Repurposing electroencephalogram monitoring of general anaesthesia for building biomarkers of brain ageing: an exploratory study

Sabbagh, David; Cartailler, Jérôme; Touchard, Cyril; Joachim, Jona; Mebazaa, Alexandre; Vallée, Fabrice; Gayat, Étienne; Gramfort, Alexandre; Engemann, Denis A.

doi:10.1016/j.bjao.2023.100145

Cited by 4 publications

(3 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A key benefit of multimodal or group stacking is that it allows for modality-specific encoding strategies and while approaching inference at the simplified level of the 2 nd level model combining the modality-wise predictions or activations. This strategy has been used to explore importance of distinct types of brain activity at different frequencies for age prediction (Sabbagh et al 2023;Engemann et al 2020). While stacking is easy to implement with standard software e.g.…”

Section: Related Workmentioning

confidence: 99%

Variable Importance in High-Dimensional Settings Requires Grouping

Chamma,

Thirion,

Engemann

2024

AAAI

View full text Add to dashboard Cite

Explaining the decision process of machine learning algorithms is nowadays crucial for both model’s performance enhancement and human comprehension. This can be achieved by assessing the variable importance of single variables, even for high-capacity non-linear methods, e.g. Deep Neural Networks (DNNs). While only removal-based approaches, such as Permutation Importance (PI), can bring statistical validity, they return misleading results when variables are correlated. Conditional Permutation Importance (CPI) bypasses PI’s limitations in such cases. However, in high-dimensional settings, where high correlations between the variables cancel their conditional importance, the use of CPI as well as other methods leads to unreliable results, besides prohibitive computation costs. Grouping variables statistically via clustering or some prior knowledge gains some power back and leads to better interpretations. In this work, we introduce BCPI (Block-Based Conditional Permutation Importance), a new generic framework for variable importance computation with statistical guarantees handling both single and group cases. Furthermore, as handling groups with high cardinality (such as a set of observations of a given modality) are both time-consuming and resource-intensive, we also introduce a new stacking approach extending the DNN architecture with sub-linear layers adapted to the group structure. We show that the ensuing approach extended with stacking controls the type-I error even with highly-correlated groups and shows top accuracy across benchmarks. Furthermore, we perform a real-world data analysis in a large-scale medical dataset where we aim to show the consistency between our results and the literature for a biomarker prediction.

show abstract

Section: Related Workmentioning

confidence: 99%

Variable Importance in High-Dimensional Settings Requires Grouping

Chamma,

Thirion,

Engemann

2024

AAAI

View full text Add to dashboard Cite

show abstract

“…In this work, the ShallowNet and Riemann benchmarks reached a score around 84% (Table S2 in supplement). This argues for the utility of the generative modeling framework and the covariance-based models derived from it beyond its initial exploration for age prediction and brain age (Banville et al, 2023;Mellot et al, 2023;Sabbagh et al, 2020Sabbagh et al, , 2023.…”

Section: /54mentioning

confidence: 99%

“…We focused on ML approaches for subject-level prediction, where one data point contains one EEG recording and one single outcome measure (Fruehwirt et al, 2017;Sabbagh et al, 2023;Wu et al, 2020) as compared to event-related modeling in cognitive decoding (King & Dehaene, 2014;Stokes et al, 2015) or brain-computer interfaces (BCI) (Abiri et al, 2019;Congedo et al, 2017). As ML requires training data, age and sex prediction are promising example problems that are easily accessible across data resources and have received increasing attention in human neuroscience.…”

Section: Introductionmentioning

confidence: 99%

Machine learning of brain-specific biomarkers from EEG

Bomatter,

Paillard,

Garces

et al. 2023

Preprint

View full text Add to dashboard Cite

Electroencephalography (EEG) has a long history as a clinical tool to study brain function, and its potential to derive biomarkers for various applications is far from exhausted. Machine learning (ML) can guide future innovation by harnessing the wealth of complex EEG signals to isolate relevant brain activity. Yet, ML studies in EEG tend to ignore physiological artifacts, which may cause problems for deriving biomarkers specific to the central nervous system (CNS). We present a framework for conceptualizing machine learning from CNS versus peripheral signals measured with EEG. A common signal representation across the frequency spectrum based on Morlet wavelets allowed us to define traditional brain activity features (e.g. log power) and alternative inputs used by state-of-the-art ML approaches (covariance matrices). Using more than 2600 EEG recordings from large public databases (TUAB, TDBRAIN), we studied the impact of peripheral signals and artifact removal techniques on ML models in exemplary age and sex prediction analyses. Across benchmarks, basic artifact rejection improved model performance whereas further removal of peripheral signals using ICA decreased performance. Our analyses revealed that peripheral signals enable age and sex prediction. However, they explained only a fraction of the performance provided by brain signals. We show that brain signals and body signals, both reflected in the EEG, allow for prediction of personal characteristics. While these results may depend on specific prediction problems, our work suggests that great care is needed to separate these signals when the goal is to develop CNS-specific biomarkers using ML.

show abstract

Do try this at home: Age prediction from sleep and meditation with large-scale low-cost mobile EEG

Banville,

Jaoude,

Wood

et al. 2024

Imaging Neuroscience

View full text Add to dashboard Cite

Electroencephalography (EEG) is an established method for quantifying large-scale neuronal dynamics which enables diverse real-world biomedical applications, including brain-computer interfaces, epilepsy monitoring, and sleep staging. Advances in sensor technology have freed EEG from traditional laboratory settings, making low-cost ambulatory or at-home assessments of brain function possible. While ecologically valid brain assessments are becoming more practical, the impact of their reduced spatial resolution and susceptibility to noise remain to be investigated. This study set out to explore the potential of at-home EEG assessments for biomarker discovery using the brain age framework and four-channel consumer EEG data. We analyzed recordings from more than 5200 human subjects (18–81 years) during meditation and sleep, to predict age at the time of recording. With cross-validated R2 scores between 0.3-0.5, prediction performance was within the range of results obtained by recent benchmarks focused on laboratory-grade EEG. While age prediction was successful from both meditation and sleep recordings, the latter led to higher performance. Analysis by sleep stage uncovered that N2-N3 stages contained most of the signal. When combined, EEG features extracted from all sleep stages gave the best performance, suggesting that the entire night of sleep contains valuable age-related information. Furthermore, model comparisons suggested that information was spread out across electrodes and frequencies, supporting the use of multivariate modeling approaches. Thanks to our unique dataset of longitudinal repeat sessions spanning 153 to 529 days from eight subjects, we finally evaluated the variability of EEG-based age predictions, showing that they reflect both trait- and state-like information. Overall, our results demonstrate that state-of-the-art machine-learning approaches based on age prediction can be readily applied to real-world EEG recordings obtained during at-home sleep and meditation practice.

show abstract

Repurposing electroencephalogram monitoring of general anaesthesia for building biomarkers of brain ageing: an exploratory study

Cited by 4 publications

References 39 publications

Variable Importance in High-Dimensional Settings Requires Grouping

Variable Importance in High-Dimensional Settings Requires Grouping

Machine learning of brain-specific biomarkers from EEG

Do try this at home: Age prediction from sleep and meditation with large-scale low-cost mobile EEG

Contact Info

Product

Resources

About