Biology subjects

Nau, M.

Publications and source records attributed to Nau, M..

11 recordsLinked to original sources

bioRxiv2026

Brain-wide gaze-dependent activity during eyes-closed rest and sleep

Gaze behavior and brain activity are tightly coupled during perception and mental imagery. Whether this coupling extends to eyes-closed states remains largely unknown, primarily because measuring eye movements alongside brain activity is difficult when the eyes are closed. Here, we used a scalable MR-based eye-tracking approach to reconstruct gaze behavior from fMRI data during eyes-closed rest and sleep. We found widespread coupling between inferred gaze behavior and brain activity distributed through much of the cerebral cortex and cerebellum, including many regions typically associated with vision. These dynamics largely persisted across the two states. Additionally, accounting for gaze systematically altered functional connectivity estimates of large-scale brain organization. Together, these results underscore the importance of considering gaze as a hidden behavioral dimension of intrinsic fMRI signals, even in eyes-closed states. More broadly, our findings suggest that eye-brain dynamics may provide a window into eyes-closed states of consciousness.

neuroscience↗

bioRxiv2026

Optimizing MR-based gaze-decoding for eyes-closed eye-tracking in fMRI

Eye movements provide valuable insights into human cognition and are a critical variable in numerous functional magnetic resonance imaging (fMRI) studies. Yet, when the eyes are closed, camera-based eye-tracking is unavailable, making studies of eyes-closed states challenging. Here, we address this gap using DeepMReye, a deep learning framework for camera-free gaze reconstruction from the MR-signal of the eyes. We first show that fine-tuning DeepMReye on visuomotor calibration data acquired with the eyes open significantly improves gaze decoding, and that this fine-tuning does not require simultaneous camera-based data. We next assessed whether decoding could be extended to eyes-closed states using a novel auditory-guided task, in which participants gazed at learned target positions with and without visual input, and with their eyes open, blinking, or closed. While DeepMReye was originally trained exclusively on eyes-open data, the network successfully generalized to eyes-closed periods, and this generalization was further improved through task-specific fine-tuning. Finally, fine-tuning also improved decoding of eyelid-state (open versus closed) directly from the MR-signal. These findings demonstrate that gaze and eyelid-state monitoring during eyes-closed periods is feasible, enabling broader integration of eye-tracking in fMRI research.

neuroscience↗

bioRxiv2025

Emotional arousal enhances narrative memories through functional integration of large-scale brain networks

Emotional events tend to be vividly remembered. While growing evidence suggests that emotions have their basis in brain-wide network interactions, it is unclear if and how these whole-brain dynamics contribute to memory encoding. We combined fMRI, graph theory, text analyses, and pupillometry in a naturalistic context where participants recalled complex narratives in their own words. Across three independent datasets, emotionally arousing moments during narrative perception were associated with an integrated brain state characterized by increased cohesion across functional modules, which in turn predicted the fidelity of subsequent recall. Network integration mediated the influence of emotional arousal on recall fidelity, with consistent within- and between-network interactions supporting the mediation across datasets. Together, these results suggest that emotional arousal enhances memory encoding via strengthening functional integration across brain networks. Our findings advance a cross-level understanding of emotional memories that bridges large-scale brain network dynamics, affective states and ongoing cognition.

neuroscience↗

bioRxiv2024

Neural and behavioral reinstatement jointly reflect retrieval of narrative events

When recalling past events, patterns of gaze position and neural activity resemble those observed during the original experience. We hypothesized that these two phenomena, known as gaze reinstatement and neural reactivation, are linked through a common process that underlies the reinstatement of past experiences during memory retrieval. Here, we tested this proposal based on the viewing and recall of a narrative movie, which we assessed through fMRI, deep learningbased gaze prediction, and language modeling of spoken recall. In line with key predictions, gaze behavior adhered to the same principles as neural activity; it was event-specific, robust across individuals, and generalized across viewing and recall. Additionally, gaze-dependent brain activity overlapped substantially across tasks. Collectively, these results suggest that retrieval engages mechanisms that direct our eyes during natural vision, reflecting common constraints within the functional organization of the nervous system. Moreover, they highlight the importance of considering behavioral and neural reinstatement together in our understanding of remembering.

neuroscience↗

bioRxiv2024

Encoding of task regularities links grid-like signals to human timing behavior

Grid cells have been proposed to encode task regularities that allow predicting future states. Entorhinal grid-like signals might therefore mirror behavioral biases associated with relying on task regularities, like regression-to-the-mean biases in time estimation. Here, we tested this proposal using functional magnetic resonance imaging and a rapid timing task in humans. Indeed, trial-wise entorhinal activity reflected task accuracy and the degree to which interval estimates regressed towards the mean of all tested intervals. Grid-like signals were observed exclusively for the interval closest to the mean, which was explained by differences in temporal grid stability across intervals. Finally, both behavioral and entorhinal results were explained by a Bayesian observer model that assumes the integration of current-trial sensory evidence with prior expectations. Together, we find that entorhinal activity and grid-like signals reflect behavioral performance in a timing task, supporting the proposed role of grid cells in encoding task structure for predictive processing.

neuroscience↗

bioRxiv2023

Hi-Fi fMRI: High-resolution, fast-sampled and sub-second whole-brain functional MRI at 3T in humans

Functional magnetic resonance imaging (fMRI) is a methodological cornerstone of neuroscience. Most studies measure blood-oxygen-level-dependent (BOLD) signal using echo-planar imaging (EPI), Cartesian sampling, and image reconstruction with a one-to-one correspondence between the number of acquired volumes and reconstructed images. However, EPI schemes are subject to trade-offs between spatial and temporal resolutions. We make strides in overcoming these limitations by measuring BOLD with a gradient recalled echo (GRE) with a 3D radial-spiral phyllotaxis trajectory at a high sampling rate (28.49ms) on standard 3T field strength. The framework enables the reconstruction of 3D signal time courses with whole-brain coverage at simultaneously higher nominal spatial (1mm3) and temporal (up to 250ms) resolutions, as compared to optimized EPI schemes. Additionally, we apply motion correction directly to the k-space raw data, enabling flexible motion-corrected reconstructions; the desired temporal resolution to observe hemodynamic responses can be chosen after scanning. By showing activation in the calcarine sulcus of 20 participants completing an ON-OFF visual paradigm, we demonstrate the reliability of our method for applications in cognitive neuroscience research.

neuroscience↗

bioRxiv2021

Hippocampus and striatum encode distinct task regularities that guide human timing behavior

The brain encodes the statistical regularities of the environment in a task-specific yet flexible and generalizable format. Here, we seek to understand this process by converging two parallel lines of research, one centered on sensorimotor timing, and the other on cognitive mapping in the hippocampal system. By combining functional magnetic resonance imaging (fMRI) with a fastpaced time-to-contact (TTC) estimation task, we found that the hippocampus signaled behavioral feedback received in each trial as well as performance improvements across trials along with reward-processing regions. Critically, it signaled performance improvements independent from the tested intervals, and its activity accounted for the trial-wise regression-to-the-mean biases in TTC estimation. This suggests that the hippocampus supports the rapid encoding of temporal context even on short time scales in a behavior-dependent manner. Our results emphasize the central role of the hippocampus in statistical learning and position it at the core of a brain-wide network updating sensorimotor representations in real time for flexible behavior.

neuroscience↗

bioRxiv2020

MR-based camera-less eye tracking using deep neural networks

Viewing behavior provides a window into many central aspects of human cognition and health, and it is an important variable of interest or confound in many fMRI studies. To make eye tracking freely and widely available for MRI research, we developed DeepMReye: a convolutional neural network that decodes gaze position from the MR-signal of the eyeballs. It performs camera-less eye tracking at sub-imaging temporal resolution in held-out participants with little training data and across a broad range of scanning protocols. Critically, it works even in existing datasets and when the eyes are closed. Decoded eye movements explain network-wide brain activity also in regions not associated with oculomotor function. This work emphasizes the importance of eye tracking for the interpretation of fMRI results and provides an open-source software solution that is widely applicable in research and clinical settings.

neuroscience↗

bioRxiv2019

DeepInsight: A general framework for interpreting wide-band neural activity

AO_SCPLOWBSTRACTC_SCPLOWRapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data often depends on manual operations and requires considerable knowledge about the nature of the representation. Decoding provides a means to infer the information content of such recordings but typically requires highly processed data and prior knowledge of the encoding scheme. Here, we developed a deep-learning-framework able to decode sensory and behavioural variables directly from wide-band neural data. The network requires little user input and generalizes across stimuli, behaviours, brain regions, and recording techniques. Once trained, it can be analysed to determine elements of the neural code that are informative about a given variable. We validated this approach using data from rodent auditory cortex and hippocampus, identifying a novel representation of head direction encoded by putative CA1 interneurons.

neuroscience↗

bioRxiv2019

Behavior-dependent directional tuning in the human visual-navigation network

The brain derives cognitive maps from sensory experience that guide memory formation and behavior. Despite extensive efforts, it still remains unclear how the underlying population activity relates to active behavior and memory performance. To examine these processes, we here combined 7T-fMRI with a kernel-based encoding model of virtual navigation to map world-centered directional tuning across the human cortex. First, we present an in-depth analysis of directional tuning in visual, retrosplenial, parahippocampal and medial temporal cortices. Second, we show that tuning strength, width and topology of this directional code during memory-guided navigation depend on successful encoding of the environment. Finally, we show that participants locomotory state influences this tuning in sensory and mnemonic regions such as the hippocampus. We demonstrate a direct link between neural population tuning and human cognition and show that high-level memory processing interacts with network-wide environmental coding in the service of behavior.

neuroscience↗

bioRxiv2018

Deforming the metric of cognitive maps distorts memory

Environmental boundaries anchor cognitive maps that support memory. However, trapezoidal boundary geometry distorts the regular firing patterns of entorhinal grid cells proposedly providing a metric for cognitive maps. Here, we test the impact of trapezoidal boundary geometry on human spatial memory using immersive virtual reality. Consistent with reduced regularity of grid patterns in rodents and a grid-cell model based on the eigenvectors of the successor representation, human positional memory was degraded in a trapezoid compared to a square environment; an effect particularly pronounced in the trapezoids narrow part. Congruent with spatial frequency changes of eigenvector grid patterns, distance estimates between remembered positions were persistently biased; revealing distorted memory maps that explained behavior better than the objective maps. Our findings demonstrate that environmental geometry affects human spatial memory similarly to rodent grid cell activity -- thus strengthening the putative link between grid cells and behavior along with their cognitive functions beyond navigation.

neuroscience↗