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Visual stimulus-induced gamma oscillations in electroencephalogram (EEG) recordings have been recently shown to be compromised in subjects with preclinical Alzheimer’s Disease (AD), suggesting that gamma could be an inexpensive biomarker for AD diagnosis provided its characteristics remain consistent across multiple recordings. Previous magnetoencephalography studies in young subjects have reported consistent gamma power over recordings separated by a few weeks to months. Here, we assessed the consistency of stimulus-induced slow (20–35 Hz) and fast gamma (36–66 Hz) oscillations in subjects (n = 40) (age: 50–88 years) in EEG recordings separated by a year, and tested the consistency in the magnitude of gamma power, its temporal evolution and spectral profile. Gamma had distinct spectral/temporal characteristics across subjects, which remained consistent across recordings (average intraclass correlation of ~0.7). Alpha (8–12 Hz) and steady-state-visually evoked-potentials were also reliable. We further tested how EEG features can be used to identify 2 recordings as belonging to the same versus different subjects and found high classifier performance (AUC of ~0.89), with temporal evolution of slow gamma and spectral profile being most informative. These results suggest that EEG gamma oscillations are reliable across sessions separated over long durations and can also be a potential tool for subject identification.

Visual stimulus-induced narrowband gamma oscillations in electroencephalogram (EEG) recordings have been recently shown to be compromised in subjects with Mild Cognitive Impairment or Alzheimer’s 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 Alzheimer’s 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.

Gamma rhythms (∼20-70 Hz) have been reported to be abnormal in mental disorders such as autism and schizophrenia in humans, and Alzheimer’s disease (AD) models in rodents. However, the effect of normal aging on these oscillations is not known, especially for elderly subjects (>49 years) for which AD is most prevalent. In a first large-scale (236 subjects; 104 females) electroencephalogram (EEG) study on gamma oscillations on elderly subjects (aged 50-88 years), we presented full-screen Cartesian gratings that induced two distinct gamma oscillations (slow: 20-34 Hz and fast: 36-66 Hz). Power and centre frequency significantly decreased with age for both slow and fast gamma, but not alpha (8-12 Hz). Reduction was more salient for fast gamma than slow. These results were independent of microsaccades and pupillary reactivity to stimulus, as well as variations in power spectral density with age. Steady-state visual evoked potentials (SSVEPs) also reduced with age. These results are crucial first steps towards using gamma/SSVEPs as biomarkers of cognitive decline in elderly.Significance statementNo study in humans has examined visual narrow-band gamma oscillations with healthy aging in elderly subjects. In a first large-scale (236 subjects) EEG study on stimulus-induced narrow band gamma in cognitively normal elderly (>49 years) humans, we show that both power and centre frequency of slow and fast gamma (20-34 Hz and 36-66 Hz respectively) decrease with age, but alpha power does not. Steady-state visual evoked potential (SSVEPs) in gamma range also decrease with age. Any EEG-based biomarker is accessible and affordable to patients of a wide socio-economic spectrum. Our results are important steps for developing such screening/diagnostic tests for aging-related diseases, like Alzheimer’s disease.

Alzheimer’s Disease (AD) in elderly adds substantially to socio-economic burden necessitating early diagnosis. While recent studies in rodent models of AD have suggested diagnostic and therapeutic value for gamma rhythms in brain, the same has not been rigorously tested in humans. We recruited a large population (N=247; 106 females) of elderly (>49 years) individuals from the community, who viewed large gratings that induced strong gamma oscillations in their electroencephalogram (EEG). These individuals were classified as healthy (N=227), mild-cognitively-impaired (MCI; 14) or AD (6) based on clinical history and Clinical Dementia Rating scores. Surprisingly, stimulus-induced gamma rhythms, but not alpha or steady-state-visually-evoked-responses, were significantly lower in both MCI and AD patients compared to their age and gender matched controls. This reduction was not due to differences in eye movements or baseline power. Our results suggest that gamma could be used as potential diagnostic tool for MCI/AD in humans.One Sentence SummaryA large double-blinded EEG study suggests that narrow-band visual gamma rhythms are weaker in MCI/AD patients compared to cognitively healthy controls.

Alzheimer’s disease (AD), the most common cause of dementia, is a chronic illness characterized by cognitive symptoms, behavioural and psychological symptoms and difficulty in performing activities of daily living. Mild cognitive impairment (MCI) is regarded as a transitional state between healthy cognitive ageing and dementia and around 12–15% of individuals with MCI progress to dementia annually. This provides unique opportunity to initiate treatments targeted to delay or prevent the onset of AD. Based on the age at onset, AD is divided into two broad categories: early onset and late onset. Early onset AD follows the classical mendelian pattern of inheritance, whereas the late onset AD has a complex interplay of gene–environment interaction, with several lifestyle-related risk factors strongly implicated in the pathogenesis of this degenerative condition. The onset of AD pathology predates the clinical symptoms by several years. The neurodegenerative processes in AD is related to the accumulation of abnormally folded Aβ and tau proteins in amyloid plaques and neuronal tangles. Diagnosis of AD is by presence of cluster of clinical features and aided by biomarkers such as CSF Aβ42 and PET amyloid imaging, CSF tau and tau imaging, 18fluorodeoxyglucose uptake on PET and atrophy on structural magnetic resonance imaging increase the diagnostic certainty. In the absence of curative treatments, the management of AD involves pharmacological treatment to delay the onset or progression of AD and supportive care by family members. Targeting these lifestyle-related factors in young adulthood and middle age may be protective against AD. High educational achievement in early life, involvement in cognitively stimulating activity, physical activity and social engagement including rich social network have been associated with reduced risk of late-life dementia and AD.