Biology subjects

Oostenveld, R.

Publications and source records attributed to Oostenveld, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Microstructure of white matter association tracts in the temporal lobe predicts the timing of electrophysiological activity and behaviour in picture naming

White matter (WM) microstructure has been mainly associated with accuracy of picture naming. However, little is known about how WM properties relate to the timing of picture naming and its underlying electrophysiological activity. Here, we linked diffusion-weighted, electrophysiological, and behavioural data, using time-based measures to conceptually connect WM deterioration to alterations in conduction speed and precision. Our sample included individuals of varying age, with or without stroke. We correlated fractional anisotropy (FA) of bilateral WM tracts traversing the temporal lobe with mean response times (RTs) and response time variability (coefficient of variation, CV) in picture naming, and with the onset latency of neuronal activity, represented by the electrophysiological P2 component time-locked to picture onset. We selected tracts showing associations with P2 latency and naming performance separately, on which we conducted path analyses examining the relationship between structure, neuronal activity and behaviour. Depending on the tract, higher FA was associated with faster and/or more consistent naming, suggesting that ventral tracts differentially contribute to naming speed and consistency. Importantly, path modelling for the right inferior fronto-occipital fasciculus (IFOF) revealed that FA predicted P2 latency, which in turn predicted RTs, possibly reflecting a structure-activity-behaviour relationship in lexical-semantic processing. P2 source reconstruction demonstrated anatomical proximity to posterior segments of the right IFOF, supporting these findings. Overall, this study provides a proof of concept for modelling structure-activity-behaviour relationships in language research, while highlighting the need for elaboration of such relations at a more mechanistic level.

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bioRxiv2026

The Concise Language Paradigm in left and right hemisphere stroke: a longitudinal source-localized EEG study

The mechanisms underlying language recovery after stroke remain poorly understood. Most previous studies have either focused on language comprehension or production and/or relied on hemodynamic measures, with limited complementary electrophysiological evidence. Here, we combined the Concise Language Paradigm (CLaP), assessing comprehension and production, with individualized electroencephalography (EEG) source reconstruction to investigate post-stroke language function and changes in hemispheric dominance over time. In two sessions, we tested stroke-free controls, individuals with left hemisphere (LH) or right hemisphere (RH) stroke. Participants with stroke were assessed two and seven months post-stroke. EEG data were analyzed using event-related potentials and time-frequency representations, followed by source reconstruction based on state-of-the-art individual MRI-derived forward models. Laterality indices were calculated to assess changes in hemispheric dominance between sessions. Behaviorally, the LH group showed low performance at two months followed by overall improvement, whereas the RH group mainly improved in functional communication. EEG activity was significantly modulated by task condition across comprehension and production contrasts. Source-level results in controls were consistent with established language-related networks, serving as regions of interest for the stroke analyses. In the stroke groups, hemispheric dominance differed between LH and RH stroke participants, with LH participants showing systematic shifts from left- to right-hemisphere dominance between sessions, if not already right-hemisphere dominant at two months. Our study provides evidence on comprehension and production performance after stroke using a single paradigm. These findings demonstrate dynamic changes in hemispheric dominance during post-stroke recovery, ultimately highlighting the value of individualized EEG source reconstruction to investigate post-stroke plasticity.

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bioRxiv2026

STAMP-LME: A spatiotemporal surface-mapping framework for population-level iEEG

Intracranial EEG (iEEG) affords a unique opportunity to assess human neural activity with millisecond temporal resolution and millimeter spatial precision. However, group-level statistical analyses remain challenging due to sparse and heterogeneous electrode coverage, anatomical variability, and the difficulty of integrating spatiotemporal responses across individuals. Most iEEG studies therefore define brain regions of interest for the group analysis, sacrificing a spatial resolution strength of iEEG and excluding data outside those regions of interest. Here, we present a spatiotemporal analysis and mapping with permutation-based linear mixed-effects models (STAMP-LME), a cortical-mapping framework for population-level iEEG analysis independent of predefined cortical regions of interest. STAMP-LME is a complete processing pipeline spanning electrode localization, mapping to a standardized surface of the cortical mantle, including medial, orbitofrontal, and insular regions, and statistical inference at the cortical level. Electrode-level data from each participant is mapped to a standardized cortical surface using weights that account for electrode-to-cortex distance and confidence in cortical sampling. Brain signals are then combined with a spatial smoothing kernel and entered into LME models at each vertex-node and time point. Data-derived null distributions are generated through controlled permutations and used for spatiotemporal inference with threshold-free cluster enhancement (TFCE) or false-discovery-rate (FDR) correction. We validated STAMP-LME using simulations with known cortical ground truth while varying effect size, interparticipant source-location variability, electrode coverage, and spatial smoothing. The framework reliably detected simulated effects and recovered their spatiotemporal distribution while maintaining low localization error and limiting false-positive detections across different sampling conditions. We then applied the method to intracranial recordings from nine individuals performing an auditory oddball task. The empirical analysis revealed distributed auditory cortical responses consistent with conventional channel-level analyses, while providing more fine-grained group-level activation maps across sampled cortical regions. STAMP-LME provides a flexible, open-source framework for data-driven population-level cortical iEEG analysis that preserves the spatiotemporal strengths of intracranial recordings while accounting for sparse and heterogeneous electrode coverage. The structured workflow enables reproducible and anatomically precise group-level inference on a standardized cortical surface and facilitates comparison with surface-based fMRI and source-localized M/EEG results, therefore providing a practical tool for cognitive and clinical neuroscience research.

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bioRxiv2026

Flattened brain hierarchy and increased EEG complexity during unusual bodily experiences and out-of-body experiences

Experiences in which the sense of the body becomes distorted offer a unique window into how the brain constructs the bodily self. Despite their clinical relevance in conditions such as depersonalization and body-image disorders, large-scale brain dynamics underlying unusual bodily experiences (UBEs) remain poorly understood, and no prior study has examined them systematically across wakefulness and sleep. Here, we analyzed a high-density EEG dataset including 36 UBE episodes, such as floating sensations, distorted body boundaries, and out-of-body experiences, recorded across rapid eye-movement (REM) sleep, light sleep, sleep arousals, and wakefulness during meditation (N=20). We quantified the temporal irreversibility of neural dynamics, a marker of cortical hierarchical organization, and complemented these analyses with whole-brain signal complexity measures following a within-subject design. UBEs were consistently associated with a global reduction in neural irreversibility and cortical hierarchical differentiation relative to non-UBEs and wakefulness, along with a concurrent increase in whole-brain complexity to levels comparable to wakefulness. Together, these findings suggest that UBEs reflect a neural state combining high informational richness with a transient flattening of cortical hierarchy, a profile that resembles that observed in psychedelic states. Key pointsO_LIUBEs are associated with a global flattening of brain hierarchy measured by EEG. C_LIO_LIUBEs are associated with increased global EEG complexity, resembling the psychedelic state. C_LI

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bioRxiv2026

EMG-BIDS: an extension to the Brain Imaging Data Structure for electromyography

Electromyography (EMG) is fundamental to clinical assessment, rehabilitation, neuromuscular research, and human-machine interfaces. Despite decades of use, no widely adopted standard exists for organizing and sharing EMG data, limiting reusability and large-scale data aggregation. We present EMG-BIDS, an extension to the Brain Imaging Data Structure (BIDS) that standardizes the organization of EMG recordings. EMG-BIDS addresses challenges unique to EMG, including diverse electrode types (surface or intramuscular, single channel to high-density arrays), heterogeneous electrode placements across anatomical locations, montages (e.g., monopolar or bipolar sensor designs), and the critical need for transparent documentation of sensor positioning. The specification introduces hierarchical coordinate systems that link local electrode grids to anatomical landmarks, enabling precise and reproducible placement documentation. EMG-BIDS is now part of BIDS as of version 1.11.0, supported by existing tools, including MNE-BIDS and EEGLAB. We demonstrate the specification through public datasets, including high-density surface EMG recordings. EMG-BIDS provides the foundation for FAIR (Findable, Accessible, Interoperable, Reusable) EMG data sharing, enabling meta-analyses, multi-site studies, and machine learning applications that require standardized, well-documented datasets.

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bioRxiv2026

Eye-Tracking-BIDS: the Brain Imaging Data Structure extended to gaze position and pupil data

The Brain Imaging Data Structure (BIDS) is a widely adopted, community-driven standard to organize neuroimaging data and metadata. Although numerous extensions have been developed to incrementally extend coverage to new modalities and data types, an unambiguous, granular specification for eye-tracking recordings is lacking. Here, we present how BIDS will structure data and metadata produced by eye-tracking devices, including gaze position and pupil data. In addition to prescribing the organization of the unprocessed (raw) recordings and associated metadata as produced by the device, BEP20 also resolves gaps in current BIDS specifications beyond the scope of eye tracking. In particular, it adds a mechanism for including asynchronous model parameters and messages, such as contextual information, statuses, and events, such as triggers, generated by the device. BEP20 includes examples that illustrate its applicability in various experimental settings. This BIDS extension provides a robust standard that supports the development of self-adaptive, open, and automated eye-tracking data structures, thereby bolstering transparency and reliability of results in this field.

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bioRxiv2025

Using magnetoencephalography to track the propagation of 40 Hz invisible spectral flicker

Brain stimulation with novel 40 Hz invisible spectral flicker (ISF) has been proposed as a therapy for Alzheimers disease, with leading hypotheses suggesting local promotion of glymphatic clearance of amyloid as the mechanism of action. Neural signals in the gamma range and their spatial propagation over the brain can be tracked using magnetoencephalography (MEG) with high temporal and good spatial detail. However, stimulation with 40 Hz ISF requires specialised hardware which causes electromagnetic interference (EMI) with MEG equipment. MEG measures the tiny magnetic fields of the brain, which are easily distorted by external magnetic fields. Using MEG to track the propagation of 40 Hz ISF requires multiple modifications to the experimental setup. These include, at least 1) an experimental design that promotes modulation of the neural signal, but not the artifact, 2) removal of all electronics not strictly necessary for light production from the stimulators, and 3) signal processing for 40 Hz EMI artifact suppression. Here, we present an MEG study on the cortical propagation of 40 Hz ISF. In two experiments, we investigate the modulation that visual and non-visual cognitive tasks have on the power and propagation of cortical activity induced by 40 Hz ISF. With the chosen experimental setup and design, the 40 Hz EMI artifact could not be entirely disentangled from the neural signal of interest, thus rendering inference on the spatial propagation of the 40 Hz signal impossible. Further improvements need to be implemented in a follow-up experimental design. We present potential solutions to allow for future investigation of 40 Hz ISF with MEG.

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bioRxiv2023

The impact of CSF-filled cavities on scalp EEG and its implications

Previous studies have found EEG amplitude and scalp topography differences between neurotypical and neurological/neurosurgical groups, being interpreted at the cognitive level. However, these comparisons are invariably accompanied by anatomical changes. Critical to EEG are the so-called volume currents, which are affected by the spatial distribution of the different tissues in the head. We investigated the effect of CSF-filled cavities on simulated EEG scalp data. We simulated EEG scalp potentials for known sources using different volume conduction models: a reference model (i.e., unlesioned brain) and models with realistic CSF-filled cavities gradually increasing in size. We used this approach for a single source close or far from the CSF-lesion cavity, and for a scenario with a distributed configuration of sources (i.e., a "cognitive ERP effect"). Magnitude and topography error between the reference and lesion models were quantified. For the single-source simulation close to the lesion, the CSF-filled lesion modulated signal amplitude with more than 17% magnitude error, and topography with more than 9% topographical error. Negligible modulation was found for the single source far from the lesion. For the multi-source simulations of the cognitive effect, the CSF-filled lesion modulated signal amplitude with more than 6% magnitude error, and topography with more than 16% topography error in a non-monotonic fashion. In conclusion, the impact of a CSF-filled cavity cannot be neglected for scalp-level EEG data. Especially when group-level comparisons are made, any scalp-level attenuated, aberrant, or absent effects are difficult to interpret without considering the confounding effect of CSF. Impact statementPrevious studies have found EEG amplitude and scalp topography differences between neurotypical and neurological/neurosurgical groups (whose brain damage leads to the presence of a CSF-filled cavity), being interpreted at the cognitive level. Via simulations of scalp-level EEG patterns, we show that attenuated, aberrant, or absent effects in these comparisons are difficult to interpret without considering the confounding effect of CSF. FundingThis study was partly supported by grants from the Netherlands Organization for Scientific Research (Nederlandse Organisatie voor Wetenschappelijk Onderzoek [NWO]) to V. P. (451-17-003 and VI.Vidi.201.081) and to the Language in Interaction Consortium (024-001-006). CRediTVP: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Visualization, Writing - original draft, Writing - review & editing RO: Conceptualization, Methodology, Software, Writing - review & editing JMS: Conceptualization, Methodology, Software, Writing - review & editing MCP: Conceptualization, Investigation, Methodology, Visualization, Writing - review & editing

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bioRxiv2021

Sharing individualised template MRI data for MEG source reconstruction: a solution for open data while keeping subject confidentiality

The increasing requirements for adoption of FAIR data management and sharing original research data from neuroimaging studies can be at odds with protecting the anonymity of the research participants due to the person-identifiable anatomical features in the data. We propose a solution to this dilemma for anatomical MRIs used in MEG source analysis. In MEG analysis, the channel-level data is reconstructed to the source-level using models derived from anatomical MRIs. Sharing data, therefore, requires sharing the anatomical MRI to replicate the analysis. The suggested solution is to replace the individual anatomical MRIs with individualised warped templates that can be used to carry out the MEG source analysis and that provide sufficient geometrical similarity to the original participants MRIs. First, we demonstrate how the individualised template warping can be implemented with one of the leading open-source neuroimaging analysis toolboxes. Second, we compare results from four different MEG source reconstruction methods performed with an individualised warped template to those using the participants original MRI. While the source reconstruction results are not numerically identical, there is a high similarity between the results for single dipole fits, dynamic imaging of coherent sources beamforming, and atlas-based virtual channel beamforming. There is a moderate similarity between minimum-norm estimates, as anticipated due to this method being anatomically constrained and dependent on the exact morphological features of the cortical sheet. We also compared the morphological features of the warped template to those of the original MRI. These showed a high similarity in grey matter volume and surface area, but a low similarity in the average cortical thickness and the mean folding index within cortical parcels. Taken together, this demonstrates that the results obtained by MEG source reconstruction can be preserved with the warped templates, whereas the anatomical and morphological fingerprint is sufficiently altered to protect the anonymity of research participants. In cases where participants consent to sharing anatomical MRI data, it remains preferable to share the original defaced data with an appropriate data use agreement. In cases where participants did not consent to share their MRIs, the individualised warped MRI template offers a good compromise in sharing data for reuse while retaining anonymity for those research participants.

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bioRxiv2021

PET-BIDS, an extension to the brain imaging data structure for positron emission tomography

The Brain Imaging Data Structure (BIDS) is a standard for organizing and describing neuroimaging datasets. It serves not only to facilitate the process of data sharing and aggregation, but also to simplify the application and development of new methods and software for working with neuroimaging data. Here, we present an extension of BIDS to include positron emission tomography (PET) data (PET-BIDS). We describe the PET-BIDS standard in detail and share several open-access datasets curated following PET-BIDS. Additionally, we highlight several tools which are already available for converting, validating and analyzing PET-BIDS datasets.

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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

Rapid changes in brain activity during learning of grapheme-phoneme associations in adults

Learning to associate written letters with speech sounds is crucial for the initial phase of acquiring reading skills. However, little is known about the cortical reorganization for supporting letter-speech sound learning, particularly the brain dynamics during the learning of grapheme-phoneme associations. In the present study, we trained 30 Finnish participants (mean age: 24.33 years, SD: 3.50 years) to associate novel foreign letters with familiar Finnish speech sounds on two consecutive days (first day ~ 50 minutes; second day ~ 25 minutes), while neural activity was measured using magnetoencephalography (MEG). Two sets of audiovisual stimuli were used for the training in which the grapheme-phoneme association in one set (Learnable) could be learned based on the different learning cues provided, but not in the other set (Control). The learning progress was tracked at a trial-by-trial basis and used to segment different learning stages for the MEG source analysis. The learning-related changes were examined by comparing the brain responses to Learnable and Control uni/multi-sensory stimuli, as well as the brain responses to learning cues at different learning stages over the two days. We found dynamic changes in brain responses related to multi-sensory processing when grapheme-phoneme associations were learned. Further, changes were observed in the brain responses to the novel letters during the learning process. We also found that some of these learning effects were observed only after memory consolidation the following day. Overall, the learning process modulated the activity in a large network of brain regions, including the superior temporal cortex and the dorsal (parietal) pathway. Most interestingly, middle- and inferior-temporal regions were engaged during multi-sensory memory encoding after the cross-modal relationship was extracted from the learning cues. Our findings highlight the brain dynamics and plasticity related to the learning of letter-speech sound associations and provide a more refined model of grapheme-phoneme learning in reading acquisition.

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bioRxiv2019

Comparison of beamformer implementations for MEG source localization

Beamformers are applied for estimating spatiotemporal characteristics of neuronal sources underlying measured MEG/EEG signals. Several MEG analysis toolboxes include an implementation of a linearly constrained minimum-variance (LCMV) beamformer. However, differences in implementations and in their results complicate the selection and application of beamformers and may hinder their wider adoption in research and clinical use. Additionally, combinations of different MEG sensor types (such as magnetometers and planar gradiometers) and application of preprocessing methods for interference suppression, such as signal space separation (SSS), can affect the results in different ways for different implementations. So far, a systematic evaluation of the different implementations has not been performed. Here, we compared the localization performance of the LCMV beamformer pipelines in four widely used open-source toolboxes (FieldTrip, SPM12, Brainstorm, and MNE-Python) using datasets both with and without SSS interference suppression.\n\nWe analyzed MEG data that were i) simulated, ii) recorded from a static and moving phantom, and iii) recorded from a healthy volunteer receiving auditory, visual, and somatosensory stimulation. We also investigated the effects of SSS and the combination of the magnetometer and gradiometer signals. We quantified how localization error and point-spread volume vary with SNR in all four toolboxes.\n\nWhen applied carefully to MEG data with a typical SNR (3-15 dB), all four toolboxes localized the sources reliably; however, they differed in their sensitivity to preprocessing parameters. As expected, localizations were highly unreliable at very low SNR, but we found high localization error also at very high SNRs. We also found that the SNR improvement offered by SSS led to more accurate localization.

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bioRxiv2019

Reduction of spontaneous cortical beta bursts in Parkinson’s disease is linked to symptom severity

Parkinsons disease is characterized by a gradual loss of dopaminergic neurons, which are associated with altered neuronal activity in the beta band (13-30 Hz). Assessing beta band activity typically involves transforming the time-series to get the power of the signal in the frequency-domain. Such transformation assumes that the time-series can be reduced to a combination of steady-state sine-and cosine waves. However, recent studies have suggested that this approach masks relevant biophysical features in the beta band activity--for example, that the beta band exhibits transient bursts of high-amplitude activity.\n\nIn an exploratory study we used magnetoencephalography (MEG) to record cortical beta band activity to characterize how spontaneous cortical beta bursts manifest in Parkinsons patients ON and OFF dopaminergic medication, and compare this to matched healthy controls. From three minutes of MEG data, we extracted the time-course of beta band activity from the sensorimotor cortex and characterized high-amplitude epochs in the signal to test if they exhibited burst like properties. We then compared the rate, duration, inter-burst interval, and peak amplitude of the high-amplitude epochs between the Parkinsons patients and healthy controls.\n\nOur results show that Parkinsons patients OFF medication had a 6-17% lower beta bursts rate compared to healthy controls, while both the duration and the amplitude of the bursts were the same for Parkinsons patients and healthy controls and medicated state of the Parkinsons patients. These data thus support the view that beta bursts are fundamental underlying features of beta band activity, and show that changes in cortical beta band power in PD can be explained primarily by changes in the underlying burst rate. Importantly, our results also revealed a relationship between beta bursts rate and motor symptom severity in PD: a lower burst rate scaled with increased in severity of bradykinesia and postural/kinetic tremor. Beta burst rate might thus serve as neuromarker for Parkinsons disease that can help in the assessment of symptom severity in Parkinsons disease or evaluate treatment effectiveness.

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bioRxiv2019

Sensor localization using magnetic dipole-like coils: A method for highly accurate co-registration in on-scalp MEG

Source modelling in magnetoencephalography (MEG) requires precise co-registration of the sensor array and the anatomical structure of the measured individuals head. In conventional MEG, positions and orientations of the sensors relative to each other are fixed and known beforehand, requiring only localization of the head relative to the sensor array. Since the sensors in on-scalp MEG are positioned on the scalp, locations of the individual sensors depend on the subjects head shape and size. The positions and orientations of on-scalp sensors must therefore be measured at every recording. This can be achieved by inverting conventional head localization, localizing the sensors relative to the head - rather than the other way around.\n\nIn this study we present a practical method for localizing sensors using magnetic dipole-like coils attached to the subjects head. We implement and evaluate the method in a set of on-scalp MEG recordings using a 7-channel on-scalp MEG system based on high critical temperature superconducting quantum interference devices (high-Tc SQUIDs). The method provides accurate estimates of individual sensor positions and orientations with short averaging time ([≤] 2 mm and < 3 degrees, respectively, with 1-second averaging), enabling continuous sensor localization. Calibrating and jointly localizing the sensor array can further improve the localization accuracy (< 1 mm and < 2.5 degrees, respectively, with 1-second coil recordings).\n\nWe demonstrate source localization of on-scalp recorded somatosensory evoked activity based on co-registration with our method. Equivalent current dipole fits of the evoked responses corresponded well (within 5.3 mm) with those based on a commercial, whole-head MEG system.

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bioRxiv2018

Lead-DBS v2: Toward a comprehensive pipeline for deep brain stimulation imaging

Deep brain stimulation (DBS) is a highly efficacious treatment option for movement disorders and a growing number of other indications are investigated in clinical trials. To ensure optimal treatment outcome, exact electrode placement is required. Moreover, to analyze the relationship between electrode location and clinical results, a precise reconstruction of electrode placement is required, posing specific challenges to the field of neuroimaging. Since 2014 the open source toolbox Lead-DBS is available, which aims at facilitating this process. The tool has since become a popular platform for DBS imaging. With support of a broad community of researchers worldwide, methods have been continuously updated and complemented by new tools for tasks such as multispectral nonlinear registration, structural / functional connectivity analyses, brain shift correction, reconstruction of microelectrode recordings and orientation detection of segmented DBS leads. The rapid development and emergence of these methods in DBS data analysis require us to revisit and revise the pipelines introduced in the original methods publication. Here we demonstrate the updated DBS and connectome pipelines of Lead-DBS using a single patient example with state-of-the-art high-field imaging as well as a retrospective cohort of patients scanned in a typical clinical setting at 1.5T. Imaging data of the 3T example patient is co-registered using five algorithms and nonlinearly warped into template space using ten approaches for comparative purposes. After reconstruction of DBS electrodes (which is possible using three methods and a specific refinement tool), the volume of tissue activated is calculated for two DBS settings using four distinct models and various parameters. Finally, four whole-brain tractography algorithms are applied to the patients preoperative diffusion MRI data and structural as well as functional connectivity between the stimulation volume and other brain areas are estimated using a total of eight approaches and datasets. In addition, we demonstrate impact of selected preprocessing strategies on the retrospective sample of 51 PD patients. We compare the amount of variance in clinical improvement that can be explained by the computer model depending on the method of choice.\n\nThis work represents a multi-institutional collaborative effort to develop a comprehensive, open source pipeline for DBS imaging and connectomics, which has already empowered several studies, and may facilitate a variety of future studies in the field.

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bioRxiv2017

Integrated analysis of anatomical and electrophysiological human intracranial data

The exquisite spatiotemporal precision of human intracranial EEG recordings (iEEG) permits characterizing neural processing with a level of detail that is inaccessible to scalp-EEG, MEG, or fMRI. However, the same qualities that make iEEG an exceptionally powerful tool also present unique challenges. Until now, the fusion of anatomical data (MRI and CT images) with the electrophysiological data and its subsequent analysis has relied on technologically and conceptually challenging combinations of software. Here, we describe a comprehensive protocol that addresses the complexities associated with human iEEG, providing complete transparency and flexibility in the evolution of raw data into illustrative representations. The protocol is directly integrated with an open source toolbox for electrophysiological data analysis (FieldTrip). This allows iEEG researchers to build on a continuously growing body of scriptable and reproducible analysis methods that, over the past decade, have been developed and employed by a large research community. We demonstrate the protocol for an example complex iEEG data set to provide an intuitive and rapid approach to dealing with both neuroanatomical information and large electrophysiological data sets. We explain how the protocol can be largely automated, taking under an hour to complete, and readily adjusted to iEEG data sets with other characteristics.

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bioRxiv2017

Reconstruction of resting-state networks from macaqueelectrocorticographic data

The discovery of haemodynamic (BOLD-fMRI) resting-state networks (RSNs) has brought about a fundamental shift in our thinking about the role of intrinsic brain activity. The electrophysiological underpinnings of RSNs remain largely elusive and it has been shown only recently that electrophysiological cortical rhythms are organized into RSNs. Most electrophysiological studies into RSNs use magnetoencephalography (MEG) or electroencephalography (EEG), which limits the spatial scale on which RSNs can be investigated. Due to their close proximity to the cortical surface, electroencephalographic (ECoG) recordings can potentially provide a more detailed picture of the functional organization of resting-state cortical rhythms. In this study we propose using source-space independent component analysis for identifying generators of resting-state cortical rhythms as recorded with ECoG and reconstructing their network structure. Their network structure is characterized by two kinds of connectivity: instantaneous correlations between band-limited amplitude envelopes and oscillatory phase-locking. Using simulated data, we find that the reconstruction of oscillatory phase-locking is more challenging than that of amplitude correlations, particularly for low signal-to-noise levels. Specifically, phase-lags can both be over- and underestimated as a consequence of first-order and higher-order volume-conduction effects, which troubles the interpretation of interaction measures based on imaginary phase-locking or coherence. The methodology is applied to resting-state beta (15-30 Hz) rhythms within the motor system of a macaque monkey and leads to the identification of a functional network of seven cortical generators that are distributed across the sensorimotor system. The spatial extent of the identified generators, together with consistent phase-lags, suggests that these rhythms can be viewed as being spatially continuous with complex dynamics including traveling waves. Our findings illustrate the level of spatial detail attainable with source-projected ECoG and motivates wider use of the methodology for studying resting-state as well as event-related cortical dynamics in macaque and human.

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