Biology subjects

Daube, C.

Publications and source records attributed to Daube, C..

7 recordsLinked to original sources

bioRxiv2026

Backwards compatibility to classical experiments grounds beta responses to naturalistic speech in temporal acoustic forecasting

Current neuroscience is shifting from simple controlled paradigms towards rich and ecologically valid naturalistic stimuli. Correspondingly, insights from historic "impoverished" artificial paradigms are considered to be seriously challenged by generalisation to modern naturalistic contexts. Here, we argue that the reverse, backwards compatibility, is an under-appreciated and easily accessible benchmark and show that it disambiguates model comparison beyond naturalistic stimuli. We analyse magnetoencephalography (MEG) beta power responses to an audiobook stimulus using canonical correlation analysis (CCA) and replicate reported links between beta power and syntactic parsing. However, a simple stimulus-computable acoustic model predicts the same variance, suggesting a domain-general rather than linguistic function of beta responses. We therefore test the backwards compatibility of speech-trained models to classic rhythmic tones. While generalisation initially fails, reducing hidden and unnecessary degrees of freedom of the models phase responses allows successful generalisation. Crucially, this greatly improves model adjudication: Several models that perform indistinguishably on speech differ in how well they predict responses to simple tones. In this comparison, a simple forecasting deep neural network (DNN) outperforms acoustics by internalising a "slow-decay" prior as a structural mirror of sluggish speech dynamics. This grounds beta responses in canonical temporal forecasting, bridging modern naturalism to established auditory psychophysics.

neuroscience↗

bioRxiv2026

Non-invasive tracking of hippocampal theta oscillations

Hippocampal theta oscillations play a crucial role in the formation of episodic memories by binding multisensory information into coherent episodes. Brain stimulation studies suggest that memory performance can be modulated by targeting these oscillations, offering a potential way of treating memory disorders. However, to effectively engage these rhythms, precise knowledge about their presence, frequency, phase and location is required, which has been a challenge, particularly for non-invasive methods such as magnetoencephalography (MEG). Here, we first asked if hippocampal signals are detectable in MEG recordings. Using simultaneous MEG-intracranial EEG (iEEG) data, we show that invasively recorded hippocampal activity is reflected in MEG sensor signals, demonstrating the feasibility of detecting deep-brain activity non-invasively. Building on this, we introduce a MEG-based analysis pipeline to track hippocampal theta frequency over time. Applied across three independent datasets, the pipeline captured characteristic hippocampal theta frequency patterns in both rodents and humans, and MEG-derived hippocampal frequencies were consistent with those observed simultaneously in intracranial EEG. These findings provide evidence that MEG can reliably track individual hippocampal oscillatory dynamics, paving the way for future non-invasive closed-loop interventions that adapt stimulation frequency and timing to ongoing oscillations.

neuroscience↗

bioRxiv2023

The Human Infertility Single-cell Testis Atlas (HISTA): An interactive molecular scRNA-Seq reference of the human testis.

BackgroundThe Human Infertility Single-cell Testis Atlas (HISTA) is an interactive web tool and a reference for navigating the transcriptome of the human testis. It was developed using joint analyses of scRNA-Seq datasets derived from a dozen donors, including healthy adult controls, juveniles, and several infertility cases. HISTA is very different than other websites of testis scRNA-seq data, providing visualization and hypothesis testing tools on a batch-removed and integrated dataset of 23429 genes measured across 26093 cells using. ObjectiveThe main goal of this manuscript is to describe HISTA in detail and highlight its unique and novel features. MethodsTherefore, we used HISTA as a guide for its application and demonstrated HISTAs translational capacity to follow up on two observations of biological relevance. ResultsOur first analytical vignette identifies novel groupings of tightly regulated long non-coding RNA (lncRNA) molecules throughout spermatogenesis, suggesting specific functional genomics of these groupings. This analysis also found highly controlled expression of pairs of sense and antisense transcripts, suggesting conjoined regulatory mechanisms. In the next investigative vignette, we examined gene patterns in undifferentiated spermatogonia (USgs). We found the NANOS family of genes function as key drivers of transcriptomic signatures involved in human spermatogonial self-renewal programming; for the first time, demonstrating the relationship of NANOS1/2/3 transcripts in humans with scRNA-seq. Discussion and ConclusionsUsing HISTA, we found new observations that contribute to unraveling the mechanisms behind transcriptional regulation and maintenance germ cells across spermatogenesis. Furthermore, our findings provide guidance on future validation studies and experimental direction. Overall, HISTA continues to be utilized in testis-related research, and thus is updated regularly with new analytical methods, visualizations, and data. We aim to have it serve as a research environment for a broad range of investigators looking to explore the testis tissue and male infertility. Availability and ImplementationHISTA is available as an interactive web tool: https://conradlab.shinyapps.io/HISTA Source code and documentation for HISTA are provided on GitHub: https://github.com/eisascience/HISTA

bioinformatics↗

bioRxiv2021

Comparison of undirected frequency-domain connectivity measures for cerebro-peripheral analysis

Analyses of cerebro-peripheral connectivity aim to quantify ongoing coupling between brain activity (measured by MEG/EEG) and peripheral signals such as muscle activity, continuous speech, or physiological rhythms (such as pupil dilation or respiration). Due to the distinct rhythmicity of these signals, undirected connectivity is typically assessed in the frequency domain. This leaves the investigator with two critical choices, namely a) the appropriate measure for spectral estimation (i.e., the transformation into the frequency domain) and b) the actual connectivity measure. As there is no consensus regarding best practice, a wide variety of methods has been applied. Here we systematically compare combinations of six standard spectral estimation methods (comprising fast Fourier and continuous wavelet transformation, bandpass filtering, and short-time Fourier transformation) and six connectivity measures (phase-locking value, Gaussian-Copula mutual information, Rayleigh test, weighted pairwise phase consistency, magnitude squared coherence, and entropy). We provide performance measures of each combination for simulated data (with precise control over true connectivity), a single-subject set of real MEG data, and a full group analysis of real MEG data. Our results show that, overall, wppc and gcmi tend to outperform other connectivity measures, while entropy was the only measure sensitive to bimodal deviations from a uniform phase distribution. For group analysis, choosing the appropriate spectral estimation method appeared to be more critical than the connectivity measure. We discuss practical implications (sampling rate, SNR, computation time, and data length) and aim to provide recommendations tailored to particular research questions.

neuroscience↗

bioRxiv2018

Phoneme-level processing in low-frequency cortical responses to speech explained by acoustic features

When we listen to speech, we have to make sense of a waveform of sound pressure. Hierarchical models of speech perception assume that before giving rise to its final semantic meaning, the signal is transformed into unknown intermediate neuronal representations. Classically, studies of such intermediate representations are guided by linguistically defined concepts such as phonemes. Here we argue that in order to arrive at an unbiased understanding of the mechanisms of speech comprehension, the focus should instead lie on representations obtained directly from the stimulus. We illustrate our view with a strongly data-driven analysis of a dataset of 24 young, healthy humans who listened to a narrative of one hour duration while their magnetoencephalogram (MEG) was recorded. We find that two recent results, a performance gain of an encoding model based on acoustic and annotated linguistic features over a model based on acoustic features alone as well as the decoding of subgroups of phonemes from phoneme-locked responses, can be explained with an encoding model entirely based on acoustic features. These acoustic features capitalise on acoustic edges and outperform Gabor-filtered spectrograms, features with the potential to describe the spectrotemporal characteristics of individual phonemes. We conclude that models of brain responses based on linguistic features can serve as excellent benchmarks. However, we put forward that linguistic concepts are better used when interpreting models, not when building them. In doing so, we find that the results of our analyses favour syllables over phonemes as candidate intermediate speech representations visible with fast non-invasive neuroimaging.

neuroscience↗

bioRxiv2018

Stimulus-driven brain rhythms within the alpha band: The attentional-modulation conundrum

Two largely independent research lines use rhythmic sensory stimulation to study visual processing. Despite the use of strikingly similar experimental paradigms, they differ crucially in their notion of the stimulus-driven periodic brain responses: One regards them mostly as synchronised (entrained) intrinsic brain rhythms; the other assumes they are predominantly evoked responses (classically termed steady-state responses, or SSRs) that add to the ongoing brain activity. This conceptual difference can produce contradictory predictions about, and interpretations of, experimental outcomes. The effect of spatial attention on brain rhythms in the alpha-band (8 - 13 Hz) is one such instance: alpha-range SSRs have typically been found to increase in power when participants focus their spatial attention on laterally presented stimuli, in line with a gain control of the visual evoked response. In nearly identical experiments, retinotopic decreases in entrained alpha-band power have been reported, in line with the inhibitory function of intrinsic alpha. Here we reconcile these contradictory findings by showing that they result from a small but far-reaching difference between two common approaches to EEG spectral decomposition. In a new analysis of previously published human EEG data, recorded during bilateral rhythmic visual stimulation, we find the typical SSR gain effect when emphasising stimulus-locked neural activity and the typical retinotopic alpha suppression when focusing on ongoing rhythms. These opposite but parallel effects suggest that spatial attention may bias the neural processing of dynamic visual stimulation via two complementary neural mechanisms.\n\nSIGNIFICANCE STATEMENTAttending to a visual stimulus strengthens its representation in visual cortex and leads to a retinotopic suppression of spontaneous alpha rhythms. To further investigate this process, researchers often attempt to phase-lock, or entrain, alpha through rhythmic visual stimulation under the assumption that this entrained alpha retains the characteristics of spontaneous alpha. Instead, we show that the part of the brain response that is phase-locked to the visual stimulation increased with attention (in line with steady-state evoked potentials), while the typical suppression was only present in non-stimulus-locked alpha activity. The opposite signs of these effects suggest that attentional modulation of dynamic visual stimulation relies on two parallel cortical mechanisms - retinotopic alpha suppression and increased temporal tracking.

neuroscience↗

bioRxiv2016

Large-scale network dynamics of beta-band oscillations underlie auditory perceptual decision making

Perceptual decisions vary in the speed at which we make them. Evidence suggests that translating sensory information into behavioral decisions relies on distributed interacting neural populations, with decision speed hinging on power modulations of neural oscillations. Yet, the dependence of perceptual decisions on the large-scale network organization of coupled neural oscillations has remained elusive. We measured magnetoencephalography signals in human listeners who judged acoustic stimuli made of carefully titrated clouds of tone sweeps. These stimuli were used under two task contexts where the participants judged the overall pitch or direction of the tone sweeps. We traced the large-scale network dynamics of source-projected neural oscillations on a trial-by-trial basis using power envelope correlations and graph-theoretical network discovery. Under both tasks, faster decisions were predicted by higher segregation and lower integration of coupled beta-band (~16-28 Hz) oscillations. We also uncovered brain network states that promoted faster decisions and emerged from lower-order auditory and higher-order control brain areas. Specifically, decision speed in judging tone-sweep direction critically relied on nodal network configurations of anterior temporal, cingulate and middle frontal cortices. Our findings suggest that global network communication during perceptual decision-making is implemented in the human brain by large-scale couplings between beta-band neural oscillations.\n\nAuthor SummaryThe speed at which we make perceptual decisions varies. This translation of sensory information into behavioral decisions hinges on dynamic changes in neural oscillatory activity. However, the large-scale neural network embodiment supporting perceptual decision-making is unclear. Alavash et al. address this question by experimenting two auditory perceptual decision-making situations. Using graph-theoretical network discovery, they trace the large-scale network dynamics of coupled neural oscillations to uncover brain network states supporting the speed of auditory perceptual decisions. They find that higher network segregation of coupled beta-band oscillations supports faster auditory perceptual decisions over trials. Moreover, when auditory perceptual decisions are relatively difficult, the decision speed benefits from higher segregation of frontal cortical areas, but lower segregation and integration of auditory cortical areas.

neuroscience↗