Measures of resting state EEG rhythms for clinical trials in Alzheimer's disease: Recommendations of an expert panel

Abstract

The Electrophysiology Professional Interest Area (EPIA) and Global Brain Consortium endorsed recommendations on candidate electroencephalography (EEG) measures for Alzheimer's disease (AD) clinical trials. The Panel reviewed the field literature. As most consistent findings, AD patients with mild cognitive impairment and dementia showed abnormalities in peak frequency, power, and "interrelatedness" at posterior alpha (8-12 Hz) and widespread delta (< 4 Hz) and theta (4-8 Hz) rhythms in relation to disease progression and interventions. The following consensus statements were subscribed: (1) Standardization of instructions to patients, resting state EEG (rsEEG) recording methods, and selection of artifact-free rsEEG periods are needed; (2) power density and "interrelatedness" rsEEG measures (e.g., directed transfer function, phase lag index, linear lagged connectivity, etc.) at delta, theta, and alpha frequency bands may be use for stratification of AD patients and monitoring of disease progression and intervention; and (3) international multisectoral initiatives are mandatory for regulatory purposes.

Keywords: Alzheimer's disease; The Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment (ISTAART); biomarkers; clinical trials; dementia; electroencephalography (EEG); eyes-closed resting state condition.

Figure.1:

Modified combinatorial nomenclature of the 10–10-system of scalp electrode montage for clinical electroencephalographic (EEG) recordings, extended with anterior and posterior electrodes in the inferior chain. Adapted from [18], (courtesy from the Publisher).

Figure 2:

Schematic overview of the scale of spatial and temporal resolution of measurement methods used for electrophysiology and functional neuroimaging. Measurement methods are EEG, magnetoencephalography (MEG), near-infrared spectroscopy (NIRS), functional magnetic resonance imaging (fMRI), electrocorticography (ECOG), local field potential (LFP) recordings, micro-electrode array (MEA) recordings, and microelectrode (ME) recordings. Non-invasive methods are shown in blue and invasive methods are shown in red. Adapted from [192] (courtesy from the Publisher).

Figure 3:

Decomposition of EEG rhythms into an EEG power spectrum. A sketch illustrates example of sinusoidal EEG waveforms at frequency binds od delta, alpha and beta rhythms and how they can be represented (i) when summed each other at given phases or (ii) used as an input for the calculation of an EEG power density spectrum. Modified [193].

Figure 4.

Tentative physiological model of the generation of resting state eyes-closed EEG (rsEEG) rhythms in the brain of age-matched old cognitively unimpaired (CU) persons and Alzheimer’s disease (AD) patients. In the normal brain, dominant EEG rhythms are observed at alpha frequencies (8-12 Hz), which would denote the background, spontaneous synchronization around 10 Hz of neural networks regulating the fluctuation of the subject’s global arousal and consciousness states. These networks would span neural populations of cerebral cortex, thalamus, basal forebrain and brainstem, including glutamatergic, cholinergic, dopaminergic and serotoninergic parts of the reticular ascending systems. In the brain of AD patients, the amplitude of these rhythms is reduced (i.e., tonic background desynchronization) together with an amplitude increase of the pathological rsEEG slow frequencies spanning delta (< 4 Hz) and theta (4-7 Hz) rhythms. This “slowing” of rsEEG rhythms would mainly reflect a sort of thalamocortical “disconnection mode”.