Parallel Neurophysiological Abnormalities in Long COVID and Alzheimer's Disease: An EEG Perspective

Table of Contents

Overall Summary

Overview

This systematic review investigates the parallel electrophysiological abnormalities detected by electroencephalography (EEG) in individuals with Long COVID and Alzheimer's disease and related dementias (ADRD). The review found that COVID-19 patients, even months after the initial infection, frequently display abnormal EEG activity, including a slowing of brain waves and epileptiform activity, similar to patterns observed in early ADRD. These shared EEG abnormalities suggest potential overlapping pathologies, such as neuroinflammation, astrocyte reactivity, hypoxia, and neurovascular injury. The review proposes that routine EEG could be a valuable tool for monitoring COVID-19 patients, predicting long-term outcomes, and assessing treatment effectiveness, offering insights into the shared mechanisms and potential therapeutic targets for both Long COVID and ADRD. The analysis included keyword searches across multiple databases, expert panel review, and consideration of co-authors' research and expert communications.

Key Findings

Strengths

Areas for Improvement

Significant Elements

Figure 4

Description: This infographic compares the complex brain pathophysiology of COVID-19 and ADRD, using diagrams and microscopic images to illustrate shared mechanisms, such as neuroinflammation, vascular dysfunction, neurodegeneration, and synaptic dysfunction. It connects these pathophysiological changes to EEG slowing, brain dysfunction, and network hyperexcitability.

Relevance: This figure visually demonstrates the parallel pathophysiological mechanisms between COVID-19 and ADRD, supporting the hypothesis that similar processes contribute to EEG abnormalities and cognitive dysfunction in both conditions.

Figure 5

Description: This figure proposes a model illustrating the shared mechanisms between COVID-19 and ADRD that lead to similar EEG abnormalities and cognitive dysfunction. It represents the pathways from initial pathologies to their impact on astrocyte reactivity, neurodegeneration, synaptic dysfunction, EEG changes, and cognitive functions.

Relevance: This figure summarizes the key argument of the review by visually representing the complex interplay between pathologies, EEG changes, and cognitive impairments in both COVID-19 and ADRD.

Conclusion

This review highlights the potential of EEG as a valuable tool for understanding and managing the neurological complications of Long COVID. The findings suggest that Long COVID and ADRD share similar neurophysiological abnormalities, detectable through routine EEG, potentially stemming from overlapping pathophysiological mechanisms such as neuroinflammation, astrocyte reactivity, and synaptic dysfunction. Future research should focus on validating EEG as a biomarker for Long COVID severity and cognitive decline risk, investigating the long-term neurological consequences of COVID-19, and exploring potential therapeutic interventions targeting shared mechanisms with ADRD. Further research is crucial to determine the specificity of EEG abnormalities in Long COVID compared to other conditions, to establish standardized EEG protocols for Long COVID assessment, and to explore the impact of diverse demographic factors on EEG findings. This will pave the way for personalized medicine approaches and targeted interventions for individuals with Long COVID.

Section Analysis

Abstract

Overview

This abstract summarizes a systematic review conducted by an expert panel on the parallel electrophysiological abnormalities observed in individuals with Long COVID and those with Alzheimer's disease and related dementias (ADRD). The review found that COVID-19 patients, even months after acute infection, often exhibit abnormal EEG activity, including slowing (reduced alpha, increased slow waves) and epileptiform activity, mirroring patterns seen in early ADRD. The panel proposes that these similar EEG abnormalities stem from parallel pathologies like neuroinflammation, astrocyte reactivity, hypoxia, and neurovascular injury. They suggest that routine EEG could be valuable for monitoring COVID-19 patients, predicting long-term outcomes, and assessing treatment efficacy.

Key Aspects

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Suggestions for Improvement

Introduction

Overview

This introduction emphasizes the importance of neurophysiological evaluations, particularly using EEG, in COVID-19 patients. It highlights the similarities between the neurological impacts of COVID-19 and neurodegenerative diseases like Alzheimer's. The introduction points out that abnormal electrophysiological brain activity, especially reduced alpha rhythm and increased delta rhythms in resting-state EEG, is a common characteristic in COVID-19 patients experiencing cognitive impairments like "brain fog." These EEG abnormalities, also observed in MCI and ADRD, suggest overlapping neurophysiological mechanisms between Long COVID and ADRD.

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Long COVID, Neurological and Cognitive Symptoms, and EEG Signatures

Overview

This section describes the neurological and cognitive symptoms associated with Long COVID, particularly focusing on their connection to EEG signatures. It explains that Long COVID can lead to persistent symptoms like brain fog, memory problems, and attention disorders, which are reflected in abnormal EEG patterns. These EEG abnormalities, such as reduced alpha rhythm and increased delta/theta power, resemble those observed in mild cognitive impairment (MCI) and Alzheimer's disease and related dementias (ADRD). The section also discusses the prevalence of neurological symptoms in Long COVID patients and highlights the impact of these symptoms on their quality of life.

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Methodology of the Review

Overview

This section outlines the methodology employed for the systematic review. The review focused on EEG signatures of Long COVID, comparing them with existing literature on Alzheimer's disease and related dementias (ADRD). The researchers used a multi-pronged approach, starting with keyword searches across various databases like PubMed, Google Scholar, NIH, and CDC. They refined their search by setting specific criteria for EEG signatures in Long COVID, ensuring relevance to EEG studies in COVID-19 positive individuals. The expert panel, comprised of electrophysiology professionals, was further enhanced by inviting specialists in neurophysiology, astrocyte reactivity, and neuroinflammation. The review process also incorporated co-authors' research, publication recommendations, and expert communications. Finally, the authors acknowledge two caveats: the broad diagnostic criteria for Long COVID brain fog and the heterogeneity of EEG data analysis procedures across studies.

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Non-Text Elements

figure 1

This flowchart details the step-by-step process used for selecting relevant articles for this systematic review. It starts with the initial identification of articles, then screening, eligibility check, and finally, inclusion in the review. Each step shows the number of articles considered and the number excluded, along with reasons for exclusion.

First Mention

Text: "(Figure 1)"

Context: those deemed relevant, novel, and impactful in relation to EEG correlation in persons with (Long) COVID-19 were included (Figure 1).

Relevance: This flowchart is crucial for understanding the methodology of the systematic review. It provides transparency and allows readers to assess the rigor and comprehensiveness of the article selection process.

Critique
Visual Aspects
  • The flowchart is clear and easy to follow.
  • The reasons for exclusion are clearly stated.
  • The use of different colors or shading could further enhance visual distinction between stages.
Analytical Aspects
  • The flowchart clearly defines the inclusion and exclusion criteria, which is essential for a systematic review.
  • The specific numbers of articles included and excluded at each stage are helpful for understanding the scope of the review.
  • The flowchart could benefit from adding information about the databases searched and the search terms used.
Numeric Data
  • Articles initially identified:
  • Screened articles: 113 articles
  • Excluded articles (screening): 32 articles
  • Full-text articles assessed for eligibility: 83 articles
  • Excluded articles (eligibility): 23 articles
  • Articles included in review: 60 articles
figure 2

This network diagram visually represents the overlap and interconnectedness between the research literature on COVID-19 and Alzheimer's disease and related dementias (ADRD). Each circle represents a research paper, and the size of the circle corresponds to its impact, as measured by citations. Connections between circles indicate relationships between the papers.

First Mention

Text: "(Figure 2)"

Context: Aiming to cover all related EEG and long-COVID studies, we also benefited from co-authors’ research on COVID-19 and EEG, publication recommendations, and personal communications from the experts. Figure 2 shows the interconnectedness of literatures of COVID-19 and those in AD/ADRD.

Relevance: This diagram visually demonstrates the significant overlap in the research areas of COVID-19 and ADRD, justifying the investigation into shared pathologies and neurophysiological mechanisms.

Critique
Visual Aspects
  • The diagram effectively uses circle size to represent the impact of each paper.
  • The connections between circles clearly show the relationships between publications.
  • Different colors could be used to represent different research themes or clusters within the network.
Analytical Aspects
  • The diagram provides a visual overview of the research landscape, highlighting key areas of overlap.
  • The use of citations as a measure of impact is a reasonable approach.
  • The diagram could be improved by providing more information about the specific relationships between papers, such as shared authors or similar methodologies.
Numeric Data

EEG Studies in COVID-19 Patients

Overview

This section explores EEG findings in COVID-19 patients, both during the acute phase of infection and in Long COVID. It notes that common EEG abnormalities include a generalized slowing of brain activity, particularly in the frontal regions, often manifesting as increased delta and theta power and decreased alpha power. More severe EEG alterations are observed in patients with pre-existing conditions like epilepsy or cognitive impairment. The section also discusses Alpha Coma (AC), a specific EEG pattern seen in some severe acute COVID-19 cases, and highlights the potential of EEG abnormalities as biomarkers for brain damage.

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Non-Text Elements

table 1

Table 1 summarizes acute COVID-19 EEG studies. It lists authors, study types, affected brain sites, neurological abnormalities, EEG findings, and EEG markers. Each row represents a different study. For example, Antony and Haneef (2020) conducted a literature review of 617 patients, finding diffuse slowing (68%), focal slowing (17%), and other abnormalities, with frontal epileptiform discharges and frontal monomorphic biphasic slow waves as EEG markers. Other studies listed provide similar data, showing EEG slowing, epileptiform activity, and other abnormalities in acute COVID-19.

First Mention

Text: "(Table 1)"

Context: Acute COVID-19 causes characteristic rsEEG patterns (Table 1) with a generalized 'slowing' in the rsEEG activity.

Relevance: This table is important because it gathers evidence from multiple studies, demonstrating that acute COVID-19 infection often leads to abnormal EEG patterns, particularly slowing. This supports the idea that COVID-19 can directly impact brain activity.

Critique
Visual Aspects
  • The table is well-organized and easy to read.
  • The use of percentages helps quantify the prevalence of EEG findings.
  • Using consistent terminology for EEG findings across studies would improve clarity.
Analytical Aspects
  • The table effectively summarizes key information from various studies.
  • Including the sample sizes for each study would provide valuable context.
  • A brief explanation of the different EEG markers and their significance would be helpful for readers unfamiliar with EEG terminology.
Numeric Data
table 2

Table 2 summarizes Long COVID-19 EEG studies. It presents information on authors, study types, affected brain sites, neurological abnormalities, EEG findings, and EEG markers for each study. For instance, Borhani et al. (2021) found slowing in frontal regions, marked by increased delta and theta/alpha ratio. Other studies listed also report slowing, altered dominant rhythms, and hemispheric asymmetries in Long COVID patients.

First Mention

Text: "(Table 2)"

Context: More severe rsEEG alterations (summarized in Tables 1-2) in COVID-19 patients were associated with prior pathological conditions.

Relevance: This table is important as it compiles evidence showing that EEG abnormalities, especially slowing, persist in Long COVID patients. This suggests that COVID-19 can have long-lasting effects on brain activity, contributing to cognitive issues like brain fog.

Critique
Visual Aspects
  • The table is well-structured and easy to understand.
  • Consistent terminology for EEG findings would enhance clarity.
  • Adding a column for the time since initial COVID-19 infection would provide valuable context.
Analytical Aspects
  • The table effectively summarizes key information from different Long COVID EEG studies.
  • Including sample sizes would strengthen the analysis.
  • A brief explanation of the EEG markers and their clinical significance would benefit readers unfamiliar with EEG.
Numeric Data
table 2

Table 2 summarizes studies on EEG findings in Long COVID patients. It presents information on the authors, study type, affected brain regions, neurological abnormalities, EEG findings, and EEG markers identified in each study. The table highlights the common finding of 'slowing' of EEG rhythms in Long COVID patients, often characterized by increased delta and theta power and reduced alpha power. It also notes other EEG abnormalities like hemispheric asymmetries and diffuse delta activity.

First Mention

Text: "Table 2"

Context: EEG studies highlighting the features of rsEEG abnormalities in Long COVID patients showed interesting results summarized in Table 2.

Relevance: This table is highly relevant as it gathers evidence from multiple studies on EEG abnormalities in Long COVID. This information is key to understanding the neurological impact of Long COVID and its potential overlap with neurodegenerative diseases.

Critique
Visual Aspects
  • The table is well-organized and easy to read.
  • The use of clear headings makes it easy to understand the information presented.
  • Using different colors or shading to highlight key findings, such as the consistent 'slowing' of EEG, could improve readability.
Analytical Aspects
  • The table effectively summarizes key information from multiple studies.
  • Providing more details on the sample sizes and demographics of each study would be helpful.
  • A brief explanation of the different EEG markers listed (e.g., individual alpha frequency, cortical current source density) would make the table more accessible to a broader audience.
Numeric Data

EEG Slowing in COVID-19

Overview

This section explores the potential reasons behind the EEG slowing observed in COVID-19 patients, drawing parallels with similar patterns seen in Alzheimer's disease and related dementias (ADRD). It discusses how amyloid and tau pathologies, often associated with ADRD, might be linked to COVID-19 and contribute to the observed EEG slowing. Additionally, the section investigates the role of hypoxia, heart rate variability (HRV), and their connection to abnormal EEG activity in Long COVID. It suggests that hypoxia, resulting from various factors including respiratory issues and heart-brain connection abnormalities, can lead to EEG slowing and cognitive symptoms like brain fog. The section also touches upon the EEG pattern of burst suppression, seen in some slow-recovering COVID-19 patients, and its association with reduced brain metabolism.

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Possible Neurophysiological Abnormalities in Long COVID: Parallel to ADRD

Overview

This section explores the potential neurophysiological mechanisms underlying the neurological and cognitive symptoms observed in Long COVID, drawing parallels with Alzheimer's disease and related dementias (ADRD). It focuses on the role of neuroinflammation, chronic glial reactivity, and cerebrovascular injury in both conditions. The section discusses how these factors can lead to synaptic dysfunction and neuronal abnormalities, which are reflected in abnormal EEG patterns. It also highlights the role of complement C3 and glutamate transport dysfunction in synapse loss and impaired brain function, suggesting similar mechanisms might be at play in both Long COVID and ADRD.

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Non-Text Elements

figure 3

This infographic contrasts healthy brain activity with brain activity in individuals with COVID-19. It illustrates normal EEG waveforms (Beta, Alpha, Theta, Delta) alongside a scalp EEG recording and heart-brain interaction in healthy brains. In COVID-19 affected brains, it depicts slowed EEG waveforms, cognitive event-related potentials, and lists neurological and cognitive impairments like headache, fatigue, seizures, and brain fog. The figure connects network abnormalities and hypoxia to neurological and cognitive impairments.

First Mention

Text: "FIGURE 3 Electroencephalographic (EEG) abnormality in healthy versus coronavirus disease 2019 (COVID-19) positive individuals."

Context: This figure illustrates the differences in EEG patterns between healthy individuals and those with COVID-19, both in the acute and long COVID phases. It highlights the slowing of EEG signals and the presence of epileptiform activity in COVID-19 patients, particularly in frontal brain regions.

Relevance: Figure 3 visually summarizes the core argument of the paper: COVID-19 impacts brain activity, leading to neurological and cognitive issues. The comparison with healthy brain activity underscores the abnormalities and their potential link to neurodegenerative diseases.

Critique
Visual Aspects
  • Clear visual distinction between healthy and COVID-19 affected brain activity.
  • Effective use of waveform illustrations and labels.
  • Could benefit from clearer labeling of the scalp EEG recording and heart-brain interaction.
Analytical Aspects
  • Provides a good overview of the key EEG changes in COVID-19.
  • Could be strengthened by briefly explaining what event-related potentials represent.
  • Could benefit from a more explicit connection between the listed neurological/cognitive dysfunctions and the observed EEG abnormalities.
Numeric Data
figure 4

This infographic compares the complex brain pathophysiology of COVID-19 and Alzheimer's disease and related dementias (ADRDs). It uses diagrams, microscopic images, and EEG/brain dysfunction representations to illustrate shared mechanisms. It details how COVID-19, through blood immune cell activation, vascular damage, and glial cell activation, can lead to neuroinflammation, vascular dysfunction, neurodegeneration, and synaptic dysfunction. Microscopic images show cellular changes, including vasculature and astrocyte changes, Aβ/microglia interaction, tauopathies, and Aβ/astrocyte interactions. The figure connects these pathophysiological changes to EEG slowing, brain dysfunction, and network hyperexcitability, suggesting shared pathways between COVID-19 and ADRD.

First Mention

Text: "FIGURE 4 Complex brain pathophysiology and pathology of coronavirus disease 2019 (COVID-19) infection and Alzheimer's disease and related dementias (ADRDs)."

Context: This figure provides a detailed illustration of the complex pathophysiological processes involved in both COVID-19 and ADRD, highlighting the similarities in their effects on the brain, including vascular damage, neuroinflammation, and neuronal injury.

Relevance: Figure 4 is crucial as it visually demonstrates the parallel pathophysiological mechanisms between COVID-19 and ADRD, supporting the hypothesis that similar processes contribute to EEG abnormalities and cognitive dysfunction in both conditions.

Critique
Visual Aspects
  • Effective use of diagrams and microscopic images to illustrate complex processes.
  • Clear labeling and color-coding.
  • Could benefit from simplified illustrations for a broader audience.
Analytical Aspects
  • Comprehensive representation of the shared pathophysiology between COVID-19 and ADRD.
  • Could be strengthened by briefly explaining key terms like 'glycocalyx,' 'glia cell activation,' and 'cytokines.'
  • Could benefit from a more explicit link between the cellular changes depicted in the microscopic images and the overall pathophysiological processes.
Numeric Data
figure 5

This figure proposes a model illustrating parallel pathologies and shared mechanisms between COVID-19 and Alzheimer's Disease/Related Dementias (AD/ADRD) that lead to similar EEG abnormalities and cognitive dysfunction. It visually represents the pathways from initial pathologies to their impact on astrocyte reactivity, neurodegeneration, synaptic dysfunction, EEG changes (slowing and hyperexcitability), cognitive functions (executive function, attention, memory, spatial/visual processing, mental fog), and ultimately, driving impairment.

First Mention

Text: "(Figure 5)"

Context: To summarize the contrasts and commonalities in COVID-19 and AD/ADRD, we built a model from pathology, EEG, and cognitive impairment (Figure 5).

Relevance: This figure is crucial for summarizing the key argument of the review: that COVID-19 and AD/ADRD share similar pathological mechanisms leading to comparable EEG and cognitive impairments. It provides a visual representation of the complex interplay between these factors.

Critique
Visual Aspects
  • The figure effectively uses color-coding and clear labels to distinguish between COVID-19 and AD/ADRD pathways.
  • The visual representation of increasing driving impairment (from walking to multiple parked cars) is intuitive and easy to understand.
  • The figure could benefit from clearer visual separation between the different stages of the model (pathology, astrocyte reactivity, EEG changes, etc.).
Analytical Aspects
  • The model clearly illustrates the proposed shared mechanisms between COVID-19 and AD/ADRD.
  • The inclusion of both EEG and cognitive outcomes strengthens the argument for parallel pathologies.
  • The figure could be improved by providing more details about the specific mechanisms involved at each stage. For example, how exactly does astrocyte reactivity lead to synaptic dysfunction?
Numeric Data

Unresolved Issues and Potential EEG Neural Biomarkers for Intervention

Overview

This section discusses unresolved questions regarding EEG abnormalities in Long COVID, particularly their specificity and relation to other conditions. It also explores potential EEG biomarkers for monitoring COVID-19 severity, predicting long-term outcomes, and assessing treatment efficacy. The section highlights the need for further research to establish specific EEG features associated with COVID-19 and to investigate the impact of factors like fatigue, age, sex, education, and race on EEG indicators.

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Future Directions and Conclusion

Overview

This section reiterates the significance of Long COVID's neurological effects, especially brain fog, as a major challenge, particularly its potential link to ADRD. It emphasizes the growing research interest in understanding the shared neurophysiological basis of cognitive issues in both Long COVID and ADRD, advocating for EEG as a cost-effective and accessible tool to investigate these questions. The section then lists specific future research directions, including determining the value of EEG for monitoring COVID-19 severity and exploring EEG markers as proxies for synaptic dysfunction. It concludes by summarizing the review's key messages: the potential of EEG for identifying neurological complications in Long COVID, the importance of understanding overlapping pathophysiology with ADRD, and the insights gained from ADRD research about reactive astrocytes' role in COVID-19.

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Future Directions

Overview

This section focuses on future research directions to better understand the neurological impact of COVID-19, particularly its relationship with Alzheimer's disease and related dementias (ADRD). It emphasizes the need for studies that investigate the value of EEG monitoring in assessing COVID-19 severity, predicting long-term cognitive outcomes, and evaluating treatment efficacy. It also highlights the importance of exploring EEG markers as indicators of synaptic dysfunction and identifying specific EEG features associated with COVID-19 and ADRD. Finally, it calls for research assessing variations in EEG indicators across diverse populations.

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Conclusion

Overview

This conclusion summarizes the review's findings, emphasizing the potential of EEG abnormalities as indicators of neurological complications in Long COVID. It also highlights the shared impact of COVID-19 and ADRD on synaptic and neurovascular function, involving astrocyte reactivity and neuroinflammation. The conclusion suggests that these shared pathologies contribute to similar neurophysiological abnormalities observed in both conditions, detectable through routine EEG. It underscores the importance of further research into cognitive EEG and MCI in Long COVID.

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Supporting Information

Overview

This section indicates where to find author disclosures regarding potential conflicts of interest.

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