Matthias Nau

Cognitive neuroscientist at the National Institute of Mental Health, Bethesda, USA

Keywords: vision & viewing behavior - space & memory - fMRI - machine learning

How vision, gaze & memory combine

I am a postdoctoral researcher and Alexander von Humboldt fellow in the lab of Chris Baker at the National Institute of Mental Health in Bethesda (USA) and guest researcher at the Max-Planck-Institute for Human Cognitive and Brain Sciences in Leipzig (Germany).

I study the links between human vision, viewing behavior and memory, which to me are not separate concepts. Our ongoing experience is grounded in our behavior and vice versa, and both depend on and determine what we remember. This inseparability poses a challenge: mental processes and behavior must be studied together in the light of task demands, and neural activity needs to be interpreted with their connection in mind.

My work seeks to address this challenge, both on a theoretical and methodological level, by characterizing how human brain activity, mental processes (such as visual perception and memory), and complex behaviors (such as viewing and navigation) are grounded in each other. To do so, I combine neuroimaging and eye tracking with naturalistic stimuli and virtual reality, and I develop open-source machine-learning approaches to push the boundaries of how human behavior can be studied using neuroimaging.

This is a personal website and I do not represent the NIH here


Since 11/2020 - Postdoctoral researcher
National Institute of Mental Health (NIMH), Laboratory of Brain and Cognition (LBC), Bethesda, MD, USA.
PI: Christopher I. Baker

Supported by a Feodor Lynen Research Fellowship
by the Alexander von Humboldt foundation.

01/2020 - 10/2020 - Postdoctoral researcher
Kavli Institute for Systems Neuroscience, Centre for Neural Computation, NTNU, Trondheim, Norway.
PI: Christian F. Doeller

09/2016 - 01/2020 - PhD Candidate
Kavli Institute for Systems Neuroscience, Centre for Neural Computation, NTNU, Trondheim, Norway.
PI: Christian F. Doeller

04/2016 - 08/2016 - Research Assistant
Donders Institute for Brain, Cognition & Behaviour, Nijmegen, The Netherlands.
PI: Christian F. Doeller

11/2014 - 03/2016 - Research Assistant
Werner Reichardt Centre for Integrative Neuroscience, Tübingen, Germany.
PI: Andreas Bartels

11/2014 - Master of Science
Neurobiology, University of Tübingen, Tübingen, Germany.

Since 04/2019 - Guest researcher
Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.

11/2014 - 03/2016 - Guest researcher
Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany.

Publications & Preprints

*Shared-first author, † Shared-senior author

Franceschiello B., Rumac S., Hilbert T., Nau M., Dziadosz M., Degano G., Roy C.W., Gaglianese A., Petri G., Yerly J., Stuber M., Kober T., van Heeswijk R.B., Murray M.M., Fornari E. (2023). Hi-Fi fMRI: High-resolution, fast-sampled and sub-second whole-brain functional MRI at 3T in humans.

Polti I.* & Nau M.*, Kaplan R., Van Wassenhove V., Doeller C.F. (2022). Rapid encoding of task regularities in the human hippocampus guides sensorimotor timing.

Kidder A.* & Silson E.H.*, Nau M., Baker C.I. (2022). Distributed cortical regions for the recall of people, places and objects.

Allen E.J., St-Yves G., Wu Y., Breedlove J.L., Dowdle L.T., Nau M., Caron B., Pestilli F., Charest I., Hutchinson J.B., Naselaris T.†, Kay K.† (2022).
A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence.
Nature Neuroscience
News & Views article by Botch, Robertson & Finn

Frey M.* & Nau M.*†, Doeller C.F.† (2021). Magnetic resonance-based eye tracking using deep neural networks.
Nature Neuroscience
News & Views article by Krajbich I.

Levitis E.* & Gould van Praag C.D.* & Gau R.* & Heunis S.* ... Nau M., ... Maumet C. (2021). Centering inclusivity in the design of online conferences.
GigaScience (Shortened author list, n = 110)

Frey M., Tanni S., Perrodin C., O'Leary A., Nau M. Kelly J., Banino, A., Bendor, D., Lefort, J., Doeller C.F., Barry, C. (2021). Interpreting wide-band neural activity using convolutional neural networks.

Nau M., Navarro Schröder T., Frey M., Doeller C.F. (2020). Behavior-dependent directional tuning in the human visual-navigation network.
Nature Communications

Frey M., Nau M. (2020). Memory efficient brain tumor segmentation using an autoencoder-regularized U-Net.
Springer: Lecture Notes in Computer Science

Nau M. (2020). Perception & the cognitive map: deriving a stable world from visual inputs.
Dissertation - NTNU Open

Bellmund J.L.S., Ruiter T.A., Nau M., Doeller C.F. (2020). Deforming the metric of cognitive maps distorts memory.
Nature Human Behavior

Navarro Schröder T., Towse B.W., Nau M., Burgess N., Barry C., Doeller C.F. (2020). Environmental anchoring of grid-like representations minimizes spatial uncertainty during navigation.

Nau M. (2019). Functional imaging of the medial temporal lobe - A neuroscientist's guide to fMRI pulse sequence optimization.
Open Science Framework

Nau M.* & Julian J.B.*, Doeller C.F. (2018). How the brain's navigation system shapes our visual experience.
Trends in Cognitive Sciences
Featured cover article

Nau M., Navarro Schröder T., Bellmund J.L.S., Doeller C.F. (2018). Hexadirectional coding of visual space in human entorhinal cortex.
Nature Neuroscience
News & Views article by Killian N.J. & Buffalo E.A.

Nau M., Schindler A., Bartels A. (2018). Real-motion signals in human early visual cortex.

Magnetic resonance-based eye tracking using deep neural networks

DeepMReye is an open-source deep-learning framework for eye tracking in fMRI without camera. It reconstructs viewing behavior from the MR-signal of the eyeballs. It works even in existing datasets and when the eyes are closed.

Read more about it in our paper Paper!

The Code, Data & User Documentation are open access!

Functional imaging of the human medial temporal lobe.
A neuroscientist's guide to fMRI pulse sequence optimization

The medial temporal lobe (MTL) is difficult to image with fMRI due to magnetic field inhomogeneities and low signal-to-noise ratios. Here, I compiled some information about fMRI pulse sequences and how they affect your data, along with a few tips on how to get a good signal in the MTL. Find it here on Open Science Framework!

Behavioral encoding modeling

This code creates, fits & tests an encoding model of virtual head direction using simulated fMRI voxel time courses.

It can be easily adapted for other behavioral domains & imaging techniques (MEG, 2p-imaging...) to study the neural underpinnings of behavior across species.


HERE you find my fMRI crash course slides, covering the MR-imaging basics, data preprocessing, the general linear model and simple analyses.

HERE you find my lecture slides on visual field mapping.