Biology subjects

Haufe, S.

Publications and source records attributed to Haufe, S..

18 recordsLinked to original sources

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.

neuroscience↗

bioRxiv2025

Effect of MRI Defacing on EEG Forward and Inverse Modeling

Recent years have seen improvements in facial recognition software, which has increased the risk of reidentification of patients structural neuroimaging scans, e.g. obtained from magnetic resonance imaging (MRI). Thus, an anonymization procedure known as defacing has become the norm when publicly sharing patients scans. Defacing removes some of the facial features from the data, making it improbably to re-identify a patient from a 3D rendering of the image. However, certain tasks, such as localization of the sources of electroencephalographic (EEG) signals, require the creation of individual electrical volume conductor models of the human head from structural MRI data. Defacing could affect the co-registeration of MRI and EEG sensor positions and, more importantly, the model of electrical current flow itself. This study quantifies and maps the effect of defacing on individual volume conductor models and the localization accuracy of inverse solutions based on these models in a sample of ten subjects with known ground-truth (non-defaced) anatomy. Boundary and finite element modeling approaches (B/FEM) are compared. Clinical relevanceThis study enables clinicians, e.g. epileptologists, and neuroscientists to gauge the EEG source localization errror that is incurred due to using defaced MRI data for forward model construction, and identifies the regions most prone to mislocalization. Based on the results of the study, it is recommended to construct EEG volume conductor models before defacing if accurate source localization is desired.

neuroscience↗

bioRxiv2025

How forgiving are M/EEG inverse solutions to noise level misspecification? An excursion into the BSI-Zoo.

AO_SCPLOWBSTRACTC_SCPLOWBrain source imaging (BSI), also known as source localization, from magneto- and electroencephalographic (M/EEG) data, is a challenging ill-posed inverse problem. Accurate source estimation is sensitive to modeling parameters, such as regularization strength and noise level, where misconfigurations can lead to under- or overfitting. Different BSI methods, however, may vary in their robustness to suboptimal parameter choices. Here we conducted extensive simulations of brain sources superimposed by varying degrees of sensor noise to study the ranges of noise misspecification within which different BSI approaches can still localize well. Using the Earth Movers Distance (EMD) and other metrics, we compare the performance of smooth linear inverse solutions with that of sparse non-linear Bayesian learning solutions. Additionally, we assess the effectiveness of various noise estimation and cross-validation techniques to select hyperparameters close to those achieving optimal localization. Methods and experiments are made available within the BSI-Zoo Python package.

neuroscience↗

bioRxiv2024

Shared pathway-specific network mechanisms of dopamine and deep brain stimulation for the treatment of Parkinson's disease

Deep brain stimulation is a brain circuit intervention that can modulate distinct neural pathways for the alleviation of neurological symptoms in patients with brain disorders. In Parkinsons disease, subthalamic deep brain stimulation clinically mimics the effect of dopaminergic drug treatment, but the shared pathway mechanisms on cortex - basal ganglia networks are unknown. To address this critical knowledge gap, we combined fully invasive neural multisite recordings in patients undergoing deep brain stimulation surgery with normative MRI-based whole-brain connectomics. Our findings demonstrate that dopamine and stimulation exert distinct mesoscale effects through modulation of local neural population activity. In contrast, at the macroscale, stimulation mimics dopamine in its suppression of excessive interregional network synchrony associated with indirect and hyperdirect cortex - basal ganglia pathways. Our results provide a better understanding of the circuit mechanisms of dopamine and deep brain stimulation, laying the foundation for advanced closed-loop neurostimulation therapies.

neuroscience↗

bioRxiv2023

Dopamine and neuromodulation shorten the latency from motor intention to execution in Parkinson's disease

The ability to initiate volitional action is fundamental to human behaviour. Loss of dopaminergic neurons in Parkinsons disease is associated with impaired action initiation, also termed akinesia. Both dopamine and subthalamic deep brain stimulation (DBS) can alleviate akinesia, but the underlying mechanisms are unknown. An important question is whether dopamine and DBS facilitate de novo build-up of neural dynamics for motor execution or accelerate existing cortical movement initiation signals through shared modulatory circuit effects. Answering these questions can provide the foundation for new closed-loop neurotherapies with adaptive DBS, but the objectification of neural processing delays prior to performance of volitional action remains a significant challenge. To overcome this challenge, we studied readiness potentials and trained brain signal decoders on invasive neurophysiology signals in 25 DBS patients (12 female) with Parkinsons disease during performance of self-initiated movements. Combined sensorimotor cortex electrocorticography (ECoG) and subthalamic local field potential (LFP) recordings were performed OFF therapy (N=22), ON dopaminergic medication (N=18) and ON subthalamic deep brain stimulation (N=8). This allowed us to compare their therapeutic effects on neural latencies between the earliest cortical representation of movement intention as decoded by linear discriminant analysis classifiers and onset of muscle activation recorded with electromyography (EMG). In the hypodopaminergic OFF state, we observed long latencies between motor intention and motor execution for readiness potentials and machine learning classifications. Both, dopamine and DBS significantly shortened these latencies, hinting towards a shared therapeutic mechanism for alleviation of akinesia. To investigate this further, we analysed directional cortico-subthalamic oscillatory communication with multivariate granger causality. Strikingly, we found that both therapies independently shifted cortico-subthalamic oscillatory information flow from antikinetic beta (13-35 Hz) to prokinetic theta (4-10 Hz) rhythms, which was correlated with latencies in motor execution. Our study reveals a shared brain network modulation pattern of dopamine and DBS that may underlie the acceleration of neural dynamics for augmentation of movement initiation in Parkinsons disease. Instead of producing or increasing preparatory brain signals, both therapies modulate oscillatory communication. These insights provide a link between the pathophysiology of akinesia and its therapeutic alleviation with oscillatory network changes in other non-motor and motor domains, e.g. related to hyperkinesia or effort and reward perception. In the future, our study may inspire the development of clinical brain computer interfaces based on brain signal decoders to provide temporally precise support for action initiation in patients with brain disorders.

neuroscience↗

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.

neuroscience↗

bioRxiv2023

Bayesian Inference for Brain Source Imaging with Joint Estimation of Structured Low-rank Noise

The inverse problem in brain source imaging is the reconstruction of brain activity from non-invasive recordings of electroencephalography (EEG) and magnetoencephalography (MEG). One key challenge is the efficient recovery of sparse brain activity when the data is corrupted by structured noise that is low-rank noise. This is often the case when there are a few active sources of environmental noise and the MEG/EEG sensor noise is highly correlated. In this paper, we propose a novel robust empirical Bayesian framework which provides us a tractable algorithm for jointly estimating a low-rank noise covariance and brain source activity. Specifically, we use a factor analysis model for the structured noise, and infer a sparse set of variance parameters for source activity, while performing Variational Bayesian inference for the noise. One key aspect of this algorithm is that it does not require any additional baseline measurements to estimate the noise covariance from the sensor data. We perform exhaustive experiments on both simulated and real datasets. Our algorithm achieves superior performance as compared to several existing benchmark algorithms.

bioengineering↗

bioRxiv2022

Identifying best practices for detecting inter-regional functional connectivity from EEG

Aggregating voxel-level statistical dependencies between multivariate time series is an important intermediate step when characterising functional connectivity (FC) between larger brain regions. However, there are numerous ways in which voxel-level data can be aggregated into inter-regional FC, and the advantages of each of these approaches are currently unclear. In this study we generate ground-truth data and compare the performances of various pipelines that estimate directed and undirected linear phase-to-phase FC between regions. We test the ability of several existing and novel FC analysis pipelines to identify the true regions within which connectivity was simulated. We test various inverse modelling algorithms, strategies to aggregate time series within regions, and connectivity metrics. Furthermore, we investigate the influence of the number of interactions, the signal-to-noise ratio, the noise mix, the interaction time delay, and the number of active sources per region on the ability of detecting phase-to-phase FC. Throughout all simulated scenarios, lowest performance is obtained with pipelines involving the absolute value of coherency. Further, the combination of dynamic imaging of coherent sources (DICS) beamforming with directed FC metrics that aggregate information across multiple frequencies leads to unsatisfactory results. Pipeline that show promising results with our simulated pseudo-EEG data involve the following steps: (1) Source projection using the linearly-constrained minimum variance (LCMV) beamformer. (2) Principal component analysis (PCA) using the same fixed number of components within every region. (3) Calculation of the multivariate interaction measure (MIM) for every region pair to assess undirected phase-to-phase FC, or calculation of time-reversed Granger Causality (TRGC) to assess directed phase-to-phase FC. We formulate recommendations based on these results that may increase the validity of future experimental connectivity studies. We further introduce the free ROIconnect plugin for the EEGLAB toolbox that includes the recommended methods and pipelines that are presented here. We show an exemplary application of the best performing pipeline to the analysis EEG data recorded during motor imagery.

neuroscience↗

bioRxiv2022

Performance reserves in brain-imaging-based phenotype prediction

Machine learning studies have shown that various phenotypes can be predicted from structural and functional brain images. However, in most such studies, prediction performance ranged from moderate to disappointing. It is unclear whether prediction performance will substantially improve with larger sample sizes or whether insufficient predictive information in brain images impedes further progress. Here, we systematically assess the effect of sample size on prediction performance using sample sizes far beyond what is possible in common neuroimaging studies. We project 3-9 fold improvements in prediction performance for behavioral and mental health phenotypes when moving from one thousand to one million samples. Moreover, we find that moving from single imaging modalities to multimodal input data can lead to further improvements in prediction performance, often on par with doubling the sample size. Our analyses reveal considerable performance reserves for neuroimaging-based phenotype prediction. Machine learning models may benefit much more from extremely large neuroimaging datasets than currently believed.

neuroscience↗

bioRxiv2021

Joint Learning of Full-structure Noise in Hierarchical Bayesian Regression Models

We consider the reconstruction of brain activity from electroencephalography (EEG). This inverse problem can be formulated as a linear regression with independent Gaussian scale mixture priors for both the source and noise components. Crucial factors influencing the accuracy of the source estimation are not only the noise level but also its correlation structure, but existing approaches have not addressed the estimation of noise covariance matrices with full structure. To address this shortcoming, we develop hierarchical Bayesian (type-II maximum likelihood) models for observations with latent variables for source and noise, which are estimated jointly from data. As an extension to classical sparse Bayesian learning (SBL), where across-sensor observations are assumed to be independent and identically distributed, we consider Gaussian noise with full covariance structure. Using the majorization-maximization framework and Riemannian geometry, we derive an efficient algorithm for updating the noise covariance along the manifold of positive definite matrices. We demonstrate that our algorithm has guaranteed and fast convergence and validate it in simulations and with real MEG data. Our results demonstrate that the novel framework significantly improves upon state-of-the-art techniques in the real-world scenario where the noise is indeed non-diagonal and full-structured. Our method has applications in many domains beyond biomagnetic inverse problems.

neuroscience↗

bioRxiv2021

Alterations in rhythmic and non-rhythmic resting-state EEG activity and their link to cognition in older age

While many structural and biochemical changes in the brain have been previously associated with aging, the findings concerning electrophysiological signatures, reflecting functional properties of neuronal networks, remain rather controversial. To try resolve this issue, we took advantage of a large population study (N=1703) and comprehensively investigated the association of multiple EEG biomarkers (power of alpha and theta oscillations, individual alpha peak frequency (IAF), the slope of 1/f power spectral decay), aging, and aging and cognitive performance. Cognitive performance was captured with three factors representing processing speed, episodic memory, and interference resolution. Our results show that not only did IAF decline with age but it was also associated with interference resolution over multiple cortical areas. To a weaker extent, 1/f slope of the PSD showed age-related reductions, mostly in frontal brain regions. Finally, alpha power was negatively associated with the speed of processing in the right frontal lobe, despite the absence of age-related alterations. Our results thus demonstrate that multiple electrophysiological features, as well as their interplay, should be considered when investigating the association between age, neuronal activity, and cognitive performance.

neuroscience↗

bioRxiv2020

Relationship between Regional White Matter Hyperintensities and Alpha Oscillations in Older Adults

Aging is associated with increased white matter hyperintensities (WMHs) and with the alterations of alpha oscillations (7-13 Hz). However, a crucial question remains, whether changes in alpha oscillations relate to aging per se or whether this relationship is mediated by age-related neuropathology like WMHs. Using a large cohort of cognitively healthy older adults (N=907, 60-80 years), we assessed relative alpha power, alpha peak frequency, and long-range temporal correlations (LRTC) from resting-state EEG. We further associated these parameters with voxel-wise WMHs from 3T MRI. We found that a higher prevalence of WMHs in the superior and posterior corona radiata as well as in the thalamic radiation was related to elevated alpha power, with the strongest association in the bilateral occipital cortex. In contrast, we observed no significant relation of the WMHs probability with alpha peak frequency and LRTC. Finally, higher age was associated with elevated alpha power via total WMH volume. Although an increase in alpha oscillations due to WMH can have a compensatory nature, we rather suggest that an elevated alpha power is a consequence of WMH affecting a spatial organization of alpha sources.

neuroscience↗

bioRxiv2020

Unification of Sparse Bayesian Learning Algorithms for Electromagnetic Brain Imaging with the Majorization Minimization Framework

Methods for electro- or magnetoencephalography (EEG/MEG) based brain source imaging (BSI) using sparse Bayesian learning (SBL) have been demonstrated to achieve excellent performance in situations with low numbers of distinct active sources, such as event-related designs. This paper extends the theory and practice of SBL in three important ways. First, we reformulate three existing SBL algorithms under the majorization-minimization (MM) framework. This unification perspective not only provides a useful theoretical framework for comparing different algorithms in terms of their convergence behavior, but also provides a principled recipe for constructing novel algorithms with specific properties by designing appropriate bounds of the Bayesian marginal likelihood function. Second, building on the MM principle, we propose a novel method called LowSNR-BSI that achieves favorable source reconstruction performance in low signal-to-noise-ratio (SNR) settings. Third, precise knowledge of the noise level is a crucial requirement for accurate source reconstruction. Here we present a novel principled technique to accurately learn the noise variance from the data either jointly within the source reconstruction procedure or using one of two proposed cross-validation strategies. Empirically, we could show that the monotonous convergence behavior predicted from MM theory is confirmed in numerical experiments. Using simulations, we further demonstrate the advantage of LowSNR-BSI over conventional SBL in low-SNR regimes, and the advantage of learned noise levels over estimates derived from baseline data. To demonstrate the usefulness of our novel approach, we show neurophysiologically plausible source reconstructions on averaged auditory evoked potential data.

neuroscience↗

bioRxiv2020

Robust Estimation of Noise for Electromagnetic Brain Imaging with the Champagne Algorithm

Robust estimation of the number, location, and activity of multiple correlated brain sources has long been a challenging task in electromagnetic brain imaging from M/EEG data, one that is significantly impacted by interference from spontaneous brain activity, sensor noise, and other sources of artifacts. Recently, we introduced the Champagne algorithm, a novel Bayesian inference algorithm that has shown tremendous success in M/EEG source reconstruction. Inherent to Champagne and most other related Bayesian reconstruction algorithms is the assumption that the noise covariance in sensor data can be estimated from "baseline" or "control" measurements. However, in many scenarios, such baseline data is not available, or is unreliable, and it is unclear how best to estimate the noise covariance. In this technical note, we propose several robust methods to estimate the contributions to sensors from noise arising from outside the brain without the need for additional baseline measurements. The incorporation of these methods for noise covariance estimation improves the robust reconstruction of complex brain source activity under high levels of noise and interference, while maintaining the performance features of Champagne. Specifically, we show that the resulting algorithm, Champagne with noise learning, is quite robust to initialization and is computationally efficient. In simulations, performance of the proposed noise learning algorithm is consistently superior to Champagne without noise learning. We also demonstrate that, even without the use of any baseline data, Champagne with noise learning is able to reconstruct complex brain activity with just a few trials or even a single trial, demonstrating significant improvements in source reconstruction for electromagnetic brain imaging.

bioengineering↗

bioRxiv2020

Sensorimotor functional connectivity: a neurophysiological factor related to BCI performance

Brain-Computer Interfaces (BCIs) are systems that allow users to control devices using brain activity alone. However, the ability of participants to command BCIs varies from subject to subject. For BCIs based on the modulation of sensorimotor rhythms as measured by means of electroen-cephalography (EEG), about 20% of potential users do not obtain enough accuracy to gain reliable control of the system. This lack of efficiency of BCI systems to decode users intentions requires the identification of neuro-physiological factors determining good and poor BCI performers. Given that the neuronal oscillations, used in BCI, demonstrate rich a repertoire of spatial interactions, we hypothesized that neuronal activity in sensorimotor areas would define some aspects of BCI performance. Analyses for this study were performed on a large dataset of 80 inexperienced participants. They took part in calibration and an online feedback session in the same day. Undirected functional connectivity was computed over sensorimotor areas by means of the imaginary part of coherency. The results show that post-as well as pre-stimulus connectivity in the calibration recordings is significantly correlated to online feedback performance in and feedback frequency bands. Importantly, the significance of the correlation between connectivity and BCI feedback accuracy was not due to the signal-to-noise ratio of the oscillations in the corresponding post and pre-stimulus intervals. Thus, this study shows that BCI performance is not only dependent on the amplitude of sensorimotor oscillations as shown previously, but that it also relates to sensorimotor connectivity measured during the preceding training session. The presence of such connectivity between motor and somatosensory systems is likely to facilitate motor imagery, which in turn is associated with the generation of a more pronounced modulation of sen-sorimotor oscillations (manifested in ERD/ERS) required for the adequate BCI performance. We also discuss strategies for the up-regulation of such connectivity in order to enhance BCI performance.

neuroscience↗

bioRxiv2019

Power-Law Dynamics in Cortical Excitability as probed by Early Somatosensory Evoked Responses

Brain responses vary considerably from moment to moment, even to identical sensory stimuli. This has been attributed to changes in instantaneous neuronal states determining the systems excitability. Yet the spatio-temporal organization of these dynamics remains poorly understood. Here we test whether variability in stimulus-evoked activity can be interpreted within the framework of criticality, which postulates dynamics of neural systems to be tuned towards the phase transition between stability and instability as is reflected in scale-free fluctuations in spontaneous neural activity. Using a novel non-invasive approach in 33 male participants, we tracked instantaneous cortical excitability by inferring the magnitude of excitatory post-synaptic currents from the N20 component of the somatosensory evoked potential. Fluctuations of cortical excitability demonstrated long-range temporal dependencies decaying according to a power law across trials - a hallmark of systems at critical states. As these dynamics covaried with changes in pre-stimulus oscillatory activity in the alpha band (8-13 Hz), we establish a mechanistic link between ongoing and evoked activity through cortical excitability and argue that the co-emergence of common temporal power laws may indeed originate from neural networks poised close to a critical state. In contrast, no signatures of criticality were found in subcortical or peripheral nerve activity. Thus, criticality may represent a parsimonious organizing principle of variability in stimulus-related brain processes on a cortical level, possibly reflecting a delicate equilibrium between robustness and flexibility of neural responses to external stimuli. Significance StatementVariability of neural responses in primary sensory areas is puzzling, as it is detrimental to the exact mapping between stimulus features and neural activity. However, such variability can be beneficial for information processing in neural networks if it is of a specific nature, namely if dynamics are poised at a so-called critical state characterized by a scale-free spatio-temporal structure. Here, we demonstrate the existence of a link between signatures of criticality in ongoing and evoked activity through cortical excitability, which fills the long-standing gap between two major directions of research on neural variability: The impact of instantaneous brain states on stimulus processing on the one hand and the scale-free organization of spatio-temporal network dynamics of spontaneous activity on the other.

neuroscience↗

bioRxiv2018

Effect of head volume conduction on directed connectivity estimated between reconstructed EEG sources

Electrical activity recorded on the scalp using electroencephalography (EEG) results from the mixing of signals originating from different regions of the brain as well as from artefactual sources. In order to investigate the role of distinct brain areas in a given experiment, the signal recorded on the sensors is typically projected back into the brain (source reconstruction) using algorithms that address the so-called EEG \"inverse problem\". Once that the activity of sources located inside of the brain has been reconstructed, it is often desirable to study the statistical dependencies among them, in particular to quantify directional dynamical interactions between brain areas. Unfortunately, even when performing source reconstruction, the superposition of signals that is due to the propagation of activity from sources to sensors cannot be completely undone, resulting in potentially biased estimates of directional functional connectivity. Here we perform a set of simulations involving interacting sources, and quantify source connectivity estimation performance as a function of the location of the sources, their distance to each other, the noise level, the source reconstruction algorithm, and the connectivity estimator. The generated source activity was projected onto the scalp and projected back to the cortical level using two source reconstruction algorithms, Linearly Constrained Minimum Variance (LCMV) beamforming and Exact Low-resolution Tomography (eLORETA). In source space, directed connectivity was estimated using Multi-Variate Granger Causality (MVGC), Time-Reversed Granger Causality (TRGC) and Partial Directed Coherence (PDC), and the estimated connectivity was compared with the imposed ground truth. Our results demonstrate that all considered factors significantly affect the connectivity estimation performance.

neuroscience↗

bioRxiv2017

Elucidating relations between fMRI, ECoG and EEG through a common natural stimulus

Human brain mapping relies heavily on fMRI, ECoG and EEG, which capture different physiological signals. Relationships between these signals have been established in the context of specific tasks or during resting state, often using spatially confined concurrent recordings in animals. But it is not certain whether these correlations generalize to other contexts relevant for human cognitive neuroscience. Here, we address the case of complex naturalistic stimuli and ask two basic questions. First, how reliable are the responses evoked by a naturalistic audio-visual stimulus in each of these imaging methods, and second, how similar are stimulus-related responses across methods? To this end, we investigated a wide range of brain regions and frequency bands. We presented the same movie clip twice to three different cohorts of subjects (NEEG = 45, NfMRI = 11, NECoG = 5) and assessed stimulus-driven correlations across viewings and between imaging methods, thereby ruling out task-irrelevant confounds. All three imaging methods had similar repeat-reliability across viewings when fMRI and EEG data were averaged across subjects, highlighting the potential to achieve large signal-to-noise ratio by leveraging large sample sizes. The fMRI signal correlated positively with high-frequency ECoG power across multiple task-related cortical structures but positively with low-frequency EEG and ECoG power. In contrast to previous studies, these correlations were as strong for low-frequency as for high frequency ECoG. We also observed links between fMRI and infra-slow EEG voltage fluctuations. These results extend previous findings to the case of natural stimulus processing.

neuroscience↗