOLOGIES (HI-POCT); v. N/A, p. 4-pg., 2017-01-01.

Abstract

Alzheimer's Disease (AD) is a neurodegenerative syndrome affecting millions of people worldwide. Also, individuals with mild cognitive impairment (MCI) are in a group of risk that should be followed and treated since there is a high probability of evolution to AD. In this study we carried out an Event-Related Potential (ERP) analysis on patient and control groups from 32-channel EEG recorded during N-back working memory (WM) tasks with the aim of finding an ERP-based biomarker for early diagnosis of both AD and MCI. Participants were 15 AD patients, 20 individuals diagnosed with MCI and 26 age-matched healthy elderly (HE) controls. Subjects underwent a three-level visual N-back task with ascending memory load difficulty. Nonparametric Kruskal-Wallis tests with cluster correction and 5% significance level were used for statistical analysis. A considerable amount of significant differences between patient and control groups were found in the ERP during execution of the WM tasks, predominantly in fronto-centro-parietal electrodes. Such results are promising in the direction of achieving an early EEG-based diagnosis of MCI and AD. (AU)

• FAPESP's process: 15/09510-7 - Computational EEG analysis for early Alzheimer's Disease diagnosis
• Grantee: Francisco José Fraga da Silva
• Support Opportunities: Regular Research Grants