Electrophysiological Biomarkers of Epileptogenicity in Alzheimer's Disease.

Tingting Yu,Xiao Liu,Qun Wang,Jianping Wu

doi:10.3389/fnhum.2021.747077

Abstract

Cortical network hyperexcitability is an inextricable feature of Alzheimer’s disease (AD) that also might accelerate its progression. Seizures are reported in 10–22% of patients with AD, and subclinical epileptiform abnormalities have been identified in 21–42% of patients with AD without seizures. Accurate identification of hyperexcitability and appropriate intervention to slow the compromise of cognitive functions of AD might open up a new approach to treatment. Based on the results of several studies, epileptiform discharges, especially those with specific features (including high frequency, robust morphology, right temporal location, and occurrence during awake or rapid eye movement states), frequent small sharp spikes (SSSs), temporal intermittent rhythmic delta activities (TIRDAs), and paroxysmal slow wave events (PSWEs) recorded in long-term scalp electroencephalogram (EEG) provide sufficient sensitivity and specificity in detecting cortical network hyperexcitability and epileptogenicity of AD. In addition, magnetoencephalogram (MEG), foramen ovale (FO) electrodes, and computational approaches help to find subclinical seizures that are invisible on scalp EEGs. We performed a comprehensive analysis of the aforementioned electrophysiological biomarkers of AD-related seizures.

Highlights

Brain rhythms are fundamental in maintaining normal cognition and behavior, and neuronal hyperexcitability has emerged as an important electrical abnormality that could lead to memory failure in the early stage of Alzheimer’s disease (AD) and contribute to the progression of the disease (Noebels, 2011; Vossel et al, 2013; Busche and Konnerth, 2015, 2016; Palop and Mucke, 2016)
Seizures are reported in 10–22% of patients with AD (Friedman et al, 2011; Vossel et al, 2013; Cretin et al, 2016; Sarkis et al, 2016), Abbreviations: 5xFAD, 5x familial AD; AD, Alzheimer’s disease; AEDs, antiepileptic drugs; aMCI, amnesic mild cognitive impairment; APOE4, the E4 variant of apolipoprotein E; AUROC, area under receiver operating characteristic; blood-brain barrier dysfunction (BBBd), bloodbrain barrier dysfunction; EEG, electroencephalogram; eLORETA, exact low-resolution brain electromagnetic tomography; FO, foramen ovale; MEG, magnetoencephalogram; mTL, mesial temporal lobe; PSWEs, paroxysmal slow wave events; REM, rapid eye moment; SSS, small sharp spikes; Temporal intermittent rhythmic delta activity (TIRDA), temporal inter mitten rhythmic delta activity
We performed a comprehensive analysis of the potential electrophysiological biomarkers of AD-related seizures in humans and discuss their feasibility in clinical practice and their potential to predict the subsequent development of clinical seizures and epilepsy

Summary

INTRODUCTION

Brain rhythms are fundamental in maintaining normal cognition and behavior, and neuronal hyperexcitability has emerged as an important electrical abnormality that could lead to memory failure in the early stage of Alzheimer’s disease (AD) and contribute to the progression of the disease (Noebels, 2011; Vossel et al, 2013; Busche and Konnerth, 2015, 2016; Palop and Mucke, 2016). For patients with high suspicion of cortical hyperexcitability without visible epileptiform discharge on scalp EEG, MEG examination can be chosen to individually guide the usage of AEDs. Robust morphology, right temporal location, occur during awake or REM state, and higher frequency Unilateral SSS-like waveforms and frequent frequency (>100 per 24 h) Occur during awake or REM state –. Many studies have demonstrated that though there is a lack of visible epileptiform activity on scalp EEG, non-specific subtle and quantitative changes in local or long-distance networks induced by mTL spikes or seizures (Tyvaert et al, 2009; Vulliemoz et al, 2011; Cunningham et al, 2012; Aghakhani et al, 2015; Khoo et al, 2017; Naftulin et al, 2018) can be detected indirectly using artificial intelligence approaches. Once the computational approaches become established, patients with AD may obtain more accurate guidance on antiepileptic therapy

Findings

DISCUSSION

CONCLUSION