Biology subjects

van Es, M. W. J.

Publications and source records attributed to van Es, M. W. J..

15 recordsLinked to original sources

bioRxiv2026

Anterior default mode brain state dynamics predict depressive symptom severity before and during TMS treatment

Transcranial magnetic stimulation (TMS) targeting the left dorsolateral prefrontal cortex is known to progressively reduce symptoms of depression. However, the neural mechanisms supporting this effect are poorly understood. To address this gap, we analysed longitudinal EEG recordings from 70 people undergoing TMS therapy and fitted an established dynamic network model of resting-state activity. Greater baseline symptom severity was associated with reduced occupancy of and fewer transitions into an anterior default mode brain state, alongside increased activity in a posterior default mode state. During treatment, decreases in anterior default mode state engagement following TMS predicted symptom improvement in the latter half of the intervention. Brain state activity exhibited structured, cyclical dynamics, with slower cycles linked to greater baseline severity. These findings suggest that symptoms of depression are characterised by gradual alterations in brain state dynamics, highlighting a central and dissociable role of default mode brain states in the persistence and remission of symptoms.

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bioRxiv2025

Canonical Hidden Markov Model Networks for Studying M/EEG

Dynamic brain networks identified in magneto/encephalography (M/EEG) recordings provide new insights into human brain activity. One established method uses Hidden Markov Models (HMMs) and has been shown to infer reproducible, fast-switching brain networks in a variety of cognitive and disease conditions. Often these studies are done on small bespoke (boutique) datasets (N < 100) and analysed in isolation of other M/EEG datasets. Instead of training a new model for each boutique study, which is computationally expensive, we propose the use of a canonical HMM. This provides a common reference through which different studies can be described using the same set of networks. We provide HMMs for a range of model orders (4-16 states) in parcellated source space and sensor space. These HMMs were trained on 1849 MEG recordings (N = 621, 18-88 years old, 194 hours), capturing population variability in both rest and task data. We illustrate applications of this canonical HMM approach in parcellated source space using boutique MEG and EEG datasets. Applying the canonical HMM in parcel space requires the boutique dataset to be preprocessed and source reconstructed in the same way as the canonical HMM training data. Applying the canonical HMM in the sensor space requires the same sensor layout and preprocessing as the canonical HMM training data. The canonical HMMs have been made publicly available as an open-access resource, providing sets of canonical brain networks that can be used to compare individuals within and across a range of datasets. HighlightsO_LIInstead of training a new HMM for every study, which is computationally expensive and can lead to less reliable results, we propose a canonical HMM instead. C_LIO_LIThe canonical HMM provides a common reference through which different studies can be described using the same set of networks. C_LIO_LIThe canonical HMM was learnt from 1849 MEG recordings (194 hours of data; resting state and task) in participants aged 18-88 years. C_LIO_LIWe demonstrate applications of the canonical HMM on three parcellated source space boutique studies: an Alzheimers resting-state MEG dataset, a working memory task MEG dataset, and a resting-state EEG dataset. C_LIO_LIWe transfer the parcel-level canonical HMM to sensor space, avoiding the need for source reconstruction. C_LIO_LIWe release the canonical HMM (for varying model orders) as an open-access resource for M/EEG research. C_LI

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bioRxiv2025

Towards decoding inner speech from EEG and MEG

Despite the prevalence of inner speech in everyday life, research on this has been limited, particularly when it comes to non-invasive methods. This preprint aims to fill this gap by using EEG and MEG to collect data from three different inner speech paradigms, and by conducting an initial decoding analysis. Specifically, we tested silent reading, repetitive inner speech, and generative inner speech tasks. We collect a high number of inner speech trials from a few participants. Besides comparing across recording modalities we also compare across inner speech types. Our aim is to analyse the decodability of inner speech within each task and between tasks by the use of transfer learning. We find that in both EEG and MEG, silent reading can be decoded relatively well with 30-40% accuracy across 5 words. However, the decoding performance of both types of inner speech is mostly at chance level. This prohibited further transfer learning investigations between tasks. While the inner speech results are primarily negative, we believe our exploration of data size and various decoding methods is valuable. The dataset itself is useful for the research community as it contains a much larger number of trials within one participant than any other inner speech dataset. Having multiple sessions also allows for testing across-session performance. Finally, we systematically compare silent reading decoding performance within 3 participants across four non-invasive modalities. These are EEG, 2 types of MEG machines, Elekta and CTF, and optically-pumped magnetometers (OPMs). We also compare the spatiotemporal dynamics of silent reading between these modalities. This is especially aimed at validating OPMs as a new kind of non-invasive brain recording technology. We find comparable performance to EEG, but OPM performance did not reach traditional MEG.

neuroscience↗

bioRxiv2025

Robust and replicable effects of ageing on resting state brain electrophysiology measured with MEG.

Non-invasive recordings using brain electrophysiology provide credible insights into decline in neuronal functioning with age. New approaches are required to translate these results into compelling clinical metrics and meet the global challenge of preserving brain health in ageing. Changes in neuronal dynamics with ageing are observable in the power spectra of EEG and MEG recordings. Highly promising candidates for electrophysiological markers have been identified, but progress is hindered by substantial methodological variability across studies. This makes it challenging to establish a clear consensus on the frequency, location, and direction of any single reported effect within the larger body of research. We estimate a full-frequency whole-head profile of the ageing effect on eyes-closed resting-state MEG using the GLM-Spectrum. This data representation is easily sharable, facilitates meta-analyses and provides a framework for estimating statistical power of age effects for future study planning. We use this to show that the effect of age replicates across open-access MEG datasets and is robust to modelling of common covariates. Distinct components within the full frequency profile have different effect sizes, indicating that sample-size planning for ageing effects must consider the specific features of interest. The frequency profile of ageing is strongly robust to a range of common covariates and partially robust to modelling of grey matter volume. We establish that what seems a well-powered study may become underpowered when analyses target an age effect that is linearly separable from an age-relevant covariate such as grey matter volume. These results provide a pathway towards formal comparison and assessment of candidate markers for brain health in ageing. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=151 SRC="FIGDIR/small/668093v3_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@1a9c351org.highwire.dtl.DTLVardef@563837org.highwire.dtl.DTLVardef@84b898org.highwire.dtl.DTLVardef@1730472_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIThere is a strong effect of ageing across the adult lifespan on neuronal oscillations across a range of frequencies and spatial locations. C_LIO_LIThe effect is replicable across multiple open-access datasets. C_LIO_LIThe effect is robust to a range of sources of inter-subject variability. C_LIO_LIThe age effect has heterogeneous effect size across space and frequency with implications for future study planning. C_LI

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bioRxiv2024

Effects of age on resting-state cortical networks

Understanding how ageing affects brain function remains a central challenge in neuroscience. Electrophysiological brain imaging techniques provide a near-direct measure of neuronal activity, which is useful for characterising neurophysiological health. They offer us the ability to track large-scale networks of functional activity with high temporal precision. The effects of healthy ageing on these networks remain poorly understood, in part due to small sample sizes and limited control for confounding factors in previous studies. Here, we analysed resting-state source-reconstructed magnetoencephalography (MEG) data from a large cross-sectional cohort of healthy adults (N =612, 18-88 years old) to characterise the effect of age using not only time-averaged (static), but also transient (dynamic) network activity. We examined time-averaged power and coherence across canonical frequency bands ({delta}, {theta}, , {beta}, {gamma}), as well as transient network dynamics identified using Hidden Markov Modelling. We included many confounding variables known to be affected by age, such as brain volume, as well as head size and position, which have previously been overlooked. Ageing was associated with frequency-specific changes in oscillatory power, with decreases in low-frequency ({delta}, {theta}) power and increases in high-frequency ({beta}) power. Coherence increased across all frequency bands and was positively associated with cognitive performance. Transient network analyses additionally revealed that frontal network occurrences declined with age, with evidence suggesting a compensatory role in supporting cognition. These findings provide a more comprehensive electrophysiological signature for healthy ageing and establish a baseline for detecting pathological change. HighlightsO_LIUsing source reconstructed magnetoencephalography data, we studied cortical functional networks of oscillatory activity from a large healthy population (612 subjects, 18-88 years old). C_LIO_LITime-averaged (static) network analysis in canonical frequency bands ({delta}, {theta}, , {beta}, {gamma}) showed: {beta} power and coherence increased with age; {delta}/{theta} power decreased with age, but coherence increased. Coherence in all frequencies increased with cognitive performance. C_LIO_LITransient (dynamic) network analysis showed frontal networks decreased in occurrence with age and their relation to cognitive performance showed that these changes were compensatory. The other networks showed a decreased occurrence with age. C_LIO_LIIn testing age and cognitive performance effects for statistical significance, we included a comprehensive set of confounds, such as brain volumes, head size and position, which has previously been overlooked. C_LIO_LIWe provide a comprehensive electrophysiological signature for healthy ageing and establish a baseline for detecting pathological change. C_LI

neuroscience↗

bioRxiv2024

Comparison between EEG and MEG of static and dynamic resting-state networks

The characterisation of resting state networks (RSNs) using neuroimaging techniques has significantly contributed to our understanding of the organisation of brain activity. Prior work has demonstrated the electrophysiological basis of RSNs and their dynamic nature, revealing transient activations of brain networks with millisecond timescales. While previous research has confirmed the comparability of RSNs identified by electroencephalography (EEG) to those identified by magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI), most studies have utilised static analysis techniques, ignoring the dynamic nature of brain activity. Often, these studies use high-density EEG systems, which limit their applicability in clinical settings. Addressing these gaps, our research studies RSNs using medium-density EEG systems (61 sensors), comparing both static and dynamic brain network features to those obtained from a high-density MEG system (306 sensors). We assess the qualitative and quantitative comparability of EEG-derived RSNs to those from MEG, including their ability to capture age-related effects, and explore the reproducibility of dynamic RSNs within and across the modalities. Our findings suggest that both MEG and EEG offer comparable static and dynamic network descriptions, albeit with MEG offering some increased sensitivity and reproducibility. Such RSNs and their comparability across the two modalities remained consistent qualitatively but not quantitatively when the data were reconstructed without subject-specific structural MRI images.

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bioRxiv2024

Dynamic Network Analysis of Electrophysiological Task Data

An important approach for studying the human brain is to use functional neuroimaging combined with a task. In electrophysiological data this often involves a time-frequency analysis, in which recorded brain activity is time-frequency transformed and epoched around task events of interest, followed by trial-averaging of the power. Whilst this simple approach can reveal fast oscillatory dynamics, the brain regions are analysed one at a time. This causes difficulties for interpretation and a debilitating number of multiple comparisons. In addition, it is now recognised that the brain responds to tasks through the coordinated activity of networks of brain areas. As such, techniques that take a whole-brain network perspective are needed. Here, we show how the oscillatory task responses from conventional time-frequency approaches, can be represented more parsimoniously at the network level using two state-of-the-art methods: the HMM (Hidden Markov Model) and DyNeMo (Dynamic Network Modes). Both methods reveal frequency-resolved networks of oscillatory activity with millisecond resolution. Comparing DyNeMo, HMM and traditional oscillatory response analysis, we show DyNeMo can identify task activations/deactivations that the other approaches fail to detect. DyNeMo offers a powerful new method for analysing task data from the perspective of dynamic brain networks. HighlightsO_LIWe show how oscillatory task response analysis can be carried out at the network level using two state-of-the-art methods: the Hidden Markov Model (HMM) and Dynamics Network Modes (DyNeMo). C_LIO_LIThe HMM and DyNeMo can identify oscillatory task responses that conventional time-frequency methods fail to detect. C_LIO_LIDyNeMo provides a more interpretable and precise network decomposition of the data compared to the HMM. C_LIO_LIThe dataset and (Python) scripts used to perform dynamic network analysis on task data are made publicly available. C_LI

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bioRxiv2023

osl-dynamics: A toolbox for modelling fast dynamic brain activity

Neural activity contains rich spatio-temporal structure that corresponds to cognition. This includes oscillatory bursting and dynamic activity that span across networks of brain regions, all of which can occur on timescales of a tens of milliseconds. While these processes can be accessed through brain recordings and imaging, modelling them presents methodological challenges due to their fast and transient nature. Furthermore, the exact timing and duration of interesting cognitive events is often a priori unknown. Here we present the OHBA Software Library Dynamics Toolbox (osl-dynamics), a Python-based package that can identify and describe recurrent dynamics in functional neuroimaging data on timescales as fast as tens of milliseconds. At its core are machine learning generative models that are able to adapt to the data and learn the timing, as well as the spatial and spectral characteristics, of brain activity with few assumptions. osl-dynamics incorporates state-of-the-art approaches that can be, and have been, used to elucidate brain dynamics in a wide range of data types, including magneto/electroencephalography, functional magnetic resonance imaging, invasive local field potential recordings and electrocorticography. It also provides novel summary measures of brain dynamics that can be used to inform our understanding of cognition, behaviour and disease. We hope osl-dynamics will further our understanding of brain function, through its ability to enhance the modelling of fast dynamic processes. HighlightsO_LIAn open-source toolbox for identifying and describing brain dynamics in neuroimaging data on fast timescales. C_LIO_LIIncludes visualisation and quantification of oscillatory bursting and network dynamics. C_LIO_LIProvides novel summary measures and group analysis tools of brain dynamics that can be used to inform our understanding of cognition, behaviour and disease. C_LIO_LIImplemented in Python and makes use of TensorFlow. C_LIO_LIIncludes comprehensive documentation and tutorials. C_LI

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bioRxiv2023

Large-scale cortical networks are organized in structured cycles

The brain needs to perform a diverse set of cognitive functions essential for survival, but it is unknown how it is organized to ensure that each of these functions is fulfilled within a reasonable period. One way in which this requirement can be met is if each of these cognitive functions occur as part of a repeated cycle. Here, we studied the temporal evolution of canonical large-scale cortical networks, and show that while network dynamics are stochastic, the overall ordering of their activity forms a robust cyclical pattern. This cyclical structure groups states with similar function and spectral content at specific phases of the cycle and occurs at timescales of (300-1000 ms). These results are reproduced in five large magnetoencephalography (MEG) datasets. Moreover, we show that metrics that characterize the cycle strength and speed are heritable, and relate to age, cognition, and behavioural performance. These results reveal for the first time that the activations of a canonical set of large-scale cortical networks are organised in an inherently cyclical manner, ensuring periodic activation of essential cognitive functions.

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bioRxiv2023

Interpretable full-epoch multiclass decoding for M/EEG

Multivariate pattern analysis (MVPA) of Magnetoencephalography (MEG) and Electroencephalography (EEG) data is a valuable tool for understanding how the brain represents and discriminates between different stimuli. Identifying the spatial and temporal signatures of stimuli is typically a crucial output of these analyses. Such analyses are mainly performed using linear, pairwise, sliding window decoding models. These allow for relative ease of interpretation, e.g. by estimating a time-course of decoding accuracy, but are computationally intensive and can have limited decoding performance. On the other hand, full epoch decoding models, commonly used for brain-computer interface (BCI) applications, can provide better decoding performance. However, they lack methods for interpreting the contributions of spatial and temporal features. In this paper, we propose an approach that combines a multiclass, full epoch decoding model with supervised dimensionality reduction, while still being able to reveal the contributions of spatiotemporal and spectral features using permutation feature importance. We demonstrate the approach on 3 different task MEG datasets using image presentations. Our results demonstrate that this approach consistently achieves higher accuracy than the peak accuracy of a sliding window decoder while estimating the relevant spatiotemporal features in the MEG signal. Finally, we show that our multiclass model can also be used for pairwise decoding, eliminating the computational burden of training separate models for each pairwise combination of stimuli.

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bioRxiv2022

The relationship between frequency content and representational dynamics in the decoding of neurophysiological data

Decoding of high temporal resolution, stimulus-evoked neurophysiological data is increasingly used to test theories about how the brain processes information. However, a fundamental relationship between the frequency spectra of the neural signal and the subsequent decoding accuracy timecourse is not widely recognised. We show that, in commonly used instantaneous signal decoding paradigms, each sinusoidal component of the evoked response is translated to double its original frequency in the subsequent decoding accuracy timecourses. We therefore recommend, where researchers use instantaneous signal decoding paradigms, that more aggressive low pass filtering is applied with a cut-off at one quarter of the sampling rate, to eliminate representational alias artefacts. However, this does not negate the accompanying interpretational challenges. We show that these can be resolved by decoding paradigms that utilise both a signals instantaneous magnitude and its local gradient information as features for decoding. On a publicly available MEG dataset, this results in decoding accuracy metrics that are higher, more stable over time, and free of the technical and interpretational challenges previously characterised. We anticipate that a broader awareness of these fundamental relationships will enable stronger interpretations of decoding results by linking them more clearly to the underlying signal characteristics that drive them. HighlightsO_LIWe investigate different decoding paradigms applied to epoched data and characterise the information content available to each over time. C_LIO_LIUnder commonly used instantaneous signal decoding paradigms, sinusoidal components of the evoked response are translated to double their original frequency in decoding accuracy metrics, presenting technical and interpretational challenges. C_LIO_LIWhen instantaneous signal decoding is used, we recommend using low pass filters with a cut-off less than one quarter of the sampling rate to eliminate spurious representational alias artefacts. C_LIO_LIThe interpretational issues associated with instantaneous signal decoding can be resolved with alternative paradigms such as complex spectrum decoding. C_LIO_LIWe show that complex spectrum decoding results in decoding accuracy metrics that are higher, more stable over time, and free of representational aliasing. C_LI

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bioRxiv2021

Reducing the efforts to create reproducible analysis code with FieldTrip

The analysis of EEG and MEG data typically requires a lengthy and complicated sequence of analysis steps, often requiring large amounts of computations, which are ideally represented in analysis scripts. These scripts are often written by researchers without formal training in computer science, resulting in the quality and readability of these analysis scripts to be highly dependent on individual coding expertise and style. Even though the computational outcomes and interpretation of the results can be correct, the inconsistent style and quality of analysis scripts make reviewing the details of the analysis difficult for other researchers that are either involved in the study or not, and the quality of the scripts might compromise the reproducibility of obtained results. This paper describes the design and implementation of a strategy that allows complete reproduction of MATLAB-based scripts with little extra efforts on behalf of the user, which we have implemented as part of the FieldTrip toolbox. Starting from the researchers idiosyncratic pipeline scripts, this new functionality allows researchers to automatically create and publish analysis pipeline scripts in a standardized format, along with all relevant intermediate data. We demonstrate the functionality and validate its effectiveness by applying it to the analysis of a recently published MEG study.

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bioRxiv2020

Phasic modulation of visual representations during sustained attention

Sustained attention has long been thought to benefit perception in a continuous fashion, but recent evidence suggests that it affects perception in a discrete, rhythmic way. Periodic fluctuations in behavioral performance over time, and modulations of behavioral performance by the phase of spontaneous oscillatory brain activity point to an attentional sampling rate in the theta or alpha frequency range. We investigated whether such discrete sampling by attention is reflected in periodic fluctuations in the decodability of visual stimulus orientation from magnetoencephalographic (MEG) brain signals. In this exploratory study, human subjects attended one of two grating stimuli while MEG was being recorded. We assessed the strength of the visual representation of the attended stimulus using a support vector machine (SVM) to decode the orientation of the grating (clockwise vs. counterclockwise) from the MEG signal. We tested whether decoder performance depended on the theta/alpha phase of local brain activity. While the phase of ongoing activity in visual cortex did not modulate decoding performance, theta/alpha phase of activity in the FEF and parietal cortex, contralateral to the attended stimulus did modulate decoding performance. These findings suggest that phasic modulations of visual stimulus representations in the brain are caused by frequency- specific top-down activity in the fronto-parietal attention network.

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bioRxiv2020

Investigating the neuronal oscillatory correlates of the Gratton effect

Rhythmic brain activity may provide a functional mechanism that facilitates dynamic interareal interactions and thereby give rise to complex behavior. It has been shown that low and high frequency oscillations propagate in opposite directions, but interactions between brain areas in various frequency bands are poorly understood. We investigated local and long-range synchrony in a brain-wide network and their relation to behavior, while human subjects executed a variant of the Simon task during MEG recording. We hypothesized that the behavioral difference for stimulus-response congruent (C) and incongruent (IC) trials is caused by differences in cortical synchrony, and that the relative behavioral benefit for trials following instances with the same stimulus-response contingency (i.e. the Gratton effect) is caused by contingency-induced changes in the state of the network. This would be achieved by temporarily upregulating the connectivity strength between behaviorally relevant network nodes. We identified regions-of-interest that differed in local synchrony during the response phase of the Simon task. Within this network, spectral power in none of the nodes in either of the studied frequencies was significantly different in the pre-cue window of the subsequent trial. Nor was there a significant difference in coherence between the task-relevant nodes that could explain the superior performance after compatible consecutive trials.

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bioRxiv2018

Stimulus-induced Gamma Power Predicts the Amplitude of the Subsequent Visual Evoked Response

The efficiency of neuronal information transfer in activated brain networks may affect behavioral performance. Gamma-band synchronization has been proposed to be a mechanism that facilitates neuronal processing of behaviorally relevant stimuli. In line with this, it has been shown that strong gamma-band activity in visual cortical areas leads to faster responses to a visual go cue. We investigated whether there are directly observable consequences of trial-by-trial fluctuations in non-invasively observed gamma-band activity on the neuronal response. Specifically, we hypothesizedthat the amplitude of the visual evoked response to a go cue can be predicted by gamma power in the visual system, in the window preceding the evoked response. Thirty-three human subjects (22 female) performed a visual speeded response task while their magnetoencephalogram (MEG) was recorded. The participants had to respond to a pattern reversal of a concentric moving grating. We estimated single trial stimulus-induced visual cortical gamma power, and correlated this with the estimated single trial amplitude of the most prominent event-related field (ERF) peak within the first 100 ms after the pattern reversal. In parieto-occipital cortical areas, the amplitude of the ERF correlated positively with gamma power, and correlated negatively with reaction times. No effects were observed for the alpha and beta frequency bands, despite clear stimulus onset induced modulation at those frequencies. These results support a mechanistic model, in which gamma-band synchronization enhances the neuronal gain to relevant visual input, thus leading to more efficient downstream processing and to faster responses.\n\nSignificance statementGamma-band activity has been associated with many cognitive functions and improved behavioral performance. For example, high amplitude gamma-band activity in visual cortical areas before a go cue leads to faster reaction times. However, it remains unclear through which neural mechanism(s) gamma-band activity eventually affects behavior. We tested whether the strength of induced gamma-band activity affects evoked activity elicited by a subsequent visual stimulus. We found enhanced amplitudes of early visual evoked activity, and faster responses with higher gamma power. This suggests that gamma-band activity affects the neuronal gain to new sensory input, and thus these results bridge the gap between gamma power and behavior, and support the putative role of gamma-band activity in the efficiency of cortical processing.

neuroscience↗