Combining magnetoencephalography with MRI enhances learning of surrogate-biomarkers

For a plain English summary, consider the eLife digest by Helena Perez Valle. To get an overview on the paper, consider this Twitter thread: I am very excited to share our latest work published in @eLife! We combined #MEG, #fMRI and #MRI for #BrainAge prediction & #biomarker development. Each modality added unique information & enhanced brain-behavior mapping! Thread👇 — Denis A. Engemann (@dngman) May 19, 2020

Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states

For a high-level presentation of the main findings check-out our OHBM 2020 Poster. Or visit this Twitter thread: Excited to share our paper @NeuroImage_EiC by @DavSabbagh with @PierreAblin @GaelVaroquaux @agramfort Nonlinear subject-level regression on M/EEG using linear models without source localization: theory + empirical benchmarks Thread👇 — Denis A. Engemann (@dngman) May 18, 2020

Robust EEG-based cross-site and cross-protocol classification of states of consciousness

For a discussion of the neurophysiologicla implications, please consider the companion article by Rodika Sokoliuk and Damian Cruse Should you have no time to read the paper, this Twitter thread gives you the gist: 1/ New paper out! @Brain1878 Robust #MachineLearning of diagnosis from #EEG in disorders of #consciousness powered by @CEAParisSaclay @cdf1530 @Coma_Science @dc_uba @ERC_Research @Inria_Saclay @Inserm @mne_news @NeuroSpin_91 @icm_institute @scikit_learn — Denis A. Engemann (@dngman) October 4, 2018