Measures of resting state EEG rhythms for clinical trials in alzheimer's disease patients : recommendations of an expert panel

Background and Aim: Eyes-closed resting state electroencephalographic (rsEEG) rhythms reflect neurophysiological oscillatory mechanisms of synchronization/desynchronization of activity within neural populations of ascending reticular activating brain systems and thalamus-cortical circuits involved in quite vigilance regulation. Currently, they are not considered as biomarkers of Alzheimer’s disease (AD) in the amyloid, tau and neurodegeneration (ATN) Framework of Alzheimer’s Association and National Institute of Aging (AA-NIA). The Electrophysiology Professional Interest Area (EPIA) of AA and Global Brain Consortium endorsed this article written by a multidisciplinary Expert Panel to provide recommendations on candidate rsEEG measures for AD clinical trials. Method: The Panel revised the field literature and reached consensus about the rsEEG measures consistently associated with clinical phenotypes and neuroimaging markers of AD in previous international multicentric clinical trials. Most consistent findings: AD patients with mild cognitive impairment and dementia displayed reduced peak frequency, power, and paired-electrode “interrelatedness” in posterior alpha (8-12 Hz) rhythms and topographically widespread increases in delta (< 4 Hz) and theta (4-8 Hz) rhythms. Recommendations: (i) Careful multi-center standardization of instructions to patients, rsEEG recordings, and selection of artifact-free rsEEG periods; (ii) extraction of rsEEG power density and paired-electrode “interrelatedness” (e.g., directed transfer function, phase lag index, linear lagged connectivity, etc.) rsEEG measures computed at delta, theta, and alpha frequency bands by validated open-access software platforms for replicability; (iii) valid use of those measures in stratification of AD patients and monitoring of disease progression and intervention; and iv) international initiatives to cross-validate rsEEG measures (including nonlinear) for disease monitoring and intervention