Biology subjects

Nikulin, V.

Publications and source records attributed to Nikulin, V..

At least 19 recordsLinked to original sources

bioRxiv2026

Matching Corticospinal and Cortical Excitability along the motor cortex: TMS-EMG-EEG study

There is still no consensus on how to choose intensities for transcranial magnetic stimulation (TMS). The resting motor threshold (RMT), the intensity needed to evoke muscle contractions in the contralateral limb muscles, is a robust muscle-specific index of corticospinal excitability. RMT from one of the hand muscles is commonly used as a reference for TMS neuromodulation interventions and TMS-electroencephalography (EEG) studies. However, it only provides an indirect measure of cortical excitability, and even for the primary motor cortex (M1), we do not understand how cortical and corticospinal excitability correspond to each other. Indeed, it remains unclear whether cortical responses to TMS follow similar location-dependent patterns along the M1, and whether subthreshold stimulation can reliably engage cortical circuits relevant to motor output. Combining TMS with electromyography (EMG) and EEG, TMS-EMG-EEG, enables simultaneous assessment of corticospinal and cortical excitability to answer these questions. We investigated how cortical excitability thresholds (CETs, measured with EEG), relate to corticospinal excitability thresholds (RMTs, measured with EMG) at the hand, arm, and leg hotspots along the M1 in 24 subjects. RMTs and CETs were strongly correlated, and both increased from hand to arm to leg. CETs were generally lower than RMTs, indicating that subthreshold stimulation was enough to evoke robust early TMS evoked potentials (TEPs). Within subjects, CETs differed from the corresponding RMTs at all three M1 hotspots, and this difference increased from hand to leg representations. The findings of the present study argue against using a default hand-muscle RMT to set cortical excitability dosage along the M1 and beyond. Online TMS-EEG can enable real-time dosing and could improve stimulation precision, efficiency, and generalizability, but the site-specific variability calls for adaptive control.

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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

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

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

Somatosensory high frequency oscillations across the human central nervous system

Information transmission in the central nervous system (CNS) relies on the propagation of action potentials (neuronal spikes), traditionally thought to be inaccessible with electroencephalography (EEG). Converging evidence from human and animal recordings however demonstrates that high frequency oscillations (HFOs) in EEG recordings represent neuronal population spiking, complementing standard low-frequency EEG recordings in cortex. Here, we developed an approach to simultaneously record HFOs to somatosensory stimulation across the entire human CNS noninvasively: from spinal over subcortical to cortical stages. Our approach provides a detailed temporal and spectral characterization of HFOs at each CNS level, delineating how they evolve across the somatosensory processing hierarchy. Critically, we show that low and high frequency electrophysiological responses represent at least partly independent processing channels across the CNS. We thus demonstrate the feasibility of studying the full spectrum of neuronal information processing across the CNS, offering a multi-level window into human neurophysiology in health and disease.

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bioRxiv2024

Network mechanisms in rapid-onset dystonia-parkinsonism

BackgroundRapid-onset dystonia-parkinsonism (RDP) is a rare neurological disorder caused by mutations in the ATP1A3 gene. Symptoms are characterized by a dystonia-parkinsonism. Recently, experimental studies have shown that the pathophysiology of the disease is based on a combined dysfunction of the cerebellum (CB) and basal ganglia (BG) and that blocking their interaction can alleviate the symptoms. The underlying network mechanisms have not been studied so far. ObjectiveOur aim was to characterize neuronal network activity in the BG and CB and motor cortex in the ouabain model of RDP by site-specific infusion of ouabain. MethodsRats were chronically infused with ouabain either in the CB, striatum (STR) or at both places simultaneously. Motor behavior was scored using published rating systems. Parallel in vivo recordings of local field potentials (LFP) from M1, deep cerebellar nuclei (DCN) and substantia nigra reticulata (SNr) were performed. Data were compared to untreated controls. ResultsOuabain infusion into the cerebellum produced severe dystonia that was associated with increased high-frequency gamma oscillations in the DCNs, which were subsequently transmitted to the BG and M1. Striatal infusion led to parkinsonism and elevated beta- oscillations in SNr that were transmitted to the CB and M1. The simultaneous application of STRs and CB with ouabain resulted in dystonia-parkinsonism and increased beta oscillations in BG, CB, and M1. ConclusionWe demonstrate that symptom-specific beta and gamma oscillations can be transmitted between the BG and CB, which is likely to be very important for the understanding of disease mechanisms.

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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

Evaluating noise correction approaches for non-invasive electrophysiology of the human spinal cord

The spinal cord is a vital component of the central nervous system for the processing of sensorimotor information transmitted between the body and the brain. Electrospinography (ESG) is the most accessible non-invasive technique for recording spinal signals in humans, but the vast and detrimental impact of physiological noise (mostly of cardiac nature) has prevented widespread adoption. Here, we aim to address this issue by examining various denoising algorithms for cardiac artefact reduction - including principal component analysis-based techniques (PCA), independent component analysis-based approaches (ICA) and signal space projection (SSP). We observed that in situations where large numbers of spinal electrodes are used, SSP offers the best results in terms of balancing the removal of harmful noise and preserving neural information of interest. In cases where only a small number of electrodes are available, an approach based on PCA is deemed helpful. Approaches based on ICA were found to be unsuitable for cardiac artefact removal in ESG, due to a suboptimal balance of artefact removal and signal preservation. Finally, we also approached this issue from a signal-enhancement perspective and observed that in cases where extensive electrode arrays are used in the context of task-based designs, a spatial filtering technique based on canonical correlation analysis (CCA) reveals clear evoked spinal potentials even with single-trial resolution. Taken together, there are several appropriate algorithms for physiological noise removal in ESG, rendering this an accessible and easy-to-use technique for non-invasive assessments of human spinal cord function.

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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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bioRxiv2024

Heartbeat perception is causally linked to frontal delta oscillations

The ability to accurately perceive ones own bodily signals, such as the heartbeat, plays a vital role in physical and mental health. However, the neurophysiological mechanisms underlying this ability, termed interoception, are not fully understood. Converging evidence suggests that cardiac rhythms are linked to frontal brain activity, particularly oscillations in the delta (0.5 - 4 Hz) band, but their causal relationship remained elusive. Using amplitude-modulated transcranial alternating current stimulation (AM-tACS), a method to enhance or suppress brain oscillations in a phase-specific manner, we investigated whether frontal delta oscillations are causally linked to heartbeat perception. We found that enhancement of delta phase synchrony suppressed heartbeat detection accuracy, while suppression of delta phase synchrony enhanced heartbeat detection accuracy. These findings suggest that frontal delta oscillations play a critical role in heartbeat perception, paving the way for causal investigations of interoception and potential clinical applications. SignificanceAlthough bodily signals are known to influence perception and behavior, little is known about the underlying neurophysiological mechanisms. Here, we show that perception of the heartbeat is anticorrelated with phase synchrony of frontal delta oscillations, and that modulating these oscillations with transcranial electric stimulation influences heartbeat perception. Our results suggest that delta oscillations play a key role in processing bodily signals, with potential implications for theories of emotions and clinical neuroscience.

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