Biology subjects

Schwenk, J. C. B.

Publications and source records attributed to Schwenk, J. C. B..

9 recordsLinked to original sources

bioRxiv2026

Improved detection and spatiotemporal spectral analysis of neural traveling waves

Traveling waves (TWs) are a fundamental mode of neural dynamics, yet existing detection methods are limited by sensor geometry, spatial-frequency resolution, signal amplitude, and ambiguity between propagating and standing-wave patterns. Here we introduce the Traveling Wave Index (TWINDEX), a framework for three-dimensional spatiotemporal spectral analysis of TWs across temporal frequency, spatial frequency, and propagation direction. TWINDEX generalizes to irregular sensor layouts, and quantifies wave strength as the reduction in circular phase variance produced by a candidate planar wave. This normalization yields robust behavior at both low and high spatial frequencies and suppresses coherent in-phase activity. Directional moments further separate planar from standing waves. We derive analytical links between TWINDEX, parametric planar-wave fit, and distance-phase correlation, and introduce projected distance-phase correlation (ProDPC) for sensitive single-trial planar-wave detection. Applying these methods to large-scale marmoset ECoG and human EEG, we identify alpha/low-beta TWs localized in spatial and temporal frequency, with physiologically plausible propagation speeds. Across trials and epochs, waves occur in oppositely directed propagation modes, demonstrating that alpha/beta activity is associated with both feedforward- and feedback-directed large-scale dynamics.

neuroscience↗

bioRxiv2026

The aging rhythm: spatio-temporal dynamics of resting alpha oscillations in young and older brains

Aging is associated with substantial alterations in brain oscillatory activity, particularly within the alpha band (8-12 Hz). Yet, little is known about how aging affects the spatial propagation of alpha oscillations across cortical networks. In addition, although previous EEG studies have consistently reported age-related slowing of alpha peak frequency and changes in alpha power, the interpretation of these findings remains debated because oscillatory measures are influenced by age-related modifications in the aperiodic component of the power spectrum. Here, we investigated age-related changes in both the spectral and spatiotemporal properties of alpha activity using resting-state EEG data from a large cohort of younger (N = 326) and older adults (N = 108). To address the debate in the literature, analyses explicitly accounted for the aperiodic component of the EEG power spectrum. Consistent with previous literature, older adults exhibited a robust slowing of the individual alpha peak frequency, along with reductions in the aperiodic exponent and offset. Importantly, alpha-band power was also significantly reduced in older adults even after correcting for aperiodic activity, indicating that age-related alpha alterations cannot be fully explained by non-oscillatory spectral changes alone. Beyond conventional spectral measures, we characterized alpha-band traveling waves and identified age-related alterations in their propagation dynamics, particularly within frontal regions. Older adults showed enhanced medial-to-lateral and interhemispheric propagation patterns, suggesting reduced hemispheric segregation and increased bilateral coordination of rhythmic activity. These findings extend current models of cognitive aging by demonstrating that aging affects not only the spectral characteristics of alpha oscillations but also their large-scale spatiotemporal organization. Together, the results support the view that aging involves a functional reorganization of cortical communication dynamics, potentially reflecting compensatory mechanisms within distributed neural networks.

neuroscience↗

bioRxiv2026

Neural Oscillatory Signatures of Predictive Processing in Visual Statistical Learning

Several studies suggest that neural oscillations play a role in cognition, including predictive processing. Recent models propose that alpha-band traveling waves reflect prediction (top-down, frontal to occipital), while bottom-up waves reflect prediction errors. We tested this hypothesis using a visual statistical learning task in which participants (N=31) detected target shapes presented with varying levels of predictability. EEG and pupillometry were recorded throughout the task. Behavioral results showed faster responses for predictable targets, and pupil dilation increased for unexpected shapes. ERP analyses revealed predictability effects on the P300 component across occipital, central, and frontal electrodes, along with modulation of alpha oscillations. However, traveling-wave analyses did not show a clear effect of predictability between conditions. Instead, wave patterns varied with participants cognitive strategies: individuals who relied more on statistical regularities showed stronger top-down alpha-band waves. These findings suggest that alpha traveling waves may reflect general cognitive strategies rather than specific predictive processes.

neuroscience↗

bioRxiv2026

Oscillatory traveling waves during visual entrainment in autistic and neurotypical adults

Neural oscillations play a crucial role in cognition, and several studies emphasize the importance of considering them as traveling waves that propagate through brain regions. In scalp EEG, waves typically travel along the anterior-posterior axis: forward waves (occipital-to-frontal) predominate during sensory stimulation, while backward waves (frontal-to-occipital) emerge during rest and top-down modulation. Within the predictive coding framework, backward waves are proposed to convey predictive signals, whereas forward waves reflect sensory processes and feedforward propagation of prediction errors. In this study, we investigated traveling wave dynamics during a visual entrainment task in neurotypical (N=25) and autistic (Autism Spectrum Disorder - ASD) adults (N=24). Our results replicated previous findings in neurotypical participants, as we observed an increase in backward waves during rhythmic visual stimulation, consistent with enhanced top-down predictions. Notably, we observed the opposite pattern in the ASD group, characterized by a pronounced increase in forward waves at the entrained frequency during visual stimulation. These results align with predictive coding accounts of autistic perception, which hypothesize an imbalance between predictions and sensory evidence. Specifically, an increase in forward wave may reflect a bias toward bottom-up sensory signaling over predictive feedback, due to atypical hierarchical communication across brain regions in ASD. Together, our findings shed new light on the oscillatory dynamics involved in visual entrainment in neurotypical adults and provide novel evidence in favor of predictive coding accounts of autistic perception, as well as a consequent bias toward bottom-up sensory signaling over predictive feedback in a context of visual entrainment. Significance statementTraveling waves reflect the spatiotemporal propagation of neural oscillations, providing a window into hierarchical brain communication. By examining traveling wave dynamics during visual entrainment, we show that neurotypical adults exhibit increased backward (frontal-to-occipital) waves, consistent with enhanced top-down predictive signaling. In contrast, adults with Autism Spectrum Disorder (ASD) display a marked increase in forward (occipital-to-frontal) waves, indicating stronger bottom-up sensory drive. These findings provide electrophysiological evidence for atypical predictive processing in ASD, supporting predictive coding theories that propose an imbalance between sensory evidence and prior expectations. Our results highlight traveling waves as a sensitive neural marker of hierarchical signaling and predictive dynamics across typical and atypical perceptual systems.

neuroscience↗

bioRxiv2025

Detection and quantification of planar traveling waves in the EEG using spherical phase fitting

Recent years have seen an increasing interest in the spatiotemporal dynamics of oscillatory brain activity. Recordings at various scales have shown oscillations propagating as waves over cortical space, both at small and large scales. The most common waves observed in the EEG are planar waves, formed by a synchronized wavefront propagating in a consistent direction (e.g., posterior-anterior). These waves have been linked to diverse perceptual and cognitive measures. However, their quantification has faced issues due to the inherently noisy EEG signal and its ambiguous spatial localization. Existing methods employ restrictive windows of analysis (e.g., lines of electrodes) or rely on wave motifs extracted from the data. A specific algorithm for detecting planar waves is still lacking. Here, we present a comprehensive analysis pipeline for this purpose, comprising three steps: first, genuine oscillatory activity is extracted from the EEG as clusters. The spatial phase gradient is then fit using a spherical wave model. Finally, stable waves are extracted from the time series of fits. We validate our method using simulations over a physiological range of parameters. Using a forward model, we test EEG wave detection for propagation along different pathways at the source level. Lastly, we apply our analysis to real EEG recordings, targeting alpha oscillations during visual stimulation and at rest. In summary, our method provides a reliable algorithm for detecting planar waves in the EEG. Given the emerging functional roles of traveling waves in perception and cognition, this could potentially be utilized in a wide range of future studies.

neuroscience↗

bioRxiv2024

A hierarchical multiscale model of forward and backward alpha-band traveling waves in the visual system

Recent studies have shown that low-frequency oscillations in the cortex are often organized as traveling waves. The dynamical properties of these waves, that span different scales, have been linked to both sensory processing and cognitive functions. In EEG recordings, alpha-band ([~]10Hz) traveling waves propagate predominantly in both directions of the occipital-frontal axis, with forward waves being most prominent during visual processing, while backward waves dominate at rest and during sensory suppression. While a previous study has proposed a functional model to explain their generation and propagation, a multi-scale, biologically plausible implementation is still lacking. Here, we present a multi-scale network model with mean-field dynamics that, building on known interlaminar and cortico-cortical projections, reproduces the dynamics of alpha-band traveling waves observed in EEG recordings. We show that scalp-level forward and backward waves can arise from two distinct sub-networks that are connected in infragranular layers at each area. At rest, the network generates spontaneous backward waves and switches to a forward state upon bottom-up sensory stimulation, reproducing the dynamics we observed in EEG recordings in healthy participants.. We then expand our model to a cortico-thalamic network with a parallel feedforward pathway through the pulvinar. Our results show that this pathway biases the cortical dynamics to the forward state and that high pulvinar engagement leads to spontaneous forward waves without external input. This result is in line with previous studies suggesting a key role for the pulvinar in directing information flow in the cortex, and provide a computational basis to investigate the role of the pulvinar in cortical dynamics. In summary, our model provides a biologically plausible architecture for modeling the dynamics of macroscale traveling waves. Importantly, our study bridges the gap between distinct scales by connecting laminar mean-field activity to spatial patterns at the scalp level, providing a biologically grounded and comprehensive view of the generation and propagation of alpha-band traveling waves.

neuroscience↗

bioRxiv2022

Neural responses to broadband visual flicker in marmoset primary visual cortex

Temporal information is ubiquitous in natural vision and must be represented accurately in the brain to allow us to interact with a constantly changing world. Recent studies have employed a random stimulation paradigm to map the temporal response function (TRF) to luminance changes in the human EEG. This approach has revealed that the visual system, when presented with broadband visual input, actively selects distinct temporal frequencies, and retains their phase-information for prolonged periods of time. This non-linear response likely originates in primary visual cortex (V1), yet, so far it has not been investigated on a neural level. Here, we characterize the steady-state response to random broadband visual flicker in marmoset V1. In two experiments, we recorded from i) marmosets passively stimulated under general anesthesia, and ii) awake marmosets, under free viewing conditions. Our results show that LFP coupling to the stimulus was broadband and unselective under anesthesia, whereas in awake animals, it was restricted to two distinct frequency components, in the alpha and beta range. Within these frequency bands, coupling adhered to the receptive field (RF) boundaries of the local populations. The responses outside the RF did not provide evidence for a propagation of stimulus information across the cortex, contrary to results in human EEG studies. This result may be explained by short fixation durations, warranting further investigation. In summary, our findings show that during awake behavior V1 neural responses to broadband information are selective for distinct frequency bands, and that this selectivity is likely controlled actively by top-down mechanisms.

neuroscience↗

bioRxiv2021

Visual perturbation of balance suggests impaired neuromuscular stability but intact visuo-motor control in Parkinson's disease

Postural instability marks one of the most disabling features of Parkinsons disease (PD), but only reveals itself after affected brain areas have already been significantly damaged. Thus, there is a need to detect deviations in balance and postural control before visible symptoms occur. In this study, we visually perturbed balance in the anterior-posterior direction using sinusoidal oscillations of a moving room in virtual reality at different frequencies. We tested three groups: individuals with PD under dopaminergic medication, an age-matched control group, and a group of young healthy adults. We tracked their centre of pressure and their full-body motion. We investigated sway amplitudes and applied newly introduced phase-locking analyses to investigate responses across participants bodies. Patients exhibited significantly higher sway amplitudes as compared to the control subjects. However, their sway was phase-locked to the visual motion like that of age-matched and young healthy adults. Furthermore, all groups successfully compensated for the visual perturbation by - most likely reflexively - phase-locking their sway to the stimulus. As frequency of the perturbation increased, distribution of phase-locking (PL) across the body revealed a shift of the highest PL-values from the upper body towards the hip-region for young healthy adults, which could not be observed in patients and elderly healthy adults. Our findings suggest an impaired neuromuscular stability, but intact visuomotor processing in early stages of PD, while less flexibility to adapt postural strategy to different perturbations revealed to be an effect of age rather than disease. New & NoteworthyA better understanding of visuomotor control in Parkinsons disease (PD) potentially serves as a tool for earlier diagnosis, which is crucial for improving patients quality of life. In our study, we assess body sway responses to visual perturbations of the balance control system in patients with early-to-mid stage PD, using motion tracking along with recently established phase-locking techniques. Our findings suggest patients at this stage to have an impaired muscular stability but intact visuomotor control.

neuroscience↗

bioRxiv2021

Coding of interceptive saccades in parietal cortex of macaque monkeys

The oculomotor system can initiate remarkably accurate saccades towards moving targets (interceptive saccades) the processing of which is still under debate. The generation of these saccades requires the oculomotor centers to have information about the motion parameters of the target that then must be extrapolated to bridge the inherent processing delays. We investigated to what degree the information about motion of a saccade target is available in the lateral intra-parietal area (area LIP) of macaque monkeys for generation of accurate interceptive saccades. When a multi-layer neural network was trained based on neural discharges from area LIP around the time of saccades towards stationary targets it was also able to predict the end points of saccades directed towards moving targets. This prediction, however, lagged behind the actual post-saccadic position of the moving target by ~80 ms when the whole neuronal sample of 105 neurons was used. We further found that single neurons differentially code for the motion of the target. Selecting neurons with the strongest representation of target motion reduced this lag to ~30 ms which represents the position of the moving target approximately at the onset of the interceptive saccade. We conclude that - similarly to recent findings from the Superior Colliculus (Goffart et al., 2017) - there is a continuum of contributions of individual LIP neurons to the accuracy of interceptive saccades. A contribution of other gaze control centers (like the cerebellum or the frontal eye field) that further increase the saccadic accuracy is, however, likely.

neuroscience↗