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Templates and automation no longer simplest save time and confirm consistency in verbal exchange however additionally aid track performance and optimize your prospecting strategy. * * * As a B2B marketer, your work does not birth or conclusion with passing along generated ends up in the earnings team. What’s greater critical is empowering them with focused content assets to convey a more personalised and engaging experience to possibilities and patrons. The package for the implementation of the 2021 HPC equipment was launched nowadays The kit for the implementation of the 2021 HPC equipment become released these days. The HPC kit contains: A Step-by means of-Step ebook to increase the HNO and HRP. The HNO and HRP templates. Complementary suggestions on the Joint Intersectoral evaluation Framework (JIAF) for the HNO. Complementary tips on Response analysis, goals and focused on for the HRP. The Step-with the aid of-Step e book and the HNO and HRP templates are updates of the identical files that international locations used last year. they’ve been adjusted to replicate the instructions learned from final 12 months’s first introduction of the ‘superior’ HPC. They aim to assist container groups sequence the quite a lot of actions required to increase the HNO and HRP, and to mirror the effects in a greater streamlined way in written documents. They also contain a powerful emphasis on inclusivity in accordance with our commitments on incapacity, gender equality and prevention of GBV, as showcased already within the GHRP and the imminent GHO. The JIAF counsel responds to a clear gap identified ultimate yr and is the effect of many months of consultations with UN corporations, global Clusters, NGOs and donors a part of the Joint Intersectoral evaluation group (JIAG). The complementary assistance for the HRP additionally responds to gaps recognized last year and limitations referred to in HRP files, and to expectations from donors for more evidently centered and prioritized responses, and clear funding requests. As of now, the files are shared as notice information, whereas conversions to InDesign and translations in French, Arabic and Spanish were initiated. The wholly formatted and translated information could be shared with you as quickly as feasible, with a bit of luck no later than conclusion August. Julich-mind: A 3D probabilistic atlas of the human brain’s cytoarchitecture a gift-day atlas of the human mind Defining mind regions and demarking their spatial extent are crucial desires in neuroscience. a latest map of the brain’s cellular constitution, a cytoarchitectonic atlas, should deliver maps of areas in three dimensions, integrate contemporary advantage about mind parcellation, consider diversifications between individual brains, count on reproducible workflows, and provide net-based links to other materials and databases. Amunts et al. created such an atlas in accordance with serial histological sections of mind. They developed a computational framework and sophisticated the existing boundaries of the human brain in accordance with cytoarchitectural patterns. This method can conveniently be transferred to build brain atlases for other species or a spatial framework for other organs, other modalities, or multimodal maps for regions of interest at larger spatial scales. This research makes equivalent future makes an attempt concurrently reproducible and versatile. Science, this concern p. 988 summary Cytoarchitecture is a primary precept of microstructural brain parcellation. We introduce Julich-mind, a third-dimensional atlas containing cytoarchitectonic maps of cortical areas and subcortical nuclei. The atlas is probabilistic, which permits it to account for adaptations between particular person brains. building such an atlas become highly facts- and labor-intensive and required the building of nested, interdependent workflows for detecting borders between mind areas, records processing, provenance monitoring, and flexible execution of processing chains to address giant amounts of facts at distinctive spatial scales. Full cortical insurance turned into done by the inclusion of hole maps to enrich cortical maps. The atlas is dynamic and may be tailored as mapping progresses; it’s overtly accessible to assist neuroimaging reviews as well as modeling and simulation; and it is interoperable, enabling connection to different atlases and resources. Maps of the microstructural segregation of the human mind can offer enhanced knowing of the organic substrates of mind capabilities, dysfunctions, and habits. Cytoarchitecture—the association of cells, their distribution, composition, and layering—is a huge precept of microstructural mind corporation. it is closely linked to the connectivity sample of a region and its characteristic (1). in addition, cytoarchitecture allows for numerous points of brain corporation (equivalent to myeloarchitecture, molecular architecture, gene expression, and activation or resting-state networks) to be referenced to a common ground that serves as the interface to represent and integrate the diverse points of mind organization (2). it’s largely approved that a multifaceted but built-in method is a prerequisite for analysis into mind corporation (three, 4). Brodmann’s cytoarchitectonic map from 1909 was probably the most first maps of its form and remains known. It has a couple of limitations; for example, it indicates only the left hemisphere of a single mind and for this reason cannot account for intersubject variability. evidence has been obtained that the number of cortical areas is in the latitude of 180 or extra (2, 5, 6), as in comparison to forty three areas in Brodmann’s map. Subcortical buildings had been mapped with the identical level of detail (7) however aren’t part of the Brodmann atlas. without analyzing and processing lots of histological sections per mind with constantly high great, the variable cytoarchitecture of areas and nuclei in a cytoarchitectonic map cannot be captured with adequate spatial resolution (eight). We created the Julich-mind atlas in our labs in Jülich and Düsseldorf (Fig. 1). it is a cytoarchitectonic atlas containing probabilistic maps of cortical areas and subcortical nuclei. Having began this activity in the mid-1990s, we extra recently resorted to “crowdsourcing” techniques (however on a high expert level and in keeping with profound advantage), which in turn required massive records–equipped processing workflows. the entire necessary steps—education of human brain tissue, microstructural mapping, analysis, and complicated statistics processing—are information-, time-, and labor-intensive, all of the greater so with expanding pattern sizes and better spatial decision. it’s accordingly not possible to provide whole-mind maps with ample detail via single researchers or small teams in an appropriate time body. extended computing vigor and storage capacities, as well as more desirable algorithms and workflows for facts processing, now allow tons quicker and greater mighty processing at high spatial decision. Fig. 1 Cytoarchitectonic highest chance maps of Julich-brain in MNI-Colin27 reference area. Areas have distinctive colors; views of the left and appropriate hemispheres are shown. The decrease panel suggests structures located in the depths of the mind. Datasets of published areas are freely available through the Julich-brain and HBP data portals. each net-based mostly interfaces enable the visualization and inspection of probabilistic and optimum probability maps as floor (pial, smoothed white count number, inflated) and volume representations. besides the fact that children, now not all datasets and analyses benefit equally from improved records acquisition options. Our cytoarchitectonic mapping efforts begun greater than 25 years in the past. The brains have already been histologically processed, and neither new high-box magnetic resonance imaging (MRI) records nor high-decision blockface photos may also be acquired later on. The excellent of MRI data is therefore constrained with the aid of the nice accessible at the time of acquisition. This may also from time to time prevent the use of modern imaging tools and strategies, as a result of these are sometimes geared towards currently attainable statistics great. specific statistics processing ideas considering that both recent and older datasets are necessary. To make certain accuracy, reproducibility, and consistency of statistics and processing steps over the complete records lifestyles cycle, automatic and reproducible workflows governed through provenance monitoring are essential. We hence developed a modular, flexible, and adaptive framework to create probabilistic cytoarchitectonic maps, on account of the evaluation of 10 postmortem human brains, for each and every area (Fig. 2). Maps had been aligned to 2 ordinary stereotaxic areas, MNI-Colin27 and ICBM152casym area (9), and superimposed. The Julich-mind atlas permits evaluation of purposeful activations, networks, genetic expression patterns, anatomical buildings, and different facts obtained across diverse reviews in a common stereotaxic reference house (Fig. three). The framework relies on long-standing capabilities for handling whole human postmortem brains, cytoarchitectonic mapping of a lot of cortical and subcortical regions, and computational advantage to increase robust and adaptive equipment, the usage of both native clusters and supercomputers. All of these elements have modified over time, and the introduction of a uniform, reproducible, and probabilistic mind atlas depends on their convergence. Fig. 2 Workflow of the 3D reconstruction of serial histological sections and alignment of brain information to a reference area, cytoarchitectonic evaluation in second images, and the computing of the probabilistic Julich-mind atlas. (A) To improve the 3D shape of a postmortem mind, we applied a mix of linear and nonlinear processing steps at distinct scales in keeping with the undistorted MRI dataset, and non-compulsory on blockface images, bought throughout sectioning. The digitized histological pictures have been repaired and corrected for illumination and optical imbalances. A rigid area-to-part alignment turned into computed to create a primary approximate 3D reconstruction. It served to align the MRI dataset to the corresponding area planes by a rigid-body transformation. The sections had been nonlinearly registered to the sections of the MRI by using an elastic method. The alignment turned into more suitable by using re-registering the output several instances, area with the aid of area, to a median filtered version. (B) The cytoarchitecture was analyzed in consecutive histological sections overlaying the finished extent of a local and characterised by using the gray-degree index (16). Contour strains of the areas were submitted to a knowledge repository, 3D-reconstructed, and topologically normalized. Linear and nonlinear transformations have been utilized to the areas, and areas were superimposed to form the cytoarchitectonic likelihood map. (C) volume- and surface-based highest likelihood maps of the Julich-brain atlas had been computed. To easily organize the intensive computations, we applied a data processing management gadget that allowed disbursed processing of a big number of datasets throughout diverse CPU cores. It changed into designed to scale up smartly from a single core desktop equipment up to hundreds of computing nodes in a high-efficiency computing ambiance (12). See fig. S1, A to C, for particulars. Fig. three Examples of applications of the Julich-brain atlas. (A) Screenshot of the net-based atlas device in the Human brain Atlas of the HBP displaying chance maps in diverse views. (B and C) JuGEx allows evaluation of differential gene expression in cytoarchitectonic maps (B) and linkage and exploration of DTI-based mostly connectivity (e.g., left Broca’s area forty five) (C). (D) Superposition of the probabilistic map of Broca’s enviornment forty four on a dataset of a patient with a mind lesion and aphasia. The maps permit genuine statements concerning the microanatomical place of a neuroimaging discovering, and are a device to quantify these findings through the use of the maps as a mask. The Julich-brain atlas is based on histological sections of 23 postmortem brains (11 female, 12 male; suggest age = sixty four years, age range = 30 to 86 years; suggest postmortem extend = 12 hours; table S1) acquired from the physique donor courses of the Anatomical Institute of the university of Düsseldorf. The brains had been fixed in formalin or Bodian answer, subjected to MRI, embedded in paraffin, and serially reduce with a microtome into 20-μm sections (10). telephone bodies have been stained using a modified Merker formula. Histological sections were digitized with flatbed scanners at 10 μm, reduced to an isotropic decision of 20 μm, framed to a hard and fast picture measurement, and stored as lossless compressed gray-degree photos. Two brains represent finished sequence [the “BigBrain datasets,” one of which was published in (11)], where each section changed into stained and digitized. The different brains have been stained with intervals of up to 15 sections. This resulted in more than 24,000 histological sections. Histological processing and marking, together with mounting of sections and removing of small wrinkles and folds, entailed some diploma of native deformation, damage, or staining inhomogeneity. however this become unavoidable, fewer than 1% of the sections confirmed irretrievable hurt (e.g., lack of sizeable parts of the tissue), and 20 to 30% had small, native damages. To correct for distortions in histological sections, we used the corresponding MRI datasets for 3D reconstruction (Fig. 2). fairly extreme areas of damage in images of histological sections had been manually and, where relevant, semiautomatically corrected (11) (fig. S1A). The time-ingesting repairs and the appreciable quantity of computing time for processing the BigBrain datasets required a workflow using supercomputers. The colossal number of datasets, in combination with the complexity and variety of the common reconstruction workflow, required time- and resource-beneficial computational processing. This in turn required superior management of huge datasets, computing, and provenance monitoring (fig. S1). On the basis of the reconstruction of BigBrain 1 (11), an tailored workflow become developed for the reconstruction of BigBrain 2, which includes an complicated data provenance monitoring device. It served as the groundwork for a widespread-aim dataflow administration system that allowed limiting the recalculation to simplest these images that have been suffering from subsequent repairs (fig. S1A), resulting in a considerable discount of computing time (12). furthermore, the pipeline of the BigBrain 2 dataset became carefully linked to the universal workflow and become utilized in an analogous manner as for the other 21 postmortem brains. To recover the original shape and topology of the mind quantity, we computed a 3D reconstruction of histological sections (fig. S3); for a fresh survey of methods, see (13). The strategy is in line with a multistep method ranging from an at the start 3D dataset at a resolution of 0.3 mm3 and is defined in the supplementary substances. Human brains show a variable sample of sulci and gyri, plus intersubject variability healthy, localization, and extent of cytoarchitectonic regions (2). To make brains related, we in the beginning transferred 3D reconstructed histological datasets to the stereotaxic space of the one-discipline MNI-Colin27 template (fig. S3). In distinction to templates resulting from an “ordinary” of many mind datasets [e.g., the MNI305 template (9)], the individual reference mind indicates an in depth (however not representative) anatomy, for this reason enabling a exact registration of the gross anatomy of the postmortem brains to that area. as a result of suggest community datasets are well authorised in the neuroimaging neighborhood, nonlinear transformations into the ICBM2009casym space were also computed. This template represents a compromise between the designated but certain anatomy of the MNI-Colin27 brain and the extra familiar but smoother MNI305 template. The illustration of the maps in these two spaces makes Julich-brain interoperable with other atlases and elements [e.g., (6, 7, 14, 15)] and connects it to massive cohort reviews such as the Human Connectome project (HCP; www.humanconnectome.org) and UK Biobank (www.ukbiobank.ac.uk) (see also supplementary substances). diverse techniques can be found to register postmortem brains to each other [for an overview, see (13)]. To strengthen an atlas with both cortical areas and subcortical nuclei, we started with a volume-based approach, which provided a consistent registration framework for each cortical and subcortical constructions. An elastic 3D registration turned into utilized with a neatly-matched parameter set that become also used for the 2d registrations. The formula showed high reliability in each postmortem and in vivo datasets. The registration of all postmortem brains to the MNI-Colin27 and the ICBM152casym reference brains resulted in an identical folding sample and shape of the 3D-reconstructed datasets and the template (fig. S3). The 3D vector box transformations of each 3D-reconstructed histological dataset have been kept and have been later utilized to the mapped cytoarchitectonic areas. up to now, 41 tasks have resulted in maps of 248 cytoarchitectonic areas (Fig. 1). projects have been conducted by doctoral college students, researchers, and guest scientists and had been posted in peer-reviewed scientific journals [for an overview, see (2)]. These publications provide particulars of cytoarchitecture, localization with admire to sulci/gyri and stereotaxic area, intersubject variability, and other aspects; a few of them also check with the relationship of the areas to purposeful imaging stories, receptor architecture, and/or area-certain gene expression. each map is in line with analyses of 10 postmortem brains (5 male, 5 feminine), which have been selected from the pool of 23 brains on the basis of their folding sample, the presence of already mapped neighboring areas, the orientation of the slicing aircraft, etc. as a result, overlapping and often identical samples have been analyzed for diverse areas. reckoning on the measurement and form of a structure, each fifteenth to 60th part changed into mapped over the entire extent of a cytoarchitectonic region. Borders between cortical areas were recognized the usage of photo evaluation and statistical criteria to make mapping reproducible (16). The positions of borders have been labeled within the digitized sections, and a closed polygon (contour line) marked its extent in the section (fig. S4). For subcortical nuclei, the outer boundaries of nuclei had been recognized in histological sections and labeled as closed polygonal traces. Contour strains had been also used for a quality check of each and every map over its full extent (fig. S4). As a subsequent step, particular person shrinkage-corrected volumes for each and every enviornment/nucleus, hemisphere, and brain have been calculated (see supplementary materials). The analysis of the one hundred twenty at present accessible areas showed considerable intersubject differences in extent (fig. S5), however variations between the two hemispheres were not giant at a global degree, nor had been changes between male and feminine brains (see desk S2 for full counsel on these volumes). The record of volumes is a starting to be aid together with the probabilistic maps of areas and nuclei, made available and at all times up-to-date during the knowledge graph of the european Human mind undertaking (HBP; see https://ebrains.ecu). The comparison of the degree of intersubject variability suggests variations between brain regions (fig. S5). for instance, we found excessive variability (i.e., low values for chance) in Broca’s area with areas 44 and forty five and the advanced parietal lobule, whereas the occipital pole with the primary and secondary visual cortices (BA17/18) and enviornment Te3 in the temporal lobe looked less variable. previous and ongoing mapping projects resulted in additional than 10,616 XML files containing eighty five,210 contour strains with 3,737,771 features and a total length of 1961 m. Stacks with contour traces have been managed using the open-source edition handle software Subversion, which automatically manages information and directories with a view to document the finished background of how the localization of an areal border could have modified over its life cycle (fig. S1B). adjustments may additionally take place when a brand new mapping task requires reanalysis of an already present map, however these were small in the past. The areas and nuclei had been also modified to the stereotaxic MNI-Colin27 and ICBM2009casym reference space, using the computed complete-mind transformations, and superimposed (see supplementary materials). The resulting probabilistic, cytoarchitectonic maps were kept as quantity statistics data. via projecting the chance values onto the surface, a surface-based mostly representation of cytoarchitectonic cortical maps turned into computed (Fig. 1 and fig. S6). The ensuing values, indicating the likelihood of a neighborhood or nucleus being localized in a given voxel (0% to a hundred% overlap), range from 0.0 to 1.0; this quantity gives a measure for adaptations of a given area in localization and extent from brain to mind (i.e., intersubject variability). These probabilistic maps overlap with every other; it’s, voxels in reference space can usually be assigned to a couple of area, each and every with a well-defined chance, summing as much as a hundred% (fig. S6). We discovered that about 50% of the voxels were associated with a single area or nucleus, 35% with two, and 15% with three or greater. To reduce complexity and to visualize the extent of areas similar to Brodmann’s map, we calculated a optimum probability map (MPM). each voxel in the reference mind become assigned to the cytoarchitectonic area with the optimum probability at that place (17). The usual overlap fraction of all presently mapped and adjoining areas consequences in a threshold cost of about 35%. beneath the belief that this threshold additionally applies to MPM borders to yet unmapped areas, this threshold is used to reduce the MPM towards unmapped regions. The visualization of neighboring areas demonstrates that gyri may well be occupied with the aid of one or more areas that fluctuate in cytoarchitecture and performance. Conversely, single areas may well be discovered on a couple of gyrus; examples are given for the auditory cortex in fig. S7. The manifold of relationships between areas and sulci/gyri illustrates the potential and higher precision of cytoarchitectonic probabilistic maps as compared to macroscopic mind maps. At present, about 70% of the cortical surface has been lined by using completed and posted mapping initiatives. however, there are nevertheless areas that have not been mapped and signify projects for future research. To supply whole-brain coverage for the cortex (fig. S8), we’ve mixed ingredients of the cortex that have not yet been charted into a couple of “hole maps,” pooling these uncharted areas in a given mind place (see supplementary substances and fig. S9). The distributions have been modeled so that probabilistic gap maps had been computed in analogy to the other maps. As mapping progresses, new maps are continuously replacing hole maps whereas the process is captured and documented by provenance monitoring. because of this, the atlas is not static (as is, e.g., Brodmann’s map) but somewhat represents a “dwelling map”—a concept that is wide-spread, as an example, from geography for navigating complicated areas. hole maps allow computation of a parcellation masking the whole cortical floor and the unambiguous assignment of every position to a cortical area. along side the expanding variety of probabilistic maps of subcortical nuclei, gap maps make a contribution to a whole-mind human atlas. Maps in Julich-mind will also be combined with findings in other atlases and maps; one example includes the study of microstructural correlates of activations from neuroimaging reviews of in shape subjects and sufferers (Fig. three). furthermore, Julich-brain contributes to brain modeling and simulation via informing the model by a functionally sound microstructural parcellation. it is expected that this may open new avenues to generate fashions of brain exercise equivalent to those used in the remedy of epilepsy, where customized brain models are used to foretell the propagation of seizures (18). The modular, flexible, and extensible workflows cowl a wide latitude of steps from photograph acquisition to 3D reconstruction and the generation of probabilistic maps, which may also be present in several areas of analysis. The methodical framework (or materials of it) will also be prolonged to brains of other species, and it can be used to system area photographs labeled by way of different suggestions (e.g., immunohistology). New modules will also be added to the workflows for applications comparable to mapping mind areas on the groundwork of deep gaining knowledge of (19). The Julich-brain atlas is a freely available resource (www.julich-brain.org). Maps have been made available via different tools and web sites such because the SPM anatomy toolbox (www.fz-juelich.de/inm/inm-7/JuelichAnatomyToolbox), FSL (https://fsl.fmrib.ox.ac.uk), FreeSurfer (https://surfer.nmr.mgh.harvard.edu), and the EBRAINS analysis infrastructure of the HBP (https://ebrains.ecu/capabilities/atlases). The maps can be linked to diffusion tensor imaging (DTI)–based mostly connectivity information (Fig. 3C) and to gene expression records provided by way of the Allen Institute for mind Science (https://alleninstitute.org/what-we-do/brain-science) during the JuGEx device (20) to allow a multimodal point of view on human brain company (Fig. 3B). Julich-brain represents a new kind of human mind atlas that’s (i) cytoarchitectonic to replicate a simple principle of the mind’s microstructural parcellation; (ii) total-brain,to cover each the cerebral cortex and subcortical nuclei; (iii) 3D-probabilistic, to agree with adaptations between individual brains in stereotaxic area; (iv) dynamic, a residing atlas, to be supplemented by way of maps of latest areas or subdivision of existing maps of areas (e.g., when new stories suggest a finer or new parcellation); (v) flexible, to enable for adjustments of modules within the workflows for different facts modalities, organs, or species; (vi) open-access and in keeping with fair concepts, to make a contribution to stories through different researchers addressing structure-function relationships and community company; and (vi) interoperable, to link it to different atlases and materials that provide complementary tips about brain firm. References and Notes ↵ ↵ ↵ ↵ ↵ ↵ ↵ J. k. Mai et al., Atlas of the Human mind (educational Press, ed. four, 2015). ↵ ↵ ↵ ↵ ↵ H. Mohlberg et al., in brain-impressed Computing: 2nd international Workshop, BrainComp 2015, ok. Amunts et al., Eds. (Springer, 2016), pp. 15–27. ↵ ↵ H. Damasio, Human mind Anatomy in Computerized pictures (Oxford Univ. Press, ed. 2, 2005). ↵ ↵ ↵ ↵ ↵ H. Spitzer et al., in clinical image Computing and computing device Assisted Intervention—MICCAI 2018, part III, A. F. Frangi et al., Eds. (Springer, 2018), pp. 663–671. ↵ ↵ M. Gibb et al., in foreign convention on Computational strategies in systems Biology, D. Gilbert, M. Heiner, Eds. (Springer, 2012), pp. 2–16. M. Ono et al., Atlas of the Cerbral Sulci (Thieme, ed. 1, 1990). okay. Brodmann, Vergleichende Lokalisationslehre der Grosshirnrinde in ihren Prinzipien dargestellt auf Grund des Zellenbaues (Barth, Leipzig, 1909). C. von Economo, G. N. Koskinas, Die Cytoarchitektonik der Hirnrinde des erwachsenen Menschen (Springer, 1925). ↵ Acknowledgments: We thank more than 41 postdocs, doctoral college students, guests, and colleagues who contributed to the mapping of 248 cytoarchitectonic areas and nuclei; the technical assistants of the C. and O. Vogt Institute of Heinrich Heine institution Düsseldorf and analysis Centre Jülich; S. Eickhoff, R. Hübbers, P. Pieperhoff, N. Palomero-Gallagher, S. Caspers, T. Dickscheid, and A. C. Evans for intensive discussions; A. Schleicher for developing the common observer-impartial mapping approach; our partners of the foreign Consortium for Human mind Mapping who motivated and actively supported this analysis; and our colleagues at the Jülich Supercomputing Centre, in certain B. Tweddell and T. Lippert. Funding: Supported by the Portfolio Theme “Supercomputing and Modeling for the Human brain” of the Helmholtz affiliation, the european Union Seventh Framework Programme (FP7/2007-2013, HBP), and the eu Union’s Horizon 2020 research and Innovation Programme, provide agreements 604102 (HBP SGA1), 785907 (HBP SGA2), and 945539 (HBP SGA3). writer contributions: k.A. and k.Z. developed the conception of 3D probabilistic cytoarchitectonic brain mapping and atlas. ok.A. is overseeing the atlas projects, contributed to methodological developments, and drafted the manuscript. H.M. developed and tailored the methodology and the software for information processing and atlas constructing, and drafted the manuscript. S.B. developed new equipment for analysis and mapping of cytoarchitectonic areas. All authors contributed to the writing of the manuscript. Competing interests: The authors declare no competing hobbies. facts and materials availability: All data are available often textual content or the supplementary substances. numerous statistics modalities of the already posted maps of brain areas and the comprehensive atlas are available on-line at www.julich-mind.org and via EBRAINS (https://ebrains.ecu/) (DOI: 10.25493/TAKY-64D). prior to now developed parts of the workflows have been published earlier; new scripts for computing the Julich-brain Atlas and for inspecting contour lines and gap maps (https://doi.org/10.5281/zenodo.3906413) are provided by means of the git repository, https://github.com/JulichBrainAtlas..

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