Biology subjects

Engel, A. K.

Publications and source records attributed to Engel, A. K..

At least 19 recordsLinked to original sources

bioRxiv2026

Causal modulation of cortical amplitude coupling through dual-site amplitude-modulated tACS

BackgroundIntrinsic functional coupling at multiple temporal scales is a hallmark of human brain dynamics. Among these coupling modes, slow co-fluctuations of oscillatory amplitudes, termed amplitude coupling, are thought to represent a key organizing principle of the large-scale functional architecture, constraining and gating network activity. Yet, despite extensive correlational evidence, direct causal access to amplitude coupling remains limited, restricting insight into its functional relevance. ObjectivesHere, we investigated whether dual-site amplitude-modulated transcranial alternating current stimulation (AM-tACS) can selectively modulate interhemispheric amplitude coupling in human resting-state networks. MethodsTwenty-eight participants received AM-tACS with a carrier frequency in the beta-band whose amplitude was modulated by low-frequency, scale-free dynamics. By applying dual-site AM-tACS either coherently or incoherently across bilateral parieto-occipital cortices, we tested whether stimulation could systematically enhance or disrupt amplitude co-fluctuations in the electrophysiological aftereffect. ResultsIncoherent AM-tACS significantly reduced interhemispheric amplitude coupling between targeted parieto-occipital cortices, with the strongest effects observed in the stimulated beta-band carrier frequency range. This modulation occurred independently of changes in local power or inter-areal phase coupling, indicating a selective effect of AM-tACS on amplitude-based connectivity. Moreover, reductions in amplitude coupling were correlated with the induced electric field strength, suggesting a dose-dependent relationship between stimulation intensity and coupling modulation. ConclusionsOur findings demonstrate that dual-site AM-tACS can causally and selectively modulate amplitude coupling in the human brain. By establishing causal control over lasting amplitude coupling dynamics, this work provides a methodological foundation for future investigations into the functional and behavioral relevance of amplitude coupling in both healthy and pathological brain states. HighlightsO_LIDual-site AM-tACS selectively modulates amplitude coupling in humans C_LIO_LIAM-tACS was designed to mimic natural, scale-free amplitude fluctuations C_LIO_LIStimulation effects are spatially confined to interactions between target regions C_LIO_LIE-field strength predicts the change in amplitude coupling, suggesting a dose-response relationship C_LIO_LIAmplitude coupling modulations are not mediated by band-limited power changes C_LI

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bioRxiv2026

Oscillatory sensory stimulation in the delta-band enhancestemporal prediction performance

Predicting the time point when an event will occur is fundamental for adaptive behavior, yet it remains unresolved whether temporal prediction can be influenced by low-frequency rhythmic modulation of sensory stimuli. Here, we tested whether external rhythmic sensory stimulation at a frequency in the delta range (0.5 - 3 Hz) alters performance in a visual temporal prediction task. Participants judged whether a moving visual stimulus reappeared too early or too late after disappearing behind an occluder, while the temporal structure of crossmodal sensory input was manipulated across two behavioral sessions. Results indicated that in the visual-auditory conditions, oscillatory stimulation in either the visual or auditory modality improved performance, whereas decaying sensory intensity over time impaired performance. In visual-tactile conditions, oscillatory visual stimulation also enhanced sensitivity, but rhythmic tactile stimulation did not produce a comparable benefit in performance. Critically, tactile stimulation improved performance only when aligned to the expected disappearance of the visual stimulus, demonstrating that the phase relationship between sensory input and intrinsic delta oscillations is behaviorally relevant. Together, these findings indicate that temporal prediction depends on the temporal structure of sensory input and support the relevance of delta-band oscillations in predictive behavior across and within sensory modalities. Hence, rhythmic modulation of sensory stimuli may provide a tool to enhance temporal prediction accuracy by stimulating oscillatory neural dynamics.

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bioRxiv2025

Low-Rank Tensor Decomposition for Cross-Bispectral Analysis of EEG Data

Cross-bispectral measures provide a rich description of interactions in EEG signals, but their third-order tensor structure poses substantial challenges for interpretation and dimensionality reduction. We introduce a low-rank tensor decomposition framework specifically designed for cross-bispectral EEG data. The model expresses the bispectrum as a product of a single spatial mixing matrix and a compact source-interaction tensor, yielding a structured and interpretable representation of the data. Unlike classical tensor decompositions such as Tucker and its special case PARAFAC, where either the core tensor is unconstrained or restricted to rank-one terms, our formulation incorporates a source-space interpretation directly through the tensor Qmnp, making the decomposition well suited for identifying dominant coupling components. After obtaining the low-rank representation, spatial demixing is performed using MOCA to derive clear and interpretable source maps. Through simulations, we show that the method consistently retrieves the dominant structure of the bispectrum across a wide range of conditions, including changes in SNR, dipole orientation, amplitude balance, and source complexity. Applied to resting-state EEG, the method produces anatomically plausible parietal generators associated with the strongest alpha-band bispectral interactions. Overall, the framework provides a principled and computationally efficient approach for reducing and interpreting cross-bispectral EEG data, offering source-level insights that are difficult to obtain using standard tensor factorization techniques.

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bioRxiv2025

Inter-areal coupling for cognition through coincident oscillatory transients

How do large-scale functional brain networks dynamically emerge to enable cognition? Correlated oscillations, a mechanism for inter-areal interactions, can be expressed as phase coherence or correlated amplitude fluctuations. While the functional role of phase coherence has been addressed in the past, it remains unknown how large-scale amplitude coupling dynamically supports the rapid network reconfigurations necessary for adaptive behavior. Here, we demonstrate that cognitive processing relies on the temporal alignment of brief, high-amplitude oscillatory events across distant cortical regions, rather than sustained coupling of oscillations. Capitalizing on the spatio-temporal resolution of magnetoencephalography (MEG) during a task probing attention and decision-making, we show that oscillatory event coincidences form functionally relevant, transient networks: First, coincidences of high-amplitude oscillatory events in the alpha and beta frequency range increase prior to correct decisions within a parieto-frontal network. Second, transient theta/alpha oscillatory event coincidences within a medial parietal, temporo-parietal junction and lateral prefrontal network track the dynamics of spontaneous, covert spatial attention reallocation. In contrast, transient aperiodic and sustained network components exhibit only weak functional modulations, revealing a spatial and spectral dissociation between transient and sustained amplitude coupling. Overall, our findings demonstrate that efficient cognition relies on large-scale interactions rapidly formed through co-occurring transient oscillatory events. We propose that the brain-wide orchestration of high-amplitude oscillatory events can provide a new framework for understanding how the brain dynamically integrates information to guide flexible behavior.

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bioRxiv2025

Modulation of temporal prediction by STN-DBS in Parkinson's disease: Links between behavior and cortical oscillations

BACKGROUNDAccurate temporal prediction is essential for adaptive behavior and relies on coordinated neural activity within cortico-basal ganglia circuits. Parkinsons disease (PD), with the main hallmark of dopaminergic depletion and abnormal neural synchrony, impairs this ability. Deep brain stimulation deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a widely used treatment for reducing motor symptoms in PD, but its effects on temporal prediction remain not fully understood. OBJECTIVESThis study aimed to investigate how STN-DBS influences temporal prediction performance and its underlying oscillatory dynamics in PD patients, with a particular focus on beta-band power and delta-band inter-trial phase consistency (ITPC). METHODS13 PD patients (5 female, age: 64 {+/-} 5.7 years; disease duration: 11.8 {+/-} 1.8 years) with STN-DBS performed a temporal prediction task with (DBS ON) and without (DBS OFF) stimulation, while 64-channel-electroencephalography (EEG) was recorded. 20 age-matched healthy controls completed the same task. Behavioral performance was assessed using psychometric function slopes. Time-frequency analyses and source-level EEG measures examined beta power and delta ITPC. RESULTSPD patients showed impaired temporal prediction performance compared to controls, reflected in shallower psychometric slopes. DBS significantly improved performance to a level comparable to those of controls. EEG revealed reduced beta suppression in PD patients during DBS OFF, while beta suppression in DBS ON was comparable to controls. Both DBS OFF and ON exhibited reduced delta ITPC compared to controls. In DBS ON, source-level delta ITPC was positively correlated with temporal prediction accuracy. CONCLUSIONSTN-DBS improves temporal prediction performance in PD, likely through modulation of beta and delta oscillatory activity. While beta power suppression is partially restored, deficits in delta phase alignment persist, suggesting frequency-specific DBS effects on temporal prediction processes.

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bioRxiv2025

Differential contributions of low-frequency phase and power in crossmodal temporal prediction: A MEG study

BACKGROUNDOur representation of time is embedded within multisensory perception, based on sight, sound, or touch. However, despite being a crucial aspect of daily life, the neural dynamics of cross-modal temporal predictions remain elusive. The objective of this study was to investigate neural correlates of tactile-to-visual influences on temporal prediction using magnetencephalography (MEG). We hypothesized to observe increased inter-trial phase consistency (ITPC) in the low-frequency delta range (0.5-4 Hz) due to their involvement in temporal prediction. In addition, stronger ITPC values should correlate with a steeper slope of the psychometric function, indicating phase alignments as a likely cause of more consistent temporal predictions. METHODSThe study was conducted within one MEG session employing a modified version of the time prediction task by Roth et al. [2013] and Daume et al. [2021]. Participants (N=23) observed a visual stimulus moving towards an occluder. Shortly before reaching the occluder, the visual stimulus faded in luminance to make the visual offset less informative. Instead, participants received a brief tactile stimulus to the ipsilateral hand at the time point of disappearance, generating a temporal expectation regarding its reappearance on the opposite side of the occluder. After variable time intervals, a visual stimulus reappeared, and participants had to indicate whether this was too early or too late compared to the movement before disappearance. A non-predictive control condition involved participants judging the variable luminance of the reappearing visual stimulus compared to its initial luminance at the beginning of the trial. Psychometric curves were fitted to the behavioral data of each participant and condition, and MEG recordings were analyzed using time-frequency representations obtained by wavelet convolution. To compare spectral power and ITPC estimates between conditions within frequency bands showing significant differences to the pre-stimulus baseline, we used cluster-based permutation statistics. Pearsons correlations were used to examine the relationship between ITPC or power estimates and the steepness of each participants psychometric function. RESULTSITPC analysis revealed strong increases in the delta range around stimulus disappearance and reappearance. Delta ITPC was significantly stronger during temporal prediction compared to the control condition. Only delta ITPC, but not delta power, correlated with the consistency of temporal prediction. Furthermore, temporal prediction led to increased alpha power in the dorsolateral and medial prefrontal cortex, as well as the right superior temporal sulcus and middle temporal gyrus, whereas beta power did not show differences between conditions. CONCLUSIONOur findings suggest that increased delta ITPC is likely caused by phase resets driven by the temporal prediction process rather than evoked neural activity. Furthermore, our findings indicate that phase alignments occur during crossmodal visuo-tactile-to-visual temporal predictions, even with a combination of non-rhythmic and discrete stimulation. This highlights the broad applicability of phase resets of neural oscillations as a mechanism for predicting timing across various types of stimuli.

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bioRxiv2025

The sensorimotor basis of subjective experience in social synchronization behavior

1.The sensorimotor contingency (SMC) theory addresses action and perception as constitutive factors to one another - what is being perceived depends on what we do and vice versa. Accordingly, perception is seen as an active process of probing the environment and receiving feedback from it. These action-effect patterns may also be a predominant factor in social interactions. They can manifest in the phenomenon of synchronization, for example, when applause, gait, or posture in conversations synchronize unintentionally and form the basis of the concept of socially deployed sensorimotor contingencies (socSMCs). In this study, we introduce and compare measures used to study complex systems in order to quantify the information-theoretic basis of socSMCs. Two human participants had to synchronize arm movements in a full body virtual reality (VR) environment with each other. We aimed to evaluate the information-theoretic measures transfer entropy and mutual information in this complex motion synchronization task. Furthermore, in our experiment participants shared a mutual sensation of synchronicity and creativity for their interaction solely based on their movements. These subjective ratings of synchronicity and creativity can be predicted using transfer entropy and mutual information, showing that informational coupling between agents is relevant to subjective experience of interaction as described by the concept of socSMCs.

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bioRxiv2024

Source to sensor coupling (SoSeC) as an effective tool to localize interacting sources from EEG and MEG data

A standard approach to estimate interacting sources from EEG or MEG data is to first calculate a coupling between all pairs of voxels on a predefined grid within the brain and then average or maximize this coupling matrix along each column or row. Depending on the chosen coupling measure and grid size this approach can be computationally very costly, in particular when a bias is supposed to be removed. We here suggest to replace this approach by a maximization of coupling between each source and the signal in sensor space. The idea is that any neuronal activity which can be estimated from recorded data must be present in sensor space in the first place. Using the imaginary part of coherency as coupling measure makes sure that we do not confuse this source to sensor coupling with a coupling of a source to itself. We found that this approach is hundreds of times faster than the conventional approach. Results for EEG resting state data indicate that the new approach has more statistical power than the conventional approach. The presentation of this specific method is augmented with a discussion of conceptual issues for various forms of vector beamformers and eLoreta.

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bioRxiv2024

Delta phase-dependent modulation of temporal predictions by parietal transcranial alternating current stimulation

BackgroundPrevious research has shown that temporal prediction processes are associated with phase resets of low-frequency delta oscillations in a network of parietal, sensory and frontal areas during non-rhythmic sensory stimulation. Transcranial alternating current stimulation (tACS) modulates perceptually relevant brain oscillations in a frequency and phase-specific manner, allowing the assessment of their functional qualities in certain cognitive functions like temporal prediction. ObjectiveWe addressed the relation between oscillatory activity and temporal prediction by using tACS to manipulate brain activity in a sinusoidal manner. This enables the investigation of the relevance of low-frequency oscillations phase for temporal prediction. MethodsDelta tACS was applied over the left and right parietal cortex in two separate unimodal and crossmodal experiments. Participants judged either the visual or the tactile reappearance of a uniformly moving visual stimulus, which shortly disappeared behind and occluder. Using an intermittent electrical stimulation protocol, tACS was applied with six different phase shifts relative to sensory stimulation in both experiments while participants performed a temporal prediction task. Additionally, a computational model was developed and analysed to elucidate oscillation-based functional principles for the generation of temporal predictions. ResultsOnly in the unimodal experiment, the application of delta tACS resulted in a phase-dependent modulation of temporal prediction performance. By considering the effect of sustained tACS in the computational model, we demonstrate that the entrained dynamics can phase-specifically modulate temporal prediction accuracy. ConclusionOur findings suggest that phase-specific neuromodulation through delta tACS can influence temporal prediction accuracy in a unimodal context. This provides support to the notion that low-frequency delta oscillations are of causal relevance for temporal prediction.

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bioRxiv2024

Sampling bias corrections for accurate neural measures of redundant, unique, and synergistic information

Shannon Information theory has long been a tool of choice to measure empirically how populations of neurons in the brain encode information about cognitive variables. Recently, Partial Information Decomposition (PID) has emerged as principled way to break down this information into components identifying not only the unique information carried by each neuron, but also whether relationships between neurons generate synergistic or redundant information. While it has been long recognized that Shannon information measures on neural activity suffer from a (mostly upward) limited sampling estimation bias, this issue has largely been ignored in the burgeoning field of PID analysis of neural activity. We used simulations to investigate the limited sampling bias of PID computed from discrete probabilities (suited to describe neural spiking activity). We found that PID suffers from a large bias that is uneven across components, with synergy by far the most biased. Using approximate analytical expansions, we found that the bias of synergy increases quadratically with the number of discrete responses of each neuron, whereas the bias of unique and redundant information increase only linearly or sub-linearly. Based on the understanding of the PID bias properties, we developed simple yet effective procedures that correct for the bias effectively, and that improve greatly the PID estimation with respect to current state-of-the-art procedures. We apply these PID bias correction procedures to datasets of 53117 pairs neurons in auditory cortex, posterior parietal cortex and hippocampus of mice performing cognitive tasks, deriving precise estimates and bounds of how synergy and redundancy vary across these brain regions.

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bioRxiv2024

Causal Interactions between Phase- and Amplitude-Coupling in Cortical Networks

Phase coherence and amplitude correlations across brain regions are two main mechanisms of connectivity that govern brain dynamics at multiple scales. However, despite the increasing evidence that associates these mechanisms with brain functions and cognitive processes, the relationship between these different coupling modes is not well understood. Here, we study the causal relation between both types of functional coupling across multiple cortical areas. While most of the studies adopt a definition based on pairs of electrodes or regions of interest, we here employ a multichannel approach that provides us with a time-resolved definition of phase and amplitude coupling parameters. Using data recorded with a multichannel {micro}ECoG array from the ferret brain, we found that the transmission of information between both modes can be unidirectional or bidirectional, depending on the frequency band of the underlying signal. These results were reproduced in magnetoencephalography (MEG) data recorded during resting from the human brain. We show that this transmission of information occurs in a model of coupled oscillators and may represent a generic feature of a dynamical system. Together, our findings open the possibility of a general mechanism that may govern multi-scale interactions in brain dynamics.

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bioRxiv2024

Predicting social experience from dyadic interaction dynamics: the BallGame, a novel paradigm to study social engagement

Theories of embodied cognition suggest that a shared environment and ongoing sensorimotor interaction are central for interpersonal learning and engagement. To investigate the embodied, distributed and hence dynamically unfolding nature of social cognitive capacities, we present a novel laboratory-based coordination task: the BallGame. Our paradigm requires continuous sensing and acting between two players who jointly steer a virtual ball around obstacles towards as many targets as possible. By analysing highly resolved measures of movement coordination and gaming behaviour, game-concurrent experience ratings, semi-structured interviews, and personality questionnaires, we reveal contributions from different levels of observation on social experience. In particular, successful coordination (number of targets collected) and intermittent periods of high versus low movement coordination (variability of relation) emerged as prominent predictors of social experience. Importantly, having the same (but incomplete) view on the game environment strengthened interpersonal coordination, whereas complementary views enhanced engagement and tended to generate more complex interactive behaviour. Overall, we find evidence for a critical balance between similarity and synchrony on the one hand, and variability and difference on the other, for successful engagement in social interactions. Finally, following participant reports, we highlight how interpersonal experience emerges from specific histories of coordination that are closely related to the interaction context in both space and time.

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bioRxiv2023

Transcranial electrical stimulation over premotor cortex mimics attentional modulation of visual processing

Attentional control over sensory processing has been linked to neural alpha oscillations and related pulsed inhibition of the human cortex. Despite the wide consensus on the functional relevance of alpha oscillations for attention, precise neural mechanisms of how alpha oscillations shape perception and how this top-down modulation is implemented in cortical networks remain unclear. Here, we tested the hypothesis that alpha oscillations in premotor cortex are causally involved in top-down regulation of visual cortex responsivity to contrast. We applied intermittent transcranial alternating current stimulation (tACS) over bilateral premotor cortex to manipulate attentional preparation in a visual discrimination task. tACS was applied at 10 Hz (alpha) and controlled with 40 Hz (gamma) and sham stimulation. Importantly, we used a novel linear mixed modeling approach for statistical control of neurosensory side-effects of the electric stimulation. We found a frequency-specific effect of alpha tACS on the slope parameter, leading to enhanced low-contrast perception and decreased perception of high-contrast stimuli. Side-effects affected both threshold and slope parameters, leading to high variability in parameter estimates. Controlling the impact of side-effects on psychometric parameters by linear mixed model analysis reduced variability and clarified the existing effect. We conclude that alpha tACS over premotor cortex mimicked a state of increased endogenous attention potentially by modulation of fronto-occipital connectivity in the alpha band. We speculate that this network modulation allowed for improved sensory readout from visual cortex which led to a decrease in psychometric slope, effectively broadening the dynamic range for contrast perception. Significance statementAttention is fundamental to voluntary control of perception and behavior. Yet, despite extensive scientific efforts, precise underlying neural mechanisms remain elusive. We contribute to this ongoing discussion by providing evidence for a vital role of frontal alpha oscillations in regulating the responsivity of visual cortex. By controlled neuromodulation with intermittent transcranial alternating current stimulation (tACS), we show that alpha tACS modulates psychometric properties of visual contrast perception. This study fills an important gap between work on alpha oscillations in spatial attention and studies on the psychometrics of attention. Furthermore, we pioneered an approach for the statistical control of tACS side-effects with linear mixed modeling and thereby add to the ongoing debate on outcome variability in studies using transcranial neurostimulation methods.

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bioRxiv2023

Functional hierarchies in brain dynamics characterized by signal reversibility in ferret cortex

Brain signal irreversibility has been shown to be a promising approach to study neural dynamics. Nevertheless, the relation with cortical hierarchy and the influence of different electrophysiological features is not completely understood. In this study, we recorded local field potentials (LFPs) during spontaneous behavior, including awake and sleep periods, using custom micro-electrocorticographic (ECoG) arrays implanted in ferrets. In contrast to humans, ferrets remain less time in each state across the sleep-wake cycle. We deployed a diverse set of metrics in order to measure the levels of complexity of the different behavioral states. In particular, brain irreversibility, which allows us to quantify the level of non-equilibrium captured by the arrow of time of the signal, revealed the hierarchical organization of the ferrets cortex. We found different signatures of irreversibility and functional hierarchy of large-scale dynamics in three different brain states (active awake, quiet awake, and deep sleep), showing a lower level of irreversibility in the deep sleep stage, compared to the other. Irreversibility also allowed us to disentangle the influence of different brain regions and frequency bands in this process, showing a predominance of the parietal area and the theta band. Furthermore, when inspecting the embedded dynamic through a Hidden Markov Model, the deep sleep stage was revealed to have a lower switching rate and lower entropy production. These results suggest functional hierarchies in organization that can be revealed through thermodynamic features and information theory metrics.

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bioRxiv2023

Leading and Following: Noise Differently Affects Semantic and Acoustic Processing during Naturalistic Speech Comprehension

Despite the distortion of speech signals caused by unavoidable noise in daily life, our ability to comprehend speech in noisy environments is relatively stable. However, the neural mechanisms underlying reliable speech-in-noise comprehension remain to be elucidated. The present study investigated the neural tracking of acoustic and semantic speech information during noisy naturalistic speech comprehension. Participants listened to narrative audio recordings mixed with spectrally matched stationary noise at three signal-to-ratio (SNR) levels (no noise, 3 dB, -3 dB), and 60-channel electroencephalography (EEG) signals were recorded. A temporal response function (TRF) method was employed to derive event-related-like responses to the continuous speech stream at both the acoustic and the semantic levels. Whereas the amplitude envelope of the naturalistic speech was taken as the acoustic feature, word entropy and word surprisal were extracted via the natural language processing method as two semantic features. Theta-band frontocentral TRF responses to the acoustic feature were observed at around 400 ms following speech fluctuation onset over all three SNR levels, and the response latencies were more delayed with increasing noise. Delta-band frontal TRF responses to the semantic feature of word entropy were observed at around 200 to 600 ms leading to speech fluctuation onset over all three SNR levels. The response latencies became more leading with increasing noise and were correlated with comprehension performance and perceived speech intelligibility. While the following responses to speech acoustics were consistent with previous studies, our study revealed the robustness of leading responses to speech semantics, which suggests a possible predictive mechanism at the semantic level for maintaining reliable speech comprehension in noisy environments. HighlightsO_LILeading responses were observed in the semantic-level neural tracking, with more leading latencies as noise increased. C_LIO_LIFollowing responses were observed in the acoustic-level neural tracking, with more delayed latencies as noise increased. C_LIO_LISemantic-level neural tracking is correlated with comprehension performance and perceived intelligibility. C_LIO_LIDistinct frequency bands were involved in speech semantic and acoustic processing. C_LI

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bioRxiv2023

Personalized alpha-tACS targeting left posterior parietal cortex modulates visuo-spatial attention and posterior evoked EEG activity

BackgroundCovert visuo-spatial attention is marked by the anticipatory lateralization of neuronal alpha activity in the posterior parietal cortex. Previous applications of transcranial alternating current stimulation (tACS) at the alpha frequency, however, were inconclusive regarding the causal contribution of oscillatory activity during visuo-spatial attention. ObjectiveAttentional shifts of behavior and electroencephalography (EEG) after-effects were assessed in a cued visuo-spatial attention paradigm. We hypothesized that parietal alpha-tACS facilitates attention in the ipsilateral visual hemifield. Furthermore, we assumed that modulations of behavior and neurophysiology are related to individual electric field simulations. MethodsWe applied personalized tACS at alpha and gamma frequencies to elucidate the role of oscillatory neuronal activity for visuo-spatial attention. Personalized tACS montages were algorithmically optimized to target individual left and right parietal regions that were defined by an EEG localizer. ResultsBehavioral performance in the left hemifield was specifically increased by alpha-tACS compared to gamma-tACS targeting the left parietal cortex. This hemisphere-specific effect was observed despite the symmetry of simulated electric fields. In addition, visual event-related potential (ERP) amplitudes showed a reduced lateralization over posterior sites induced by left alpha-tACS. Neuronal sources of this effect were localized in the left premotor cortex. Interestingly, accuracy modulations induced by left parietal alpha-tACS were directly related to electric field magnitudes in the left premotor cortex. ConclusionOverall, results corroborate the notion that alpha lateralization plays a causal role in covert visuo-spatial attention and indicate an increased susceptibility of parietal and premotor brain regions of the left dorsal attention network to subtle tACS-neuromodulation.

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bioRxiv2022

Strike different neural notes at distinct stages: An integrative process of attention modulation in naturalistic speech

When we pay attention to someone, do we focus only on the sound they make, the word they use, or do we form a mental space shared with the speaker we want to pay attention to? Some would argue that the human language is no other than a simple signal, but others claim that human beings understand each other not only by relying on the words that have been said but also formed a shared ground in the specific conversation. This debate was raised early, but the conclusion remains vague. Our study aimed to investigate how attention modulates the neural coupling between the speaker and the listener in a cocktail party paradigm. The temporal response function (TRF) method was employed to reveal how the listener was coupled to the speaker at the neural level. The results showed that the neural coupling between the listener and the attended speaker peaked 5 seconds before speech onset at the delta band over the left frontal region, and was correlated with speech comprehension performance. In contrast, the attentional processing of speech acoustics and semantics occurred primarily at a later stage after speech onset and was not significantly correlated with comprehension performance. These findings suggest that our human brain might have adopted a predictive mechanism to achieve speaker-listener neural coupling for successful speech comprehension. Three key pointsO_LIListeners EEG signals coupled to the speakers 5 s before the speech onset, which revealed a "beyond the stimulus" attentional modulation. C_LIO_LISpeaker-listener attentional coupling is correlated to the listeners comprehension performance, but the speech-listeners coupling didnt. C_LIO_LIThe implementation of temporal response function methods and the neural language methods yielded novel perspectives to the analysis of the inter-brain studies. C_LI

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bioRxiv2022

Changes of oscillatory and aperiodic neuronal activity in working memory following anaesthesia: a prospective observational study

BackgroundAnaesthesia and surgery can lead to cognitive decline, especially in the elderly. However, to date, the neurophysiological underpinnings of perioperative cognitive decline remain unknown. MethodsWe included male patients, who were 60 years or older scheduled for elective radical prostatectomy under general anaesthesia. We obtained neuropsychological (NP) tests as well as a visual match-to-sample working memory (WM) task with concomitant 62-channel scalp electroencephalography (EEG) before and after surgery. ResultsA total number of 26 patients completed neuropsychological assessments and EEG pre- and postoperatively. Behavioural performance declined in the neuropsychological assessment after anaesthesia (total recall; t-tests: t25 = -3.25, Bonferroni-corrected p = 0.015 d = -0.902), while WM performance showed a dissociation between match and mis-match accuracy (rmANOVA: match*session F1,25 = 3.866, p = 0.060). Distinct EEG signatures tracked behavioural performance: Better performance in the NP assessment was correlated with an increase of non-oscillatory (aperiodic) activity, reflecting increased cortical activity (cluster permutation tests: total recall r = 0.66, p = 0.029, learning slope r = 0.66, p = 0.015), while WM accuracy was tracked by distinct temporally-structured oscillatory theta/alpha (7 - 9 Hz), low beta (14 - 18 Hz) and high beta/gamma (34 - 38 Hz) activity (cluster permutation tests: matches: p < 0.001, mis-matches: p = 0.022). ConclusionsOscillatory and non-oscillatory (aperiodic) activity in perioperative scalp EEG recordings track distinct features of perioperative cognition. Aperiodic activity provides a novel electrophysiological biomarker to identify patients at risk for developing perioperative neurocognitive decline.

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