Association of Sleep Electroencephalography-Based Brain Age Index With Dementia

Ye, Elissa; Sun, Haoqi; Leone, Michael J.; Paixao, Luis; Thomas, Robert J.; Lam, Alice; Westover, M. Brandon

doi:10.1001/jamanetworkopen.2020.17357

Cited by 29 publications

(15 citation statements)

References 42 publications

(64 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In clinical and real-world contexts in which EEG is frequently collected, fine-grained spatial information may not be present as only a few electrodes are used. This has favored alternative EEG-derived brain-age models focusing on a wealth of spectral and temporal features (Al Zoubi et al 2018) which may perform better on sparse EEG-montages and has enabled sleepbased brain age measures (Sun et al 2019;Ye et al 2020).…”

Section: Introductionmentioning

confidence: 99%

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

Engemann

Mellot

Hoechenberger

et al. 2021

Preprint

View full text Add to dashboard Cite

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

show abstract

Section: Introductionmentioning

confidence: 99%

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

Engemann

Mellot

Hoechenberger

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Another symposium speaker, Dr. Haoqi Sun, presented his work on a feature-based machine learning model that takes advantage of the fact that brain activity as recorded by EEG during sleep naturally varies with age (Leone et al, 2021;Paixao et al, 2020;Sun et al, 2019;Ye et al, 2020). Features from both time and frequency domains of each sleep stage are used to compute an overall brain age.…”

Section: Section Iv: Brain Health As Assessed By Deviations From Heal...mentioning

confidence: 99%

How Machine Learning is Powering Neuroimaging to Improve Brain Health

et al. 2022

Self Cite

View full text Add to dashboard Cite

This report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. Towards this goal, we aresharing the information presented at a symposium, “Neuroimaging Indicators of Brain Structure and Function - Closing the Gap Between Research and Clinical Application”, co-hosted by the McCance Center for Brain Health at Mass General Hospital and the MIT HST Neuroimaging Training Program on February 12, 2021. The symposium focused on the potential for machine learning approaches, applied to increasingly large-scale neuroimaging datasets, to transform healthcare delivery and change the trajectory of brain health by addressing brain care earlier in the lifespan. While not exhaustive, this overview uniquely addresses many of the technical challenges from image formation, to analysis and visualization, to synthesis and incorporation into the clinical workflow. Some of the ethical challenges inherent to this work are also explored, as are some of the regulatory requirements for implementation. We seek to educate, motivate, and inspire graduate students, postdoctoral fellows, and early career investigators to contribute to a future where neuroimaging meaningfully contributes to the maintenance of brain health.

show abstract

“…To understand whether the increased EEG-BAI in each sleep disorder is explained by the different pattern of regional EEG spectrum, we analyzed the spatial characteristics of the EEG spectral power among the 6 channels. To compute the spectral power, by applying the 3 rd order Butterworth bandpass filter, we differentiated five frequency bands: slow wave (0.5-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), sigma (12-15 Hz) and beta (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). Then, the spectral powers measured for the two channels in each frontal, central, or occipital lobe were averaged.…”

Section: Regional Spectral Power Analysismentioning

confidence: 99%

“…8 Recently, estimating individual BAIs using sleep EEG have been proposed, and these approaches successfully predicted an individual's risk for neurodegenerative disease, psychiatric disease, cognitive impairment, and mortality. 8,25,26 Yet, an unbiased, datadriven deep learning (DL) approach has not been explored, which may be fully capable of modeling the complex nature of sleep neuroeletrophysiology.…”

Section: Introductionmentioning

confidence: 99%

A novel neuroelectrophysiological age index implicates brain health and sleep disorders

Yook

Park

et al. 2022

Preprint

View full text Add to dashboard Cite

Sleep architecture and microstructures alter with aging and sleep disorder-led accelerated aging. We proposed a sleep electroencephalogram (EEG) based brain age prediction model using convolutional neural networks. We then associated the estimated brain age index (BAI) with brain structural aging features, sleep disorders and various sleep parameters. Our model also showed a higher BAI (predicted brain age minus chronological age) is associated with cortical thinning in various functional areas. We found a higher BAI for sleep disorder groups compared to healthy sleepers, as well as significant differences in the spectral pattern of EEG among different sleep disorders (lower power in slow and θ waves for sleep apnea vs. higher power in β and σ for insomnia), suggesting sleep disorder-dependent pathomechanisms of aging. Our results demonstrate that the new EEG-BAI can be a biomarker reflecting brain health in normal and various sleep disorder subjects, and may be used to assess treatment efficacy.

show abstract

Association of Sleep Electroencephalography-Based Brain Age Index With Dementia

Cited by 29 publications

References 42 publications

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

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

How Machine Learning is Powering Neuroimaging to Improve Brain Health

A novel neuroelectrophysiological age index implicates brain health and sleep disorders

Contact Info

Product

Resources

About