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373 results for “Brain tissue”
Figure 3 in Investigations of the nervous system biomarkers in the brain and muscle of freshwater fish (Oreochromis niloticus) following accumulation of nanoparticles in the tissues
Figure 3. TEM images of muscle tissue sample of fish (O. niloticus) exposed to 1 (A and B), 5 (C and D), and 25 (E and F) mg/L of CuO NPs for 14 days of uptake and 14 days of depuration periods, respectively.
Source code and experimental data of human brain tissue (visual cortex, corona radiata) for poro-viscoelastic parameter identification
<p>Computer code and experimental data that we used for our inverse parameter identification of poro-viscoelastic material parameters for two different brain regions: visual cortex (gray matter) and corona radiata (white matter). The experimental data comprises large-strain cyclic loading and compression/tension relaxation. For details see the corresponding publication: "Model-driven exploration of poro-viscoelasticity in human brain tissue: Be careful with the parameters!".</p> <p>Further explanation regarding the specimen preparation, experimental setup, as well as the assignment of regions and governing regions can be found in Hinrichsen, J., Reiter, N., Bräuer, L. et al. Inverse identification of region-specific hyperelastic material parameters for human brain tissue. Biomech Model Mechanobiol (2023). <a href="https://doi.org/10.1007/s10237-023-01739-w" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s10237-023-01739-w</a>.</p> <p>The file "<span>nonlinear-poro-viscoelasticity.cc</span>" contains our C++ Finite Element code based on the open source library deal.II. It is accompanied by an exemplary parameter file.</p> <p><strong>Funding:</strong> The support from the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) through the grants BU 3728/1-1, BU 3728/3-1, STE 544/70-1 as well as through project number 460333672 CRC1540 Exploring Brain Mechanics is gratefully acknowledged.</p>
Recommended Implementation of Quantitative Susceptibility Mapping for Clinical Research in The Brain: A Consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group
<p>Example datasets and code for the recommended implementation of Quantitative Susceptibility Mapping (QSM) in "Recommended Implementation of Quantitative Susceptibility Mapping for Clinical Research in The Brain: A Consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group".</p>
Metabolomic Profile of Cerebral Tissue After Blood-Brain Barrier Opening using Microbubble-Assisted Ultrasound: A Focus on Contralateral Side.
<p>Microbubble (MB)-assisted ultrasound (US) is an innovative modality for the non-invasive, targeted and efficient delivery of the therapeutic molecules into the brain. Previously, we reported the first metabolomic signature of blood-brain barrier opening (BBBO) induced by MB-assisted US. In the present study, the neurometabolic consequences of acoustically mediated BBBO on cerebral tissue using multimodal metabolomics approaches. Sinusoid US waves (1 MHz, peak negative pressure 0.6 MPa, burst length 10 ms, total treatment time 30 s, MB bolus dose 0.7 10<sup>5</sup> MBs/g) were applied on the right striatum (ipsilateral side). Brain was collected and both striata were then dissected 3 h, 2 days and 1 week after BBBO. After tissue preparation, the samples were analyzed using nuclear magnetic resonance spectrometry (NMRS) and high-performance liquid chromatography coupled to mass spectrometry (HPLC-MS). Our findings showed a slight disruption of metabolic pathways in contralateral striata of animals. Analyses of metabolic pathways indicated change of amino acid metabolisms. In addition, tryptophan derivate dosages revealed the perturbation of a central metabolite of the kynurenin pathway (<em>i.e.</em>, 3-hydroxykynurenin). In conclusion, the acoustically mediated BBBO of the ipsilateral cerebral hemisphere induced significant change in metabolism of contralateral one.</p>
Remarkable preservation of brain tissues in an Early Cretaceous iguanodontian dinosaur
<p>Supplementary information from the paper "Remarkable preservation of brain tissues in an Early Cretaceous iguanodontian dinosaur", features in a Geological Society Publication in memory of Professor Martin Brasier, University of Oxford.</p> <p>Datasets comprise:<br> -- A single zipped image stack from two aligned concatenated CT scans of the specimen.<br> -- A .7z split zip file of the dataset as a Drishti volume, used to render the images in the paper. <br> -- SEM images of the specimen.</p>
Dataset of "Optical Signatures of Thermal Damage on ex-vivo Brain, Lung and Heart Tissues using Time-Domain Diffuse Optical Spectroscopy"
<p>Dataset for the article entitled "Optical Signatures of Thermal Damage on ex-vivo Brain, Lung and Heart Tissues using Time-Domain Diffuse Optical Spectroscopy" </p> <p> </p> <p>Abstract:</p> <p> </p> <p>Thermal Therapies treat tumors by means of heat, greatly reducing pain, post-operation complications, and cost as compared to traditional methods. Yet, effective tools to avoid under- or over-treatment are mostly needed, to guide surgeons in laparoscopic interventions.<br>In this work, we investigated the temperature-dependent optical signatures of ex-vivo calf brain, lung, and heart tissues, based on the reduced scattering and absorption coefficients in the near-infrared spectral range (657 to 1107 nm). These spectra were measured by time domain diffuse optics, applying a step-like spatially homogeneous thermal treatment at 43 °C, 60 °C, and 80 °C.<br>We found three main increases in scattering spectra, possibly due to the denaturation of collagen, myosin, and proteins secondary structure.<br>After 75 °C, we found the rise of two new peaks at 770 and 830 nm in the absorption spectra due to the formation of a new chromophore, possibly related to hemoglobin or myoglobin.<br>This research marks a significant step forward in controlling thermal therapies with diffuse optical techniques by identifying several key markers of thermal damage. This could enhance the ability to monitor and adjust treatment in real-time, promising improved outcomes in tumor therapy.</p> <p> </p> <p> </p> <p>Authors:</p> <p>ALESSANDRO BOSSI , LEONARDO BIANCHI , PAOLA SACCOMANDI , AND ANTONIO PIFFERI<br>Politecnico di Milano</p> <p> </p> <p> </p> <p><a href="https://opg.optica.org/boe/fulltext.cfm?uri=boe-15-4-2481&id=548108" target="_blank" rel="noopener">Link to the article</a></p>
Brain–muscle tissue communication prevents muscle aging by maintaining daily physiology
<p>A network of molecular clocks is crucial for coordinating daily physiology and maintaining organismal health. However, the mechanisms underlying the interactions between these clocks and the significance of intra-tissue clock networks in muscle tissue maintenance have remained elusive. To uncover this network structure, we established a minimal clock module with the central clock (suprachiasmatic nucleus/brain) and/or a peripheral clock (muscle) in arrhythmic mice with premature aging. We find that reconstituting the brain-muscle clock network alone is sufficient to preserve fundamental daily homeostatic functions and prevent premature muscle aging. However, achieving whole muscle daily physiology requires the contribution of other peripheral clocks. Mechanistically, the muscle peripheral clock acts as a gatekeeper, selectively suppressing signals from the central clock that could be detrimental to muscle function if left uncontrolled while also integrating important muscle homeostatic functions. Our findings unveil the reciprocal interactions between central and peripheral clocks crucial for daily muscle function and highlight the significant influence of eating patterns on these interactions. These insights have implications for promoting healthier aging and reversing age-related muscle pathologies.</p>
Imaging mass cytometry analysis of brain tissues with CNS immune-related adverse events during anti-PD-1 cancer immunotherapy
<p><span>Dataset accompanying the manuscript "Anti-PD-1 cancer immunotherapy induces CNS immune-related adverse events by Spleen tyrosine kinase activation in microglia".</span></p> <p><span>The Metadata.xls file includes the metadata, the raw image data is saved as .txt file, the segmented cellular expression data is available as csv files. </span></p>
Figure 1 in Investigations of the nervous system biomarkers in the brain and muscle of freshwater fish (Oreochromis niloticus) following accumulation of nanoparticles in the tissues
Figure 1. TEM images of muscle (A) and brain (B) tissue samples of control fish (O. niloticus).
Small brains: Body shape constrains tissue allocation to the central nervous system in ant-mimicking spiders
<p>In Batesian mimicry, mimetic traits are not always as convincing as predicted by theory – in fact, inaccurate mimicry with only a superficial model resemblance is common and taxonomically widespread. The ‘selection trade-offs hypothesis’ proposes a life-history trade-off between accurate mimetic traits and one or more vital biological functions. Here, using an accurate myrmecomorphic (ant-mimicking) jumping spider species, <em>Myrmarachne smaragdina</em>, we investigate how myrmecomorphic modifications to the body shape impact the internal anatomy in a way that could be functionally limiting. Specifically, via X-ray micro-computed tomography (microCT), we quantify how the spider’s constricted prosoma, which emulates the head and thorax of ants, impacts the size of the central nervous system (CNS) and the venom glands. We found that, relative to their whole-body mass, the CNS of the ant-mimicking jumping spider was smaller when compared with a relatively closely related non-mimic jumping spider, indicating that some trade-off between mimic accuracy and size of neural anatomy, as articulated by the ‘selection trade-offs hypothesis’, is a possibility. Our explorative evidence enables and encourages broader investigation of how variable mimic accuracy impacts the neuroanatomy in ant mimics as a direct test of the ‘selection trade-offs hypothesis’. </p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 1 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the first part of 14 parts of the full dataset (1/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 300ms, 400ms, 500ms, and echo time (TE) = 10ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>Within this part, we also include the segmentation labels for each tissue.</p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 9 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the ninth part of 14 parts of the full dataset (9/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 600ms, 700ms, 800ms, and echo time (TE) = 15ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p> </p>
Evaluation of archival HIV DNA in brain and lymphoid tissues
<p>HIV reservoirs persist in anatomic compartments despite antiretroviral therapy (ART). Characterizing HIV reservoirs in central nervous system (CNS) and other tissues is crucial to inform cure strategies. We evaluated paired autopsy brain – frontal cortex (FC), occipital cortex (OCC), basal ganglia (BG) – and peripheral lymphoid tissues from 63 people with HIV. Participants passed away while virally suppressed on ART and without evidence of CNS opportunistic disease. We quantified HIV DNA and, from a subset of 14 participants, we obtained full-length HIV-envelope (FL HIV-env) sequences. We detected HIV DNA (<em>gag</em>) in most brain (65.1%) and all lymphoid tissues. Lymphoid tissues had higher HIV DNA levels compared to brain (p<0.01). Levels of HIV <em>gag</em> between BG and FC were similar (p>0.2), while OCC had the lowest levels (p=0.01). Females had higher HIV DNA levels in tissues than males (<em>gag</em>: p=0.03; 2-LTR, p=0.05), suggesting possible sex-associated mechanisms for HIV reservoir persistence. Most FL HIV-env sequences (n=143) were intact, while 42 were defective. Clonal sequences were found in eight participants; one participant had the same clonal defective sequences in brain and spleen, suggestive of cell migration. From 10 donors with paired brain and lymphoid sequences, we observed evidence of compartmentalized sequences in two participants. Our data contribute to the idea that the brain is a site for HIV reservoirs during ART where compartmentalized proviral populations may occur in a subset of people. Future studies accessing FL HIV-provirus and replication competence in vitro are needed to further evaluate the HIV reservoirs' composition in tissues.</p>
A Study to Evaluate Feasibility, Safety, and Clinical Responses of Implanting Autologous Peripheral Nerve Tissue Into the Brain for Non-motor or Motor Symptoms in Patients With Parkinson's Disease Und
ClinicalTrials.gov study NCT06683378. IPD Sharing: YES. Countries: 1. Publications: 3.
Brain Tissue Oxygen Monitoring in Traumatic Brain Injury (TBI)
ClinicalTrials.gov study NCT00974259. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Brain–muscle tissue communication prevents muscle aging by maintaining daily physiology
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Evaluation of archival HIV DNA in brain and lymphoid tissues
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Data from: Short-term sleep loss alters cytokine gene expression in brain and peripheral tissues and increases plasma corticosterone of zebra finch (Taeniopygia guttata)
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3D dataset of tissue cleared MADM mouse brain
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Data from: Comparative support for the expensive tissue hypothesis: big brains are correlated with smaller gut and greater parental investment in Lake Tanganyika cichlids
The brain is one of the most energetically expensive organs in the vertebrate body. Consequently, the energetic requirements of encephalization are suggested to impose considerable constraints on brain size evolution. Three main hypotheses concerning how energetic constraints might affect brain evolution predict covariation between brain investment and i) investment into other costly tissues, ii) overall metabolic rate, and iii) reproductive investment. To date, these hypotheses have mainly been tested in homeothermic animals and the existing data are inconclusive. However, there are good reasons to believe that energetic limitations might play a role in large-scale patterns of brain size evolution also in ectothermic vertebrates. Here we test these hypotheses in a group of ectothermic vertebrates, the Lake Tanganyika cichlid fishes. After controlling for the effect of shared ancestry and confounding ecological variables, we find a negative association between brain size and gut size. Furthermore, we find that the evolution of a larger brain is accompanied by increased reproductive investment into egg size and parental care. Our results indicate that the energetic costs of encephalization may be an important general factor involved in the evolution of brain size also in ectothermic vertebrates.
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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.