Biology subjects

Nolte, G.

Publications and source records attributed to Nolte, G..

17 recordsLinked to original sources

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.

neuroscience↗

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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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

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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bioRxiv2023

Distinguishing between- from within-site phase-amplitude coupling using antisymmetrized bispectra

Phase-amplitude coupling (PAC) is a form of cross-frequency coupling in which the amplitude of a fast oscillation is locked to the phase of a slow oscillation. PAC has been proposed as a mechanism for integrating slow large-scale networks with fast-oscillating local processes in the brain. On a signal level, PAC can be observed in single time series, reflecting local dynamics, or between two time series, potentially reflecting a functional interaction between distinct brain sites. To investigate the role of PAC as a mechanism of brain signalling, it is important to distinguish these two cases. However, when mixtures of underlying signals are observed, between-site PAC can spuriously emerge even if the true interaction is only local (within-site). This problem arises in electrophysiological recordings where mixing occurs due to volume conduction or the presence of a shared electrical reference. To address this problem, we propose to estimate PAC using the anti-symmetrized bispectrum (ASB-PAC). It has previously been shown that the cross-bispectrum can be used to measure PAC while efficiently sup-pressing Gaussian noise, and that the anti-symmetrized bispectra vanish for mixtures of independent sources. However, ASB-PAC has so far not been used to assess the presence of genuine between-site PAC. Using simulations, we here investigate the performance of different algorithms to detect PAC in a mixed signal setting as well as the performance of the same methods to distinguish genuine between-site PAC from within-site PAC. This is done in a minimal two-channel setup as well as in a more complex setting that assesses PAC on reconstructions of simulated EEG sources. We observe that bispectral PAC methods are considerably better at detecting simulated PAC in the volume conduction setting than three conventional PAC estimators. ASB-PAC achieves the highest performance in detecting genuine between-site PAC interactions while detecting the fewest spurious interactions. Using the ASB-PAC could therefore greatly facilitate the interpretation of future PAC studies when discriminating local from remote effects. We demonstrate the application of ASB-PAC on EEG data from a motor imagery experiment. Additionally, we present an upgraded version of the free ROIconnect plugin for the EEGLAB toolbox, which includes PAC and ASB-PAC metriscs based on bispectra.

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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

neuroscience↗

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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bioRxiv2021

Harmoni: a Method for Eliminating Spurious Interactions due to the Harmonic Components in Neuronal Data

Cross-frequency synchronization (CFS) has been proposed as a mechanism for integrating spatially and spectrally distributed information in the brain. However, investigating CFS in Magneto- and Electroencephalography (MEG/EEG) is hampered by the presence of spurious neuronal interactions due to the non-sinusoidal waveshape of brain oscillations. Such waveshape gives rise to the presence of oscillatory harmonics mimicking genuine neuronal oscillations. Until recently, however, there has been no methodology for removing these harmonics from neuronal data. In order to address this long-standing challenge, we introduce a novel method (called HARMOnic miNImization - Harmoni) that removes the signal components which can be harmonics of a non-sinusoidal signal. Harmonis working principle is based on the presence of CFS between harmonic components and the fundamental component of a non-sinusoidal signal. We extensively tested Harmoni in realistic EEG simulations. The simulated couplings between the source signals represented genuine and spurious CFS and within-frequency phase synchronization. Using diverse evaluation criteria, including ROC analyses, we showed that the within- and cross-frequency spurious interactions are suppressed significantly, while the genuine activities are not affected. Additionally, we applied Harmoni to real resting-state EEG data revealing intricate remote connectivity patterns which are usually masked by the spurious connections. Given the ubiquity of non-sinusoidal neuronal oscillations in electrophysiological recordings, Harmoni is expected to facilitate novel insights into genuine neuronal interactions in various research fields, and can also serve as a steppingstone towards the development of further signal processing methods aiming at refining within- and cross-frequency synchronization in electrophysiological recordings.

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bioRxiv2020

Dissociation between catecholamines and acetylcholine in the human cerebral cortex

Influential accounts postulate distinct roles of the catecholamine and acetylcholine neuromodulatory systems in cognition and behavior. But previous work found similar effects of these modulators on the response properties of individual cortical neurons. Here, we report a double dissociation between catecholamine and acetylcholine effects at the level of cortex-wide network interactions in humans. A pharmacological boost of catecholamine levels increased cortex-wide interactions during a visual task, but not rest. Conversely, an acetylcholine-boost decreased correlations during rest, but not task. Cortical circuit modeling explained this dissociation by differential changes in two circuit properties: the local excitation-inhibition balance (more strongly altered by catecholamines) and intracortical transmission (more strongly reduced by acetylcholine). The inferred catecholaminergic mechanism also predicted increased behavioral exploration, which we confirmed in human behavior during both a perceptual and value-based choice task. In sum, we identified specific circuit mechanisms for shaping cortex-wide network interactions and behavior by key neuromodulatory systems.

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bioRxiv2020

Understanding phase-amplitude coupling from bispectral analysis

Two measures of cross-frequency coupling (CFC) are Phase-Amplitude Coupling (PAC) and bicoherence. The estimation of PAC with meaningful bandwidth for the high frequency amplitude is crucial in order to avoid misinterpretations. While recommendations on the bandwidth of PACs amplitude component exist, there is no consensus yet. Here, we show that the earlier recommendations on filter settings lead to estimates which are smeared in the frequency domain, which makes it difficult to distinguish higher harmonics from other types of CFC. We also show that smearing can be avoided with a different choice of filter settings by theoretically relating PAC to bicoherence. To illustrate this, PAC estimates of simulations and empirical data are compared to bispectral analyses. We used simulations replicated from an earlier study and empirical data from human electro-encephalography and rat local field potentials. PACs amplitude component was estimated using a bandwidth with a ratio of (1) 2:1, (2) 1:1, or (3) 0.5:1 relative to the frequency of the phase component. For both simulated and empirical data, PAC was smeared over a broad frequency range and not present when the estimates comprised a 2:1- and 0.5:1-ratio, respectively. In contrast, the 1:1-ratio accurately avoids smearing and results in clear signals of CFC. Bicoherence estimates, which do not smear across frequencies by construction, were found to be essentially identical to PAC calculated with the recommended frequency setting.

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bioRxiv2019

Attention-dependent preparatory processing of naturalistic narratives is correlated with speech comprehension

While human speech comprehension is thought to be an active process that involves top-down predictions, it remains unclear how predictive information is used to prepare for the processing of upcoming speech information. We aimed to identify the neural signatures of the preparatory processing of upcoming speech. Participants selectively attended to one of two competing naturalistic, narrative speech streams, and a temporal response function (TRF) method was applied to derive event-related-like neural responses from electroencephalographic data. The phase responses to the attended speech at the delta band (1-4 Hz) were correlated with the comprehension performance of individual participants, with a latency of -200-0 ms before onset over the fronto-central and left-lateralized parietal regions. The phase responses to the attended speech at the alpha band also correlated with comprehension performance, but with a latency of 650-980 ms post-onset over fronto-central regions. Distinct neural signatures were found for the attentional modulation, taking the form of TRF-based amplitude responses at a latency of 240-320 ms post-onset over the left-lateralized fronto-central and occipital regions. Our findings reveal how the brain gets prepared to process an upcoming speech in a continuous, naturalistic speech context.

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bioRxiv2019

Mathematical relations between measures of brain connectivity estimated from electrophysiological recordings for Gaussian distributed data

A large variety of methods exist to estimate brain coupling in the frequency domain from electrophysiological data measured e.g. by EEG and MEG. Those data are to reasonable approximation, though certainly not perfectly, Gaussian distributed. This work is based on the well-known fact that for Gaussian distributed data, the cross-spectrum completely determines all statistical properties. In particular, for an infinite number of data, all normalized coupling measures at a given frequency are a function of complex coherency. However, it is largely unknown what the functional relations are. We here present those functional relations for six different measures: the weighted phase lag index, the phase lag index, the absolute value and imaginary part of the phase locking value (PLV), power envelope correlation, and power envelope correlation with correction for artifacts of volume conduction. With the exception of PLV, the final results are simple closed form formulas. We tested in simulations of linear and nonlinear dynamical systems and for empirical resting state EEG on sensor level to what extent a model, namely the respective function of coherency, can explain the observed couplings. For empirical data w e found that for measures of phase-phase coupling deviations from the model are in general minWor, while power envelope correlations systematically deviate from the model for all frequencies. For power envelope correlation with correction for artifacts of volume conduction the model cannot explain the observed couplings at all. We also analyzed power envelope correlation as a function of time and frequency in an event related experiment using a stroop reaction task and found significant event related deviations mostly in the alpha range.

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bioRxiv2019

Nonlinear Interaction Decomposition (NID): A Method for Separation of Cross-Frequency Coupled Sources in Human Brain

Cross-frequency coupling (CFC) is a phenomenon through which spatially and spectrally distributed information can be integrated in the brain. There is, however, a lack of methods decomposing brain electrophysiological data into interacting components. Here, we propose a novel framework for detecting such interactions in Magneto- and Electroencephalography (MEG/EEG), which we refer to as Nonlinear Interaction Decomposition (NID). In contrast to all previous methods for separation of cross-frequency (CF) sources in the brain, we propose that the extraction of nonlinearly interacting oscillations can be based on the statistical properties of their linear mixtures. The main idea of NID is that nonlinearly coupled brain oscillations can be mixed in such a way that the resulting linear mixture has a non-Gaussian distribution. We evaluate this argument analytically for amplitude-modulated narrow-band oscillations which are either phase-phase or amplitude-amplitude CF coupled. We validated NID extensively with simulated EEG obtained with realistic head modeling. The method extracted nonlinearly interacting components reliably even at SNRs as small as -15 (dB). Additionally, we applied NID to the resting-state EEG of 81 subjects to characterize CF phase-phase coupling between alpha and beta oscillations. The extracted sources were located in temporal, parietal and frontal areas, demonstrating the existence of diverse local and distant nonlinear interactions in resting-state EEG data.

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bioRxiv2019

Large-scale functional connectivity in multisensory cortex predicts performance

Complex behavior and task execution require fast changes of local activity and functional connectivity in cortical networks at multiple scales. The roles that changes of power and connectivity play during these processes are still not well understood. Here, we study how fluctuations of functional cortical coupling across different brain areas determine performance in an audiovisual, lateralized detection task in the ferret. We hypothesized that dynamic variations in the networks state determine the animals performance. We evaluated these by quantifying changes of local power and of phase coupling across visual, auditory and parietal regions. While power for hit and miss trials showed significant differences only during stimulus and response onset, phase coupling already differed before stimulus onset. An analysis of principal components in coupling at the single-trial level during this period allowed us to reveal the subnetworks that most strongly determined performance. Whereas higher global phase coupling of visual and auditory regions to parietal cortex was predictive of task performance, a second component revealed a reduction in coupling between subnetworks of different sensory modalities, probably to allow a better detection of the unimodal signals. Furthermore, we observed that long-range coupling became more predominant during the task period compared to the pre-stimulus baseline. Taken together, our results show that fluctuations in the network state, as reflected in large-scale coupling, are key determinants of the animals behavior.

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bioRxiv2018

Source reconstruction of broadband EEG/MEG data using the frequency-adaptive broadband (FAB) beamformer

A beamformer enhances the signal from a voxel of interest by minimising interference from all other locations represented in the sensor covariance matrix. However, the presence of narrowband oscillations in EEG/MEG implies that the spatial structure of the covariance matrix, and hence also the optimal beamformer, depends on the frequency. The frequency-adaptive broadband (FAB) beamformer introduced here exploits this fact in the Fourier domain by partitioning the covariance matrix into cross-spectra corresponding to different frequencies. For each frequency bin, an individual spatial filter is constructed. This assures optimal noise suppression across the frequency spectrum. After applying the spatial filters in the frequency domain, the broadband source signal is recovered using the inverse Fourier transform. MEG simulations using artificial data and real resting-state measurements were used to compare the FAB beamformer to the LCMV beamformer and MNE. The FAB beamformer significantly outperforms both methods in terms of the quality of the reconstructed time series. To our knowledge, the FAB beamformer is the first beamforming approach tailored for the analysis of broadband neuroimaging data. Due to its frequency-adaptive noise suppression, the reconstructed source time series is suited for further time-frequency or connectivity analysis in source space.

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bioRxiv2017

Working Memory Training Integrates Visual Cortex into Beta-Band Networks in Congenitally Blind Individuals

Congenitally blind individuals activate the visual cortex during non-visual tasks. Such crossmodal reorganization is likely associated with changes in large-scale functional connectivity, the spectral characteristics of which can be assessed by analysis of neural oscillations. To test visual cortical integration into working memory networks, we recorded magnetoencephalographic data from congenitally blind and sighted individuals during resting state as well as during a voice-based working memory task prior to and following working memory training with voices, or tactile stimuli or a training-control condition. Auditory training strengthened beta-band (17.5-22.5 Hz) connectivity (imaginary coherency) in the blind and theta-band (2.5-5 Hz) connectivity in the sighted during the task, suggesting different neural coupling mechanisms. In the sighted, theta-band connectivity increased between brain areas involved in auditory working memory (inferior frontal, superior temporal, insular cortex). In the blind, beta-band networks largely emerged during the training, and connectivity increased between brain areas involved in auditory working memory and the visual cortex. The prominent involvement of the right fusiform face area in this beta-band network suggests a task-specific integration of visual cortex. Our findings highlight large-scale interactions as a key mechanism of functional reorganization following congenital blindness, and provide new insights into the spectral characteristics of the mechanism.

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bioRxiv2017

Catecholamines, not acetylcholine, alter cortical and perceptual dynamics in line with increased excitation inhibition ratio

The ratio between excitatory and inhibitory neurons (E/I ratio) is vital for cortical circuit dynamics, computation, and behavior. This ratio may be under the dynamic control of neuromodulatory systems, which are in turn implicated in several neuropsychiatric disorders. In particular, the catecholaminergic (dopaminergic and noradrenergic) and cholinergic systems have highly specific effects on excitatory and inhibitory cortical neurons, which might translate into changes in the local net E/I ratio. Here, we assessed and compared their net effects on net E/I ratio in human cortex, through an integrated application of computational modeling, placebo-controlled pharmacological intervention, magnetoencephalographic recordings of cortical activity dynamics, and perceptual psychophysics. We found that catecholamines, but not acetylcholine, altered both the temporal structure of intrinsic activity fluctuations in visual and parietal cortex, and the volatility of perceptual inference based on ambiguous visual input. Both effects indicate that catecholamines increase the net E/I ratio in visual and parietal cortex.

neuroscience↗