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1,064 results for “human brain”
The physiological effects of non-invasive brain stimulation fundamentally differ across the human cortex
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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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Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas
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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>
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>
Dataset Activation of Lactate Receptor HCAR1 Down-modulates Neuronal Activity in Rodent and Human Brain Tissue
<p>This dataset is related to the study: </p> <p>Briquet M, Rocher AB, Alessandri M, Rosenberg N, de Castro Abrantes H, Wellbourne-Wood J, Schmuziger C, Ginet V, Puyal J, Pralong E, Daniel RT, Offermanns S, Chatton JY. Activation of lactate receptor HCAR1 down-modulates neuronal activity in rodent and human brain tissue. J Cereb Blood Flow Metab. 2022 Mar 3:271678X221080324. doi: 10.1177/0271678X221080324. Epub ahead of print. PMID: 35240875.</p>
Summary statistics accompanying the article "Genome-wide association study of the human brain functional connectome reveals strong vascular component underlying global network efficiency" in Scientific Reports (2022)
<p>Summary statistics for genome-wide association studies reported in:</p> <p>Bell, S., Tozer, D.J., & Markus H.S. (2022). Genome-wide association study of the human brain functional connectome reveals strong vascular component underlying global network efficiency. <em>Scientific Reports</em>, DOI: <a href="https://dx.doi.org/10.1038/s41598-022-19106-7">10.1038/s41598-022-19106-7</a>. </p> <p><strong>Abstract</strong></p> <p>Complex brain networks play a central role in integrating activity across the human brain, and such networks can be identified in the absence of any external stimulus. We performed 10 genome-wide association studies of resting state network measures of intrinsic brain activity in up to 36,150 participants of European ancestry in the UK Biobank. We found that the heritability of global network efficiency was largely explained by blood oxygen level-dependent (BOLD) resting state fluctuation amplitudes (RSFA), which are thought to reflect the vascular component of the BOLD signal. RSFA itself had a significant genetic component and we identified 24 genomic loci associated with RSFA, 157 genes whose predicted expression correlated with it, and 3 proteins in the dorsolateral prefrontal cortex and 4 in plasma. We observed correlations with cardiovascular traits, and single-cell RNA specificity analyses revealed enrichment of vascular related cells. Our analyses also revealed a potential role of lipid transport, store-operated calcium channel activity, and inositol 1,4,5-trisphosphate binding in resting-state BOLD fluctuations. We conclude that that the heritability of global network efficiency is largely explained by the vascular component of the BOLD response as ascertained by RSFA, which itself has a significant genetic component.</p> <p> </p> <p>Further information on the files uploaded here can be found in the README. Users interested in bulk downloading these summary statistics may find <a href="https://github.com/dvolgyes/zenodo_get">zenodo_get</a> helpful.</p>
7 Tesla MRI of the ex vivo human brain at 100 micron resolution
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Human Brain MRI Template and Myelin Atlas
<p>The structural template, quantitative myelin water imaging atlases, tissue segmentations, and regions of interest (ROIs) generated and analyzed for <em>An atlas for human brain myelin content throughout the adult life span</em></p> <p><a href="https://www.nature.com/articles/s41598-020-79540-3">https://www.nature.com/articles/s41598-020-79540-3</a></p>
Small-angle X-ray scattering datasets for imaging crossing fibers in mouse, pig, monkey, and human brain
<p>Small-angle X-ray scattering datasets for resolving crossing fibers (myelinated neuronal axon bundles), as described in</p> <p>"<strong><em>Imaging crossing fibers in mouse, pig, monkey, and human brain using small-angle X-ray scattering</em></strong>"</p> <p>deposited in bioRxiv:</p> <p>https://doi.org/10.1101/2022.09.30.510198</p>
Realistic modeling of mesoscopic ephaptic coupling in the human brain
<p>Comsol models with E-field distributions generated by dipole sources in a realistic head model and a stylized 'toy' model representing a sulcus.</p>
Three dimensional MRF obtains highly repeatable and reproducible multi-parametric estimations in the healthy human brain at 1.5T and 3.0T
<p>3D MR Fingerprinting T1/T2/M0 maps of twelve healthy volunteers obtained in eight different sites (1.5T and 3.0T scanners, single vendor). Each subject/site dataset includes two acquisitions (test-retest) to assess repeatability of the measurement.</p>
Data from: Identification of neural oscillations and epileptiform changes in human brain organoids.
<p>Seurat object containing processed single-cell RNA sequencing data described in:</p> <p>Samarasinghe, R.A., Miranda, O.A., Buth, J.E. <em>et al.</em> Identification of neural oscillations and epileptiform changes in human brain organoids. <em>Nat Neurosci</em> <strong>24, </strong>1488–1500 (2021). <a href="https://doi.org/10.1038/s41593-021-00906-5">https://doi.org/10.1038/s41593-021-00906-5</a></p> <p>Additional raw and processed data can be accessed at the Gene Expression Omnibus under accession number <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE165577">GSE165577</a>. </p> <p> </p>
Data supporting the manuscript "Sexually divergent development of depression-related brain networks during healthy human adolescence"
<p>This data supports the manuscript "Sexually divergent development of depression-related brain networks during healthy human adolescence" by Dorfschmidt et al. Part of these <a href="https://doi.org/10.6084/m9.figshare.11551602">data</a> were initially released by Váša et al. (2020) as part of their <a href="https://doi.org/10.1073/pnas.1906144117">manuscript</a>. Please cite them when using these data. </p> <p> </p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 8. Modular Hierarchical Organization of the Human Perceptual System
<p>In order to perform complex tasks, neuro-symbols have to be connected to neuro-symbolic networks. For the structural organization of this neuro-symbolic network, the modular hierarchical organization of the human perceptual cortex as described by A. Luria [27] was taken as a blueprint (see Figure 8).</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 2. Two Possible Progress Scenarios for How to Reach Towards Machines and Systems with Human- Level Cognitive Skills
<p>Having identified the need for novel methods for machine recognition, situation assessment, and decision making in order to advance further in different automation domains, an important question is by what means can we reach such sophisticated mechanisms. The long-term goal in<br> mind is to construct machines and systems showing performances comparable to or even beyond human skill levels. In a guest talk at the Vienna University of Technology in 2008, Prof. Etienne Barnard, an expert in the field of Artificial Intelligence, made an interesting “conceptual suggestion” for two possible progress scenarios to reach this goal which could be summarized as depicted in Figure 2.</p>
Results of elemental analyses of brain and liver human tissue samples performed by inductively coupled plasma mass spectrometry
<p>Human tissue samples of brain and liver were obtained after min. 24 h postmortem from the Department of Forensic Medicine, University of Lublin. Tissue samples were collected from typical anatomical locations intended for histopathological examination: A—polus frontalis (frontal pole), B—gyrus precentralis (precentral gyrus), C—gyrus postcentralis (postcentral gyrus), D—cortex cingularis (gyrus cinguli cingulate gyrus), E—hippocampus (hippocampus), F—caput nuclei caudati (head of caudate nucleus), G—fasciculus longitudinalis superior cerebri (superior longitudinal fasciculus of brain, SLF), H—fasciculus longitudinalis inferior cerebri (inferior longitudinal fasciculus of brain, ILF), I—thalamus dorsalis (dorsal thalamus), J—nucleus accumbens septi (nucleus accumbens septi, NAc), K—insula (insula), L—hepar (liver). Samples were taken with the consent of the prosecutor and the Local Bioethics Committee (Medical University of Lublin, Poland, KE-0254/152/2021, approval date 24 June 2021). The study was conducted in accordance with the World Medical Association Code of Ethics, Declaration of Helsinki, for experiments involving human subjects. The samples were mineralized to remove the organic matrix using microwave minerali-zation with nitric acid (69% suprapur HNO3, Baker, Radnor, PA, USA) in the microwave mineralization system Multiwave 5000 (Anton Paar, Graz, Austria). After mineralization step, HCl (Merck, Darmstadt, Germany) was added and diluted by ultrapure water. The elemental analysis was performed using the inductively coupled plasma mass spectrometer Agilent 8900 ICP-MS Triple Quad (Agilent, Santa Clara, CA, USA). </p>
Figure 1. Brain Structure-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>EEG data have collected from<br> desirable subjects. Each and every EEG signal has different kind of bands like Alpha, Beta,<br> Gamma, Theta, and Delta. Each band stores the particular information about the emotions. Alpha<br> band (8-13 Hz) which located in Frontal Occipital, Beta band (13-30 Hz) which located in Frontal<br> Central, Gamma band (30-100 Hz), Theta band (4- 7 Hz) which located in Midline Temp, Delta<br> band (0-4Hz) which located in Frontal Lobe. Before processing the EEG signal and extracting these<br> bands, preprocess the signal and reduce the noise. The basic brain figure is shown in below.</p>
BRAIN Journal-Computational Intelligence in a Human Brain Model-Figure 2. Processes and Interfaces of the SAH Human Brain Model
<p>The proposed SAH Human Brain Model starts assigning the main attributes to the “heavy pieces” (king, queen, rooks, bishops, knights) and assigning to pawns the interfaces as an advanced guard. The interface represents senses and processed human actions (equilibrium, movements, and speech) and it results from the brain activity (see Figure 2). </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.