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94 results for “MEG”
Human MEG recordings during sequential conflict task
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Differential brain mechanisms of selection and maintenance of information during working memory (MEG data)
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NeuroSpin hMT+ Localizer DATA (MEG & aMRI)
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Testing of Medtronic Percept PC with MEG phantom
<p>The combination of subcortical Local Field Potential (LFP) recordings and stimulation with Magnetoencephalography (MEG) in Deep Brain Stimulation (DBS) patients enables the investigation of cortico-subcortical communication patterns and provides insights into DBS mechanisms. Until now, these recordings have been carried out in post-surgical patients with externalised leads. However, a new generation of telemetric stimulators makes it possible to record and stream LFP data in chronically implanted patients. Nevertheless, whether such streaming can be combined with MEG has not been tested.</p> <p>In the present study, we tested the most commonly implanted telemetric stimulator – Medtronic Percept PC with a phantom in three different MEG systems: two cryogenic scanners (CTF and MEGIN) and an experimental Optically Pumped Magnetometry (OPM)-based system.</p> <p>The dataset and code herein make it possible to reproduce most of the figures in the paper and examine additional conditions not described in detail in the paper. The data can be useful for developing, testing and benchmarking MEG artefact removal methods.</p>
MEG dataset nonlinguistic auditory statistical learning
<p>MEG data of 24 healthy adults with an auditory nonlinguistic statistical learning paradigm plus data from two subsequent behavioral tasks. For closer description of data see data description file. </p>
Localizing On-scalp MEG Sensors using an Array of Magnetic Dipole Coils
<p>Matlab scripts and data necessary to reproduce the results from the PLOS ONE paper. For more information see README.</p>
Tracking the neurodevelopmental trajectory of beta band oscillations with OPM-MEG
<p>Optically pumped magnetometer magnetoencephalography (OPM-MEG) data were acquired during a somatosensory task using a 192-channel OPM-MEG device which is adaptable to head size and robust to participant movement.</p> <p>This dataset contains data from individuals aged between 2 and 34 years.</p> <p>Analyses and descriptions of the dataset were published in eLife (https://doi.org/10.7554/eLife.94561.1)</p> <p>Defaced, T1-weighted MR images are provided for each participant. These were generated using an individualized template anatomy approach where age-matched template MRIs were warped to an optical 3D scan of each individual's head-shape.</p> <p>Data were compressed using zip on Windows.</p> <p>Matlab (2022b) scripts used for data loading and analysis can be found on (https://github.com/LukasRier/RierRhodes_2024_Neurodevelopmental_OPMMEG)</p>
Glasser52: A parcellation for MEG-Analysis
<p><strong>Summary</strong></p> <p>A parcellation with 52 parcels that can be linked to anatomical labels of the HCP-MMP atlas and is suitable for analyses of MEG recordings.</p> <p>The parcellation was created based on the Human Connectome Project Multimodal Parcellation (HCP-MMP) atlas (Glasser et al., 2016) following the procedure suggested by Tait et al. (2021).</p> <p>The underlying idea of this procedure is to 1) calculate the influence of each of the 22 regions of the HCP-MMP atlas on MEG recordings obtained from healthy controls and PD volunteers and 2) to merge and divide regions so that 52 parcels with similar influence on the MEG sensor signal are obtained. This approach allows to start from the anatomical HCP-MMP atlas and adapts it according to functional signals obtained from MEG recordings. Thus, it enables a good compromise between solely anatomically or solely functionally derived parcellations. (For more information see the Additional Information.docx).<br><br><strong>Contents:</strong></p> <blockquote> <p><strong>Glasser52_binary_space-MNI152NLin6_res-8x8x8.nii.gz</strong><br>Parcellation for MEG-Analyses (8mm voxel resolution).</p> <p><strong>Glasser52_binary_space-MNI152NLin6_res-2x2x2.nii.gz</strong><br>Parcellation for MEG-Analyses (2mm voxel resolution).</p> <p><strong>Glasser52_binary_space-MNI152NLin6_res-1x1x1.nii.gz</strong><br>Parcellation for MEG-Analyses (1mm voxel resolution).</p> <p><strong>parcels</strong><br>Folder with images of parcels for 1mm, 2mm, and 8mm resolution.</p> <p><strong>Glasser_Merging.xlsx<br></strong>Excel sheet with detailed information on why parcels were merged or divided.</p> <p><strong>Labels_short.p</strong><br>Short Labels of the Glasser52 labels. Best loaded with <code>pickle.load(open("../Labels_short.p","rb"))</code>.</p> <p><strong>Labels.p</strong><br>Labels of the Glasser52 labels. Best loaded with <code>pickle.load(open("../Labels.p","rb"))</code>.</p> <p><strong>Additional Informatio.docx</strong><br>Description of the motivation, methods used, and creation of the Glasser52 parcellation.</p> <p><strong>parcel_names_and_mni_coordinates.docx</strong><br>Table with MNI-coordinates of Glasser52 parcel centres.</p> </blockquote>
Linking the microarchitecture of neurotransmitter systems to large-scale MEG resting state networks
<p>Information processing and communication in neuronal circuits is enabled by dynamic networks of inter-areal coupling of neuronal oscillations in which hubs play a central role for regulation of communication. Oscillations are shaped by interactions between pyramidal cells and interneurons and are locally influenced by neuromodulatory systems. Here, we set out to investigate how sparial variability in neurotransmitter receptor and transporter density influences frequency-specific large-scale networks of phase-synchrony (PS) and amplitude-correlation (AC) in human magnetoencephalography data. We found that node centrality - indexing which individual brain regions function as hubs - covaried positively with GABA, NMDA, dopaminergic, and most serotonergic receptor and transporter densities in lower frequency bands (delta to low-alpha for PS, and delta for AC) and in the gamma band, but negatively in between. These results establish how local microarchitecture influences large-scale connectivity networks of neuronal oscillations in the human brain in frequency- and spatially-specific patterns.</p>
High frequency somatosensory MEG: evoked responses, FreeSurfer reconstruction
<p>This dataset contains somatosensory evoked responses recorded with Elekta TRIUX magnetoencephalography (MEG) system. The purpose of the measurements was to examine high-frequency (HF) somatosensory responses. To this end, a large number of responses (couple of thousand) were recorded with a short interstimulus interval (randomized between 300-350 ms). A constant-current electric stimulator was used, with the electrodes placed around the median nerve at the right wrist. The magnitude of the current was individually determined so that the stimulation was slightly below motor threshold; it was approximately 7 mA. The length of the current pulse was set at 200 microseconds.</p> <p>Measurements from two subjects are included. The dataset also contains anatomical MRIs and FreeSurfer reconstructions. Only evoked (averaged) MEG data is included; raw MEG data is in a separate dataset at https://doi.org/10.5281/zenodo.889295</p> <p> </p>
Tutorial data for Imaging in neuroscience: with a focus on MEG and EEG methods
<p>MEG/EEG/MRI tutorial data for the PhD course Imaging in neuroscience: with a focus on MEG and EEG methods at Karolinska Institutet, Stockholm, Sweden. For more information, see: https://github.com/natmegsweden/meeg_course</p> <p>Data from a single subject. The participant received 160 tactile stimulation to all five fingers of the right hand at a rate of 0.3 Hz while watching a silent movie. Continuous HPI was measured for the duration of the recording.</p> <p>MEG was recorded with a Neuromag Triux MEG scanner with 102 magnetometers and 204 planar gradiometers at a sample rate of 1000Hz. Simultaneously recorded electroencephalography (EEG) with 128 channels. Electrocardiogram (ECG) or electrooculogram (EOG) was measured together with MEG to control for artefacts from heartbeats and eye-blinks. Structural MRI.</p>
MEG data during the presentation of Gabor patterns and word sets
<p><strong>Subjects</strong></p> <p>MEG was recorded in 7 healthy subjects (5 young right-handed and 1 left-handed and 1 elderly) in the waking state with their eyes open or closed, who were sitting in a comfortable chair. The experimental technique was approved by the ethical commission of the Institute of Higher Nervous Activity and Neurophysiology of RAS (protocol No. 5 dated 02.12.2020).</p> <p><strong>Equipment</strong></p> <p>MEG was recorded on a VectorView device (Elekta Neuromag Oy, Finland), which was placed inside a magnetically protected chamber made of multilayer permalloy (AK3b, Vacuumschmelze GmbH, Germany). Before MEG recording, the coordinates of anatomical reference points (left and right preauricular points and nasion) were determined, as well as indicator coils attached to the surface of the scalp of the subject in the upper part of the forehead and behind the auricles. These points were determined using a FASTRAK 3D digitizer (Polhemus, USA). Each subject had a virtual model of the brain and head obtained from an anatomical 3D MRI taken the day before (file: MRI_V1_7.zip).</p> <p><strong>Registration and pre-processing</strong></p> <p>The subject's head was covered by a helmet, which is part of a fiberglass Dewar vessel with an array of sensors immersed in liquid helium. The subject sat down in such a way that the surface of the head was as close as possible to the sensors. The magnetic signal was recorded from 102 triplets, each of which consisted of 1 magnetometer and 2 gradiometers at rest with eyes closed and upon presentation of visual and speech stimuli. Recording was performed with a sampling frequency of 1000 Hz in a bandwidth of 0.1–330 Hz and was processed by the MaxFilter program (Elekta Neuromag Oy, Finland), which eliminates artifacts (the tSSS method—spatio-temporal separation of signals). The signal levels were corrected in accordance with the data on the position of the subject's head in relation to the MEG sensors. The position of the head during the experiment was controlled using special inductors.</p> <p><strong>Visual and verbal stimuli</strong></p> <p>After recording the background MEG for 3 minutes with closed eyes, the subject opened his eyes on command and observed the fixation point on the projection screen. After 15 seconds, stimulation was started and the subject's responses were received in the form of pressing a button. In response to the 0 degrees and 90 degrees stimuli, the subject had to press the button with the index finger, and to the 45 degrees and 135 degrees inclined stimuli, the adjacent button with the middle finger. Stimuli lasting 100 ms were presented randomly every 3100±100 ms (intervals between stimuli varied randomly). In two series, 42 stimuli of each orientation were presented. Between the series, the subject rested for 2-3 minutes. Visual stimuli in the form of Gabor contrast gratings (1.9 cycles per angular degree) with dimensions of 5.25 angular degrees and an average brightness of 4 lux were projected onto a screen located at a distance of 95 cm from the subject's eyes using a Panasonic PT-stimulating projector D7700E-K, which is part of the MEG facility. Visual stimulus patterns were generated at http://www.cogsci.nl/pages/gabor-generator with edge parameters: Circular (sharp edge). The samples are contained in the GaborStim.zip file (the names of the sample files correspond to their name in the script file, but do not match their geometric meaning, see table below). The stimulator was programmed using the Presentation software (USA, Neurobehavioral Systems, Inc). Stimulation scripts are contained in the sce.zip file.</p> <p><strong>Table</strong></p> <p><em>Stimulus or response code Type of stimulus or response</em></p> <p>STI101_1 Fixation point<br> STI101_2 90 degrees<br> STI101_4 135 degrees</p> <p>STI101_8 0 degrees<br> STI101_16 45 degrees<br> STI101_32 First button (index finger)</p> <p>STI101_64 Second button (middle finger)</p> <p>After 2 series of visual stimuli, the subject closed his eyes and was presented with 3 series of speech stimuli for 2 minutes with a break of 1 minute. In each series, recordings of audio files of 8 separate adjectives of the Russian language were presented, which were repeated 5 times in a pseudo-random order. The series began with 3 words, which were not taken into account in further analysis. The subject listened to the words and had to press the button after he understood the meaning of the presented word. After pressing or no response, the next word followed in 2±1 s. The audio files are contained in the words101_343.zip file (the names correspond to the script file).</p> <p><strong>Data received</strong></p> <p>The records are contained in files with the name of the type V1m24r, where V1 is the number of the subject, m is the sex (m/f), 24 is the age, and r is the right-handed subject. This dataset can be easily loaded into the Brainstorm program. Spontaneous and evoked MEG can be used for source localization and reconstruction of traveling waves.</p> <p><strong>Acknowledgments</strong></p> <p>The reported study was funded by RFBR, project number 20-015-00475.</p>
Test-Retest Reliability of the Human Connectome: An OPM-MEG study
<p>OPM-MEG data was acquired during naturalistic viewing of a 600s clip from the film "Dog Day Afternoon".</p> <p>Two sets of MEG data were acquired in each of the 10 scanned subjects.</p> <p>Defaced, T1 weighted MRIs are available for each subject and OPM sensor locations and orientations are given relative to subject anatomy to allow for source reconstruction.</p> <p>An example of MATLAB code used to analyse these data can be found on <a href="https://github.com/LukasRier/Rier2022_OPM_connectome_test-retest">GitHub</a>, which includes all code used to produce the results described in "Test-Retest Reliability of the Human Connectome: An OPM-MEG study" (<a href="https://biorxiv.org/cgi/content/short/2022.12.21.521184v1">biorxiv.org/cgi/content/short/2022.12.21.521184v1</a>)<br> ______________________________________________________________<br> Updates:<br> v1.0.1<br> Added missing meshes and AAL source location files</p> <p>v1.1.0<br> Added video file used in the experiment</p>
Supporting data (MEG and fMRI) for brain model
<p>Supporting data (MEG and fMRI) for the brain model published here: https://doi.org/10.5281/zenodo.7988965.</p>
Linking the microarchitecture of neurotransmitter systems to large-scale MEG resting state networks
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MEG dataset for the article "MEG signature of social conformity: Evidence from evoked and induced responses"
<p>The data set contains averaged MEG measurements of the event-related fields and 3d reconstructions of individual cortical surfaces. Data analysis was performed with Brainstorm (Tadel et al. 2011), which is documented and freely available for download online under the GNU general public license (http://neuroimage.usc.edu/brainstorm). The data is organized in brainstorm database format and can be imported directly using Brainstorm software. The participant's identities are encoded, individual facial features are blurred and any other personal information is also removed from the data. </p>
High frequency somatosensory evoked magnetic fields recorded with MEG (OLD)
<p>** Old version - do not use **</p> <p>This dataset contains somatosensory evoked responses recorded with Elekta TRIUX magnetoencephalography (MEG) system. The purpose of the measurements was specifically to examine high-frequency (HF) somatosensory responses, which have very small amplitude. To this end, a large number of responses (thousands) were recorded with a short interstimulus interval (randomized between 300-350 ms). A constant-current electric stimulator was used, with the electrodes placed around the median nerve at the right wrist. The magnitude of the current was individually determined so that the stimulation was slightly below motor threshold; it was approximately 7 mA. The length of the current pulse was set at 200 microseconds.</p> <p>Both datasets contain raw FIFF files and MRIs in DICOM format. There are two recordings for subject 'A' and one for subject 'B'. The length of each recording is approximately 15 minutes. The data of subject 'A' has more prominent high-frequency responses and smaller stimulator artifact. Both subjects also have a 2-minute empty room recording, acquired just before the actual measurement.</p> <p>MEG recording parameters: sampling frequency 3000 Hz, analog lowpass 1000 Hz. Single-shot HPI was done at the beginning of the measurement, but continuous HPI was not used. No active shielding systems were in use. The data were recorded at the BioMag laboratory of Helsinki University Central Hospital. The two subjects were healthy male volunteers, age 38-40 years.</p> <p> </p> <p> </p>
Dead By Daylight - Meg Thomas (DBD)
Meg Thomas is a girl from a Survival Multiplayer Horror Game called Dead By Daylight. Shes one of the Survivors. Source: Objaverse 1.0 / Sketchfab
High frequency somatosensory MEG: raw data
<p>This dataset contains somatosensory evoked responses recorded with Elekta TRIUX magnetoencephalography (MEG) system. The purpose of the measurements was to examine high-frequency (HF) somatosensory responses. To this end, a large number of responses (couple of thousand) were recorded with a short interstimulus interval (randomized between 300-350 ms). A constant-current electric stimulator was used, with the electrodes placed around the median nerve at the right wrist. The magnitude of the current was individually determined so that the stimulation was slightly below motor threshold; it was approximately 7 mA. The length of the current pulse was set at 200 microseconds.</p> <p>Measurements from two subjects are included. This dataset contains raw MEG data only. For evoked data and structural (MRI) data, see https://doi.org/10.5281/zenodo.889234</p>
Dataset: Combining video telemetry and wearable MEG for naturalistic imaging
<p>OPM-MEG and Openpose keypoint data from the study "Combining video telemetry and wearable MEG for naturalistic imaging".</p> <p><strong>Changelog</strong></p> <p><strong>v1.10</strong></p> <ul> <li>Subject 004 from v1.01 has been renamed 005 (to reflect addition of new subject recorded prior to 005 during acquisition).</li> <li><strong>NEW </strong>sub-004</li> <li>Subjects 003-004 have a proof-of-principle motor paradigm added.</li> <li>README changes</li> </ul> <p><strong>v1.01</strong></p> <ul> <li>Corrected sub-004 *_channel.json files to include bad channel identifiers</li> <li>Telemetry data zipped prior to uploading to zenondo</li> <li>Updates to README</li> </ul>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.