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94 results for “MEG”
Prospective Exploratory Study on rTMS for Migraine Under the Guidance of MEG
ClinicalTrials.gov study NCT06796725. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Voluntary and Involuntary Attention MEG dataset
<p>The details of the presented stimuli are described in https://doi.org/10.1101/2020.02.18.953653. Shortly, a second version of the dataset will be uploaded with a better explanation of the data corresponding to a peer-reviewed publication utilising the data.</p>
MEG Attention Dataset Using Musicians and Non-Musicians - Part 2
<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><strong>Part/Set 2 (pink) contains: meg data of participants 20 - 38</strong></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“ of <a href="../records/12793944" target="_blank" rel="noopener">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>
Data for the study: Comparison of beamformer implementations for MEG source localization
<p>This data set is a part of the study 'Comparison of beamformers implementations for MEG source localization'. The dataset includes 64 phantom datasets, 2 human datasets, and 50 simulated datasets. The data also include segmented MRI files from FreeSurfer for MEG phantom (Megin Oy, Helsinki, Finland) and a human subject MRI. It also includes the used versions of the four beamforming packages (MNE-Python, FieldTrip, SPM12(DAiSS), and Brainstorm) and codes used for the analysis.</p>
MEG Study of Mindfulness Based Stress Reduction
ClinicalTrials.gov study NCT00571051. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Simultaneous MEG or fMRI And INtracranial EEG
ClinicalTrials.gov study NCT02342938. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Spatial and Dynamic Characterization of Brain Activity for Language and Posture (Verticality) During Normal Aging. Magnetoencephalography (MEG) Study (MEG-AGING)
ClinicalTrials.gov study NCT04036162. IPD Sharing: Not stated. Countries: 1. Publications: 3.
High-Density EEG-MEG-wearable EEG: Comparison of Quantitative Electroencephalographic Indicators as Neurophysiological Markers of Effectiveness of Mindfulness Therapy in Addition to Standard Treatment
ClinicalTrials.gov study NCT06938178. IPD Sharing: NO. Countries: 1. Publications: 7.
MEG and DTI of Neural Function and Connectivity in Traumatic Brain Injury
ClinicalTrials.gov study NCT01298557. IPD Sharing: Not stated. Countries: 1. Publications: 5.
MaST: MEG and Brain Stimulation in Tinnitus
ClinicalTrials.gov study NCT04978142. IPD Sharing: NO. Countries: 1. Publications: 6.
Recording and Modulation of Neuronal Mechanisms During Operant Conditioning: a MEG Study
ClinicalTrials.gov study NCT01006109. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Speech and Short-term Memory Functions in Dyslexia: a Combined MEG and EEG Study
ClinicalTrials.gov study NCT02622360. IPD Sharing: Not stated. Countries: 1. Publications: 27.
Structural and Functional Connectivity in Partial Epilepsies Studied with MRI and MEG
ClinicalTrials.gov study NCT01313260. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Brain Rhythms in Fibromyalgia: A Magnetoencephalography (MEG) Study
ClinicalTrials.gov study NCT02159300. IPD Sharing: Not stated. Countries: 1. Publications: 10.
Verify the Effectiveness rTMS Using MEG
ClinicalTrials.gov study NCT01874444. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Brain Plasticity and Emotion Recognition in Patients With Facial Palsy Before & After Surgical Rehabilitation: MEG Study
ClinicalTrials.gov study NCT06809127. IPD Sharing: NO. Countries: 1. Publications: 16.
Imaging Post-Stroke Recovery: Using MEG to Evaluate Cognition
ClinicalTrials.gov study NCT04188522. IPD Sharing: YES. Countries: 1. Publications: 4.
Event-Related Coherence in Visual Cortex and Brain Noise: An MEG Study
<p>Experimental data analysed in the publication:</p> <p>Chholak, P.; Kurkin, S.A.; Hramov, A.E.; Pisarchik, A.N. Event-Related Coherence in Visual Cortex and Brain Noise: An MEG Study. <em>Appl. Sci.</em> <strong>2020</strong>, 0, 5. https://doi.org/10.3390/app0010005</p> <p> </p>
Data-driven approach for the delineation of the irritative zone in epilepsy in MEG
<p>The reliable identification of the irritative zone (IZ) is a prerequisite for the correct clinical evaluation of medically refractory patients affected by epilepsy. Given the complexity of MEG data, visual analysis of epileptiform neurophysiological activity is highly time consuming and might leave clinically relevant information undetected. We recorded and analyzed the interictal activity from seven patients affected by epilepsy (Vectorview Neuromag), who successfully underwent epilepsy surgery (Engel >= II). We visually marked and localized characteristic epileptiform activity (VIS). We implemented a two-stage pipeline for the detection of interictal spikes and the delineation of the IZ. First, we detected candidate events from peaky ICA components, and then clustered events around spatio-temporal patterns identified by convolutional sparse coding. We used the average of clustered events to create IZ maps computed at the amplitude peak (PEAK), and at the 50% of the peak ascending slope (SLOPE). We validated our approach by computing the distance of the estimated IZ (VIS, SLOPE and PEAK) from the border of the surgically resected area (RA). We identified 25 spatiotemporal patterns mimicking the underlying interictal activity (3.6 clusters/patient). Each cluster was populated on average by 22.1 [15.0-31.0] spikes. The predicted IZ maps had an average distance from the resection margin of 8.4 ± 9.3 mm for visual analysis, 12.0 ± 16.5 mm for SLOPE and 22.7 ±. 16.4 mm for PEAK. The consideration of the source spread at the ascending slope provided an IZ closer to RA and resembled the analysis of an expert observer. We validated here the performance of a data-driven approach for the automated detection of interictal spikes and delineation of the IZ. This computational framework provides the basis for reproducible and bias-free analysis of MEG recordings in epilepsy.</p>
Results on the influence of head model on MEG brain fingerprinting
<p>Different metrics for brain fingerprinting from MEG recordings obtained with different connectivity measures and with different strategies to deal with the head model.</p> <p>Identifiability matrices from which these metrics were obtained.</p> <p>Code and more info at https://github.com/MatthiasSchelf/brain-fingerprinting/tree/main</p>
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Allen Brain Atlas
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International Brain Laboratory public data
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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.