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566
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ShareScore release 0.9.0
Dataset results
566 results for “fmri”
Evaluation of Lamotrigine on Neuropathic Facial Pain Using fMRI
ClinicalTrials.gov study NCT00243152. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Neural Response to Food Stimuli: fMRI Changes Following Cognitive Behavioral Therapy for Binge Eating Disorder
ClinicalTrials.gov study NCT03604172. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Neurobehavioral Plasticity to Regular Sugar-Sweetened Beverage Intake: An fMRI Experiment
ClinicalTrials.gov study NCT03490734. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Investigation of Cocaine Addiction Using mGluR5 PET and fMRI
ClinicalTrials.gov study NCT03471182. IPD Sharing: YES. Countries: 1. Publications: 1.
Functional Effects of Botox on the Brain Using MRS and fMRI
ClinicalTrials.gov study NCT03373162. IPD Sharing: NO. Countries: 1. Publications: 1.
Exposure, D-cycloserine Enhancement, and Functional Magnetic Resonance Imaging (fMRI) in Snake Phobics
ClinicalTrials.gov study NCT01450306. IPD Sharing: YES. Countries: 1. Publications: 1.
Paroxetine/Bupropion in Depression With Suicide Attempt or Thoughts: fMRI Study
ClinicalTrials.gov study NCT01748955. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Liraglutide in Obesity and Diabetes: Identification of CNS Targets Using fMRI
ClinicalTrials.gov study NCT01562678. IPD Sharing: Not stated. Countries: 1. Publications: 2.
An Acupuncture Functional Magnetic Resonance Imaging (fMRI) Study on Chronic Pain: Response Reliability and Dose Effect
ClinicalTrials.gov study NCT01079390. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Deciphering the Role of Oxytocin in Motivation: an fMRI Study
ClinicalTrials.gov study NCT01722071. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Differentiating Unipolar and Bipolar Depression in Young Adults Using fMRI
ClinicalTrials.gov study NCT01811147. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Data from: Ultra-slow oscillations in fMRI and resting-state connectivity: Neuronal and vascular contributions and technical confounds
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Re-imagining fMRI for awake behaving infants
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Data for: Will you read how I will read? Naturalistic fMRI predictors of emergent reading
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A neural implementation of cognitive reserve: insights from a longitudinal fMRI study of set-switching in aging
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Data from: Neural correlates of multisensory enhancement in audiovisual narrative speech perception: a fMRI investigation
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Migraine monoclonal antibodies against CGRP change brain activity depending on ligand or receptor target – an fMRI study
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fMRI during lower body negative pressure (LBNP) with concurrent physiological measurements
<p>This dataset was acquired at Hannover Medical School, Hanover, Germany. The study complied with the Declaration of Helsinki, and was approved by the local ethics committee (# 3404-2016). All subjects gave written informed consent, and consent to publish their data anonimously.</p> <p>We simulated orthostatic stress by means of lower body negative pressure (LBNP). Negative pressure of 30 mmHg was built up inside a custom-made polycarbonate chamber using a vacuum cleaner and a pressure gauge. The stimulation paradigm consisted of four alternating five-minute blocks, two with and two without negative pressure, starting with the negative pressure. Functional MR images and physiological measures were continuously acquired during this cardiovascular challenge. Transitions between pressure states were excluded to avoid excessive movement.</p> <p> </p> <p><strong>Data acquisition</strong><br> <em>MRI data</em><br> All MR images were acquired on a Siemens 3T MAGNETOM Skyra using a 64-channel head/neck coil. The scanning protocol consisted of the following sequences (see Sequences.ods):</p> <ul> <li>func_i: Functional whole brain gradient-echo echo-planar images (EPI) (TR=1230 ms; TE=32 ms; 2mm isotropic resolution; simultaneous multi-slice factor=6; partial Fourier=7/8; 4x233 volumes)</li> <li>func_ref: Reference scan for motion correction and template formation; equivalent to func but without multi-band acceleration (TR=7530 ms)</li> <li>SE_AP_PA: Reference scans for unwarping: Two spin-echo images matched to func in distortion without multi-band acceleration; one with the same, the other one with inverted phase encoding direction.</li> <li>t1: T1-weighted magnetisation-prepared rapid acquisition gradient-echo image (MPRAGE) (TR=2300 ms; TE=2.95 ms; TI=900 ms; resolution: 1.1x1.1x1.2 mm3, in-plane acceleration factor=2)</li> </ul> <p> </p> <p><em>fMRI data preprocessing</em><br> Our preprocessing pipleine (JPreprocessing) is optimised for the brainstem and hypothalamus by avoiding superfluous resampling steps and unnecessary smoothing. On that account, motion correction (MCFLIRT (Jenkinson et al., 2002)) and unwarping (topup (Andersson et al.,2003)) are applied in a single transformation. Afterwards, brain extraction (BET (Smith, 2002)), grand mean scaling and high pass filtering (0.005 Hz) are applied. The data are not smoothed.</p> <p>Two study templates were generated using Advanced Normalization Tools (ANTs (Avants et al., 2008) using antsMultivariateTemplateConstruction2.sh). The first one using the unwarped EPI reference images (func_ref); the second one using the T1-images.</p> <p><br> <em>Physiological data</em><br> The following physiological measures were acquired with an MR-compatible BIOPAC MP150 system</p> <ul> <li>Blood pressure (systolic and dyastolic): Non-invasive continuous blood pressure of the digital artery (pulse decomposition analysis using CareTaker)</li> <li>Electrodermal activity</li> <li>Photoplethysmography</li> <li>Respiration (belt)</li> <li>Electrocardiography</li> </ul> <p><br> <em>Subject information</em><br> 01 m 21 a 182 cm 73 kg 129/62 mmHg<br> 02 f 18 a 167 cm 55 kg 104/58 mmHg<br> 03 m 27 a 200 cm 92 kg 127/62 mmHg<br> 04 f 22 a 160 cm 59 kg 120/73 mmHg<br> 05 f 21 a 170 cm 62 kg 125/67 mmHg<br> 06 f 22 a 171 cm 56 kg 130/70 mmHg<br> 07 f 22 a 174 cm 55 kg 119/61 mmHg<br> 08 f 37 a 171 cm 82 kg 130/71 mmHg<br> 09 m 25 a 170 cm 72 kg 129/62 mmHg<br> 10 f 21 a 173 cm 58 kg 116/71 mmHg<br> 11 m 30 a 185 cm 113 kg 125/65 mmHg<br> 12 f 27 a 157 cm 48 kg 127/76 mmHg<br> 13 m 21 a 184 cm 70 kg 127/80 mmHg<br> 14 f 26 a 173 cm 63 kg 120/68 mmHg<br> 15 m 24 a 200 cm 92 kg 129/72 mmHg<br> 16 m 38 a 196 cm 86 kg 133/67 mmHg<br> 17 m 24 a 186 cm 76 kg 125/65 mmHg<br> 18 f 19 a 178 cm 69 kg 102/53 mmHg<br> 19 f 18 a 168 cm 60 kg 125/66 mmHg<br> 20 f 26 a 171 cm 63 kg 122/70 mmHg<br> 21 f 20 a 175 cm 62 kg 122/76 mmHg<br> 22 f 24 a 169 cm 58 kg 114/64 mmHg</p> <p><br> <em>Missing data</em></p> <ul> <li> sub01: physio.acq and physio_cuts.txt</li> <li>sub14: func_3 has 209 time points</li> <li>sub15: func_2 has 167 time points</li> </ul> <p> </p> <p><strong>Data structure</strong><br> <em>Raw data</em></p> <ul> <li>acqparams.txt: Acquisition parameters needed for topup</li> <li>func_i: raw functional data (dummy volumes already deleted)</li> <li>func_ref: Reference image for motion correction and template generation</li> <li>physio.acq: Physiological measurements <ul> <li>Trigger</li> <li>Blood pressure (systolic and dyastolic)</li> <li>Electrodermal activity</li> <li>Photoplethysmogram</li> <li>Respiration</li> <li>Electrocardiogram</li> </ul> </li> <li>physio_cuts.txt: time points in the physio data that correspond to fMRI blocks (start-time end-time TR #volumes)</li> <li>SE_AP_PA: auxiliary image for unwarping</li> <li>slicetiming_i.txt: Slice timing information in seconds</li> <li>t1: Defaced T1-weighted image. (Undefaced images were used for template generation)</li> </ul> <p><br> <em>Templates and masks</em></p> <ul> <li>EPI-template: Generated from unwarped func_ref images <ul> <li>Transformations for all subjects (can be applied using antsApplyTransforms)</li> <li>wb_mask</li> </ul> </li> <li>EPI-2-T1: Transformation from EPI to T1-template</li> <li>MNI-template: T1-template warped into MNI_152 <ul> <li>hyp_mask</li> <li>lower_bs_mask</li> </ul> </li> <li>T1-template: Generated from t1 images <ul> <li>hyp_mask</li> <li>lower_bs_mask</li> </ul> </li> <li>T1-2-MNI: Transformation from T1 to MNI_152-template</li> </ul> <p><br> <em>JPreprocessing</em><br> Preprocessing pipeline</p>
EEG and fMRI_PLOS ONE
<p>subject and data information</p> <p>5_subjects</p> <p>sampling frequency: 1000 Hz</p> <p>256 channels</p>
The EEG and fMRI signatures of neural integration: An investigation of meaningful gestures and corresponding speech
<p>One of the key features of human interpersonal communication is our ability to integrate information communicated by speech and accompanying gestures. However, it is still not fully understood how this essential combinatory process is represented in the human brain. Functional magnetic resonance imaging (fMRI) studies have unanimously attested the relevance of activation in the posterior superior temporal sulcus/middle temporal gyrus (pSTS/MTG), while electroencephalography (EEG) studies have shown oscillatory activity in specific frequency bands to be associated with multisensory integration. In the current study, we used fMRI and EEG to separately investigate the anatomical and oscillatory neural signature of integrating intrinsically meaningful gestures (IMG; e.g. “Thumbs-up gesture”) and corresponding speech (e.g., “The actor did a good job”). In both the fMRI (<em>n</em>=20) and EEG (<em>n</em>=20) study, participants were presented with videos of an actor either: performing IMG in the context of a German sentence (GG), IMG in the context of a Russian (as a foreign language) sentence (GR), or speaking an isolated German sentence without gesture (SG). The results of the fMRI experiment confirmed that gesture–speech processing of IMG activates the posterior MTG (GG>GR∩GG>SG). In the EEG experiment we found that the identical integration process (GG>GR∩GG>SG) is related to a centrally-distributed alpha (7–13 Hz) power decrease within 700–1400 ms post-onset of the critical word. These new findings suggest that BOLD response increase in the pMTG and alpha power decrease represent the neural correlates of integrating intrinsically meaningful gestures with their corresponding speech.</p>
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.