Biology subjects

Krauss, P.

Publications and source records attributed to Krauss, P..

At least 19 recordsLinked to original sources

bioRxiv2026

Bacterial dispersal and host specificity shape microbiome strain sharing among great ape species in neighboring zoo exhibits

Animal microbiomes often exhibit host-species-specific structure, which could suggest a shared evolutionary history with their hosts. However, in wild populations this possibility has been difficult to disentangle from geographic or ecological barriers to microbial dispersal, particularly in taxa like non-human primates in which phylogeny and geography are closely related. To investigate microbiome transmission and adaptation in a common environment, the gut microbiomes of zoo-housed great apes were profiled longitudinally for four months. Even in a shared environment, phylogenetic structure emerged in both species- and strain-level microbiome composition. Longitudinal tracking of new strain acquisitions revealed that transmission across exhibits was possible, even between distantly related host species, suggesting that phylogenetic structure emerged in the longer term as different strains survived in different hosts. Comparison to metagenomes from humans and wild great apes revealed little evidence that human keepers, researchers, or zoo guests drive transmission between exhibits. Instead, across-exhibit transmission disproportionately involved spore-forming bacterial genera, possibly suggesting airborne spore transmission as a means of bacterial dispersal. By investigating microbiomes of different host species in a common geographic environment without direct inter-host interaction, this study identifies spatial constraints on transmission, bacterial dispersal ability, and differential survival of bacterial strains in different hosts as factors shaping the gut microbiomes of great apes.

evolutionary biology↗

bioRxiv2025

Different Multiword Verb Categories are Processed Differentially in the Brain: An Evidence from EEG Analysis and Decoding

The mental representation of multiword verb constructions is a central question in neurolinguistics: are they stored as single lexical units or completely compositional items? This study investigates the neurocognitive processing of two multiword verb types: phrasal verbs (e.g., look up) and prepositional verbs (e.g., decide on). Taking in consideration verb-to-infinitive constructions (e.g., want to go) as a control group. We analyse event-related potentials from eleven native English speakers who completed a listening task while EEG data were recorded. Grand-averaged waveforms and root-mean-square amplitudes were analysed across four-time windows. Therefore, statistical comparisons showed a significantly larger N400 amplitudes for prepositional verbs compared to phrasal verbs, while no significant differences were found between prepositional and to-infinitive constructions. Multivariate pattern analyses confirmed neural discriminability between phrasal and prepositional verbs, but not between prepositional and verb-to-infinitive structures. These results confirm that prepositional and verb-to-infinitive constructions are processed compositionally via valency-based integration, whereas phrasal verbs are stored as lexicalized units. The findings support a theoretical model in which multiword verb constructions differ in their degree of lexicalization, with measurable consequences for real-time neural processing.

neuroscience↗

bioRxiv2025

Prediction, Syntax and Semantic Grounding in the Brain and Large Language Models

Language comprehension involves continuous prediction of upcoming words, with syntactic structure and semantic meaning intertwined in the human brain. To date, few studies have used combined magnetoencephalography (MEG) and electroen-cephalography (EEG) measurements to investigate how syntactic processing, predictive coding, and semantic grounding interact in real time. Here we present the first combined MEG-EEG investigation of syntactic processing and semantic grounding under naturalistic conditions. Twenty-nine healthy participants listened to a German audio book while their neural responses were recorded. Event-related fields and event-related potentials for four word classes - nouns, verbs, adjectives, and proper nouns - showed highly reproducible, characteristic spatio-temporal signatures, including significant pre-onset activity for nouns, suggesting enhanced predictability of this word class. Source-space analyses revealed pronounced activation in the pre- and post-central gyri for nouns, suggesting a deeper semantic grounding of nouns in e.g. sensory experiences than verbs. To further investigate predictive mechanisms, we analyzed the hidden representations of the large language model Llama. By comparing the transformer-based representations to neural responses, we explored the relationship between computational language models and human brain activity, offering new insights into syntactic and semantic prediction. These findings highlight the power of simultaneous MEG-EEG recordings in unraveling the predictive, syntactic, and semantic mechanisms that underlie the comprehension of natural language.

neuroscience↗

bioRxiv2024

Cognitive Dynamics of Verb-Particle Constructions: An Eye-Tracking Study

This study investigates the cognitive processing of verb-particle constructions (VPCs) using eye-tracking data analysis method to explore how English native speakers process different types of the sequence NP-verb-particle-NP during reading tasks. While previous research has focused on phrasal verbs, our study extends this examination to include patterns with prepositions, aiming to identify distinct cognitive engagement patterns and processing efficiencies associated with each. We employed the Provo Corpus to analyse eye movements while participants read sentences containing these constructions. we focused on metrics such as first fixation duration, gaze duration, go-past times, and total reading times. Our findings indicate similarities in the lexical verbs, and significant differences in particles, indicating how these two types of constructions are processed, with phrasal verbs sometimes processed more efficiently than the prepositional counterparts. This suggests that phrasal verbs might be more deeply entrenched in the linguistic repertoire of native speakers, possibly functioning as single lexical units. This research contributes to the understanding of complex structures processing and the cognitive mechanisms that support it, offering insights that could influence linguistic theory and language education.

neuroscience↗

bioRxiv2024

Analyzing Differences in Processing Nouns and Verbs in the Human Brain using Combined EEG and MEG Measurements

Language and consequently the ability to transmit and spread complex information is unique to the human species. The disruptive event of the introduction of large language models has shown that the ability to process language alone leads to incredible abilities and, to some extent, to intelligence. However, how language is processed in the human brain remains elusive. Many insights originate from fMRI studies, as the high spatial resolution of fMRI devices provides valid information about where things happen. Nevertheless, the limited temporal resolution prevents us from gaining a deep understanding on the underlying mechanisms. In this study, we performed combined EEG and MEG measurements in 29 healthy right-handed human subjects during the presentation of continuous speech. We compared the evoked potentials (ERPs and ERFs) for different word types in source space and sensor space across the whole brain. We found characteristic spatio-temporal patterns for different word types (nouns, verbs) especially at latencies of 300ms to 1 s. This is further emphasized by the fact that we observe these effects in two pre-defined sub-samples of the data set (exploration and validation sample). We expect this study to be the starting point for further evaluations of semantic and syntactic processing in the brain.

neuroscience↗

bioRxiv2024

Temporal and Hemispheric Dynamics in Neural Processing of Auditory and Speech Stimuli Across Linguistic Complexity: A MEG Source Space Study

In this study we investigated the neural processing of auditory stimuli of varying complexity: a non-linguistic (pure tone), a simple linguistic (phoneme) and a complex linguistic (word) stimulus. We recorded brain activity of 30 healthy, righthanded participants using magnetoencephalography (MEG), and compared the resulting evoked fields (ERFs) in source space in three different time intervals, i.e. early (0-250ms), mid (250-500ms) and late (500-750ms) responses. Our results reveal a bilateral activation during early response and rightlateralized activation in the mid-phase for all stimuli. Hoewever, the late response exhibited lateralization variations. The pure tone predominantly activated the right hemisphere, consistent with pitch processing theories. The phoneme primarily engaged the left hemisphere, supporting its role in phonemic processing. Notably, the word elicited activation in both hemispheres, reflecting phonemic processing on the left and stress patterns on the right. These findings highlight the intricate interplay between temporal processing and hemispheric lateralization in speech perception, emphasizing the importance of stimulus complexity and temporal dynamics in understanding auditory and speech processing.

neuroscience↗

bioRxiv2023

Attentional modulation of the cortical contribution to the frequency-following response evoked by continuous speech

Selective attention to one of several competing speakers is required for comprehending a target speaker amongst other voices and for successful communication with them. Selective attention has been found to involve the neural tracking of low-frequency speech rhythms in the auditory cortex. Effects of selective attention have also been found in subcortical neural activities, in particular regarding the high-frequency neural response at the fundamental frequency of speech, the speech-FFR. Recent investigations have, however, shown that the speech-FFR contains cortical contributions as well. It remains unclear whether these are also modulated by selective attention. Here we employed magnetoencephalography (MEG) to assess the attentional modulation of the cortical contributions to the speech-FFR. We presented participants with two competing speech signals and analyzed the cortical responses during attentional switching between the two speakers. Our findings revealed robust attentional modulation of the cortical contribution to the speech-FFR: the neural responses were higher when the speaker was attended than when they were ignored. We also found that, regardless of attention, a voice with a lower fundamental frequency elicited a larger cortical contribution to the speech-FFR than a voice with a higher fundamental frequency. Our results show that the attentional modulation of the speech-FFR does not only occur subcortically but extends to the auditory cortex as well.

neuroscience↗

bioRxiv2023

Early subcortical response at the fundamental frequency of continuous speech measured with MEG

Most parts of speech are voiced, exhibiting a degree of periodicity with a fundamental frequency and many higher harmonics. Some neural populations respond to this temporal fine structure, in particular at the fundamental frequency. This frequency-following response to speech (speech-FFR) consists of both subcortical and cortical contributions and can be measured through electroen-cephalography (EEG) as well as through magnetoencephalography (MEG), although both differ in the aspects of neural activity that they capture: EEG is sensitive to both radial and tangential sources as well as to deep sources, while MEG is more restrained to the measurement of tangential and superficial neural activity. EEG responses to continuous speech have shown an early subcortical contribution, at a latency of around 9 ms, in agreement with MEG measurements in response to short speech tokens, whereas MEG responses to continuous speech have not yet revealed such an early component. Here we analyze MEG responses to long segments of continuous speech. We find an early subcortical response at a latency of 9 ms, followed by later right-lateralized cortical activities at delays of 20 - 57 ms as well as potential subcortical activities. Our results show that the early subcortical component of the FFR to continuous speech can be measured from MEG, and that its latency agrees with that measured with EEG. They furthermore show that the early subcortical component is temporally well separated from later cortical contributions, enabling an independent assessment of both components towards further aspects of speech processing.

neuroscience↗

bioRxiv2022

Behavioral assessment of Zwicker tone percepts in rodents

The Zwicker tone illusion can serve as an interesting model for acute tinnitus, an auditory phantom percept still not fully understood. Recent mechanistic models suggest that the underlying neural mechanisms of both percepts are similar. However, to date it is not clear if animals do perceive the Zwicker tone at all, as up to now no behavioral paradigms are available to objectively assess the presence of this phantom percept. Here we introduce, for the first time, a modified version of the gap pre-pulse inhibition of the acoustic startle reflex (GPIAS) paradigm - usually used to assess the presence of a tinnitus percept in animals - to test if it is possible to induce a Zwicker tone percept in our rodent model, the Mongolian gerbil. Furthermore, we developed a new aversive conditioning shuttle box learning paradigm and compare the two approaches. We found a significant increase in the GPIAS effect when presenting a notched noise compared to flat white noise gap pre-pulse inhibition, indicating that the animals actually perceived a Zwicker tone. However, in the aversive conditioning learning paradigm, no clear effect could be observed in the discrimination performance of the tested animals. When investigating the CR+ responses, an effect of a possible Zwicker tone percept can be seen, i.e. animals show identical behavior as if a pure tone was presented, but the paradigm needs to be further improved.

neuroscience↗

bioRxiv2022

Deep learning based decoding of local field potential events

How is information processed in the cerebral cortex? To answer this question a lot of effort has been undertaken to create novel and to further develop existing neuroimaging techniques. Thus, a high spatial resolution of fMRI devices was the key to exactly localize cognitive processes. Furthermore, an increase in time-resolution and number of recording channels of electro-physiological setups has opened the door to investigate the exact timing of neural activity. However, in most cases the recorded signal is averaged over many (stimulus) repetitions, which erases the fine-structure of the neural signal. Here, we show that an unsupervised machine learning approach can be used to extract meaningful information from electro-physiological recordings on a single-trial base. We use an auto-encoder network to reduce the dimensions of single local field potential (LFP) events to create interpretable clusters of different neural activity patterns. Strikingly, certain LFP shapes correspond to latency differences in different recording channels. Hence, LFP shapes can be used to determine the direction of information flux in the cerebral cortex. Furthermore, after clustering, we decoded the cluster centroids to reverse-engineer the underlying prototypical LFP event shapes. To evaluate our approach, we applied it to both neural extra-cellular recordings in rodents, and intra-cranial EEG recordings in humans. Finally, we find that single channel LFP event shapes during spontaneous activity sample from the realm of possible stimulus evoked event shapes. A finding which so far has only been demonstrated for multi-channel population coding.

neuroscience↗

bioRxiv2022

Neural Correlates of Linguistic Collocations During Continuous Speech Perception

Language is fundamentally predictable, both on a higher schematic level as well as low-level lexical items. Regarding predictability on a lexical level, collocations are frequent co-occurrences of words that are often characterized by high strength of association. So far, psycho-and neurolin guistic studies have mostly employed highly artificial experimental paradigms in the investigation of collocations by focusing on the processing of single words or isolated sentences. In contrast, here we analyze EEG brain responses recorded during stimulation with continuous speech, i.e. audio books. We find that the N400 response to collocations is significantly different from that of non-collocations, whereas the effect varies with respect to cortical region (anterior/ posterior) and laterality (left/right). Our results are in line with studies using continuous speech, and they mostly contradict those using artificial paradigms and stimuli. To the best of our knowledge, this is the first neurolinguistic study on collocations using continuous speech stimulation.

neuroscience↗

bioRxiv2021

Sleep as a random walk - A superstatistical analysis of EEG data across sleep stages

In clinical practice, human sleep is classified into stages, each associated with different levels of muscular activity and marked by characteristic patterns in the EEG signals. It is however unclear whether this subdivision into discrete stages with sharply defined boundaries is truly reflecting the dynamics of human sleep. To address this question, we consider one-channel EEG signals as heterogeneous random walks: stochastic processes controlled by hyper-parameters that are themselves time-dependent. We first demonstrate the heterogeneity of the random process by showing that each sleep stage has a characteristic distribution and temporal correlation function of the raw EEG signals. Next, we perform a superstatistical analysis by computing hyper-parameters, such as the standard deviation, kurtosis and skewness of the raw signal distributions, within subsequent 30-second epochs. It turns out that also the hyper-parameters have characteristic, sleep-stage-dependent distributions, which can be exploited for a simple Bayesian sleep stage detection. Moreover, we find that the hyper-parameters are not piece-wise constant, as the traditional hypnograms would suggest, but show rising or falling trends within and across sleep stages, pointing to an underlying continuous rather than subdivided process that controls human sleep.

neuroscience↗

bioRxiv2020

Analysis and Visualization of Sleep Stages based on Deep Neural Networks

Automatic sleep stage scoring based on deep neural networks has come into focus of sleep researchers and physicians, as a reliable method able to objectively classify sleep stages, would save human resources and thus would simplify clinical routines. Due to novel open-source software libraries for Machine Learning in combination with enormous progress in hardware development in recent years a paradigm shift in the field of sleep research towards automatic diagnostics could be observed. We argue that modern Machine Learning techniques are not just a tool to perform automatic sleep stage classification but are also a creative approach to find hidden properties of sleep physiology. We have already developed and established algorithms to visualize and cluster EEG data, in a way so that we can already make first assessments on sleep health in terms of sleep-apnea and consequently daytime vigilance. In the following study, we further developed our method by the innovative approach to analyze cortical activity during sleep by computing vectorial cross-correlations of different EEG channels represented by hypnodensity graphs. We can show that this measure serves to estimate the period length of sleep cycles and thus can help to find disturbances due to pathological conditions.

neuroscience↗

bioRxiv2020

Improved pure tone sensitivity after simulated hearing loss

Recently, it was proposed that a processing principle called adaptive stochastic resonance plays a major role in the auditory system, and serves to maintain optimal sensitivity even to highly variable sound pressure levels. As a side effect, in case of reduced auditory input, such as permanent hearing loss, this mechanism may eventually lead to the perception of phantom sounds like tinnitus or the Zwicker tone illusion. Using computational modeling, the biological plausibility of this processing principle was already demonstrated. Here, we provide empirical results that further support the stochastic resonance model of auditory perception. In particular, Mongolian gerbils were exposed to long-term notched noise, which mimics hearing loss for frequencies within the notch. Remarkably, the animals developed increased sensitivity, i.e. improved hearing thresholds, for the frequency centered within the notch, but nut for frequencies outside the notch. In addition, most animals treated with the new paradigm showed identical behavioral signs of phantom sound perception as animals with acoustic trauma induced tinnitus. In contrast, animals treated with broadband noise as a control condition did not show any significant threshold change, nor behavioral signs of phantom sound perception.

neuroscience↗

bioRxiv2020

Analysis of ongoing neuronal activity evoked by continuous speech with computational corpus linguistics methods

In the field of neurobiology of language, neuroimaging studies are generally based on stimulation paradigms consisting of at least two different conditions. Designing those paradigms can be very time-consuming and this traditional approach is necessarily data-limited. In contrast, in computational linguistics analyses are often based on large text corpora, which allow a vast variety of hypotheses to be tested by repeatedly re-evaluating the data set. Furthermore, text corpora also allow exploratory data analysis in order to generate new hypotheses. By drawing on the advantages of both fields, neuroimaging and corpus linguistics, we here present a unified approach combining continuous natural speech and MEG to generate a corpus of speech-evoked neuronal activity.

neuroscience↗

bioRxiv2020

The Stochastic Resonance model of auditory perception: A unified explanation of tinnitus development, Zwicker tone illusion, and residual inhibition.

Stochastic Resonance (SR) has been proposed to play a major role in auditory perception, and to maintain optimal information transmission from the cochlea to the auditory system. By this, the auditory system could adapt to changes of the auditory input at second or even sub-second timescales. In case of reduced auditory input, somatosensory projections to the dorsal cochlear nucleus would be disinhibited in order to improve hearing thresholds by means of SR. As a side effect, the increased somatosensory input corresponding to the observed tinnitus-associated neuronal hyperactivity is then perceived as tinnitus. In addition, the model can also explain transient phantom tone perceptions occurring after ear plugging, or the Zwicker tone illusion. Vice versa, the model predicts that via stimulation with acoustic noise, SR would not be needed to optimize information transmission, and hence somatosensory noise would be tuned down, resulting in a transient vanishing of tinnitus, an effect referred to as residual inhibition.

neuroscience↗

bioRxiv2020

Intrinsic noise improves speech recognition in a computational model of the auditory pathway

Noise is generally considered to harm information processing performance. However, in the context of stochastic resonance, noise has been shown to improve signal detection of weak subthreshold signals, and it has been proposed that the brain might actively exploit this phenomenon. Especially within the auditory system, recent studies suggest that intrinsic noise plays a key role in signal processing and might even correspond to increased spontaneous neuronal firing rates observed in early processing stages of the auditory brain stem and cortex after hearing loss. Here we present a computational model of the auditory pathway based on a deep neural network, trained on speech recognition. We simulate different levels of hearing loss and investigate the effect of intrinsic noise. Remarkably, speech recognition after hearing loss actually improves with additional intrinsic noise. This surprising result indicates that intrinsic noise might not only play a crucial role in human auditory processing, but might even be beneficial for contemporary machine learning approaches.

neuroscience↗

bioRxiv2020

Acute and long-term circuit-level effects in the auditory cortex after sound trauma

Harmful environmental sounds are a prevailing source for chronic hearing impairments, including noise induced hearing loss, hyperacusis, or tinnitus. How these symptoms are related to pathophysiological damage to the sensory receptor epithelia and its effects along the auditory pathway, such as functional reorganizations in the auditory cortex (ACx), have been documented in numerous studies. An open question concerns the temporal evolution of maladaptive changes after damage and their manifestation in the balance between afferent thalamocortical input and corticocortical input to the ACx. To address this, we investigated the loci of plastic reorganizations across the tonotopic axis of the auditory cortex of male Mongolian gerbils (Meriones unguiculatus) acutely after a sound trauma and after several weeks. We used a laminar residual current-source density analysis to dissociate adaptations of intracolumnar input and horizontally relayed corticocortical input to synaptic populations across cortical layers in ACx. A pure tone-based sound trauma caused acute changes of subcortical inputs and corticocortical inputs at all tonotopic regions, particularly showing a broad elimination of tone-evoked inputs at tonotopic regions with a pre-trauma best frequency between 2-8 kHz. At other cortical sites, the overall columnar activity acutely decreased, while relative contributions of lateral corticocortical inputs increased. After 4-6 weeks, cortical activity to the altered sensory inputs showed a general increase of local thalamocortical input reaching levels higher than before the trauma. Hence, our results suggest a detailed mechanism for overcompensation of altered frequency input in the auditory cortex that relies on a changing balancing of thalamocortical and intracortical input and is confined to the spectral neighborhood of the trauma frequency. Significance statementHarmful noise exposure is a major anthropogenic cause of hearing disorders and is becoming an ever-increasing burden for human health and society. Damage to the sensory epithelia elicited by harmful sounds can subsequently lead to chronic hearing loss, hyperacusis or tinnitus. We still lack an understanding of the pathophysiological plastic processes and their evolution, particularly at the circuit level of the auditory cortex (ACx), which is fundamentally involved in auditory perception. We demonstrate that plastic changes in ACx after noise induced hearing loss (NIHL) occur over several weeks, and that changes in intracortical functional connectivity compensate the acute effects in the deafferentiated subcortical inputs. Such long-term changes may underlie the temporal evolution of hearing impairments or phantom sounds after NIHL.

neuroscience↗