Abstract

BackgroundEntrainment of neural oscillations in occipital cortices by external rhythmic visual stimuli has been proposed as a novel therapy for patients with Alzheimer’s disease (AD). Despite this increased interest in visual neural oscillations in AD, little is known regarding their role in AD-related cognitive impairment and in particular during visuospatial processing.MethodsWe used source-imaged magnetoencephalography (MEG) and an established visuospatial processing task to elicit multi-spectral neuronal responses in 35 biomarker-confirmed patients on the AD spectrum and 20 biomarker-negative older adults. Neuronal oscillatory responses were imaged to the level of the cortex, and group classifications and neurocognitive relationships were modeled using logistic and linear regression, respectively.ResultsVisuospatial neuronal oscillations in the theta, alpha, and gamma ranges significantly predicted the classification of patients on the AD spectrum. Importantly, the direction of these effects differed by response frequency, such that patients on the AD spectrum exhibited weaker alpha-frequency responses in lateral occipital regions, and stronger gamma-frequency responses in the primary visual cortex, as compared to biomarker-negative older adults. In addition, alpha and gamma, but not theta, oscillations robustly predicted cognitive status (i.e., MoCA and MMSE scores), such that patients with neural responses that deviated more from those of healthy older adults exhibited poorer cognitive performance.ConclusionsWe find that the multi-spectral neural dynamics supporting visuospatial processing differentiate patients on the AD spectrum from cognitively normal, biomarker-negative older adults. Oscillations in the alpha and gamma bands also relate to cognitive status in ways that are informative for emerging clinical interventions.

Highlights

  • Entrainment of neural oscillations in occipital cortices by external rhythmic visual stimuli has been proposed as a novel therapy for patients with Alzheimer’s disease (AD)

  • As supported by stringent cluster-based permutation testing, as well as by numerous previous reports, we observed significant neural oscillatory responses to the visuospatial task stimuli in three temporally and spectrally defined windows (Fig. 2, left). These included an early synchronization in the theta band (0–350 ms; 3–6 Hz), followed by a strong de-synchronization in the alpha band (350–700 ms; 8–14 Hz) and a synchronization in the gamma band (350–550 ms; 72–84 Hz). Imaging of these responses to the level of the cortex confirmed that they all originated from bilateral occipital regions, with the alpha response being more lateral than the medial theta and gamma responses (Fig. 2, middle)

  • Visuospatial neural oscillations differentiate patients on the AD spectrum from cognitively normal older adults To examine the utility of these occipital neural dynamics for classifying patients on the AD spectrum, we computed a logistic regression model with theta, alpha, and gamma frequency oscillatory responses as predictors, group as the binary dependent variable, and age as a predictor in the null model (Fig. 3, top)

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Summary

Introduction

Entrainment of neural oscillations in occipital cortices by external rhythmic visual stimuli has been proposed as a novel therapy for patients with Alzheimer’s disease (AD). Primary sensory systems are traditionally thought to be spared until late in the AD disease course, an emerging literature has suggested that rhythmic gamma-frequency visual stimulation might attenuate amyloid-β load and rescue cognitive function, by way of microglial recruitment and enhanced hemodynamic response [2,3,4,5,6]. This groundbreaking line of research has spawned multiple clinical trials and, more generally, has Wiesman et al Alz Res Therapy (2021) 13:139 led to an increased interest in the role of rhythmic neuronal activity in AD pathology, in the visual system. Previous work has indicated that the impact of clinical disorders on such neural responses is often mediated by their spectral content [12, 13]

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