Matthias Nau

This paper improves gaze decoding in fMRI during eyes-closed states using a deep learning framework, showing it can generalize to eyes-closed conditions with task-specific fine-tuning.

This study investigates gaze-dependent brain activity during eyes-closed rest and sleep using MR-based eye-tracking, revealing widespread cortical and cerebellar coupling and altered functional connectivity when accounting for gaze.

The paper investigates how the hippocampus and neocortex contribute to symbolic compositional generalization by analyzing BOLD data during a spatial inference task.

The paper shows emotional arousal improves memory recall by enhancing functional integration of brain networks during narrative processing.

The paper shows that gaze behavior and neural activity during memory recall of narrative events follow similar principles and are linked in their reinstatement.

The paper investigates whether human medial parietal cortex (MPC) mirrors the categorical organization of ventral temporal cortex (VTC) during recall of people, places, and objects, finding no object-specific region in MPC but robust effects for people and places.

This study links grid-like signals in the entorhinal cortex to human timing behavior by showing they reflect task accuracy and regression-to-the-mean biases in time estimation.

This paper introduces a high-resolution, fast-sampled fMRI technique for whole-brain BOLD signal measurement, demonstrating improved spatial and temporal resolution in human studies.

This study shows the hippocampus encodes task-related temporal regularities to guide sensorimotor timing through real-time updates of behavioral feedback and performance improvements.

The paper introduces a large-scale 7T fMRI dataset of natural scenes to advance cognitive and computational neuroscience research.

This paper develops a deep-learning framework to decode sensory and behavioural variables from wide-band neural data with minimal user input and generalization across stimuli and brain regions.

This paper develops DeepMReye, a deep learning model that decodes gaze position from MR signals for camera-less eye tracking in fMRI studies.

This study links neural population directional tuning in the human visual-navigation network to memory performance and behavior using fMRI and encoding models.

The paper shows that aligning grid-like neural representations with environmental information axes improves navigation performance and reduces spatial uncertainty.

This study shows that trapezoidal environments distort human spatial memory, similar to how grid cell patterns are affected in rodents, suggesting a link between grid cells and cognitive functions beyond navigation.