Biology subjects

Schüller, A.

Publications and source records attributed to Schüller, A..

5 recordsLinked to original sources

bioRxiv2026

A historical cross-border Andes virus lineage reveals the origin of a cruise ship hantavirus pulmonary syndrome outbreak

Andes virus (ANDV) caused a multi-country outbreak of hantavirus pulmonary syndrome among passengers and crew of a cruise ship in 2026. To investigate the origin and evolutionary history of the virus responsible for the outbreak, we analyzed complete ANDV small (S), medium (M), and large (L) genome segment sequences from Chile alongside outbreak-associated and publicly available genome sequences. Across all three segment-specific phylogenies, the outbreak virus clustered within an ANDV Clade III cluster spanning southern Chile and northern Patagonia in Argentina and were most closely related to a human-derived ANDV (p1236) collected in Los Rios Region of Chile in 2012, representing the closest known historical relative of the outbreak-associated virus. Phylogeographic analysis showed that the genetically distinct ANDV Clade V lineage circulating in central Chile was not closely related to the cruise ship outbreak-associated genomes, thereby reducing the likelihood that the index cases acquired infection through zoonotic spillover while traveling through the Maule Region. These findings trace the geographic origin of the outbreak-associated virus to a defined corridor, the Hua Hum Pass, a cross-border zone connecting Province of Neuquen in Argentina with the Los Rios and La Araucania Regions of Chile.

evolutionary biology↗

bioRxiv2026

Deep Learning Reveals Cross-Modal Neural Representations of Auditory and Visual Mental Imagery in MEG

Mental imagery provides a unique window into the brains ability to internally simulate sensory experiences, offering valuable insights for both cognitive neuroscience and brain-computer interface (BCI) research. This study examined the neural representations of imagined auditory and visual stimuli using magnetoen-cephalography (MEG) and assessed the ability of machine learning models to decode these mental processes. MEG data were recorded from 18 right-handed participants during auditory and visual imagery tasks and source-reconstructed within modality-specific cortical regions of interest. We compared a convolutional neural network (CNN) and a linear logistic regression model within a subject-specific classification frame-work. Both approaches achieved above-chance decoding accuracies, with the CNN outperforming the linear model in the auditory task, whereas the linear model showed slightly higher accuracy for visual imagery. Notably, the CNN achieved significant decoding performance even when trained on non-task-relevant cortical regions, indicating that imagined stimuli are represented in distributed and partially overlapping neural networks across modalities. This cross-modal decoding capability highlights the potential of deep learning models to capture complex, multimodal neural patterns and suggests that future brain-computer interfaces could benefit from integrating auditory and visual information. A secondary, behavioral analysis revealed correlation of memory capacity and individual learning preferences with decoding performances, suggesting that individual cognitive differences may further shape the quality of neural representations. Together, these findings advance our understanding of cross-modal mental imagery and point toward more flexible and personalized approaches in BCI design. New and NoteworthyBy comparing linear and deep classifiers, this work shows that convolutional networks capture rich, cross-modal neural representations of auditory and visual mental imagery in MEG. Significant decoding from non-task-relevant regions indicates distributed cortical engagement, highlighting deep learnings potential for robust, modality-independent brain-computer interfaces.

neuroscience↗

bioRxiv2026

The cortical contribution to the speech-FFR is not modulated by visual information

Seeing a speakers face can significantly aid understanding, particularly in challenging acoustic environments. An early neural response implicated in audiovisual speech processing is the frequency-following response (speech-FFR), which occurs at the fundamental frequency of the speech signal. This response arises from both subcortical areas and the auditory cortex. Previous studies have shown that subcortical responses are reduced when bimodal stimulation includes visual input from the talkers face. Here, we examined the cortical contribution to the speech-FFR and its potential modulation by visual information. We recorded MEG responses to four types of audiovisual signals: a still image, an artificially generated avatar, a degraded video, and a natural video. The audio stimuli were presented in a substantial level of background noise to make behavioral audiovisual effects stand out. Speech-in-noise comprehension increased significantly from the audio-only condition to the avatar and the degraded video, and further to the natural video. Moreover, we found that all types of audiovisual stimuli yielded robust speech-FFRs in the auditory cortex at an early latency of around 30 ms. However, the magnitude of this neural response was neither enhanced nor attenuated by the videos, nor could the cortical contribution of the speech-FFR explain a significant portion of the variance in the behavioral comprehension scores. Our results suggest that visual modulation of the speech-FFR in the auditory cortex is, if existent, too small to be measurable in scenarios where speech occurs in considerable background noise.

neuroscience↗

bioRxiv2026

Talking avatars can differentially modulate cortical speech tracking in the high and in the low delta band

In noisy listening environments, visual cues from a speakers face can significantly boost speech compre-hension. The underlying audiovisual integration in the brain involves neural tracking of audiovisual speech features. Moreover, lip reading in silence is associated with tracking of the speech envelope in the low-delta frequency band (0.5 - 1 Hz). Recently, digital avatars have emerged that can support speech comprehen-sion. Yet, it remains unclear how the human brain integrates such artificial visual signals with natural speech. Here, we employed magnetoencephalography (MEG) to measure the neural response to a natural video, an avatar generated by deep neural networks, and a degraded video serving as a control. We demonstrate that the avatar can enhance speech-in-noise comprehension to a similar degree as the degraded video, although less than the natural video. We further identify a late response at 600 ms in the neural tracking of the audi-tory cortex in the high delta band (1 - 4 Hz) that predicts audiovisual speech comprehension. In contrast, we found that neural tracking in the low delta band is related to silent lip-reading performance. Importantly, the tracking in the low delta band evoked by the avatars is much weaker and occurs earlier than that elicited by the other audiovisual stimuli. Neural tracking in the theta band (4 - 8 Hz) is not involved in audiovisual integration. Our results show that the low delta band and the high delta band play clearly distinct roles in visual-only and audiovisual speech processing, and suggest potential avenues for further boosting the abilities of avatars to support speech comprehension. Significance StatementUnderstanding a conversational partner is essential for everyday communication. Yet, many people -- due to aging or other factors -- struggle to follow speech in noisy environments. Seeing the speakers face can greatly enhance speech comprehension, but visual cues are often unavailable, such as during public announcements or telephone conversations. Digital avatars offer a promising alternative, but how the brain integrates audiovisual information from such artificial sources remains unclear. Using magnetoencephalog-raphy (MEG), we investigated how the brain processes and integrates speech when visual information is provided by either natural or artificial (avatar-based) signals. Our findings reveal both shared and distinct neural mechanisms of audiovisual integration, providing critical insight into how visual input can support speech understanding in challenging listening conditions.

neuroscience↗

bioRxiv2025

NEUROLINGUA: A Neuroimaging Database Tailored to Unravel the Complexity of Multilingual Comprehension

The neural mechanisms underlying language processing involve a well-defined brain network, including mainly left perisylvian areas. Yet, the extent of individual variability remains largely unexplored, particularly in bilingual and multilingual contexts. Differences in linguistic profiles (e.g., age of acquisition, exposure, proficiency) provide an opportunity to assess how network topology is shaped by sociolinguistic factors. To address this, we developed NEUROLINGUA, a comprehensive database of functional and structural MRI data, enriched with sociodemographic, sociolinguistic, and behavioral information. It includes 725 healthy individuals aged 18-82 immersed in a Basque-Spanish multilingual environment, ranging from near-monolinguals to highly proficient multilinguals. Participants completed a functional MRI language localizer with both auditory and visual comprehension tasks, enabling cross-modal comparisons, as well as sentences involving arithmetic problem-solving. Exploratory analyses confirmed associations between structural MRI, sociodemographic, and cognitive measures. We demonstrate that NEUROLINGUAs functional MRI data localize the language comprehension network and thus capture linguistic profile effects. This integrative dataset offers an unparalleled resource to investigate factors influencing language network adaptability and variability in diverse sociolinguistic contexts.

neuroscience↗