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13,963 results for “brain”
Emotion regulation in the Ageing Brain, University of Reading, BBSRC
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A dataset recorded during development of an affective brain-computer music interface: calibration session
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A dataset recorded during development of an affective brain-computer music interface: testing session
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A dataset recorded during development of an affective brain-computer music interface: training sessions
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Structural brain network of gifted children
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Brain mechanisms underlying episodic future thinking of sustainable behaviors
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Human es-fMRI Resource: Concurrent deep-brain stimulation and whole-brain functional MRI
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Taste Quality Representation in the Human Brain
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Protecting the Aging Brain, Case-Study
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Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas
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An isotropic EPI database for rat brain resting-state fMRI
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Visual and auditory brain areas share a representational structure that supports emotion perception: fMRI data
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Finite element method (FEM) models for translational research in non-invasive brain stimulation
<p>Finite element method (FEM) models for non-invasive brain stimulation modeling using SimNIBS or other compatible software.<br> The mouse and monkey models are described in detail in Alekseichuk et al., Comparative modeling of transcranial magnetic and electric stimulation in mouse, monkey, and human, NeuroImage 2019.<br> The Petri dish model follows a typical experimental setup for in-vitro TMS, similar to what is described in Lenz et al. Repetitive magnetic stimulation induces plasticity of inhibitory synapses, Nature Communications 2016.<br> <br> The following files are included:<br> 1. Brain tissue slice in a Petri dish.<br> 2. Normal adult male nude mouse "Digimouse" (brain volume of 0.38 cm3).<br> 3. Normal adult male capuchin monkey "S" (brain volume of 68.31 cm3).<br> <br> The models include the following tissues (coded with numbers):<br> 1. White matter volume<br> 2. Grey matter volume<br> 3. CSF volume<br> 4. Skull volume<br> 5. Soft tissues volume<br> 8. Eyeballs volume<br> 1001. White matter outer surfaces<br> 1002. Grey matter outer surfaces<br> 1003. CSF outer surfaces<br> 1004. Skull outer surfaces<br> 1005. Soft tissues outer surfaces<br> 1008. Eyeballs outer surfaces<br> <br> With any questions, please, contact the corresponding authors of the relevant papers or <a href="mailto:aopitz@umn.edu">aopitz@umn.edu</a> (Alexander Opitz).</p>
Learning Dynamics of Electrophysiological Brain Signals During Human Fear Conditioning (Open Data and Open Materials)
<p><strong>Open Data and Open Materials of: Sperl, M. F. J., Wroblewski, A., Mueller, M., Straube, B., & Mueller, E. M. (2021). Learning Dynamics of Electrophysiological Brain Signals During Human Fear Conditioning. <em>NeuroImage</em>, <em>226</em>, 117569.</strong></p> <p>Electrophysiological studies in rodents allow recording neural activity during threats with high temporal and spatial precision. Although fMRI has helped translate insights about the anatomy of underlying brain circuits to humans, the temporal dynamics of neural fear processes remain opaque and require EEG. To date, studies on electrophysiological brain signals in humans have helped to elucidate underlying perceptual and attentional processes, but have widely ignored how fear memory traces <em>evolve</em> over time. The low signal-to-noise ratio of EEG demands aggregations across high numbers of trials, which will wash out transient neurobiological processes that are induced by learning and prone to habituation. Here, our goal was to unravel the plasticity and temporal emergence of EEG responses during fear conditioning. To this end, we developed a new sequential-set fear conditioning paradigm that comprises three successive acquisition and extinction phases, each with a novel CS+/CS- set. Each set consists of two different neutral faces on different background colors which serve as CS+ and CS-, respectively. Thereby, this design provides sufficient trials for EEG analyses while tripling the relative amount of trials that tap into more transient neurobiological processes. Consistent with prior studies on ERP components, data-driven topographic EEG analyses revealed that ERP amplitudes were potentiated during time periods from 33–60 ms, 108–200 ms, and 468–820 ms indicating that fear conditioning prioritizes early sensory processing in the brain, but also facilitates neural responding during later attentional and evaluative stages. Importantly, averaging across the three CS+/CS- sets allowed us to probe the temporal evolution of neural processes: Responses during each of the three time windows gradually increased from early to late fear conditioning, while long-latency (460–730 ms) electrocortical responses diminished throughout fear extinction. Our novel paradigm demonstrates how short-, mid-, and long-latency EEG responses change during fear conditioning and extinction, findings that enlighten the learning curve of neurophysiological responses to threat in humans.</p>
T1-weighted brain MRI acquired from awake and unrestrained sheep
<p>This dataset contains T1-weighted brain MRI images acquired from 6 awake sheep, 1 anesthetized sheep and the MRI acquisition parameters.</p> <p><strong>When using this data please cite: </strong>Pluchot, C., Adriaensen, H., Parias, C. <em>et al.</em> Sheep (<em>Ovis aries</em>) training protocol for voluntary awake and unrestrained structural brain MRI acquisitions. <em>Behav Res</em> (2024). <a href="https://doi.org/10.3758/s13428-024-02449-6" target="_blank" rel="noopener">https://doi.org/10.3758/s13428-024-02449-6</a> </p> <p><strong>Note:</strong> A "Version v2" was created because the original "13332_anesthetized_T1.nii" file was corrupted.</p>
muBrain - a 3D volumetric reconstruction of the mid-fetal brain
<h2><strong>File descriptions</strong></h2> <h3><strong>Volumes:</strong></h3> <table> <tbody> <tr> <td><strong>uBrain-volume.nii.gz</strong></td> <td>microBrain template volume. A 3D reconstruction of the right hemisphere of a mid-fetal brain. Voxel size: 0.15mm.</td> </tr> <tr> <td><strong>uBrain-atlas-labels.nii.gz</strong></td> <td>microBrain brain tissue labels. Brain tissue labels (n=20) for the microBrain volume.</td> </tr> <tr> <td><strong>brain-tissue-labels.txt</strong></td> <td>LUT for brain tissue labels</td> </tr> </tbody> </table> <h3><strong>Surfaces:</strong></h3> <table> <tbody> <tr> <td><strong>uBrain.R.outer.surf.gii</strong></td> <td>outer (pial) cortical surface of the microBrain volume</td> </tr> <tr> <td><strong>uBrain.R.inner.surf.gii</strong></td> <td>inner (white) cortical surface of the microBrain volume</td> </tr> <tr> <td><strong>uBrain.cortical-atlas.fetal36w-template.label.gii</strong></td> <td>microBrain cortical atlas labels projected onto the 36w timepoint of the <a href="https://gin.g-node.org/kcl_cdb/dhcp_fetal_brain_surface_atlas">dHCP fetal surface template</a></td> </tr> <tr> <td><strong>cortical-labels.txt</strong></td> <td>LUT for cortical atlas labels.</td> </tr> </tbody> </table> <h3><strong>Microarray data:</strong></h3> <table> <tbody> <tr> <td><strong>uBrain-processed-lmd-data.csv</strong></td> <td>LMD microarray data from the <a href="https://www.brainspan.org/lcm/search/index.html">BrainSpan</a> atlas aligned to the microBrain cortical labels. </td> </tr> </tbody> </table>
Micro-CT images of deep brain stimulation leads
<p>The dataset contain micro-CT images of leads used in deep brain stimulation. A lead comprises multiple electrodes and enables the delivery of electrical pulses to the brain to treat medical conditions such as Parkinson's disease, essential tremor or epilepsy. Images were acquired with a Skyscan 1276 micro-CT system from Bruker. Each image is provided in Nifti format (.nii) along with its corresponding log file (.log) generated by the scanner. The file names indicate the manufacturer and sample model. 'BS' denotes Boston Scientific.<br><br>Images can be visualized at:<br>https://activgroup.github.io/DBS-lead-microCT/<br><br>To contribute, please contact thomas.billoud@uniklinik-freiburg.de</p>
Attention-based frontal-posterior coupling for visual consciousness in the human brain
<ol> <li>DataCode_Fig1_Attentional_Capture_Image_Detectability.m</li> <li>DataCode_Fig1_Attentional_Capture_Image_Detectability.mat</li> <li>DataCode_FigS2_Attentional_Capture_Image_Detecability.mat <ul> <li>.m Code (1) using .mat Data (2 and 3) illustrate main behavioral findings in our manuscript. Panel figures shown in Figure.1 and Figure.S2 could be well replicated using these materials.<br><br></li> </ul> </li> <li>Au_Step06_0601_unit_2C.m</li> <li>Au_Step06_0601_unit_mC.m</li> <li>Train_DSVM_xilei.m</li> <li>Classify_DSVM.m</li> <li>svmclassify.m</li> <li>svmtrain_xilei.m <ul> <li>.m Code (4) and .m code (5) using child .m functions (6, 7, 8 and 9) illustrate core codes used to discriminate neural pattern differences on a 2-class issue (image presence versus image absence) or a 3-class issue (animal, object or face), respectively. </li> </ul> </li> <li>Note_Location_activeChannels_distanceTest.m</li> <li>Note_Location_activeChannels_distanceTest.mat</li> <li>Note_Location_activeChannels.mat <ul> <li>.m Code (10) using .mat Data (11 and 12) illustrate our method used to calculate distance between responsive contacts. Based on that, we also made a statistical inference against a chance-level distribution. Panel figure shown in Figure.2F could be well replicated using these materials.<br><br></li> </ul> </li> <li>easy_ImgC.m <ul> <li>.m Code (13) illustrate our method used to calculate imaginary coherence between responsive contacts. A Rayleigh Z correction was also performed and outputed.<br><br></li> </ul> </li> <li>easy_visibility.m <ul> <li>.m Code (14) illustrate our method used to calculate an index of visibility from which measures of interest tied to an invisible image was subtracted from that of a visible image. </li> </ul> </li> </ol> <p> </p>
The oxygen initial dip in the brain of anesthetized and awake mice
<p>Datasets containing the data used to generate the figures and results from <a href="https://doi.org/10.1073/pnas.2200205119"><em>The oxygen initial dip in the brain of anesthetized and awake mice</em>, Aydin et al. (PNAS, 2022)</a>, along with the corresponding MATLAB code on <a href="https://github.com/alike-aydin/InitialDip_AydinEtAl">GitHub</a>. Download the file, and unzip it in the same folder as the scripts from the GitHub repository.</p>
Genome- and transcriptome-wide association summary statistics for outcome from traumatic brain injury
<p>The dataset contains summary statistics for the genome- and transcriptome-wide association studies (GWAS, TWAS) of genetic effects on outcome in traumatic brain injury (TBI). The study participants attended hospital within 24 hours of TBI, and underwent head computed tomography imaging.</p> <p><strong>Study participants</strong></p> <p>European ancestry data set contains 4710 individuals; multi-ethnic cohort 5268 individuals, including Europeans (n = 4710), Africans (n = 245) and Admixed Americans (n = 313).</p> <p>The largest European population contribution was from CENTER-TBI (Collaborative European NeuroTrauma Effectiveness Research, https://www.center-tbi.eu), where each participating center (60 centers from 20 countries in Europe) recruited patients between December 2013 and December 2017. The patients recruited in CENTER-TBI were supplemented by subjects from cohorts recruited at two European centres (Cambridge, UK, and Turku, Finland).</p> <p>The majority of patients in the US cohort were recruited between 2014 and 2018 to TRACK-TBI (Transforming Research and Clinical Knowledge in TBI, https://tracktbi.ucsf.edu) by the 18 US participant sites. The subjects recruited to the US cohort from TRACK-TBI were supplemented by patients recruited to an institutional research initiative at Mass General Brigham (MGB).</p> <p><strong>Outcome definition</strong></p> <p>Outcomes were measured using the extended Glasgow Outcome Scale (GOSE), ranging from 1 (dead) to 8 (upper good recovery), measured 6 months post-TBI. TBI severity was specified using the Glasgow Coma Score (GCS), with TBI classified as mild (GCS 13-15), moderate (GCS 9-12), or severe (GCS 3-8).</p> <p>To account for the effect of injury severity on outcome, sliding dichotomization was used to categorize outcome as favourable or unfavourable. A GOSE ≤ 4 was used to define an unfavourable outcome for patients with either moderate (GCS 9-12) or severe (GCS 3-8) TBI, while the unfavourable group was extended to patients with GOSE ≤ 7 if they had mild (GCS 13-15) TBI.</p> <p><strong>Genotype data and imputation</strong></p> <p>Genotyping was completed at FIMM Technology Center for CENTER-TBI, Cambridge, Turku patients and the Broad Institute for TRACK-TBI, using the Illumina Global Screening Array (GSA-24v2-0 + Multi-Disease). The MGB cohort were genotyped using Illumina’s Multi-Ethnic Global array (MEGA) and the pre-releases forms, including MEGA and MEGA-Ex arrays at Illumina at the MGB Translational Genomics Core.</p> <p>A unified quality control procedure was applied for each study cohort and the array-based genotypes were imputed using the Haplotype Reference Consortium panel. Autosomal chromosomes were considered, post-imputation data was filtered by imputation quality (INFO > 0.4 for CENTER-TBI, Cambridge and Turku; R2 > 0.4 for TRACK-TBI and MGB) and MAF > 1%.</p> <p><strong>Genome-wide association analysis and meta-analysis</strong></p> <p>Genome-wide single-marker scans were performed using a penalized likelihood-based Firth logistic regression, and implemented in PLINK v2.0. Using favourable outcome as reference, models were fitted on the basis of imputed allelic dosages. Age, sex, major extracranial injury, pupillary reactivity, and the first 10 principal components were included as covariates. Study cohort (CENTER-TBI, Cambridge, Turku) was an additional covariate in the CENTER-TBI GWAS.</p> <p>Fixed-effects meta-analysis of the three European ancestry GWAS was performed using METAL. For trans-ethnic meta-analysis, summary statistics of five GWASs in patients of European, African and Admixed Americans were aggregated via MR-MEGA.</p> <p><strong>Transcriptome-wide association study</strong></p> <p>Genetically regulated gene expression (GREx) was imputed using a regression model fitted on a separate gene expression database. Elastic net models provided by PrediXcan for all available GTEx brain tissues and whole blood were used. For TWAS, the same sliding dichotomy model for outcome with the same set of covariates as in the GWAS, but PCA components were replaced with the top five principal components of the respective gene expression data. </p> <p><strong>Column headers - GWAS</strong></p> <p>rsID: variant rsID<br> Chrom: chromosome<br> Pos: position (build GRCh38)<br> A1: effect allele<br> A2: reference allele<br> EAF: allele frequency of effect allele<br> Effect: effect size of effect allele<br> StdErr: standard error of effect size<br> P: p value of association (with genomic correction)<br> N: sample size</p> <p>Note. 'Effect' and 'StdErr' are only available for the European ancestry meta-analysis.</p> <p><br> <strong>Column headers - TWAS</strong></p> <p>tissue: GTEx tissue type<br> id: ensembl gene id<br> coef: model coefficient<br> se: model standard error for coefficient<br> p: model-based p value<br> symbol: gene symbol<br> name: gene name written out<br> chr: chromosome<br> start: gene start position (build GRCh38)</p>
ScienceDex guides
Understand access before you commit
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.