Biology subjects

Villringer, A.

Publications and source records attributed to Villringer, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Individual heartbeats track distinct prediction processes during human probabilistic learning

Heart rate continuously adjusts to accommodate perception and action, and these shifts are frequently explained through predictive processes. Yet direct evidence that interbeat intervals exhibit graded scaling with prediction remains limited. Here we investigate millisecond-resolved physiological signatures of prediction processing using a mechanistically constrained analysis of beat-to-beat cardiac dynamics. We analysed trial-by-trial electrocardiogram and electroencephalogram recordings from 34 participants performing a probabilistic learning task. We quantified stimulus-locked cardiac responses as changes between consecutive interbeat intervals and accounted for cardiac phase at feedback. This single-beat approach separated anticipatory slowing, stimulus-locked parasympathetic brake, and subsequent acceleration. Anticipatory deceleration and rebound acceleration scaled with model-derived expectations, whereas the second heartbeat after feedback tracked signed prediction errors and outcome valence, particularly when feedback occurred early in the cardiac cycle. Peak stimulus-locked cardiac deceleration covaried with parietal P3b rather than prediction features. Thus, individual cardiac cycles carry separable signatures of anticipation, orienting, and feedback-based updating. These findings demonstrate how predictive processing propagates into human autonomic physiology on a beat-to-beat timescale and provide an interpretable, mechanistically grounded framework for quantifying brain-body co-modulation during adaptive behaviour.

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bioRxiv2026

Canonical Correlation Analysis and Multi-Channel Cardiography Improve Artefact Cleaning in Heartbeat-Locked Analyses

Cardiac artefacts are problematic for neurophysiological analyses, especially for heartbeat-locked brain responses where neural activity and artefacts co-occur in time. Traditional approaches using single-channel electrocardiography for artefact removal do not fully capture the multidimensional spread of cardiac fields. Moreover, even if several channels are recorded, no established methods exists for integrating them for artefact removal. Here, we propose a multivariate approach based on Canonical Correlation for cardiac artefact removal and report its effectiveness in electroencephalography recorded simultaneously with a custom 15-channel electrocardiography in 14 participants. We quantify cleaning quality via residual R-peak artefact and neural signal preservation via alpha power. Canonical Correlation systematically outperforms the traditional Independent Component Analysis in both reducing R-peak artefacts and preserving alpha power. By analysing all possible channel combinations, we found that neck or supraclavicular electrodes improve cleaning when only a few channels are available. With four or five channels, precordial electrodes in combination with limb or supraclavicular locations provide a performance comparable to the 15-channel setup. While these findings require validation in other datasets, we outline clear decision criteria for cleaning efficiency and show that canonical correlation is a reliable approach for multi-channel cardiac artefact removal.

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bioRxiv2026

Expectation Shapes Neural Preparation for AI-generated and Real Image Processing: Evidence from EEG

AbstractAI-generated images, videos, and news have become an inseparable part of daily life, leading to increasing skepticism toward online information. Understanding how expectations about the presence of AI-generated content influence perception is crucial for elucidating the underlying neural mechanisms and facilitating the generation of naturalistic avatars. In this study, we analyzed an existing EEG dataset (N = 29) in which participants viewed emotional facial photographs of only real individuals while being informed that upcoming faces were either real ("REAL") or AI-generated ("FAKE"). We examined pre-stimulus oscillatory activities in the one-second EEG interval before stimulus onset and found significantly lower alpha power when participants expected "FAKE" compared to "REAL" faces. This effect was region-specific, particularly in right occipito-parietal and temporo-parietal regions, as identified by both sensor- and source- level analyses. In addition, the modulation effect between pre-stimulus alpha activity and post-stimulus event-related potentials (ERPs) was measured. A significant correlation between changes in late positive potential (LPP) amplitudes and pre-stimulus alpha power was observed exclusively for "FAKE" smiling faces, consistent with our previous findings. These results suggest that AI-related expectations modulate neural preparatory states, with lower alpha activity presumably reflecting increased attentional demands for stimuli believed to be artificially generated. This study demonstrates that top-down beliefs systematically shape both pre- and post-stimulus neural dynamics, providing new insights into how cognitive expectations bias perceptual processing, with implications for understanding human-AI interaction and improving the design and evaluation of AI- generated content in real-world contexts.

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bioRxiv2026

Central and peripheral dynamics of acute stress: evidence from functional cortical gradients

Stress involves coordinated central and peripheral processes that unfold dynamically and can be assessed through brain, autonomic, endocrine, and subjective measures. Centrally, acute stress has been linked to altered functional connectivity, particularly in the salience (SN), frontoparietal networks (FPN), and default mode networks (DMN). Here, we used cortical gradients to characterize stress-related reconfiguration in macroscale functional space and assessed their relation to peripheral stress measures. We performed secondary analyses on data from 67 young males completing the Trier Social Stress Test or a control task with resting-state fMRI before and after, concurrent peripheral (autonomic, endocrine) and subjective measures. To assess region- and network-specific changes in functional organization, we derived eccentricity and within- and between-network dispersion for the first three cortical gradients. Acute stress was associated with selective gradient reconfigurations in the right ventral prefrontal cortex and left insula and with increased SN-DMN and SN-FPN dispersion, indicating DMN and FPN decoupling from the SN. Although no associations with peripheral or subjective stress measures survived multiple-comparison correction, nominal effects suggested partly distinct links of saliva cortisol with local gradient changes and HRV with network-level reconfiguration. Together, these findings show that acute stress selectively reconfigures macroscale cortical organization.

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bioRxiv2026

Inter- and intra-individual variability in structure-function coupling in human brain

Microstructure shapes functional brain dynamics. Yet this notion has remained largely untested for human brain signals measured with electroencephalography and magnetoencephalography (EEG/MEG). In this study, we related functional activity variance to microstructural variance across brain regions and across participants. Across regions, we used multiple independent datasets: EEG (n=209), MEG (n=507), high-resolution MRI (n=10), and the human post-mortem brain. Across participants, we used a multimodal dataset including MEG and high-resolution MRI (n=31). Between regions, alpha power correlated positively with layer IV thickness and with myelin and iron estimates at mid-cortical depth. By contrast, between participants, alpha power correlated negatively with mid-cortical myelin and iron estimates. Using computational modeling, we demonstrated that regional effects are consistent with excitatory changes, whereas inter-individual effects may be driven by inhibitory influence. This dissociation reveals distinct underlying mechanisms and indicates that different factors influence intra-versus inter-individual variations in alpha activity.

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bioRxiv2025

Non-uniform effects of remaining field spread on the estimation of M/EEG activity and connectivity between regions of interest

In M/EEG analyses, it is often convenient to extract time series of activity originating from the regions of interest (ROIs). However, due to the spread of electric and magnetic fields, M/EEG recordings capture activity from all sources within the brain. Commonly used approaches for the extraction of ROI activity only partially alleviate this problem, and field spread remains a challenge even at the level of ROI time series. Because of the remaining field spread (RFS), the extracted time series captures activity not only from the considered ROI but also from other regions (not necessarily neighboring ones). The amount of RFS can strongly affect the validity of interpretations: with more RFS, the extracted time series becomes less representative of the ROI. However, neither the amount nor the pattern of RFS is usually known. In this study, we apply the cross-talk function (CTF) to analyze contributions of all sources across the brain to the extracted ROI time series, thereby quantifying the degree of RFS. With CTF, we show that the effect of RFS on the extraction of ROI activity and on the estimation of inter-regional connectivity is highly non-uniform across the cortex. In particular, ROIs farther away from the recording sensors are more likely to capture activity and connectivity from other areas. Finally, we validate this observation in simulations and complement it by investigating spurious and ghost interactions in real data. Overall, our results illustrate how CTF can be used as a diagnostic tool to quantify the effects of RFS and to evaluate pipelines for the extraction of ROI activity.

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bioRxiv2025

EEG-decodability of facial expressions and their stereoscopic depth cues in immersive virtual reality

Face perception typically occurs in three-dimensional space, where stereoscopic depth cues enrich the perception of facial features. Yet, most neurophysiological research on face processing relies on two-dimensional displays, potentially overlooking the role of stereoscopic depth information. Here, we combine immersive virtual reality (VR), electroencephalography (EEG), and eye tracking to examine the neural representation of faces under controlled manipulations of stereoscopic depth. Thirty-four participants viewed computer-generated faces with neutral, happy, angry, and surprised expressions in frontal view under monoscopic and stereoscopic viewing conditions. Using time-resolved multivariate decoding, we show that EEG signals in immersive VR conditions can reliably differentiate facial expressions. Stereoscopic depth cues elicited a distinct and decodable neural signature, confirming the sensitivity of our approach to depth-related processing. Yet, expression decoding remained robust across depth conditions, indicating that under controlled frontal viewing, the neural representation of behaviorally distinguishable facial expressions is invariant to binocular depth cues. Eye tracking showed that expression-related gaze patterns contained comparable information but did not account for neural representations, while depth information was absent in gaze patterns--consistent with dissociable representational processes. Our findings demonstrate the feasibility of EEG-based neural decoding in fully immersive VR as a tool for investigating face perception in naturalistic settings and provide new evidence for the stability of expression representations across depth variations in three-dimensional viewing conditions.

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bioRxiv2025

Heartbeat-evoked responses in M/EEG: A systematic review of methods with suggestions for analysis and reporting

Heartbeat-evoked responses (HER), as measured by electroencephalography (EEG) or magnetoencephalography (MEG), represent neural activity time-locked to heartbeats and are widely used as a marker of cardiac interoception in the study of brain-body interactions. However, HER studies report largely variable findings, at least partially due to methodological variability. To achieve consensus on HER processing and improve the reproducibility of findings, the field urgently requires a structured summary of the methods employed so far. To this end, we conducted a systematic review of 132 HER studies using non-invasive M/EEG recordings in humans. Our results reveal substantial heterogeneity across most steps of HER analysis, ranging from data acquisition and preprocessing to HER estimation and statistical approaches. The large diversity in the processing choices is accompanied by considerable proportions of unreported methodological information across reviewed studies, reaching up to 80% for key processing steps. In addition, less than 33% of studies had enough statistical power to reliably detect meta-level HER effects, while their reported spatiotemporal locations varied substantially. We provide a comprehensive reporting and quality control checklist to aid in the development of more standardized procedures, highlighting critical steps for robust HER investigations. Additionally, we share the full extracted dataset, including an interactive version, to support other researchers in answering additional specific questions they may have. We hope that these resources will improve the robustness, reproducibility, and transparency of research in the growing HER field.

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bioRxiv2025

Voluntary movement initiation is associated with cardiac input in Libet's task.

The relationship between motor intention and initiation of voluntary movement remains a fundamental topic in neuroscience, originating from the B. Libet seminal framework introduced in 1983. Libets paradigm significantly influenced discussions on intentionality, motor control, and free will. However, methodological critiques continue to challenge its interpretations, specifically the accuracy and validity of the urge to move phenomenon. One understudied factor in this debate is the potential influence of interoceptive signals--particularly cardiac activity--in shaping the experience of motor intention and movement initiation. In our study, we addressed this gap by examining whether cardiac signals modulate participants experience of the urge to move, using behavioural and electrophysiological measures in 34 healthy human participants performing Libets task. Crucially, when participants were asked to report the perceived urge to move, their button press timings were predominantly aligned with the diastolic phase of the cardiac cycle, indicating cardiac modulation of motor intention perception. However, analysing heart evoked potential (HEP) amplitudes as a measure of cardiac input perception, we observed no differences in HEP amplitudes associated with changes in introspective demands during the task in both source and sensor spaces. Our results suggest that implicit perception of cardiac signals biases subjective experience of voluntary action initiation, independent from cortical interoceptive markers. These findings have implications for models of motor preparation, intentionality and the bodily basis of voluntary action, challenging conventional interpretations of motor intention and informing debates on volition and interoception. Significance StatementOur study provides evidence that implicit perception of cardiac signals influences the subjective experience of motor intention--the urge to move in Libets experiment. We demonstrate, for the first time, that when reporting urge to move, participants tended to initiate voluntary movements during the diastolic phase of the cardiac cycle. These findings challenge traditional views on factors affecting motor initiation, suggesting relevance of interoceptive processing. By highlighting the role of cardiac input in experiencing motor intention, our findings impact existing debates on volition, agency and free will, further underscoring the importance of integrating bodily signals into these theoretical frameworks.

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bioRxiv2025

Patterns of Structural Disconnection Driving Proprioceptive Deficits After Stroke

BackgroundStroke is a leading cause of death and disability worldwide, with proprioceptive impairments affecting up to 64% of survivors. These impairments hinder sensorimotor function and motor recovery, significantly impacting post-stroke disability and quality of life. Proprioception depends on an integrated network of brain regions but remains underexplored due to limitations in clinical assessments, making it difficult to identify precise links between stroke- related damage and functional deficits. To address this, we combined quantitative proprioceptive measurements with Connectome-based Lesion-Symptom Mapping (CLSM) to identify white matter (WM) disconnection patterns underlying proprioceptive deficits following sensorimotor stroke. MethodsIn this observational study, we investigated the relationship between WM disconnection and proprioceptive deficits in 39 chronic stroke survivors with paretic arm function (>6 months post-stroke; 13 females; aged 35-81) using CLSM and kinematic assessments. Lesions were manually delineated from 3T MRI scans, and proprioception was quantified using the Arm Position Matching (APM) task on the KINARM Exoskeleton. Patient-specific voxel-wise WM disconnection maps were generated using the Tractography Lesion Assessment Standard (TractLAS), which quantifies disconnection relative to a healthy WM connectome. Proprioceptive scores were regressed against disconnection maps using voxel-wise linear regressions (family-wise error corrected, controlled for age and sex). ResultsOur disconnectome-based approach identified a network of regions where proprioceptive deficits were significantly associated with WM disconnection (d = 0.55-1, p < .005 FWE, t = 3.48-6.35). These included tracts previously implicated in proprioceptive function (superior longitudinal fasciculus, middle longitudinal fasciculus, and arcuate fasciculus) and beyond (medial lemniscus, spinothalamic tract, posterior thalamic radiation). ConclusionWe provide evidence that post-stroke proprioceptive impairments arise from network- wide WM disconnection in several key tracts that mediate proprioceptive function. This study highlights the benefits of using CLSM to assess stroke-related proprioceptive deficits and offers a framework for network-informed assessments of functional impairments that can be used for targeted therapies post-stroke.

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bioRxiv2025

Retinal nerve fibre layer thickness reflects characteristics of brain grey and white matter

The retina is a relatively accessible part of the central nervous system compared to the brain. Using high resolution optical imaging we investigated the relationship between retinal thickness, obtained with optical coherence tomography, and structural features of the brain obtained with magnetic resonance imaging. In a population-based sample of over 500 subjects, we hypothesized: (i) that there are structural associations between circumpapillary retinal nerve fibre layer thickness and brain grey matter density and white matter microstructural properties in visual information processing areas, and specifically contralateral associations for nasal retinal fibers, and (ii) that retinal findings reflect broader changes in brain grey and white matter related to cardiovascular risk factors. In support of the first hypothesis, we showed associations of circumpapillary retinal nerve fibre layer thickness with visual cortex grey matter density and with optic radiation fractional anisotropy. These correlations were stronger for the right eye, possibly reflecting right ocular dominancy. Regarding the second hypothesis, while we confirmed the broad impact of cardiovascular risk factors such as body mass index, diabetes, and hypertension on brain structure, we didnt find (adequate) significant partial correlations between circumpapillary retinal nerve fibre layer thickness and cardiovascular risk factors to support the hypothesis. As such, we couldnt confirm that circumpapillary retinal nerve fibre layer thickness is associated with the impact of cardiovascular risk factors on the brain structure. However, when the effects of cardiovascular risk factors were accounted for statistically, circumpapillary retinal nerve fibre layer thickness (particularly on the right side) was associated with fractional anisotropy of limbic system tracts, i.e., the fornix and stria terminalis including hippocampus and amygdala. To further explore the structural associations between eye and brain, in terms of a possible common underlying pathology related to cardiovascular risk factors and progressive neurodegenerative diseases on the central nervous system, longitudinal and interventional studies are necessary.

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bioRxiv2025

Beyond beta: Aperiodic broadband power reflects Parkinson's disease severity - a multicenter study

Parkinsons disease is linked to increased beta oscillations in the subthalamic nucleus, which correlate with motor symptoms. However, findings across studies have varied. Our standardized analysis of multicenter datasets shows that small sample sizes contributed to these discrepancies--a challenge we address by pooling datasets into one large cohort (n=119). Moving beyond beta power, we disentangled spectral components reflecting distinct neural processes. Combining aperiodic offset, low beta, and low gamma oscillations explained significantly more variance in symptom severity than beta alone. Moreover, interhemispheric within-patient analyses showed that, unlike beta oscillations, aperiodic broadband power-likely reflecting spiking activity-was increased in the more affected hemisphere. These findings identify aperiodic broadband power as a potential biomarker for adaptive deep brain stimulation and provide novel insights into the relationship between subthalamic hyperactivity and motor symptoms in human Parkinsons disease.

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bioRxiv2024

Spatial and feature-selective attention interact to drive selective coding in frontoparietal cortex

Attention enables the selective processing of relevant information. Two types of selective attention, spatial and feature attention, have separable neural effects but in real life are often used together. Here, we asked how these types of attention interact to affect information coding in a frontoparietal multiple-demand (MD) network, essential for attentional control. Using functional magnetic resonance imaging (fMRI) with multivariate pattern analysis, we examined how covert attention to object features (colour or shape) and spatial locations (left or right) influences coding of task-related stimulus information. We found that spatial and feature attention interacted multiplicatively on information coding in MD and visual regions, such that there was above-chance decoding of the attended feature of the attended object and no detectable coding of visually equivalent but behaviourally irrelevant aspects of the visual display. The attended information had a multidimensional neural representation, with stimulus information (e.g., colour) and discrimination difficulty (distance from the categorical decision boundary) reflected in separate dimensions. Rather than boosting processing of whole objects or relevant features across space, our results suggest neural activity reflects precise tuning to relevant information, indicating a highly selective control process that codes behaviourally relevant information across multiple dimensions.

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bioRxiv2024

Motor Imagery Enhances Performance Beyond the Imagined Action

Motor imagery is frequently utilized to improve the performance of specific target movements in sports and rehabilitation. In this study, we show that motor imagery can facilitate learning of not only the imagined target movements but also sequentially linked overt movements. Hybrid sequences comprising imagined and physically executed segments allowed participants to learn specific movement characteristics of the executed segments when they were consistently associated with specific imagined segments. Electrophysiological recordings revealed that the degree of event-related synchronization in the alpha and beta bands during a basic motor imagery task was correlated with imagery-evoked motor learning. Thus, both behavioral and neural evidence indicate that motor imagerys benefits extend beyond the imagined movements, improving performance in linked overt movements. This provides new evidence for the functional equivalence of imagined and overt movements and suggests new applications for imagery in sports and rehabilitation.

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bioRxiv2024

The utility of explainable A.I. for MRI analysis: Relating model predictions to neuroimaging features of the aging brain

IntroductionDeep learning models highly accurately predict brain-age from MRI but their explanatory capacity is limited. Explainable A.I. (XAI) methods can identify relevant voxels contributing to model estimates, yet, they do not reveal which biological features these voxels represent. In this study, we closed this gap by relating voxel-based contributions to brain-age estimates, extracted with XAI, to human-interpretable structural features of the aging brain. MethodsTo this end, we associated participant-level XAI-based relevance maps extracted from two ensembles of 3D-convolutional neural networks (3D-CNN) that were trained on T1-weighted and fluid attenuated inversion recovery images of 2016 participants (age range 18-82 years), respectively, with regional cortical and subcortical gray matter volume and thickness, perivascular spaces (PVS) and water diffusion-based fractional anisotropy of main white matter tracts. ResultsWe found that all neuroimaging markers of brain aging, except for PVS, were highly correlated with the XAI-based relevance maps. Overall, the strongest correlation was found between ventricular volume and relevance (r = 0.69), and by feature, temporal-parietal cortical thickness and volume, cerebellar gray matter volume and frontal-occipital white matter tracts showed the strongest correlations with XAI-based relevance. ConclusionOur ensembles of 3D-CNNs took into account a plethora of known aging processes in the brain to perform age prediction. Some age-associated features like PVS were not consistently considered by the models, and the cerebellum was more important than expected. Taken together, we highlight the ability of end-to-end deep learning models combined with XAI to reveal biologically relevant, multi-feature relationships in the brain.

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bioRxiv2024

Neural Representations of Extrasystoles: A Predictive Coding Perspective

The coordinated interplay between brain and body is essential for survival, with the heart-brain axis playing a fundamental role in perceptual, cognitive and motor processes. Central to this interaction, the heart-evoked potential (HEP) represents a neural correlate of the heart activity. Further, the HEP is thought to represent a neuronal prediction for each heartbeat, raising questions about its role in arrhythmia. Yet, previous studies have primarily focused on regular heart rhythms, with only a few investigations delving into arrhythmias and, notably, none into extrasystoles, a form of benign arrhythmias that disrupt the regular heart rhythm. Extrasystoles represent a unique possibility for investigating brain responses to unexpected pronounced alterations in heart rhythm. We took advantage of the largest dataset (103 subjects with 3000 extrasystoles), including both EEG and ECG recordings, and analysed the neural response to both types of extrasystoles (supraventricular and ventricular) using multiverse approaches and control analysis in time and source space. We found that the HEP was significantly reduced for ventricular extrasystoles with underlying sources in the left insular. For the postextrasystolic beat of both types of extrasystoles, we found a significantly earlier and increased HEP originating from sources in the left frontal orbital cortex and the anterior cingulate gyri. The reduced HEP response to the ventricular extrasystole might result from inactive interoceptive cardiac pathways. In contrast, increased HEP of the postextrasystolic beat along with the anatomical neural HEP generators may reflect an interoceptive prediction error arising from a naturally occurring discrepancy between the predicted and actual heart rhythm, with a main source in the anterior cingulate gyri, a primary location for prediction error processing.

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bioRxiv2024

Validating genuine changes in Heartbeat Evoked Potentials using Pseudotrials and Surrogate Procedures

The brain continuously receives interoceptive information about the state and function of our internal organs. For instance, each time the heart beats, the brain responds by generating time-locked activity, known as heartbeat evoked potentials (HEP). When investigating HEPs, it is essential to adequately control for heartbeat-independent confounding activity to avoid false interpretation. In the present study, we highlight the pitfalls of uncontrolled analyses and advocate for the use of surrogate heartbeat analysis and pseudotrial correction, which are promising tools to control for spurious results. Surrogate heartbeat analysis involves shuffling the timing of heartbeats to verify the time-locking of HEP effects. Pseudotrial correction works by subtracting heartbeat-independent activity from HEPs. In this study we employ both procedures, validate them in simulations and apply them to real EEG data. Using EEG recordings obtained during the performance of an auditory novelty oddball task in a large population, we show that, without control analyses, pre-stimulus HEPs appear inversely related to task-related measures such as P300 event-related potential amplitudes and reaction time speed. However, these effects disappear after carefully controlling for heartbeat-unrelated EEG activity. Additionally, in real and simulated data, we show that pseudotrial correction has the potential to remove task-related confounds from HEPs, thereby uncovering real heartbeat-related effects that otherwise could be missed. This study therefore highlights issues that can arise when analyzing HEPs during tasks, provides solutions to overcome them, and gives recommendations for future studies to avoid pitfalls when analyzing and designing behavioral paradigms with HEPs.

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bioRxiv2024

Excitability modulations of somatosensory perception do not depend on feedforward neuronal population spikes

Neural states shape perception at earliest cortical processing levels. Previous work in humans showed a relationship between initial cortical excitation, as indicated by the N20 component of the somatosensory evoked potential (SEP), pre-stimulus alpha oscillations, and the perceived intensity in a somatosensory discrimination paradigm. Here we address the follow-up question whether these excitability dynamics reflect changes in feedforward or feedback signals. We leveraged high-frequency oscillations (HFO) as a metric for neuronal population spiking activity of the first excitatory feedforward volley in the somatosensory cortex, non-invasively extracted from electroencephalography (EEG) data of 32 male human participants. Using Bayesian statistics, we found evidence against the involvement of HFO in moment-to-moment variability of perceived stimulus intensity, in contrast to previously observed pre-stimulus alpha and N20 effects. Given that the N20 component presumably reflects backpropagating membrane potentials towards the apical dendrites, we argue that top-down feedback processes (e.g., related to alpha oscillations) may thus rely on modulations at distal dendritic sites of involved pyramidal cells rather than on output firing changes at their basal compartments.

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