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94 results for “MEG”
Data from Real-time, model-based magnetic field correction for moving, wearable MEG
<p>OP-MEG data used to generate figures from manuscript titled "Real-time, model-based magnetic field correction for moving, wearable MEG". Each zipped folder relates to a different experiment: EnvironmentalNoise relates to the environmental noise experiments, AEF is the auditory evoked response experiment and ExternalCoils relates to the recordings using a set of external coils to produce interference presented in the supplementary material of the paper.</p>
Processed MEG data used for revealing dynamic brain reconfiguration using MAPPER
<p>This is MEG data used for topological data analysis for revealing dynamic brain reconfiguration.</p>
MEG Study of Acute STX209 Effects in ASD
ClinicalTrials.gov study NCT02278328. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Mobility Exercises for Gait (MEG Neuroplasticity Project)
ClinicalTrials.gov study NCT03555708. IPD Sharing: YES. Countries: 1. Publications: 0.
Activating Effects of Sleep Deprivation on Synchronized MEG-EEG Recordings of Epilepsy Patients With Non-Diagnostic EEG
ClinicalTrials.gov study NCT00071370. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Non-Invasive Seizure Localization in Patients With Medically Refractory Localization Related Epilepsy: Synchronized MEG-EEG Recordings
ClinicalTrials.gov study NCT00071305. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Characterization of an Optically Pumped Magnetometer (OPM) Magnetoencephalography (MEG) Array
ClinicalTrials.gov study NCT04950309. IPD Sharing: YES. Countries: 1. Publications: 0.
meg-3 meg-4 mutants are defective in exogenous RNAi signal amplification
GEO Series GSE134627. Caenorhabditis elegans. 12 samples. Type: Non-coding RNA profiling by high throughput sequencing.
meg-3 meg-4 upregulate endogenous sRNAs targeting RNAi genes and downregulate their mRNAs
GEO Series GSE134628. Caenorhabditis elegans. 18 samples. Type: Non-coding RNA profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Gene expression data in Meg-01 cells following transfection with platelet-enriched microRNA-1225-3p
GEO Series GSE41573. Homo sapiens. 10 samples. Type: Expression profiling by array.
Runx1 (Aml1) knockdown in AMkL Meg-01 cells
GEO Series GSE17311. Homo sapiens. 4 samples. Type: Expression profiling by array.
The hrde-1 mutation suppresses the rde-11 and sid-1 sRNA upregulation phenotype of meg-3 meg-4 mutants
GEO Series GSE134629. Caenorhabditis elegans. 6 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Meg-01 cell line expression data after propionate stimulation
GEO Series GSE202712. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
MEG Attention Dataset Using Musicians and Non-Musicians - Part 1
<h2><strong>Data location</strong></h2> <p>The data is split into 3 Zenodo locations as it is too large for one location. In total the data set contains meg data of 58 participants. An overview of the participants and the amount of musical training the have conducted is also available. Each of the 3 Zenodo uploads contains the participant overview file + Set#.zip.</p> <p><strong>Part/Set 1 (blue) contains: meg data of participants 1 - 19 + audio folder</strong></p> <p>Part/Set 2 (pink) contains: meg data of participants 20 - 38 (can be found <a href="../records/12794769" target="_blank" rel="noopener">here</a>)</p> <p>Part/Set 3 (yellow) contains: meg data of participants 39 - 58 (can be found <a href="../records/12794808" target="_blank" rel="noopener">here</a>)</p> <p> </p> <h2><strong>Experimental design</strong></h2> <p>We used four German audiobooks (all published by <a href="https://www.hoerbuch-hamburg.de/" target="_blank" rel="noopener">Hörbuch Hamburg Verlag</a> and available online): </p> <p>1. „Frau Ella“ (narrated by lower pitched (LP) speaker and attended by participants)</p> <p>2. „Darum“ (narrated by LP speaker and ignored by participants)</p> <p>3. „Den Hund überleben“ (narrated by higher pitched (HP) speaker and attended by participants)</p> <p>4. „Looking for Hope“ (narrated by HP speaker and ignored by participants)</p> <p>The participants listened to 10 audiobook chapters. There were always 2 audiobooks presented at the same time (one narrated by a HP speaker and one by a LP speaker) and the participants attended one and ignored the other speaker. The structure of the chapters was as follows:</p> <p>Chapter 1 of audiobook 1 + random part of audiobook 4</p> <p>3 comprehension questions</p> <p>Chapter 1 of audiobook 3 + random part of audiobook 2</p> <p>3 comprehension questions</p> <p>Chapter 2 of audiobook 1 + random part of audiobook 4</p> <p>3 comprehension questions</p> <p>Chapter 2 of audiobook 3 + random part of audiobook 2</p> <p>3 comprehension questions</p> <p>Chapter 3 of audiobook 1 + random part of audiobook 4</p> <p>3 comprehension questions</p> <p>Chapter 3 of audiobook 3 + random part of audiobook 2</p> <p>3 comprehension questions</p> <p>Chapter 4 of audiobook 1 + random part of audiobook 4</p> <p>3 comprehension questions</p> <p>Chapter 4 of audiobook 3 + random part of audiobook 2</p> <p>3 comprehension questions</p> <p>Chapter 5 of audiobook 1 + random part of audiobook 4</p> <p>3 comprehension questions</p> <p>Chapter 5 of audiobook 3 + random part of audiobook 2</p> <p>3 comprehension questions</p> <p> </p> <h2><strong>MEG Data structure</strong></h2> <p>MEG data of 58 participants is contained in this data set. </p> <p>Each participant has a folder with its participant number as folder name (1,2,3,…). </p> <p>In the participant folder are two subfolders. One (LP_speaker_attended) containing the MEG data when the participant was attending the LP speaker (ignoring the HP speaker) and one (HP_speaker_attended) containing the MEG data measured when the participant was attending the HP speaker (ignoring the LP speaker). Note that after each chapter the participants switched the attention from LP to HP and vice versa but for evaluation we concatenated the data of the LP speaker attended/ HP speaker ignored mode and the HP speaker attended/ LP speaker ignored mode.</p> <p>The data of attending the HP speaker is of shape (248, 959416) (ca 16 minutes). That of the LP speaker is of shape (248, 1247854) (ca 21 minutes)</p> <p><code>#The meg data can be loaded with the <a href="https://mne.tools/stable/index.html" target="_blank" rel="noopener">mne python library</a></code></p> <p><code>meg = mne.read_raw_fif(“…/data_meg.fif“)</code></p> <p><code>#The data can be accessed:</code></p> <p><code>meg_data = meg.get_data()</code></p> <p>Exemplary code for performing source reconstruction and trf evaluation can be found in our <a href="https://github.com/Al2606/MEG-Analysis-Pipeline" target="_blank" rel="noopener">git repository</a>.</p> <p> </p> <h2><strong>Audio Data structure</strong></h2> <p>The original audio chapters of the audio books are stored in the folder „Audio“ in <a href="../records/12793944">Part 1</a>.</p> <p>There are two subfolders. One (attended_speech) contains the ten audiobook chapters which were attended by the participant (audiobook1_#, audiobook3_#). The other subfolder (ignored_speech) contains the ten audiobook chapters which were ignored by the participant (audiobook2_#, audiobook4_#).</p> <p>We recommend the <a href="https://librosa.org/doc/latest/index.html" target="_blank" rel="noopener">librosa library</a> for audio loading and processing.</p> <p>Audio data is provided with a sampling frequency of 44.1 kHz</p> <p>Each audio book is provided in 5 chapters as they were presented to the participants. The corresponding meg file as described above already contains the concatenated measured data of all five chapters. </p> <p>If you resample the audio data to 1000Hz and concatenate the chapters, the audio shape (n_times) will be equal to the corresponding n_times of the meg data. </p> <p> </p> <h2><strong>Processing of meg data</strong></h2> <p>The meg data was filtered analog with a 1.0 - 200 Hz filter and preprocessed offline using a notch filter (Firwin, 0.5 Hz bandwidth) to remove power line interference at frequencies 50, 100, 150 and 200 Hz.</p> <p>The data was then resampled from 1017.25 Hz to 1000 Hz. </p> <p> </p> <h2><strong>Technical details</strong></h2> <p>The meg system with which the data was recorded was a 248 magnetometer system (4D Neuroimaging, San Diego, CA, USA)</p> <p>The audio signal was presented through loud speakers outside the magnetic chamber and passed on to the participant via tubes of 2 m length and 2 cm diameter leading to a delay of the acoustic signal of 6 ms. The audio was presented diotically (both the attended and the ignored audio stream were presented in both ears) with a sound pressure level of 67 dB(A).</p> <p>The measurement setup was provided by a former study by Schilling et al (<a href="https://doi.org/10.1080/23273798.2020.1803375">https://doi.org/10.1080/23273798.2020.1803375</a>).</p> <p> </p> <h2><strong>Papers to cite when using this data</strong></h2> <ul> <li>Riegel et al., "No Influence of Musical Training on the Cortical Contribution to the Speech-FFR and its Modulation Through Selective Attention" eneuro in print (<a href="https://doi.org/10.1101/2024.07.25.605057" target="_blank" rel="noopener">https://doi.org/10.1101/2024.07.25.605057</a>).</li> <li>Schüller, Mücke et al. "Assessing the Impact of Selective Attention on the Cortical Tracking of the Speech Envelope in the Delta and Theta Frequency Bands and How Musical Training Does (Not) Affect it", under review (<a href="https://doi.org/10.1101/2024.08.01.606154" target="_blank" rel="noopener">https://doi.org/10.1101/2024.08.01.606154</a>).</li> <li>Schüller et al., "Attentional Modulation of the Cortical Contribution to the Frequency-Following Response Evoked by Continuous Speech“ (<a href="https://doi.org/10.1523/JNEUROSCI.1247-23.2023">https://doi.org/10.1523/JNEUROSCI.1247-23.2023</a>).</li> </ul>
MEG Attention Dataset Using Musicians and Non-Musicians - Part 3
<h2><strong>Data location</strong></h2> <p>The data is split into 3 Zenodo locations as it is too large for one location. In total the data set contains meg data of 58 participants. An overview of the participants and the amount of musical training the have conducted is also available. Each of the 3 Zenodo uploads contains the participant overview file + Set#.zip.</p> <p>Part/Set 1 (blue) contains: meg data of participants 1 - 19 + audio folder (can be found <a href="../records/12793944" target="_blank" rel="noopener">here</a>)</p> <p>Part/Set 2 (pink) contains: meg data of participants 20 - 38 (can be found <a href="../records/12794769" target="_blank" rel="noopener">here</a>)</p> <p><strong>Part/Set 3 (yellow) contains: meg data of participants 39 - 58</strong></p> <p> </p> <h2><strong>Experimental design</strong></h2> <p>We used four German audiobooks (all published by <a href="https://www.hoerbuch-hamburg.de/" target="_blank" rel="noopener">Hörbuch Hamburg Verlag</a> and available online. </p> <p>1. „Frau Ella“ (narrated by lower pitched (LP) speaker and attended by participants)</p> <p>2. „Darum“ (narrated by LP speaker and ignored by participants)</p> <p>3. „Den Hund überleben“ (narrated by higher pitched (HP) speaker and attended by participants)</p> <p>4. „Looking for Hope“ (narrated by HP speaker and ignored by participants)</p> <p>The participants listened to 10 audiobook chapters. There were always 2 audiobooks presented at the same time (one narrated by a HP speaker and one by a LP speaker) and the participants attended one and ignored the other speaker. The structure of the chapters was as follows:</p> <p>Chapter 1 of audiobook 1 + random part of audiobook 4</p> <p>3 comprehension questions</p> <p>Chapter 1 of audiobook 3 + random part of audiobook 2</p> <p>3 comprehension questions</p> <p>Chapter 2 of audiobook 1 + random part of audiobook 4</p> <p>3 comprehension questions</p> <p>Chapter 2 of audiobook 3 + random part of audiobook 2</p> <p>3 comprehension questions</p> <p>Chapter 3 of audiobook 1 + random part of audiobook 4</p> <p>3 comprehension questions</p> <p>Chapter 3 of audiobook 3 + random part of audiobook 2</p> <p>3 comprehension questions</p> <p>Chapter 4 of audiobook 1 + random part of audiobook 4</p> <p>3 comprehension questions</p> <p>Chapter 4 of audiobook 3 + random part of audiobook 2</p> <p>3 comprehension questions</p> <p>Chapter 5 of audiobook 1 + random part of audiobook 4</p> <p>3 comprehension questions</p> <p>Chapter 5 of audiobook 3 + random part of audiobook 2</p> <p>3 comprehension questions</p> <p> </p> <h2><strong>MEG Data structure</strong></h2> <p>MEG data of 58 participants is contained in this data set. </p> <p>Each participant has a folder with its participant number as folder name (1,2,3,…). </p> <p>In the participant folder are two subfolders. One (LP_speaker_attended) containing the MEG data when the participant was attending the LP speaker (ignoring the HP speaker) and one (HP_speaker_attended) containing the MEG data measured when the participant was attending the HP speaker (ignoring the LP speaker). Note that after each chapter the participants switched the attention from LP to HP and vice versa but for evaluation we concatenated the data of the LP speaker attended/ HP speaker ignored mode and the HP speaker attended/ LP speaker ignored mode.</p> <p>The data of attending the HP speaker is of shape (248, 959416) (ca 16 minutes). That of the LP speaker is of shape (248, 1247854) (ca 21 minutes)</p> <p><code>#The meg data can be loaded with the <a href="https://mne.tools/stable/index.html" target="_blank" rel="noopener">mne python library</a></code></p> <p><code>meg = mne.read_raw_fif(“…/data_meg.fif“)</code></p> <p><code>#The data can be accessed:</code></p> <p><code>meg_data = meg.get_data()</code></p> <p>Exemplary code for performing source reconstruction and trf evaluation can be found in our <a href="https://github.com/Al2606/MEG-Analysis-Pipeline" target="_blank" rel="noopener">git repository</a>.</p> <p> </p> <h2><strong>Audio Data structure</strong></h2> <p>The original audio chapters of the audio books are stored in the folder „Audio“ in <a href="../records/12793944">Part 1</a>.</p> <p>There are two subfolders. One (attended_speech) contains the ten audiobook chapters which were attended by the participant (audiobook1_#, audiobook3_#). The other subfolder (ignored_speech) contains the ten audiobook chapters which were ignored by the participant (audiobook2_#, audiobook4_#).</p> <p>We recommend the <a href="https://librosa.org/doc/latest/index.html" target="_blank" rel="noopener">librosa library</a> for audio loading and processing.</p> <p>Audio data is provided with a sampling frequency of 44.1 kHz</p> <p>Each audio book is provided in 5 chapters as they were presented to the participants. The corresponding meg file as described above already contains the concatenated measured data of all five chapters. </p> <p>If you resample the audio data to 1000Hz and concatenate the chapters, the audio shape (n_times) will be equal to the corresponding n_times of the meg data. </p> <p> </p> <h2><strong>Processing of meg data</strong></h2> <p>The meg data was filtered analog with a 1.0 - 200 Hz filter and preprocessed offline using a notch filter (Firwin, 0.5 Hz bandwidth) to remove power line interference at frequencies 50, 100, 150 and 200 Hz.</p> <p>The data was then resampled from 1017.25 Hz to 1000 Hz. </p> <p> </p> <h2><strong>Technical details</strong></h2> <p>The meg system with which the data was recorded was a 248 magnetometer system (4D Neuroimaging, San Diego, CA, USA)</p> <p>The audio signal was presented through loud speakers outside the magnetic chamber and passed on to the participant via tubes of 2 m length and 2 cm diameter leading to a delay of the acoustic signal of 6 ms. The audio was presented diotically (both the attended and the ignored audio stream were presented in both ears) with a sound pressure level of 67 dB(A).</p> <p>The measurement setup was provided by a former study by Schilling et al (<a href="https://doi.org/10.1080/23273798.2020.1803375">https://doi.org/10.1080/23273798.2020.1803375</a>).</p> <p> </p> <h2><strong>Papers to cite when using this data</strong></h2> <ul> <li>Riegel et al., "No Influence of Musical Training on the Cortical Contribution to the Speech-FFR and its Modulation Through Selective Attention" eneuro in print (<a href="https://doi.org/10.1101/2024.07.25.605057" target="_blank" rel="noopener">https://doi.org/10.1101/2024.07.25.605057</a>).</li> <li>Schüller, Mücke et al. "Assessing the Impact of Selective Attention on the Cortical Tracking of the Speech Envelope in the Delta and Theta Frequency Bands and How Musical Training Does (Not) Affect it", under review (<a href="https://doi.org/10.1101/2024.08.01.606154" target="_blank" rel="noopener">https://doi.org/10.1101/2024.08.01.606154</a>).</li> <li>Schüller et al., "Attentional Modulation of the Cortical Contribution to the Frequency-Following Response Evoked by Continuous Speech“ (<a href="https://doi.org/10.1523/JNEUROSCI.1247-23.2023">https://doi.org/10.1523/JNEUROSCI.1247-23.2023</a>).</li> </ul>
Word-Pair Associative Recognition MEG data and subject performance
<p>MEG and behavioural data from the publication "Tracking cognitive processing stages with MEG: A spatio-temporal model<br> of associative recognition in the brain" by Borst et al. Code for loading and analyzing this data can be found in <a href="https://github.com/Seanny123/nengo_learn_assoc_mem/blob/2f10c7fb67a242fea6022ebaf690e8022bc4289d/making_plots.ipynb">this code</a> which is a part of the thesis "Learning neural memories to improve with practice" by Sean Aubin.</p>
Simultaneous optogenetic activation and MEG source imaging show non-human primate brain circuits
<p>MEG and MRI data used to generate Figures 2-5 of manuscript titled, "Simultaneous optogenetic activation and MEG source imaging show non-human primate brain circuits". </p>
MEG Versus EEG HR for the Localization of the Epileptogenic Zone as Part of the Pre-surgical Assessment of Epilepsy
ClinicalTrials.gov study NCT03893916. IPD Sharing: NO. Countries: 1. Publications: 0.
Optically Pumped 4He Magnetometers Performances Compared With Medical Reference Methods (ECG and MEG)
ClinicalTrials.gov study NCT02389257. IPD Sharing: Not stated. Countries: 1. Publications: 0.
OPM MEG in Pre-surgical Mapping for Patients With Epilepsy
ClinicalTrials.gov study NCT07378397. IPD Sharing: NO. Countries: 1. Publications: 0.
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.