Are there consistent abnormalities in event‐related EEG oscillations in patients with Alzheimer’s disease compared to other diseases belonging to dementia?

Güntekin, Bahar; Aktürk, Tuba; Arakaki, Xianghong; Bonanni, Laura; Percio, Claudio Del; Edelmayer, Rebecca M.; Farina, Francesca R; Ferri, Raffaele; Hanoğlu, Lütfü; Kumar, Sanjeev; Lizio, Roberta; Lopez, Susanna; Murphy, Brian; Noce, Giuseppe; Randall, Fiona; Sack, Alexander T.; Stocchi, Fabrizio; Yener, Görsev; Yıldırım, Ebru; Babiloni, Claudio

doi:10.1111/psyp.13934

Cited by 24 publications

(19 citation statements)

References 247 publications

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“…Measures of oscillatory brain activity are also widely used in clinical and translational research, often with the goal of defining etiological mechanisms, establishing tools for diagnostic assessment, or identifying novel treatment targets. Güntekin et al (2022) provide a review addressing a key question in the neurophysiology of Alzheimer’s disease, namely the extent to which divergent oscillatory dynamics often observed in this condition are a specific marker of the disease. The authors highlight the promise of lower frequency event‐related oscillations as potential neurophysiological biomarkers of Alzheimer’s disease and discuss how studies in other dementias are needed to establish specificity.…”

Section: Overview Of Special Issue Articlesmentioning

confidence: 99%

Introduction to the special issue of human oscillatory brain activity: Methods, models, and mechanisms

Weisz

Keil

2022

Psychophysiology

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Section: Overview Of Special Issue Articlesmentioning

confidence: 99%

Introduction to the special issue of human oscillatory brain activity: Methods, models, and mechanisms

Weisz

Keil

2022

Psychophysiology

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“…Over the past decades, important progress has been made in clinical neuroscience, resulting in improvements to clinical diagnosis and treatment (Walhovd et al 2010;Ewers et al 2011). Backed by increasingly advanced analytical methods, this has enabled fine-grained characterization of neurodegenerative conditions (Gaubert et al 2019;Schumacher et al 2021;Güntekin et al 2021). Yet, from a public-health perspective, rather than focusing on pathology, it is essential to detect risk factors early within the general population in order to provide actionable feedback for preventive medicine, e.g., by targeting life-style changes.…”

Section: Introductionmentioning

confidence: 99%

A reusable benchmark of brain-age prediction from M/EEG resting-state signals

Engemann

Mellot

Hoechenberger

et al. 2021

Preprint

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Population-level modeling can define quantitative measures of individual aging by applying machine learning to large volumes of brain images. These measures of brain age, obtained from the general population, helped characterize disease severity in neurological populations, improving estimates of diagnosis or prognosis. Magnetoencephalography (MEG) and Electroencephalography (EEG) have the potential to further generalize this approach towards prevention and public health by enabling assessments of brain health at large scales in socioeconomically diverse environments. However, more research is needed to define methods that can handle the complexity and diversity of M/EEG signals across diverse real-world contexts. To catalyse this effort, here we propose reusable benchmarks of competing machine learning approaches for brain age modeling. We benchmarked popular classical machine learning pipelines and deep learning architectures previously used for pathology decoding or brain age estimation in 4 international M/EEG cohorts from diverse countries and cultural contexts, including recordings from more than 2500 participants. Our benchmarks were built on top of the M/EEG adaptations of the BIDS standard, providing tools that can be applied with minimal modification on any M/EEG dataset provided in the BIDS format. Our results suggest that, regardless of whether classical machine learning or deep learning was used, the highest performance was reached by pipelines and architectures involving spatially aware representations of the M/EEG signals, leading to R^2 scores between 0.60-0.71. Hand-crafted features paired with random forest regression provided robust benchmarks even in situations in which other approaches failed. Taken together, this set of benchmarks, accompanied by open-source software and high-level Python scripts, can serve as a starting point and quantitative reference for future efforts at developing M/EEG-based measures of brain aging. The generality of the approach renders this benchmark reusable for other related objectives such as modeling specific cognitive variables or clinical endpoints.Highlights- We provide systematic reusable benchmarks for brain age from M/EEG signals- The benchmarks were carried out on M/EEG from four countries > 2500 recordings- We compared machine learning pipelines capable of handling the non-linear regression task of relating biomedical outcomes to M/EEG dynamics, based on classical machine learning and deep learning- Next to data-driven methods we benchmarked template-based source localization as a practical tool for generating features less affected by electromagnetic field spread- The benchmarks are built on top of the MNE ecosystem and the braindecode package and can be applied on any M/EEG dataset presented in the BIDS format

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“…Although changes can be observed in brain activity at rest, specific task-related delays in the implementation, maintenance, or interruption of communications across brain regions could be highly sensitive to age and pathology effects. Recent work (Babiloni et al, 2020 ; Güntekin et al, 2021 ) highlighted the clinical value of investigating oddball-related brain activity to detect pathology effects. Finally, from an epidemiological perspective, future studies should also consider middle-aged individuals (40–60 years) to clarify the association between changes in brain dynamics and later cognitive trajectories.…”

Section: Discussionmentioning

confidence: 99%

M/EEG Dynamics Underlying Reserve, Resilience, and Maintenance in Aging: A Review

2022

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Cognitive reserve and resilience refer to the set of processes allowing the preservation of cognitive performance in the presence of structural and functional brain changes. Investigations of these concepts have provided unique insights into the heterogeneity of cognitive and brain changes associated with aging. Previous work mainly relied on methods benefiting from a high spatial precision but a low temporal resolution, and thus the temporal brain dynamics underlying these concepts remains poorly known. Moreover, while spontaneous fluctuations of neural activity have long been considered as noise, recent work highlights its critical contribution to brain functions. In this study, we synthesized the current state of knowledge from magnetoencephalography (MEG) and electroencephalography (EEG) studies that investigated the contribution of maintenance of neural synchrony, and variability of brain dynamics, to cognitive changes associated with healthy aging and the progression of neurodegenerative disease (such as Alzheimer's disease). The reviewed findings highlight that compensations could be associated with increased synchrony of higher (>10 Hz) frequency bands. Maintenance of young-like synchrony patterns was also observed in healthy older individuals. Both maintenance and compensation appear to be highly related to preserved structural integrity (brain reserve). However, increased synchrony was also found to be deleterious in some cases and reflects neurodegenerative processes. These results provide major elements on the stability or variability of functional networks as well as maintenance of neural synchrony over time, and their association with individual cognitive changes with aging. These findings could provide new and interesting considerations about cognitive reserve, maintenance, and resilience of brain functions and cognition.

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Are there consistent abnormalities in event‐related EEG oscillations in patients with Alzheimer’s disease compared to other diseases belonging to dementia?

Cited by 24 publications

References 247 publications

Introduction to the special issue of human oscillatory brain activity: Methods, models, and mechanisms

Introduction to the special issue of human oscillatory brain activity: Methods, models, and mechanisms

A reusable benchmark of brain-age prediction from M/EEG resting-state signals

M/EEG Dynamics Underlying Reserve, Resilience, and Maintenance in Aging: A Review

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