Browsing by Author "Oostenveld, Robert"

Filter results by typing the first few letters
Now showing 1 - 6 of 6
  • A 204-subject multimodal neuroimaging dataset to study language processing
    (2019-04-03) Schoffelen, Jan Mathijs; Oostenveld, Robert; Lam, Nietzsche H.L.; Uddén, Julia; Hultén, Annika; Hagoort, Peter
     Data Article
    This dataset, colloquially known as the Mother Of Unification Studies (MOUS) dataset, contains multimodal neuroimaging data that has been acquired from 204 healthy human subjects. The neuroimaging protocol consisted of magnetic resonance imaging (MRI) to derive information at high spatial resolution about brain anatomy and structural connections, and functional data during task, and at rest. In addition, magnetoencephalography (MEG) was used to obtain high temporal resolution electrophysiological measurements during task, and at rest. All subjects performed a language task, during which they processed linguistic utterances that either consisted of normal or scrambled sentences. Half of the subjects were reading the stimuli, the other half listened to the stimuli. The resting state measurements consisted of 5 minutes eyes-open for the MEG and 7 minutes eyes-closed for fMRI. The neuroimaging data, as well as the information about the experimental events are shared according to the Brain Imaging Data Structure (BIDS) format. This unprecedented neuroimaging language data collection allows for the investigation of various aspects of the neurobiological correlates of language.
  • Comparison of beamformer implementations for MEG source localization
    (2020-08-01) Jaiswal, Amit; Nenonen, Jukka; Stenroos, Matti; Gramfort, Alexandre; Dalal, Sarang S.; Westner, Britta U.; Litvak, Vladimir; Mosher, John C.; Schoffelen, Jan Mathijs; Witton, Caroline; Oostenveld, Robert; Parkkonen, Lauri
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    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 (MNE-Python, FieldTrip, DAiSS (SPM12), and Brainstorm) using datasets both with and without SSS interference suppression. We 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 the signal-to-noise ratio (SNR) in all four toolboxes. When 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 for the first three toolboxes while Brainstorm showed greater robustness but with lower spatial resolution. We also found that the SNR improvement offered by SSS led to more accurate localization.
  • Good practice for conducting and reporting MEG research
    (2013) Gross, Joachim; Baillet, Sylvain; Barnes, Gareth R.; Henson, Richard N.; Hillebrand, Arjan; Jensen, Ole; Jerbi, Karim; Litvak, Vladimir; Maess, Burkhard; Oostenveld, Robert; Parkkonen, Lauri; Taylor, Jason R.; Wassenhovek, Virginie van; Wibraln, Michael; Schoffelen, Jan-Mathjis
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Magnetoencephalographic (MEG) recordings are a rich source of information about the neural dynamics underlying cognitive processes in the brain, with excellent temporal and good spatial resolution. In recent years there have been considerable advances in MEG hardware developments and methods. Sophisticated analysis techniques are now routinely applied and continuously improved, leading to fascinating insights into the intricate dynamics of neural processes. However, the rapidly increasing level of complexity of the different steps in a MEG study make it difficult for novices, and sometimes even for experts, to stay aware of possible limitations and caveats. Furthermore, the complexity of MEG data acquisition and data analysis requires special attention when describing MEG studies in publications, in order to facilitate interpretation and reproduction of the results. This manuscript aims at making recommendations for a number of important data acquisition and data analysis steps and suggests details that should be specified in manuscripts reporting MEG studies. These recommendations will hopefully serve as guidelines that help to strengthen the position of the MEG research community within the field of neuroscience, and may foster discussion in order to further enhance the quality and impact of MEG research.
  • Localizing on-scalp MEG sensors using an array of magnetic dipole coils
    (2018-05-01) Pfeiffer, Christoph; Andersen, Lau M.; Lundqvist, Daniel; Hämäläinen, Matti; Schneiderman, Justin F.; Oostenveld, Robert
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Accurate estimation of the neural activity underlying magnetoencephalography (MEG) signals requires co-registration i.e., determination of the position and orientation of the sensors with respect to the head. In modern MEG systems, an array of hundreds of low-Tc SQUID sensors is used to localize a set of small, magnetic dipole-like (head-position indicator, HPI) coils that are attached to the subject’s head. With accurate prior knowledge of the positions and orientations of the sensors with respect to one another, the HPI coils can be localized with high precision, and thereby the positions of the sensors in relation to the head. With advances in magnetic field sensing technologies, e.g., high-Tc SQUIDs and optically pumped magnetometers (OPM), that require less extreme operating temperatures than low-Tc SQUID sensors, on-scalp MEG is on the horizon. To utilize the full potential of on-scalp MEG, flexible sensor arrays are preferable. Conventional co-registration is impractical for such systems as the relative positions and orientations of the sensors to each other are subject-specific and hence not known a priori. Herein, we present a method for co-registration of on-scalp MEG sensors. We propose to invert the conventional co-registration approach and localize the sensors relative to an array of HPI coils on the subject’s head. We show that given accurate prior knowledge of the positions of the HPI coils with respect to one another, the sensors can be localized with high precision. We simulated our method with realistic parameters and layouts for sensor and coil arrays. Results indicate co-registration is possible with sub-millimeter accuracy, but the performance strongly depends upon a number of factors. Accurate calibration of the coils and precise determination of the positions and orientations of the coils with respect to one another are crucial. Finally, we propose methods to tackle practical challenges to further improve the method.
  • The Open Brain Consent: Informing research participants and obtaining consent to share brain imaging data
    (2021-05) Bannier, Elise; Barker, Gareth; Borghesani, Valentina; Broeckx, Nils; Clement, Patricia; Emblem, Kyrre E.; Ghosh, Satrajit; Glerean, Enrico; Gorgolewski, Krzysztof J.; Havu, Marko; Halchenko, Yaroslav O.; Herholz, Peer; Hespel, Anne; Heunis, Stephan; Hu, Yue; Hu, Chuan Peng; Huijser, Dorien; de la Iglesia Vayá, María; Jancalek, Radim; Katsaros, Vasileios K.; Kieseler, Marie Luise; Maumet, Camille; Moreau, Clara A.; Mutsaerts, Henk Jan; Oostenveld, Robert; Ozturk-Isik, Esin; Pascual Leone Espinosa, Nicolas; Pellman, John; Pernet, Cyril R.; Pizzini, Francesca Benedetta; Trbalić, Amira Šerifović; Toussaint, Paule Joanne; Visconti di Oleggio Castello, Matteo; Wang, Fengjuan; Wang, Cheng; Zhu, Hua
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Having the means to share research data openly is essential to modern science. For human research, a key aspect in this endeavor is obtaining consent from participants, not just to take part in a study, which is a basic ethical principle, but also to share their data with the scientific community. To ensure that the participants' privacy is respected, national and/or supranational regulations and laws are in place. It is, however, not always clear to researchers what the implications of those are, nor how to comply with them. The Open Brain Consent (https://open-brain-consent.readthedocs.io) is an international initiative that aims to provide researchers in the brain imaging community with information about data sharing options and tools. We present here a short history of this project and its latest developments, and share pointers to consent forms, including a template consent form that is compliant with the EU general data protection regulation. We also share pointers to an associated data user agreement that is not only useful in the EU context, but also for any researchers dealing with personal (clinical) data elsewhere.
  • Similarities and differences between on-scalp and conventional in-helmet magnetoencephalography recordings
    (2017-07-01) Andersen, Lau M.; Oostenveld, Robert; Pfeiffer, Christoph; Ruffieux, Silvia; Jousmäki, Veikko; Hämäläinen, Matti; Schneiderman, Justin F.; Lundqvist, Daniel
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    The development of new magnetic sensor technologies that promise sensitivities approaching that of conventional MEG technology while operating at far lower operating temperatures has catalysed the growing field of on-scalp MEG. The feasibility of on-scalp MEG has been demonstrated via benchmarking of new sensor technologies performing neuromagnetic recordings in close proximity to the head surface against state-of-the-art in-helmet MEG sensor technology. However, earlier work has provided little information about how these two approaches compare, or about the reliability of observed differences. Herein, we present such a comparison, based on recordings of the N20m component of the somatosensory evoked field as elicited by electric median nerve stimulation. As expected from the proximity differences between the on-scalp and in-helmet sensors, the magnitude of the N20m activation as recorded with the on-scalp sensor was higher than that of the in-helmet sensors. The dipole pattern of the on-scalp recordings wasalso more spatially confined than that of the conventional recordings. Our results furthermore revealed unexpected temporal differences in the peak of the N20m component. An analysis protocol was therefore developed for assessing the reliability of this observed difference. We used this protocol to examine our findings in terms of differences in sensor sensitivity between the two types of MEG recordings. The measurements and subsequent analysis raised attention to the fact that great care has to be taken in measuring the field close to the zero-line crossing of the dipolar field, since it is heavily dependent on the orientation of sensors. Taken together, our findings provide reliable evidence that on-scalp and in-helmet sensors measure neural sources in mostly similar ways.