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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Clark, Shaunna L.; Costin, Blair N; Chan, Robin F.; Johnson, Alexander W.; +8 Authors

    Background: Recent reviews have highlighted the potential use of blood‐based methylation biomarkers as diagnostic and prognostic tools of current and future alcohol use and addiction. Due to the substantial overlap that often exists between methylation patterns across different tissues, including blood and brain, blood‐based methylation may track methylation changes in brain; however, little work has explored the overlap in alcohol‐related methylation in these tissues. Methods: To study the effects of alcohol on the brain methylome and identify possible biomarkers of these changes in blood, we performed a methylome‐wide association study in brain and blood from 40 male DBA/2J mice that received either an acute ethanol (EtOH) or saline intraperitoneal injection. To investigate all 22 million CpGs in the mouse genome, we enriched for the methylated genomic fraction using methyl‐CpG binding domain (MBD) protein capture followed by next‐generation sequencing (MBD‐seq). We performed association tests in blood and brain separately followed by enrichment testing to determine whether there was overlapping alcohol‐related methylation in the 2 tissues. Results: The top result for brain was a CpG located in an intron of Ttc39b (p = 5.65 × 10−08), and for blood, the top result was located in Espnl (p = 5.11 × 10−08). Analyses implicated pathways involved in inflammation and neuronal differentiation, such as CXCR4, IL‐7, and Wnt signaling. Enrichment tests indicated significant overlap among the top results in brain and blood. Pathway analyses of the overlapping genes converge on MAPKinase signaling (p = 5.6 × 10−05) which plays a central role in acute and chronic responses to alcohol and glutamate receptor pathways, which can regulate neuroplastic changes underlying addictive behavior. Conclusions: Overall, we have shown some methylation changes in brain and blood after acute EtOH administration and that the changes in blood partly mirror the changes in brain suggesting the potential for DNA methylation in blood to be biomarkers of alcohol use. READMECpG LocationsContains RaMWAS formatted files of CpG locationsCpG_locations.zipPhenotype FileContains phenotype data used in association analyses. Please see README file for data packets for description of variables.phenotype_file.txtCoverageContains RaMWAS formatted coverage (i.e., CpG score) files for each CpGcoverage.tar.gz

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ DANS-EASYarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    DANS-EASY
    Dataset . 2018
    Data sources: B2FIND
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    DRYAD; ZENODO; NARCIS
    Dataset . 2018
    License: CC 0
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ DANS-EASYarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      DANS-EASY
      Dataset . 2018
      Data sources: B2FIND
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      DRYAD; ZENODO; NARCIS
      Dataset . 2018
      License: CC 0
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Bilgic, Berkin; Langkammer, Christian; Marques, José P.; Meineke, Jakob; +2 Authors

    This repository contains information about submitted solutions and resulting analysis metrics of the 2019 Quantitative Susceptibility Mapping Reconstruction Challenge. The original susceptibility maps submitted for participation in the challenge are available here and here. The package contains seven Comma-Separated Values (CSV) files and two PDF files: master_stage1_anonymized.csv: Results of stage 1 of the challenge at the time of presentation at the workshop (fully-blinded); master_stage2_snr1_anonymized.csv: Results of stage 2 of the challenge using the high noise dataset at the time of presentation at the workshop (fully-blinded); master_stage2_snr2_anonymized.csv: Results of stage 2 of the challenge using the low noise dataset at the time of presentation at the workshop (fully-blinded); submission_form_stage1.pdf: PDF export of the online form used in stage 1; submission_form_stage2.pdf: PDF export of the online form used in stage 2. For the manuscript, we analyzed these CSV files with scripts reported here. Each csv file contains metrics for all submitted solutions along with detailed information about the algorithm used, provided by the participant at the time of submission. The very first record in each file is a header containing a list of field names: normalized rmse: Whole-brain root-mean-squared error relative to ground truth; rmse_detrend_tissue: Root-mean-squared error relative to ground truth (after detrending) in grey and white matter mask; rmse_detrend_blood: Root-mean-squared error relative to ground truth (after detrending) using a one-pixel dilated vein mask; rmse_detrend_DGM: Root-mean-squared error relative to ground truth (after detrending) in a deep gray matter mask (substantia nigra & subthalamic nucleus, red nucleus, dentate nucleus, putamen, globus pallidus and caudate); DeviationFromLinearSlope: Absolute difference between the slope of the average value of the six deep gray matter regions vs. the prescribed mean value and 1.0; CalcStreak: Estimation of the impact of the streaking artifact in a region of interest surrounding the calcification through the standard deviation of the difference map between reconstruction and the ground truth; DeviationFromCalcMoment: Absolute deviation from the volumetric susceptibility moment of the reconstructed calcification, compared to the ground truth (computed at in the high-resolution model); Submission Identifier: Self-chosen unique identifier of the submission; Submission Identifier of the corresponding Stage 1 submission: This is the Submission Identifier of the solution submitted to Stage 2 that was calculated with a similar algorithm in Stage 1; Changes with respect to Stage 1 submission: Self-reported information about modifications made to the algorithm for Stage 2; Number of submissions in Stage 2: The number of solutions that were submitted to Stage 2 with a similar algorithm; Sim1/Sim2: Filename of the submitted solutions for Stage 1; File name of the zip-file you are going to upload: Filename of the file uploaded to Stage 2; Full name of the algorithm: Self-reported full name of the algorithm used; Preferred Acronym: Self-reported acronym of the algorithm used; Algorithm-type: Self-reported type of algorithm used; Does your algorithm incorporate information derived from magnitude images?: Self-reported Yes/No; Regularization terms: Self-reported types of regularization terms involved; Did your algorithm use the provided frequency map or the four individual echo phase images?: Self-reported information about involved magnitude information; Publication-ready description of the reconstruction technique: Self-reported description of the algorithm; Publications that describe the algorithm: Self-reported literature reference; Algorithm publicly available?: Self-reported public availability of the algorithm; If your algorithm is not yet publicly available, would you be willing to make it available at the end of the challenge?: Self-reported willingness to share the algorithm code with the public; Specific information about this solution: Self-reported detailed information about the solution; Herewith, I permit the QSM Challenge committee to publish my uploaded files (calculated maps) after the completion of the challenge: Self reported agreement with publication of submitted solution; Ground truth was not explicitly or implicitly incorporated into your algorithm or solution: Self-reported confirmation that the ground truth was not incorporated in the solution.

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    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Stenroos Petteri; Guillemain Isabelle; Tesler Federico; Montigon Olivier; +6 Authors

    This repository contains measured and simulated data for the manuscript titled "How Absence Seizures Impair Sensory Perception: Insights from Awake fMRI and Simulation Studies in Rats" by Stenroos P. et al. Electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) data was recorded simultaneously from awake GAERS; a rat model of absence epilepsy. Visual and whisker stimulation was experimentally applied, and visual stimulation was simulated during interictal and ictal states and whole brain hemodynamic and neural responsiveness was compared between states.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Macrì, Simone; Di-Poï, Nicolas;

    4K movie displaying the 3D rendering of the head and both the morphological features and spatial organization of the main encephalic subdivisions of the Golden Flying Snake. The brain reconstruction was obtained from a microCT scan of a iodine-stained specimen through manual segmentation using the software Amira 5.5.0. Other videos can be found here. If you are interested in reptile brain evolution and behavior, please, have a look to our recent publication: "Comparative analysis of squamate brains unveils multi-level variation in cerebellar architecture associated with locomotor specialization" Simone Macrì, Yoland Savriama, Imran Khan & Nicolas Di-Poï Nature Communications 10, 5560 (2019) For any inquiries or additional information, please, refer to the contacts provided in the article.

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    ZENODO
    Audiovisual . 2020
    License: CC BY NC ND
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Audiovisual . 2020
    License: CC BY NC ND
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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      ZENODO
      Audiovisual . 2020
      License: CC BY NC ND
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Audiovisual . 2020
      License: CC BY NC ND
      Data sources: Datacite
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    Authors: D.A. Barrière; R. Magalhães; A. Novais; P. Marques; +11 Authors

    The SIGMA templates and atlases for the Wistar Rat Brain The current document is a short description of the second version of the SIGMA resources for the Wistar rat brain. For a full description of the resources and the methodologies used to create them please consult the main publication [Barriere D.A. et al 2019]. The SIGMA resources are a set of standardized MRI compatible templates and atlases meant to support the analysis of multimodal MRI data of the rat brain. They were developped as part of the SIGMA project, a collaborative project between French (CEA and INSERM) and Portuguese (ICVS) institutions (FCT-ANR/NEU-OSD/0258/2012). They provide a unified and standardized framework for the analysis of multimodal rat brain imaging data, allowing the reporting of results within the coordinate system of the Paxinos-Watson atlas. In this second version, standardized MRI compatible templates have been built from the original acquired data (11.7 Tesla Bruker Scanner at Neuropsin center https://www.cea.fr/drf/joliot/en/Pages/research_entities/NeuroSpin.aspx) and emulated using the methods developed by Gabriel A. Devenyi (https://github.com/gdevenyi) and available here : https://github.com/CoBrALab/optimized_antsMultivariateTemplateConstruction. This pipeline is a re-implementation of the ANTs template construction pipeline requiring ANTs for the primary commands, and running on our cluster facilities using qbatch (https://islande.hub.inrae.fr/infrastructure). Using this methodology we firstly, updated the previous SIGMA spaces (T2sw, T2w, T1w) previously generated using the DARTEL Methods implemented in SPM8 and normalized the whole head images instead of brain. Secondly, we updated the probabilistic maps of the rat brain which are mandatory for the automatic segmentation of the rat brain and standardisation of morphometric analysis. Namely, we created new maps of Grey Matter, White Matter, CSF, Skull and outbrain. Those maps allow the use of SIGMA with the latter release of SPM12, a popular neuroimaging software dedicated to brain imaging analysis but also with ANTs, FSL and AFNI. Additionnally, we revised the Grey Matter/White Matter segmentations since the limits of which (particularly at the thalamic level) were a matter to debate with some users in the previous version of SIGMA. Thirdly, additionnal templates have been created using the optimized ANTs methodology to create from original unpublished data diffusion templates (B0, FA, etc.) at both ex-vivo and in-vivo resolutions. Eventually, using the same strategy, we created a CT/18FDG reference space from data obtained previously [Barrière D.A. et al 2018] which has been normalized with the MRI ex-vivo SIGMA template allowing to the SIGMA resource to propose a multimodal space for CT/TEP/MRI normalisation. Organisation of the SIGMA resources The SIGMA resources have been organized as four sections : anatomical Imaging, functional imaging, atlases and TEP/CT imaging Anatomical Imaging In this section a set of templates, priors and brain masks is available for ex-vivo and in-vivo data normalization Ex-vivo T2*-weighted T2*-weighted template + T2*-weighted map + associated probabilistics maps (GM, WM, CSF, Skull, outbrain) + brain mask. Spatial resolution 0.09x0.09x0.09mm. SIGMA_ExVivo_Anatomical_Brain_csf.nii.gz SIGMA_ExVivo_Anatomical_Brain_gm.nii.gz SIGMA_ExVivo_Anatomical_Brain_mask.nii.gz SIGMA_ExVivo_Anatomical_Brain_out.nii.gz SIGMA_ExVivo_Anatomical_Brain_skull.nii.gz SIGMA_ExVivo_Anatomical_Brain_t2starmap.nii.gz SIGMA_ExVivo_Anatomical_Brain_template.nii.gz SIGMA_ExVivo_Anatomical_Brain_wm.nii.gz Ex-vivo diffusion B0 template + FA template + associated probabilistics maps (GM, WM, CSF, Skull, outbrain) + brain mask. Spatial resolution 0.25x0.25x0.25mm. SIGMA_ExVivo_Diffusion_Brain_b0.nii.gz SIGMA_ExVivo_Diffusion_Brain_csf.nii.gz SIGMA_ExVivo_Diffusion_Brain_fa.nii.gz SIGMA_ExVivo_Diffusion_Brain_gm.nii.gz SIGMA_ExVivo_Diffusion_Brain_mask.nii.gz SIGMA_ExVivo_Diffusion_Brain_out.nii.gz SIGMA_ExVivo_Diffusion_Brain_skull.nii.gz SIGMA_ExVivo_Diffusion_Brain_wm.nii.gz In-vivo T2-weighted T2-weighted template + associated probabilistics maps (GM, WM, CSF, Skull, outbrain) + brain mask. Spatial resolution 0.15x0.15x0.15mm. SIGMA_InVivo_Anatomical_Brain_csf.nii.gz SIGMA_InVivo_Anatomical_Brain_gm.nii.gz SIGMA_InVivo_Anatomical_Brain_mask.nii.gz SIGMA_InVivo_Anatomical_Brain_out.nii.gz SIGMA_InVivo_Anatomical_Brain_skull.nii.gz SIGMA_InVivo_Anatomical_Brain_template.nii.gz SIGMA_InVivo_Anatomical_Brain_wm.nii.gz In-vivo diffusion T2-weighted template + B0 template + FA template + ADC template + brain mask. Spatial resolution 0.375x0.375x0.375mm. SIGMA_InVivo_Diffusion_Brain_adc.nii.gz SIGMA_InVivo_Diffusion_Brain_b0.nii.gz SIGMA_InVivo_Diffusion_Brain_fa.nii.gz SIGMA_InVivo_Diffusion_Brain_mask.nii.gz SIGMA_InVivo_Diffusion_Brain_t2.nii.gz Functional Imaging T2-weighted template + associated probabilistics maps (GM, WM, CSF, Skull, outbrain) + brain mask. Spatial resolution 0.375x1x0.375mm. SIGMA_InVivo_Functional_Brain_csf.nii.gz SIGMA_InVivo_Functional_Brain_epi.nii.gz SIGMA_InVivo_Functional_Brain_gm.nii.gz SIGMA_InVivo_Functional_Brain_mask.nii.gz SIGMA_InVivo_Functional_Brain_t2.nii.gz SIGMA_InVivo_Functional_Brain_wm.nii.gz SIGMA Rat Brain Atlas Version 2.0 : Waxholm atlas Feat. SIGMA In this second version of the SIGMA resources we deliver a new SIGMA brain atlas obtained by the normalization of the Waxholm space published by Kleven, H. et al. Nat Methods (2023, https://doi.org/10.1038/s41592-023-02034-3). The Waxholm rat brain atlas is currently the best numerical 3D atlas of the rat brain. In accordance with authors of this paper we are authorized to modify and embed the WHS atlas within the SIGMA environement to standardize the identification of brain territories. We provide a normalized version the WHS for both ex-vivo and in-vivo of the anatomical SIGMA templates. Finally, we offer linear and non-linear transformations to enable your data to commute between the SIGMA and WHS ex-vivo environments using ANTs commands. Ex-vivo atlas WHS rat brain atlas normalized in ex-vivo T2*-weighted SIGMA template + List of 222 labels created in ITKSnap Format + linear and non-linear transformations for SIGMA-WHS journeys (WHS-to-SIGMA_Transformations folder). Spatial resolution 0.09x0.09x0.09mm. SIGMA_ExVivo_Anatomical_Brain_Atlas.nii.gz SIGMA_ExVivo_Anatomical_Brain_Atlas.txt ./WHS-to-SIGMA_Transformations/reference_SIGMA.nii.gz ./WHS-to-SIGMA_Transformations/reference_WHS.nii.gz ./WHS-to-SIGMA_Transformations/WHS-in-SIGMA_transform_01_InverseWarp.nii.gz ./WHS-to-SIGMA_Transformations/WHS-in-SIGMA_transform_01_Warp.nii.gz ./WHS-to-SIGMA_Transformations/WHS-in-SIGMA_transform_02_GenericAffine.mat ./WHS-to-SIGMA_Transformations/WHS-in-SIGMA_transform_03_GenericAffine.mat ./WHS-to-SIGMA_Transformations/WHS_SD_rat_atlas_v4.nii.gz ./WHS-to-SIGMA_Transformations/WHS-to-SIGMA_byANTS.txt In-vivo atlas WHS rat brain atlas normalized in in-vivo T2 SIGMA anatomical template + List of 222 labels created in ITKSnap Format. Spatial resolution 0.15x0.15x0.15mm. SIGMA_InVivo_Anatomical_Brain_Atlas.nii.gz SIGMA_InVivo_Anatomical_Brain_Atlas.txt SIGMA brain meshes Rat brain mesh created using BrainNet viewers commands in matlab (https://www.nitrc.org/projects/bnv/). Spatial resolution 0.09x0.09x0.09mm. SIGMA_Anatomical_Brain_Atlas_mesh.nv SIGMA functional atlas In the original publication of the SIGMA resources, we developed a functional atlas for the rat brain, using a group ICA analysis validated through a RAICAR approach. From this analysis, we identified 59 bilateral ROIs covering cortical, sub-cortical and brainstem structures that are functionally distinct. Despite having been derived from purely functional data, this atlas broadly, if not precisely, correlates with the general anatomical boundaries and many are associated with specific anatomical structures. A primary motivation for the creation of this atlas is derived from the need to perform brain segmentations which is optimized for functional MRI analysis, since the signal sources do not necessarily match typical anatomical boundaries. A similar requirement has been identified by those performing human studies, resulting in efforts to generate more diverse, multi-modal atlases. SIGMA rat brain functional atlas normalized in in-vivo T2 SIGMA functional template + List of 59 labels created in ITKSnap Format. Spatial resolution 0.375x1x0.375mm. SIGMA_Functional_Brain_Atlas_Labels.txt SIGMA_Functional_Brain_Atlas_ListOfStructures.csv SIGMA_InVivo_Functional_Brain_Atlas.nii.gz SIGMA CT/TEP template In this version of the SIGMA resources we included a CT/TEP template built from the data that previously published (Barriere D.A. et al 2018 , Sci Rep. 2018 Jan 11;8(1):424. doi: 10.1038/s41598-017-18896-5) and acquired on a Triumph™ PET/CT dual modality imaging platform (Gamma Medica, Inc., Northridge, CA, USA), consisting of a LabPET™ avalanche photodiode-based digital PET scanner with a 7.5 cm axial field-of-view capable of achieving an isotropic spatial resolution. A caudal injection of approximately 30 MBq of [18F]-FDG was applied followed by a static acquisition to evaluate [18F]-FDG uptake within brain. CT images were acquired from the high-resolution X-ray computed tomography (CT) modality. Images were reconstructed using the Triumph™ PET/CT software. using the following parameters: 20 iterations, span of 63, field of view of 80 mm with a final matrix resolution of 160 × 160 × 128 and a voxel size of 0.5 × 0.5 × 0.597 mm. Brain dynamic [18F]-FDG images were reconstructed using the same protocol but we generated 32 frames (10 for 5 s, 7 for 10 s, 6 for 30 sec, 6 for 120 s, 2 for 240 s and 1 for 300 s). [18F]-FDG images were reconstructed using 3-D MLEM algorithm providing 0.5 × 0.5 × 0.597 mm images. CT scans were reconstructed using the standard FBP kernel analytical reconstruction algorithms, providing an isotropic image of 512 slices with a final resolution of 0.165 µm isotropic. Both [18F]-FDG and CT data were spatially normalized to the SIGMA ex-vivo template using the previously described methods. Spatial resolution 0.09x0.09x0.09mm. SIGMA_InVivo_18FDG_Brain_template.nii.gz SIGMA_InVivo_CT_Brain_template.nii.gz Important Note SIGMA ressources are provided at the scanner resolution and are oriented in anterior commisure/posterior commisure axis. Center of the images have been set at the anterior commisure level (Bregma 0 mm). Nevertheless, users are invited to increase the resolution of the current images for using in SPM or FSL for accurate coregistration and normalization steps (we recommand x10 increasing). No manipulation of image resolution are required with ANTs. Not tested with AFNI. For any questions regarding the SIGMA ressource, please email the SIGMA Team (sigma.preclinical.resources@gmail.com) or Email directly David A. Barrière (david.barriere@cnrs.fr). REFERENCES Barrière, D.A. et al. The SIGMA rat brain templates and atlases for multimodal MRI data analysis and visualization. Nat Commun 10, 5699 (2019). https://doi.org/10.1038/s41467-019-13575-7 Kleven, H. et al. Waxholm Space atlas of the rat brain: a 3D atlas supporting data analysis and integration. Nat Methods 20, 1822–1829 (2023). https://doi.org/10.1038/s41592-023-02034-3 Barrière, D.A. et al. Combination of high-fat/high-fructose diet and low-dose streptozotocin to model long-term type-2 diabetes complications. Sci Rep. 2018 Jan 11;8(1):424. doi: 10.1038/s41598-017-18896-5. PMID: 29323186; PMCID: PMC5765114. RELATED WORKS USING THE SIGMA RESSOURCES Grandjean J. et al. A consensus protocol for functional connectivity analysis in the rat brain. Nat Neurosci. 2023 Apr;26(4):673-681. doi: 10.1038/s41593-023-01286-8. Epub 2023 Mar 27. Erratum in: Nat Neurosci. 2023 Jun;26(6):1127-1128. PMID: 36973511; PMCID: PMC10493189. Vidal B. et al. Inter-subject registration and application of the SIGMA rat brain atlas for regional labeling in functional ultrasound imaging. J Neurosci Methods. 2021 May 1;355:109139. doi: 10.1016/j.jneumeth.2021.109139. Epub 2021 Mar 16. PMID: 33741345. Barrière D.A. et al. Paracetamol is a centrally acting analgesic using mechanisms located in the periaqueductal grey. Br J Pharmacol. 2020 Apr;177(8):1773-1792. doi: 10.1111/bph.14934. Epub 2020 Jan 22. PMID: 31734950; PMCID: PMC7070177 Barrière D.A. et al. Structural and functional alterations in the retrosplenial cortex following neuropathic pain. Pain. 2019 Oct;160(10):2241-2254. doi: 10.1097/j.pain.0000000000001610. PMID: 31145220. Magalhães, R. et al Resting-State Functional MR Imaging and Spectroscopy Study of the Dorsal Hippocampus in the Chronic Unpredictable Stress Rat Model. J Neurosci. 2019 May 8;39(19):3640-3650. doi: 10.1523/JNEUROSCI.2192-18.2019. Epub 2019 Feb 25. PMID: 30804096; PMCID: PMC6510342. Magalhães, R. et al The dynamics of stress: a longitudinal MRI study of rat brain structure and connectome. Mol Psychiatry. 2018 Oct;23(10):1998-2006. doi: 10.1038/mp.2017.244. Epub 2017 Dec 5. PMID: 29203852.

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    ZENODO
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      ZENODO
      Dataset . 2019
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    Authors: Ikkai, Akiko; Dandekar, Sangita; Curtis, Clayton E.;

    Attending to a task-relevant location changes how neural activity oscillates in the alpha band (8–13Hz) in posterior visual cortical areas. However, a clear understanding of the relationships between top-down attention, changes in alpha oscillations in visual cortex, and attention performance are still poorly understood. Here, we tested the degree to which the posterior alpha power tracked the locus of attention, the distribution of attention, and how well the topography of alpha could predict the locus of attention. We recorded magnetoencephalographic (MEG) data while subjects performed an attention demanding visual discrimination task that dissociated the direction of attention from the direction of a saccade to indicate choice. On some trials, an endogenous cue predicted the target’s location, while on others it contained no spatial information. When the target’s location was cued, alpha power decreased in sensors over occipital cortex contralateral to the attended visual field. When the cue did not predict the target’s location, alpha power again decreased in sensors over occipital cortex, but bilaterally, and increased in sensors over frontal cortex. Thus, the distribution and the topography of alpha reliably indicated the locus of covert attention. Together, these results suggest that alpha synchronization reflects changes in the excitability of populations of neurons whose receptive fields match the locus of attention. This is consistent with the hypothesis that alpha oscillations reflect the neural mechanisms by which top-down control of attention biases information processing and modulate the activity of neurons in visual cortex. IkkaiDataUpload

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    DRYAD; ZENODO; NARCIS
    Dataset . 2017
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    Dataset . 2016
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      DRYAD; ZENODO; NARCIS
      Dataset . 2017
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      DANS-EASY
      Dataset . 2016
      Data sources: B2FIND
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    Authors: Yu, Ke; Zhang, Mingjie;

    A 53-year-old man with a recent history of left temporal lobe hemorrhage presented with weakness of left upper limb for 4 days. Paragonimus eggs were detected in patient's sputum. Work up revealed paragonimiasis, a lung fluke worm infection endemic to East Africa, West Africa and South America which rarely involves in brain. Characteristic tunnel sign was visualized on brain MRI. Notably, susceptibility-weighted imaging revealed a migratory pattern of the parasite from the left temporal lobe to the right parietal lobe and resultant patchy hemorrhage. Treatment with praziquantel resulted in favorable outcome without residual deficits.

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    DRYAD; ZENODO
    Dataset . 2022
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      DRYAD; ZENODO
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    Authors: Du, Andrew; Zipkin, Andrew M.; Hatala, Kevin G.; Renner, Elizabeth; +4 Authors

    A large brain is a defining feature of modern humans, yet there is no consensus regarding the patterns, rates, and processes involved in hominin brain size evolution. We use a reliable proxy for brain size in fossils, endocranial volume (ECV), to better understand how brain size evolved at both clade- and lineage-level scales. For the hominin clade overall, the dominant signal is consistent with a gradual increase in brain size. This gradual trend appears to have been generated primarily by processes operating within hypothesized lineages – 64% or 88% depending on whether one uses a more or less speciose taxonomy, respectively. These processes were supplemented by the appearance in the fossil record of larger-brained Homo species and the subsequent disappearance of smaller-brained Australopithecus and Paranthropus taxa. When the estimated rate of within-lineage ECV increase is compared to an exponential model that operationalizes generation-scale evolutionary processes, it suggests that the observed data were the result of episodes of directional selection interspersed with periods of stasis and/or drift; all of this occurs on too fine a time scale to be resolved by the current human fossil record, thus producing apparent gradual trends within lineages. Our findings provide a quantitative basis for developing and testing scale-explicit hypotheses about the factors that led brain size to increase during hominin evolution. Appendix S1Supplementary methods, results, figures, and tables for the analysis.Du et al 2018 revised Appendix S1 R2_ESM_FINAL changes accepted.docxTable S1Excel spreadsheet with the raw data used for all analyses. Each row is a separate specimen along with its ID. Columns include the “lumper’s” and “splitter’s” taxonomy used in the random effects ANOVA to get inter-observer error (“lump.taxon” and “split.taxon”), the less and more speciose lineages used in the lower-taxonomic additive partitioning analyses (“lump.part” and “split.part”), region where each specimen comes from which aided in the allocation of specimens to lineages (“region”), grade for coding points in fig. 2 (“grade”), which specimens were excluded for the damaged specimens sensitivity analysis (“reliab.sens”), ECV replicate measurements from different researchers (“ecv1” to “ecv6”), and the various dates for each specimen (“min.date”, “max.date”, “mean.date”, and “sd.date”) and their respective age error distribution (“date.dist”).ProcB SI ECV dataset FINAL.xlsxR scriptR script for (1) running variance partitioning analyses to get inter-observer endocranial volume (ECV) error, (2) fitting evolutionary mode models to the hominin clade-level ECV data using the R package "paleoTS", (3) calculating R2 and model parameters for the gradualism model, and (4) running the lower taxonomic level additive partitioning analyses.Du et al R script_ESM.txt

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    DANS-EASY
    Dataset . 2018
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    DRYAD; ZENODO; NARCIS
    Dataset . 2018
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      DANS-EASY
      Dataset . 2018
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      DRYAD; ZENODO; NARCIS
      Dataset . 2018
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    Authors: Andy R. Eugene; Wayne T. Nicholson;

    Apharmacodynamic model,with parameters in the table below, may be used to visualize the propranolol concentration-effect (β-blockade) relationship in patients suffering from angina pectoris. These results have been adapted from the Pine et al article published in Circulation in 1975 which identified a linear relationship plasma Propranolol (ng/mL) to an effect of % β-Adrenergic Blockade in a single-oral dose of 40mg Propranolol in exercising individuals (Pine et al., 1975). https://www.edusoft.ro/brain/index.php/brain/issue/view/30

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    ZENODO
    Image . 2015
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Image . 2015
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Image . 2015
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Image . 2015
      License: CC BY
      Data sources: ZENODO
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    Authors: van der Burgh, Hannelore;

    Objective: To understand the progressive nature of amyotrophic lateral sclerosis (ALS) by investigating differential brain patterns of gray and white matter involvement in clinically or genetically defined subgroups of patients using cross-sectional, longitudinal and multimodal MRI. Methods: We assessed cortical thickness, subcortical volumes and white matter connectivity from T1-weighted and diffusion-weighted MRI in 292 ALS patients (follow-up: n=150) and 156 controls (follow-up: n=72). Linear mixed-effects models were used to assess changes in structural brain measurements over time in patients compared to controls. Results: Patients with a C9orf72 mutation (n=24) showed widespread gray and white matter involvement at baseline, and extensive loss of white matter integrity in the connectome over time. In C9orf72-negative patients, we detected cortical thinning of motor and frontotemporal regions, and loss of white matter integrity of connections linked to the motor cortex. Spinal-onset patients displayed widespread white matter involvement at baseline and gray matter atrophy over time, whereas bulbar-onset patients started out with prominent gray matter involvement. Patients with unaffected cognition or behavior displayed predominantly motor system involvement, while widespread cerebral changes, including frontotemporal regions with progressive white matter involvement over time, were associated with impaired behavior or cognition. Progressive loss of gray and white matter integrity typically occurred in patients with shorter disease durations (<13 months), independent of progression rate. Conclusions: Heterogeneity of phenotype and C9orf72 genotype relates to distinct patterns of cerebral degeneration. We demonstrate that imaging studies have the potential to monitor disease progression and early intervention may be required to limit cerebral degeneration. Supplemental Data corresponding to research paper 'A multimodal longitudinal study of structural brain involvement in ALS' It contains appendices, supplemental figures and supplemental tables for this paper.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Clark, Shaunna L.; Costin, Blair N; Chan, Robin F.; Johnson, Alexander W.; +8 Authors

    Background: Recent reviews have highlighted the potential use of blood‐based methylation biomarkers as diagnostic and prognostic tools of current and future alcohol use and addiction. Due to the substantial overlap that often exists between methylation patterns across different tissues, including blood and brain, blood‐based methylation may track methylation changes in brain; however, little work has explored the overlap in alcohol‐related methylation in these tissues. Methods: To study the effects of alcohol on the brain methylome and identify possible biomarkers of these changes in blood, we performed a methylome‐wide association study in brain and blood from 40 male DBA/2J mice that received either an acute ethanol (EtOH) or saline intraperitoneal injection. To investigate all 22 million CpGs in the mouse genome, we enriched for the methylated genomic fraction using methyl‐CpG binding domain (MBD) protein capture followed by next‐generation sequencing (MBD‐seq). We performed association tests in blood and brain separately followed by enrichment testing to determine whether there was overlapping alcohol‐related methylation in the 2 tissues. Results: The top result for brain was a CpG located in an intron of Ttc39b (p = 5.65 × 10−08), and for blood, the top result was located in Espnl (p = 5.11 × 10−08). Analyses implicated pathways involved in inflammation and neuronal differentiation, such as CXCR4, IL‐7, and Wnt signaling. Enrichment tests indicated significant overlap among the top results in brain and blood. Pathway analyses of the overlapping genes converge on MAPKinase signaling (p = 5.6 × 10−05) which plays a central role in acute and chronic responses to alcohol and glutamate receptor pathways, which can regulate neuroplastic changes underlying addictive behavior. Conclusions: Overall, we have shown some methylation changes in brain and blood after acute EtOH administration and that the changes in blood partly mirror the changes in brain suggesting the potential for DNA methylation in blood to be biomarkers of alcohol use. READMECpG LocationsContains RaMWAS formatted files of CpG locationsCpG_locations.zipPhenotype FileContains phenotype data used in association analyses. Please see README file for data packets for description of variables.phenotype_file.txtCoverageContains RaMWAS formatted coverage (i.e., CpG score) files for each CpGcoverage.tar.gz

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    DANS-EASY
    Dataset . 2018
    Data sources: B2FIND
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    DRYAD; ZENODO; NARCIS
    Dataset . 2018
    License: CC 0
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      DANS-EASY
      Dataset . 2018
      Data sources: B2FIND
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      DRYAD; ZENODO; NARCIS
      Dataset . 2018
      License: CC 0
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    Authors: Bilgic, Berkin; Langkammer, Christian; Marques, José P.; Meineke, Jakob; +2 Authors

    This repository contains information about submitted solutions and resulting analysis metrics of the 2019 Quantitative Susceptibility Mapping Reconstruction Challenge. The original susceptibility maps submitted for participation in the challenge are available here and here. The package contains seven Comma-Separated Values (CSV) files and two PDF files: master_stage1_anonymized.csv: Results of stage 1 of the challenge at the time of presentation at the workshop (fully-blinded); master_stage2_snr1_anonymized.csv: Results of stage 2 of the challenge using the high noise dataset at the time of presentation at the workshop (fully-blinded); master_stage2_snr2_anonymized.csv: Results of stage 2 of the challenge using the low noise dataset at the time of presentation at the workshop (fully-blinded); submission_form_stage1.pdf: PDF export of the online form used in stage 1; submission_form_stage2.pdf: PDF export of the online form used in stage 2. For the manuscript, we analyzed these CSV files with scripts reported here. Each csv file contains metrics for all submitted solutions along with detailed information about the algorithm used, provided by the participant at the time of submission. The very first record in each file is a header containing a list of field names: normalized rmse: Whole-brain root-mean-squared error relative to ground truth; rmse_detrend_tissue: Root-mean-squared error relative to ground truth (after detrending) in grey and white matter mask; rmse_detrend_blood: Root-mean-squared error relative to ground truth (after detrending) using a one-pixel dilated vein mask; rmse_detrend_DGM: Root-mean-squared error relative to ground truth (after detrending) in a deep gray matter mask (substantia nigra & subthalamic nucleus, red nucleus, dentate nucleus, putamen, globus pallidus and caudate); DeviationFromLinearSlope: Absolute difference between the slope of the average value of the six deep gray matter regions vs. the prescribed mean value and 1.0; CalcStreak: Estimation of the impact of the streaking artifact in a region of interest surrounding the calcification through the standard deviation of the difference map between reconstruction and the ground truth; DeviationFromCalcMoment: Absolute deviation from the volumetric susceptibility moment of the reconstructed calcification, compared to the ground truth (computed at in the high-resolution model); Submission Identifier: Self-chosen unique identifier of the submission; Submission Identifier of the corresponding Stage 1 submission: This is the Submission Identifier of the solution submitted to Stage 2 that was calculated with a similar algorithm in Stage 1; Changes with respect to Stage 1 submission: Self-reported information about modifications made to the algorithm for Stage 2; Number of submissions in Stage 2: The number of solutions that were submitted to Stage 2 with a similar algorithm; Sim1/Sim2: Filename of the submitted solutions for Stage 1; File name of the zip-file you are going to upload: Filename of the file uploaded to Stage 2; Full name of the algorithm: Self-reported full name of the algorithm used; Preferred Acronym: Self-reported acronym of the algorithm used; Algorithm-type: Self-reported type of algorithm used; Does your algorithm incorporate information derived from magnitude images?: Self-reported Yes/No; Regularization terms: Self-reported types of regularization terms involved; Did your algorithm use the provided frequency map or the four individual echo phase images?: Self-reported information about involved magnitude information; Publication-ready description of the reconstruction technique: Self-reported description of the algorithm; Publications that describe the algorithm: Self-reported literature reference; Algorithm publicly available?: Self-reported public availability of the algorithm; If your algorithm is not yet publicly available, would you be willing to make it available at the end of the challenge?: Self-reported willingness to share the algorithm code with the public; Specific information about this solution: Self-reported detailed information about the solution; Herewith, I permit the QSM Challenge committee to publish my uploaded files (calculated maps) after the completion of the challenge: Self reported agreement with publication of submitted solution; Ground truth was not explicitly or implicitly incorporated into your algorithm or solution: Self-reported confirmation that the ground truth was not incorporated in the solution.

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    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: Datacite
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    Authors: Stenroos Petteri; Guillemain Isabelle; Tesler Federico; Montigon Olivier; +6 Authors

    This repository contains measured and simulated data for the manuscript titled "How Absence Seizures Impair Sensory Perception: Insights from Awake fMRI and Simulation Studies in Rats" by Stenroos P. et al. Electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) data was recorded simultaneously from awake GAERS; a rat model of absence epilepsy. Visual and whisker stimulation was experimentally applied, and visual stimulation was simulated during interictal and ictal states and whole brain hemodynamic and neural responsiveness was compared between states.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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    Authors: Macrì, Simone; Di-Poï, Nicolas;

    4K movie displaying the 3D rendering of the head and both the morphological features and spatial organization of the main encephalic subdivisions of the Golden Flying Snake. The brain reconstruction was obtained from a microCT scan of a iodine-stained specimen through manual segmentation using the software Amira 5.5.0. Other videos can be found here. If you are interested in reptile brain evolution and behavior, please, have a look to our recent publication: "Comparative analysis of squamate brains unveils multi-level variation in cerebellar architecture associated with locomotor specialization" Simone Macrì, Yoland Savriama, Imran Khan & Nicolas Di-Poï Nature Communications 10, 5560 (2019) For any inquiries or additional information, please, refer to the contacts provided in the article.

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    ZENODO
    Audiovisual . 2020
    License: CC BY NC ND
    Data sources: ZENODO
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    ZENODO
    Audiovisual . 2020
    License: CC BY NC ND
    Data sources: Datacite
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      ZENODO
      Audiovisual . 2020
      License: CC BY NC ND
      Data sources: ZENODO
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      ZENODO
      Audiovisual . 2020
      License: CC BY NC ND
      Data sources: Datacite
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