Stimulus-induced narrowband gamma oscillations are test-retest reliable in healthy elderly in human EEG

Visual stimulus-induced narrowband gamma oscillations in electroencephalogram (EEG) recordings have been recently shown to be compromised in subjects with Mild Cognitive Impairment or Alzheimers Disease (AD), suggesting that gamma could be an inexpensive and easily accessible biomarker for early diagnosis of AD. However, to use gamma as a biomarker, its characteristics should remain consistent across multiple recordings, even when separated over long intervals. Previous magnetoencephalography studies in young subjects have reported that gamma power remains consistent over recordings separated by a few weeks to months. Here, we assessed the consistency of slow (20-35 Hz) and fast gamma (36-66 Hz) oscillations induced by static full-field gratings in male (N=20) and female (N=20) elderly subjects (>49 years) in EEG recordings separated by more than a year, and tested the consistency in the magnitude of gamma power, its temporal evolution and spectral profile. Gamma oscillations had distinct spectral and temporal characteristics across subjects, which remained consistent across recordings (average intraclass correlation, ICC of [~]0.7). Alpha oscillations (8-12 Hz) and steady-state-visually-evoked-potentials (SSVEPs) were also found to be reliable. We further tested how EEG features can be used to identify two recordings as belonging to the same versus different subjects and found high classifier performance (area under ROC curve of [~]0.89), with the temporal evolution of slow gamma and spectral profile emerging as the most informative features. These results suggest that EEG gamma oscillations are reliable across recordings and can be used as a clinical biomarker as well as a potential tool for subject identification. Significance statementWe demonstrate the reliability of stimulus-induced gamma oscillations in elderly humans for the first time in EEG. Since gamma has recently been shown to be compromised in patients with Mild Cognitive Impairment or early Alzheimers Disease (AD), together these results mark the first steps towards an EEG based clinical biomarker for early diagnosis of AD. We observed high reliability in the power spectrum, gamma power and its temporal characteristics, within the test-retest period of one year. Alpha and steady-state-visually-evoked potential power were also found to be reliable. These spectral and temporal features could also be used to identify EEG recordings as belonging to the same versus different subjects with high performance, suggesting a potentially key role in subject identification also.