Publications of Denis A. Engemann

Studenova, A., Forster, C., Engemann, D. A., Hensch, T., Sanders, C., Mauche, N., … Nikulin, V. V. (2023). Event-related modulation of alpha rhythm explains the auditory P300-evoked response in EEG. ELife, 12. doi:10.7554/eLife.88367

Sabbagh, D., Cartailler, J., Touchard, C., Joachim, J., Mebazaa, A., Vallée, F., … Engemann, D. A. (2023). Repurposing electroencephalogram monitoring of general anaesthesia for building biomarkers of brain ageing: An exploratory study. BJA Open, 7. doi:10.1016/j.bjao.2023.100145

Engemann, D. A., Mellot, A., Höchenberger, R., Banville, H., Sabbagh, D., Gemein, L., … Gramfort, A. (2022). A reusable benchmark of brain-age prediction from M/EEG resting-state signals. NeuroImage, 262. doi:10.1016/j.neuroimage.2022.119521

Denissen, S., Engemann, D. A., De Cock, A., Costers, L., Baijot, J., Laton, J., … Nagels, G. (2022). Brain age as a surrogate marker for cognitive performance in multiple sclerosis. European Journal of Neurology, 29, 3039–3049.

Vieira, B. H., Liem, F., Dadi, K., Engemann, D. A., Gramfort, A., Bellec, P., … Varoquaux, G. (2022). Predicting future cognitive decline from non-brain and multimodal brain imaging data in healthy and pathological aging. Neurobiology of Aging, 118, 55–65.

Banville, H., Wood, S. U. N., Aimone, C., Engemann, D. A., & Gramfort, A. (2022). Robust learning from corrupted EEG with dynamic spatial filtering. NeuroImage, 251. doi:10.1016/j.neuroimage.2022.118994

Dadi, K., Varoquaux, G., Houenou, J., Bzdok, D., Thirion, B., & Engemann, D. A. (2021). Population modeling with machine learning can enhance measures of mental health. GigaScience, 10. doi:10.1093/gigascience/giab071

Banville, H., Chehab, O., Hyvärinen, A., Engemann, D. A., & Gramfort, A. (2021). Uncovering the structure of clinical EEG signals with self-supervised learning. Journal of Neural Engineering, 18. doi:10.1088/1741-2552/abca18

Touchard, C., Cartailler, J., Levé, C., Serrano, J., Sabbagh, D., Manquat, E., … Vallée, F. (2020). Propofol requirement and EEG alpha band power during general anesthesia provide complementary views on preoperative cognitive decline. Frontiers in Aging Neuroscience, 12. doi:10.3389/fnagi.2020.593320

Sabbagh, D., Ablin, P., Varoquaux, G., Gramfort, A., & Engemann, D. A. (2020). Predictive regression modeling with MEG/EEG: From source power to signals and cognitive states. NeuroImage, 222. doi:10.1016/j.neuroimage.2020.116893

Bzdok, D., Engemann, D. A., & Thirion, B. (2020). Inference and prediction diverge in biomedicine. Patterns, 1. doi:10.1016/j.patter.2020.100119

Engemann, D. A., Kozynets, O., Sabbagh, D., Lemaître, G., Varoquaux, G., Liem, F., & Gramfort, A. (2020). Combining magnetoencephalography with magnetic resonance imaging enhances learning of surrogate-biomarkers. ELife, 9. doi:10.7554/eLife.54055

Studenova, A., Villringer, A., Forster, C., Engemann, D. A., Hensch, T., Sander, C., … Nikulin, V. V. (2024). Unifying evoked responses and oscillatory neuronal dynamics. In Journal of Computational Neuroscience (Vol. 52, pp. S166–S166). Boston: Kluwer Academic Publishers.

Denissen, S., Engemann, D. A., De Cock, A., Costers, L., Baijot, J., Laton, J., … Nagels, G. (2022). Brain age as a surrogate marker for IPS in MS. In Multiple Sclerosis Journal (Vol. 28). London: Sage. Retrieved from http://hdl.handle.net/21.11116/0000-000B-1337-2

Studenova, A., Forster, C., Engemann, D. A., Hensch, T., Sander, C., Mauche, N., … Nikulin, V. V. Unifying evoked responses and oscillatory neuronal dynamics. Poster presented at the 32nd Annual Computational Neuroscience Meeting (CNS 2023), Leipzig, Germany. Retrieved from http://hdl.handle.net/21.11116/0000-000D-7B80-8

Banville, H., Jaoude, M. A., Wood, S. U. N., Aimone, C., Holst, S. C., Gramfort, A., & Engemann, D. A. (2023, April 29). Do try this at home: Age prediction from sleep and meditation with large-scale low-cost mobile EEG. BioRxiv. doi:10.1101/2023.04.29.538328