Denis A. Engemann

Research in Psychology, Neurophysiology, Data Science

Research Scientist, Principal Investigator

Parietal, INRIA Saclay

About me

I am an experimental psychologist by training and work as research scientist at (INRIA). One of my big questions is: How can we improve clinical practice in neurology and psychiatry? With my team, we address this question by bringing research in computer science and neuroscience into medical applications. Over the past seven years, I have specialized in large-scale data analysis and predictive modeling with brain data i.e., electroencephalography (EEG), magnetoencephalography (MEG) and magnetic resonance imaging (MRI).

Most of my work can be described using three dimensions: (i) empirically derived proxy measures of brain health and mental health, (ii) statistical methods and algorithms for analyzing brain data at the population scale, (iii) open source tools for analyzing neural time series.

For an overview of my current research please consider my latest recorded talks e.g. my lecture at The Neuro.

Interests

  • Cognition, brain function and diseases
  • Biomarker development
  • Computational methods for non-invasive neurophysiology

Education

  • PhD in Psychology, 2015

    University of Cologne, Germany

  • Diploma in Psychology, 2010

    University of Gießen, Germany

Recent & Upcoming Talks

Combining magnetic resonance imaging and magnetoencephalography enhances modeling of brain age
Sep 2, 2020 9:00 AM — 9:30 AM CompAge 2020 conference, Paris, France
Challenges of building clinical biomarkers from M/EEG: multimodal modeling with missing data and robust regression on power spectra
Jul 15, 2020 4:00 PM — 5:00 PM Montreal Neurological Insititute 2020, Feindel Virtual Lecture series, Montreal, Canada
Analysis of MEG/EEG with MNE-Python
Dec 3, 2019 9:00 AM — Dec 5, 2019 6:00 PM Brain and Spine Institute (ICM), Paris, France
Large-scale analysis of electrophysiology data in cognitive neurology.
Nov 4, 2019 3:00 PM — 4:00 PM Max-Planck-Institute for Cognitive and Brain Sciences, Leipzig, Germany
Analyzing EEG and MEG in Python and MNE
Jun 25, 2019 — Jun 26, 2019 University of Glasgow, School of Psychology, UK
See all

Key Publications

Beyond brain age: Empirically-derived proxy measures of mental health

PDF Code DOI
Beyond brain age: Empirically-derived proxy measures of mental health

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!
PDF Code Project DOI
Combining magnetoencephalography with MRI enhances learning of surrogate-biomarkers

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 https://t.
PDF Code Project Poster DOI
Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states

Manifold-regression to predict from MEG/EEG brain signals without source modeling

PDF Code Project Project Poster Slides
Manifold-regression to predict from MEG/EEG brain signals without source modeling

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:
PDF Code Project DOI
Robust EEG-based cross-site and cross-protocol classification of states of consciousness
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Recent Publications

Filter publications

Florence Baillin, Aline Lefebvre, Amandine Pedoux, Yann Beauxis, Denis A Engemann, Anna Maruani, Frederique Amsellem, JA Scott Kelso, Thomas Bourgeron, Richard Delorme, Guillaume Dumas (2020). Interactive Psychometrics for Autism With the Human Dynamic Clamp: Interpersonal Synchrony From Sensorimotor to Sociocognitive Domains. Frontiers in Psychiatry.

PDF Project DOI

Cyril Touchard, Jérôme Cartailler, Charlotte Levé, José Serrano, David Sabbagh, Elsa Manquat, Jona Joachim, Joaquim Mateo, Etienne Gayat, Denis Engemann, Fabrice Vallée (2020). Propofol Requirement and EEG Alpha Band Power During General Anesthesia Provide Complementary Views on Preoperative Cognitive Decline. Frontiers in Aging Neuroscience.

PDF Project DOI

Hubert Banville, Omar Chehab, Aapo Hyvarinen, Denis Engemann, Alexandre Gramfort (2020). Uncovering the structure of clinical EEG signals with self-supervised learning. Journal of Neural Engineering.

PDF DOI

Danilo Bzdok, Denis Engemann, Bertrand Thirion (2020). Inference and Prediction Diverge in Biomedicine. bioRxiv preprint.

PDF Code DOI

Kamalaker Dadi, Gael Varoquaux, Josselin Houenou, Danilo Bzdok, Bertrand Thirion, Denis Engemann (2020). Beyond brain age: Empirically-derived proxy measures of mental health. bioRxiv.

PDF Code DOI

Denis A Engemann, Oleh Kozynets, David Sabbagh, Guillaume Lemaître, Gael Varoquaux, Franz Liem, Alexandre Gramfort (2020). Combining magnetoencephalography with MRI enhances learning of surrogate-biomarkers. eLife.

PDF Code Project DOI

David Sabbagh, Pierre Ablin, Gael Varoquaux, Alexandre Gramfort, Denis A Engemann (2020). Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states. NeuroImage.

PDF Code Project Poster DOI

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Projects

Automated processing of M/EEG

Automated processing of M/EEG

Tools for large-scale analysis

MNE software for analyzing and visualizing M/EEG data

MNE software for analyzing and visualizing M/EEG data

Coding, mentoring and dissemination for the MNE project.

Prediction of perioperative cerebral fragility from EEG using statistical learning

Prediction of perioperative cerebral fragility from EEG using statistical learning

ML/AI controled drug-dosage during anesthesia.

Tackling disorders of consciousness with Cognitive Neuroscience, EEG and Machine Learning

Tackling disorders of consciousness with Cognitive Neuroscience, EEG and Machine Learning

Supplementing diagnosis in disorders of consciousnes with EEG and ML

Ad-hoc Reviewer

Experience

 
 
 
 
 

Research Scientist

INRIA Saclay / Neurospin (Parietal)

Jan 2017 – Present France
Principal investigator
 
 
 
 
 

Postdoc

Neurospin (Unicog) & ICM (Picnic)

Apr 2014 – Dec 2016 France
Postdoc in clinical neuroscience
 
 
 
 
 

PhD student

Jülich Research Center (INM-3) & University of Cologne

Nov 2010 – Mar 2014 Germany
PhD student in cognitive social neuroscience
 
 
 
 
 

Diploma student, intern (Tomasello lab)

Max-Planck-Institute for Evolutionary Anthropology

Sep 2007 – Jan 2009 Germany
Intern & diploma thesis in developmental social cognition

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