Publications of Denis A. Engemann

Engemann, D. A.; Mellot, A.; Höchenberger, R.; Banville, H.; Sabbagh, D.; Gemein, L.; Ball, T.; Gramfort, A.: A reusable benchmark of brain-age prediction from M/EEG resting-state signals. NeuroImage 262, 119521 (2022)

Denissen, S.; Engemann, D. A.; De Cock, A.; Costers, L.; Baijot, J.; Laton, J.; Penner, I. K.; Grothe, M.; Kirsch, M.; D’hooghe, M. B. et al.; D’Haeseleer, M.; Dive, D.; De Mey, J.; Van Schependom, J.; Sima, D. M.; Nagels, G.: Brain age as a surrogate marker for cognitive performance in multiple sclerosis. European Journal of Neurology 29 (10), pp. 3039 - 3049 (2022)

Vieira, B. H.; Liem, F.; Dadi, K.; Engemann, D. A.; Gramfort, A.; Bellec, P.; Craddock, R. C.; Damoiseaux, J. S.; Steele, C. J.; Yarkoni, T. et al.; Langer, N.; Margulies, D. S.; Varoquaux, G.: Predicting future cognitive decline from non-brain and multimodal brain imaging data in healthy and pathological aging. Neurobiology of Aging 118, pp. 55 - 65 (2022)

Banville, H.; Wood, S. U.N.; Aimone, C.; Engemann, D. A.; Gramfort, A.: Robust learning from corrupted EEG with dynamic spatial filtering. NeuroImage 251, 118994 (2022)

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

Banville, H.; Chehab, O.; Hyvärinen, A.; Engemann, D. A.; Gramfort, A.: Uncovering the structure of clinical EEG signals with self-supervised learning. Journal of Neural Engineering 18 (4), 046020 (2021)

Touchard, C.; Cartailler, J.; Levé, C.; Serrano, J.; Sabbagh, D.; Manquat, E.; Joachim, J.; Mateo, J.; Gayat, E.; Engemann, D. A. et al.; Vallée, F.: Propofol requirement and EEG alpha band power during general anesthesia provide complementary views on preoperative cognitive decline. Frontiers in Aging Neuroscience 12, 593320 (2020)

Sabbagh, D.; Ablin, P.; Varoquaux, G.; Gramfort, A.; Engemann, D. A.: Predictive regression modeling with MEG/EEG: From source power to signals and cognitive states. NeuroImage 222, 116893 (2020)

Bzdok, D.; Engemann, D. A.; Thirion, B.: Inference and prediction diverge in biomedicine. Patterns 1 (8), 100119 (2020)

Engemann, D. A.; Kozynets, O.; Sabbagh, D.; Lemaître, G.; Varoquaux, G.; Liem, F.; Gramfort, A.: Combining magnetoencephalography with magnetic resonance imaging enhances learning of surrogate-biomarkers. eLife 9, e54055 (2020)

Denissen, S.; Engemann, D. A.; De Cock, A.; Costers, L.; Baijot, J.; Laton, J.; Penner, I.K.; Grothe, M.; Kirsch, M.; D'hooghe, M. B. et al.; D'haeseleer, M.; Dive, D.; De Mey, J.; Van Schependom, J.; Sima, D.; Nagels, G.: Brain age as a surrogate marker for IPS in MS. In Multiple Sclerosis Journal, 28 (Suppl. 2), O2. Sage, London (2022)

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

Studenova, A.; Forster, C.; Engemann, D. A.; Hensch, T.; Sander, C.; Mauche, N.; Hegerl, U.; Loeffler, M.; Villringer, A.; Nikulin, V. V.: Event-related modulation of alpha rhythm explains the auditory P300 evoked response in EEG. (2023)