Jan G. Bjaalie

• Dickscheid, Timo; Gui, Xiaoyun; Simsek, Ahmet N.; Schiffer, Christian; Mangin, Jean Francois & Leprince, Yann [Show all 11 contributors for this article] (2026). Siibra: a software tool suite for realizing a Multilevel Human Brain Atlas from complex data resources. Nature Methods. ISSN 1548-7091. doi: 10.1038/s41592-026-03159-x. Full text in Research Archive Show summary

  Computational technology opens new possibilities toward understanding the complexity of the human brain, but it requires integrating measurements from different modalities and scales in an anatomical context and exposing them in an interoperable, actionable form. Especially with growing big data resources, accessing information from different scales and modalities coherently for visual exploration, reproducible analysis and application development remains challenging. Here we present siibra, a tool suite that connects diverse data from cloud resources to reference atlases and coordinate spaces. It supports different use cases by making contents accessible through a web viewer, Python library and HTTP application programming interface. Using siibra we implemented a Multilevel Human Brain Atlas linking macro-anatomical concepts and their inter-subject variability with measurements of the microstructural composition and intrinsic variance of brain regions, building on cytoarchitecture as a reference and supporting MRI-based and microscopic templates. The atlas is integrated with the EBRAINS research infrastructure. All software and content are openly accessible.

• Puchades, Maja; Yates, Sharon Christine; Csucs, Gergely; Carey, Harry Alexander Chandler; Balkir, Arda & Leergaard, Trygve B. [Show all 7 contributors for this article] (2025). Software and pipelines for registration and analyses of rodent brain image data in reference atlas space. Frontiers in Neuroinformatics. 19, p. 1–15. doi: 10.3389/fninf.2025.1629388. Full text in Research Archive Show summary

  Advancements in methodologies for efficient large-scale acquisition of high-resolution serial microscopy image data have opened new possibilities for experimental studies of cellular and subcellular features across whole brains in animal models. There is a high demand for open-source software and workflows for automated or semi-automated analysis of such data, facilitating anatomical, functional, and molecular mapping in healthy and diseased brains. These studies share a common need to consistently identify, visualize, and quantify the location of observations within anatomically defined regions, ensuring reproducible interpretation of anatomical locations, and thereby allowing meaningful comparisons of results across multiple independent studies. Addressing this need, we have developed a suite of desktop and web-applications for registration of serial brain section images to three-dimensional brain reference atlases (QuickNII, VisuAlign, WebAlign, WebWarp, and DeepSlice) and for performing data analysis in a spatial context provided by an atlas (Nutil, QCAlign, SeriesZoom, LocaliZoom, and MeshView). The software can be utilized in various combinations, creating customized analytical pipelines suited to specific research needs. The web-applications are integrated in the EBRAINS research infrastructure and coupled to the EBRAINS data platform, establishing the foundation for an online analytical workbench. We here present our software ecosystem, exemplify its use by the research community, and discuss possible directions for future developments.

• Carey, Harry Alexander Chandler; Kleven, Heidi; Øvsthus, Martin; Yates, Sharon Christine; Csucs, Gergely & Puchades, Maja A. [Show all 10 contributors for this article] (2025). DeMBA: a developmental atlas for navigating the mouse brain in space and time. Nature Communications. 16. doi: 10.1038/s41467-025-63177-9. Full text in Research Archive Show summary

  Studies of the adult mouse brain have benefited from three-dimensional (3D) atlases providing standardised frameworks for data analysis and integration. However, few 3D atlases exist for the developing mouse brain, and none cover more than a few ages. This fails to represent the dynamic nature of development and is insufficient for researchers whose data fall between the ages of existent atlases. Existing atlases also neglect the spatial correspondence between ages, making comparison across ages difficult. We present the Developmental Mouse Brain Atlas (DeMBA), a 4D atlas encompassing every postnatal day from 4 to 56. DeMBA was created by using existing brain templates for six developmental time points and interpolating intermediate ages. DeMBA is incorporated in software for spatial registration and analysis, enabling brain-wide quantification of features of interest. Furthermore, we provide the software package CCF translator, allowing coordinates or image volumes to be transformed between ages for comparison. These resources provide age-specific spatial frameworks incorporated in accessible analytic pipelines for developmental mouse brain data.

• Puchades, Maja; Yates, Sharon Christine; Csucs, Gergely; Carey, Harry; Balkir, Arda & Leergaard, Trygve B. [Show all 7 contributors for this article] (2025). Software and pipelines for registration and analyses of rodent brain image data in reference atlas space. bioRxiv. ISSN 2692-8205. Full text in Research Archive
• Bjerke, Ingvild Elise; Carey, Harry Alexander Chandler; Bjaalie, Jan G.; Leergaard, Trygve B. & Kim, Jee Hyun (2025). The developing mouse dopaminergic system: Cortical-subcortical shift in D1/D2 receptor balance and increasing regional differentiation. Neurochemistry International. ISSN 0197-0186. 182. doi: 10.1016/j.neuint.2024.105899. Full text in Research Archive Show summary

  The dopaminergic system of the brain is involved in complex cognitive functioning and undergoes extensive reorganization during development. Yet, these changes are poorly characterized. We have quantified the density of dopamine 1- and 2-receptor (D1 and D2) positive cells across the forebrain of male and female mice at five developmental stages using validated transgenic mice expressing green fluorescent protein in cells producing D1 or D2 mRNA. After analyzing >4,500 coronal brain images, a cortico-subcortical shift in D1/D2 balance was discovered, with increasing D1 dominance in cortical regions as a maturational pattern that occurs earlier in females. We describe postnatal trajectories of D1 and D2 cell densities across major brain regions and observe increasing regional differentiation of D1 densities through development. Our results provide the most comprehensive overview of the developing dopaminergic system to date, and an empirical foundation for further experimental and computational investigations of dopaminergic signaling.

• Illes, J.; Perreault, M.L.; Bassil, K.; Bjaalie, Jan G.; Taylor-Bragge, R.L. & Chneiweiss, H. [Show all 11 contributors for this article] (2025). Two-Eyed Seeing and other Indigenous perspectives for neuroscience. Nature. ISSN 0028-0836. 638(8049), p. 58–68. doi: 10.1038/s41586-024-08437-2. Full text in Research Archive
• Carey, Harry Alexander Chandler; Kleven, Heidi; Øvsthus, Martin; Yates, Sharon Christine; Csucs, Gergely & Puchades, Maja [Show all 9 contributors for this article] (2024). DeMBA: a developmental atlas for navigating the mouse brain in space and time. bioRxiv. ISSN 2692-8205. doi: 10.1101/2024.06.14.598876. Full text in Research Archive Show summary

  Studies of the adult mouse brain have benefited from three-dimensional (3D) atlases providing standardised frameworks for data analysis and integration. However, few 3D atlases exist for the developing mouse brain, and none cover more than a few ages. This fails to represent the dynamic nature of development and is insufficient for researchers whose data fall between the ages of existent atlases. Existing atlases also neglect the spatial correspondence between ages, making comparison across ages difficult. We present the Developmental Mouse Brain Atlas (DeMBA), a 4D atlas encompassing every postnatal day from 4 to 56. DeMBA was created by using existing brain templates for six developmental time points and interpolating intermediate ages. DeMBA is incorporated in software for spatial registration and analysis, enabling brain-wide quantification of features of interest. Furthermore, we provide the software package CCF translator, allowing coordinates or image volumes to be transformed between ages for comparison. These resources provide age-specific spatial frameworks incorporated in accessible analytic pipelines for developmental mouse brain data.

• Blixhavn, Camilla Hagen; Reiten, Ingrid; Kleven, Heidi; Øvsthus, Martin; Yates, Sharon Christine & Schlegel, Ulrike [Show all 11 contributors for this article] (2024). The Locare workflow: representing neuroscience data locations as geometric objects in 3D brain atlases. Frontiers in Neuroinformatics. 18. doi: 10.3389/fninf.2024.1284107. Full text in Research Archive Show summary

  Neuroscientists employ a range of methods and generate increasing amounts of data describing brain structure and function. The anatomical locations from which observations or measurements originate represent a common context for data interpretation, and a starting point for identifying data of interest. However, the multimodality and abundance of brain data pose a challenge for efforts to organize, integrate, and analyze data based on anatomical locations. While structured metadata allow faceted data queries, different types of data are not easily represented in a standardized and machine-readable way that allow comparison, analysis, and queries related to anatomical relevance. To this end, three-dimensional (3D) digital brain atlases provide frameworks in which disparate multimodal and multilevel neuroscience data can be spatially represented. We propose to represent the locations of different neuroscience data as geometric objects in 3D brain atlases. Such geometric objects can be specified in a standardized file format and stored as location metadata for use with different computational tools. We here present the Locare workflow developed for defining the anatomical location of data elements from rodent brains as geometric objects. We demonstrate how the workflow can be used to define geometric objects representing multimodal and multilevel experimental neuroscience in rat or mouse brain atlases. We further propose a collection of JSON schemas (LocareJSON) for specifying geometric objects by atlas coordinates, suitable as a starting point for co-visualization of different data in an anatomical context and for enabling spatial data queries.

• Øvsthus, Martin; Swieten, Maaike Margot Helène van; Puchades, Maja; Tocco, Chiara; Studer, Michèle & Bjaalie, Jan G. [Show all 7 contributors for this article] (2024). Spatially integrated cortico-subcortical tracing data for analyses of rodent brain topographical organization. Scientific Data. 11. doi: 10.1038/s41597-024-04060-y. Full text in Research Archive Show summary

  The cerebral cortex extends axonal projections to several subcortical brain regions, including the striatum, thalamus, superior colliculus, and pontine nuclei. Experimental tract-tracing studies have shown that these subcortical projections are topographically organized, reflecting the spatial organization of sensory surfaces and body parts. Several public collections of mouse- and rat- brain tract-tracing data are available, with the Allen mouse brain connectivity atlas being most prominent. There, a large body of image data can be inspected, but it is difficult to combine data from different experiments and compare spatial distribution patterns. To enable co-visualization and comparison of topographical organization in mouse brain cortico-subcortical projections across experiments, we represent axonal labelling data as point data in a common 3D brain atlas space. We here present a collection of point-cloud data representing spatial distribution of corticostriatal, corticothalamic, corticotectal, and cortico

• Amunts, Katrin; Axer, Markus; Banerjee, Swati; Bitsch, Lise; Bjaalie, Jan G. & Brauner, Philipp [Show all 50 contributors for this article] (2024). The coming decade of digital brain research: A vision for neuroscience at the intersection of technology and computing. Imaging Neuroscience. 2. doi: 10.1162/imag_a_00137. Full text in Research Archive Show summary

  In recent years, brain research has indisputably entered a new epoch, driven by substantial methodological advances and digitally enabled data integration and modelling at multiple scales—from molecules to the whole brain. Major advances are emerging at the intersection of neuroscience with technology and computing. This new science of the brain combines high-quality research, data integration across multiple scales, a new culture of multidisciplinary large-scale collaboration, and translation into applications. As pioneered in Europe’s Human Brain Project (HBP), a systematic approach will be essential for meeting the coming decade’s pressing medical and technological challenges. The aims of this paper are to: develop a concept for the coming decade of digital brain research, discuss this new concept with the research community at large, identify points of convergence, and derive therefrom scientific common goals; provide a scientific framework for the current and future development of EBRAINS, a research infrastructure resulting from the HBP’s work; inform and engage stakeholders, funding organisations and research institutions regarding future digital brain research; identify and address the transformational potential of comprehensive brain models for artificial intelligence, including machine learning and deep learning; outline a collaborative approach that integrates reflection, dialogues, and societal engagement on ethical and societal opportunities and challenges as part of future neuroscience research.

• Gurdon, Brianna; Yates, Sharon Christine; Csucs, Gergely; Groeneboom, Nicolaas Ervik; Hadad, Niran & Telpoukhovskaia, Maria [Show all 19 contributors for this article] (2024). Detecting the effect of genetic diversity on brain composition in an Alzheimer’s disease mouse model. Communications Biology. 7(1). doi: 10.1038/s42003-024-06242-1. Full text in Research Archive Show summary

  Alzheimer’s disease (AD) is broadly characterized by neurodegeneration, pathology accumulation, and cognitive decline. There is considerable variation in the progression of clinical symptoms and pathology in humans, highlighting the importance of genetic diversity in the study of AD. To address this, we analyze cell composition and amyloid-beta deposition of 6- and 14-month-old AD-BXD mouse brains. We utilize the analytical QUINT workflow- a suite of software designed to support atlas-based quantification, which we expand to deliver a highly effective method for registering and quantifying cell and pathology changes in diverse disease models. In applying the expanded QUINT workflow, we quantify near-global age-related increases in microglia, astrocytes, and amyloid-beta, and we identify strain-specific regional variation in neuron load. To understand how individual differences in cell composition affect the interpretation of bulk gene expression in AD, we combine hippocampal immunohistochemistry analyses with bulk RNA-sequencing data. This approach allows us to categorize genes whose expression changes in response to AD in a cell and/or pathology load-dependent manner. Ultimately, our study demonstrates the use of the QUINT workflow to standardize the quantification of immunohistochemistry data in diverse mice, - providing valuable insights into regional variation in cellular load and amyloid deposition in the AD-BXD model.

• Kleven, Heidi; Reiten, Ingrid; Blixhavn, Camilla Hagen; Schlegel, Ulrike; Øvsthus, Martin & Papp, Eszter Agnes [Show all 10 contributors for this article] (2023). A neuroscientist’s guide to using murine brain atlases for efficient analysis and transparent reporting. Frontiers in Neuroinformatics. 17. doi: 10.3389/fninf.2023.1154080. Full text in Research Archive Show summary

  Brain atlases are widely used in neuroscience as resources for conducting experimental studies, and for integrating, analyzing, and reporting data from animal models. A variety of atlases are available, and it may be challenging to find the optimal atlas for a given purpose and to perform efficient atlas-based data analyses. Comparing findings reported using different atlases is also not trivial, and represents a barrier to reproducible science. With this perspective article, we provide a guide to how mouse and rat brain atlases can be used for analyzing and reporting data in accordance with the FAIR principles that advocate for data to be findable, accessible, interoperable, and re-usable. We first introduce how atlases can be interpreted and used for navigating to brain locations, before discussing how they can be used for different analytic purposes, including spatial registration and data visualization. We provide guidance on how neuroscientists can compare data mapped to different atlases and ensure transparent reporting of findings. Finally, we summarize key considerations when choosing an atlas and give an outlook on the relevance of increased uptake of atlas-based tools and workflows for FAIR data sharing.

• Kleven, Heidi; Bjerke, Ingvild Elise; Clascá, Francisco; Groenewegen, Henk J.; Bjaalie, Jan G. & Leergaard, Trygve B. (2023). Waxholm Space atlas of the rat brain: A 3D atlas supporting data analysis and integration. Nature Methods. ISSN 1548-7091. 20, p. 1822–1829. doi: 10.1038/s41592-023-02034-3. Full text in Research Archive Show summary

  Volumetric brain atlases are increasingly used to integrate and analyze diverse experimental neuroscience data acquired from animal models, but until recently a publicly available digital atlas with complete coverage of the rat brain has been missing. Here we present an update of the Waxholm Space rat brain atlas, a comprehensive open-access volumetric atlas resource. This brain atlas features annotations of 222 structures, of which 112 are new and 57 revised compared to previous versions. It provides a detailed map of the cerebral cortex, hippocampal region, striatopallidal areas, midbrain dopaminergic system, thalamic cell groups, the auditory system and main fiber tracts. We document the criteria underlying the annotations and demonstrate how the atlas with related tools and workflows can be used to support interpretation, integration, analysis and dissemination of experimental rat brain data.

• Bjerke, Ingvild Elise; Yates, Sharon Christine; Carey, Harry; Bjaalie, Jan G. & Leergaard, Trygve B. (2023). Scaling up cell-counting efforts in neuroscience through semi-automated methods. iScience. 26(9). doi: 10.1016/j.isci.2023.107562. Full text in Research Archive Show summary

  Quantifying how the cellular composition of brain regions vary across development, aging, sex, and disease, is crucial in experimental neuroscience, and the accuracy of different counting methods is continuously debated. Due to the tedious nature of most counting procedures, studies are often restricted to one or a few brain regions. Recently, there have been considerable methodological advances in combining semi-automated feature extraction with brain atlases for cell quantification. Such methods hold great promise for scaling up cell-counting efforts. However, little focus has been paid to how these methods should be implemented and reported to support reproducibility. Here, we provide an overview of practices for conducting and reporting cell counting in mouse and rat brains, showing that critical details for interpretation are typically lacking. We go on to discuss how novel methods may increase efficiency and reproducibility of cell counting studies. Lastly, we provide practical recommendations for researchers planning cell counting.

• Kleven, Heidi; Gillespie, Thomas; Zehl, Lyuba; Dickscheid, Timo; Bjaalie, Jan G. & E., Martone Maryann [Show all 7 contributors for this article] (2023). AtOM, an ontology model to standardize use of brain atlases in tools, workflows, and data infrastructures. Scientific Data. 10(1). doi: 10.1038/s41597-023-02389-4. Full text in Research Archive Show summary

  Brain atlases are important reference resources for accurate anatomical description of neuroscience data. Open access, three-dimensional atlases serve as spatial frameworks for integrating experimental data and defining regions-of-interest in analytic workflows. However, naming conventions, parcellation criteria, area definitions, and underlying mapping methodologies differ considerably between atlases and across atlas versions. This lack of standardized description impedes use of atlases in analytic tools and registration of data to different atlases. To establish a machine-readable standard for representing brain atlases, we identified four fundamental atlas elements, defined their relations, and created an ontology model. Here we present our Atlas Ontology Model (AtOM) and exemplify its use by applying it to mouse, rat, and human brain atlases. We discuss how AtOM can facilitate atlas interoperability and data integration, thereby increasing compliance with the FAIR guiding principles. AtOM provides a standardized framework for communication and use of brain atlases to create, use, and refer to specific atlas elements and versions. We argue that AtOM will accelerate analysis, sharing, and reuse of neuroscience data.

• Dorman, Reinder; Bos, Jeroen J.; Vinck, Martin; Marchesi, Pietro; Fiorilli, Julien & Lorteije, Jeanette A M [Show all 10 contributors for this article] (2023). Spike-based coupling between single neurons and populations across rat sensory cortices, perirhinal cortex, and hippocampus. Cerebral Cortex. ISSN 1047-3211. 33(13), p. 8247–8264. doi: 10.1093/cercor/bhad111. Full text in Research Archive
• Hawrylycz, Michael; Martone, Maryann E.; Ascoli, Giorgio A.; Bjaalie, Jan G.; Dong, Hong-Wei & Ghosh, Satrajit S. [Show all 103 contributors for this article] (2023). A guide to the BRAIN Initiative Cell Census Network data ecosystem. PLoS Biology. ISSN 1544-9173. 21(6). doi: 10.1371/journal.pbio.3002133. Full text in Research Archive Show summary

  Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.

• Carey, Harry; Pegios, Michael; Lewis, Martin; Saleeba, Christine; Turner, A. & Everett, Nicholas [Show all 10 contributors for this article] (2023). DeepSlice: rapid fully automatic registration of mouse brain imaging to a volumetric atlas. Nature Communications. 14(1). doi: 10.1038/s41467-023-41645-4. Full text in Research Archive Show summary

  Registration of data to a common frame of reference is an essential step in the analysis and integration of diverse neuroscientific data. To this end, volumetric brain atlases enable histological datasets to be spatially registered and ana- lyzed, yet accurate registration remains expertise-dependent and slow. In order to address this limitation, we have trained a neural network, DeepSlice, to register mouse brain histological images to the Allen Brain Common Coordinate Framework, retaining registration accuracy while improving speed by >1000 fold.

• Gurdon, Brianna; Yates, Sharon Christine; Csucs, Gergely; Groeneboom, Nicolaas; Hadad, Niran & Telpoukhovskaia, Maria [Show all 19 contributors for this article] (2023). Detecting the effect of genetic diversity on brain composition in an Alzheimer's disease mouse model. bioRxiv. ISSN 2692-8205. doi: 10.1101/2023.02.27.530226. Full text in Research Archive
• Blixhavn, Camilla Hagen; Haug, Finn-Mogens Smejda; Kleven, Heidi; Puchades, Maja; Bjaalie, Jan G. & Leergaard, Trygve B. (2023). A Timm-Nissl multiplane microscopic atlas of rat brain zincergic terminal fields and metal-containing glia. Scientific Data. 10(150). doi: 10.1038/s41597-023-02012-6. Full text in Research Archive Show summary

  The ability of Timm’s sulphide silver method to stain zincergic terminal fields has made it a useful neuromorphological marker. Beyond its roles in zinc-signalling and neuromodulation, zinc is involved in the pathophysiology of ischemic stroke, epilepsy, degenerative diseases and neuropsychiatric conditions. In addition to visualising zincergic terminal fields, the method also labels transition metals in neuronal perikarya and glial cells. To provide a benchmark reference for planning and interpretation of experimental investigations of zinc-related phenomena in rat brains, we have established a comprehensive repository of serial microscopic images from a historical collection of coronally, horizontally and sagittally oriented rat brain sections stained with Timm’s method. Adjacent Nissl-stained sections showing cytoarchitecture, and customised atlas overlays from a three-dimensional rat brain reference atlas registered to each section image are included for spatial reference and guiding identification of anatomical boundaries. The Timm-Nissl atlas, available from EBRAINS, enables experimental researchers to navigate normal rat brain material in three planes and investigate the spatial distribution and density of zincergic terminal fields across the entire brain.

• Reiten, Ingrid; Olsen, Grethe Mari; Bjaalie, Jan G.; Witter, Menno Peter & Leergaard, Trygve B. (2023). The efferent connections of theorbitofrontal, posterior parietal,and insular cortex of the rat brain. Scientific Data. 10(645), p. 1–17. doi: 10.1038/s41597-023-02527-y. Full text in Research Archive Show summary

  The orbitofrontal, posterior parietal, and insular cortices are sites of higher-order cognitive processing implicated in a wide range of behaviours, including working memory, attention guiding, decision making, and spatial navigation. To better understand how these regions contribute to such functions, we need detailed knowledge about the underlying structural connectivity. Several tract-tracing studies have investigated specific aspects of orbitofrontal, posterior parietal and insular connectivity, but a digital resource for studying the cortical and subcortical projections from these areas in detail is not available. We here present a comprehensive collection of brightfield and fluorescence microscopic images of serial coronal sections from 49 rat brain tract-tracing experiments, in which discrete injections of the anterograde tracers biotinylated dextran amine and/or Phaseolus vulgaris leucoagglutinin were placed in the orbitofrontal, parietal, or insular cortex. The images are spatially registered to the Waxholm Space Rat brain atlas. The image collection, with corresponding reference atlas maps, is suitable as a reference framework for investigating the brain-wide efferent connectivity of these cortical association areas.

• Fiorilli, Julien; Marchesi, Pietro; Ruikes, Thijs; Veld, Gerjan Huis in t; Buckton, Rhys & Reiten, Ingrid [Show all 8 contributors for this article] (2023). Neural correlates of object identity and reward outcome in the corticohippocampal hierarchy: double dissociation between perirhinal and secondary visual cortex. BioRxiv. ISSN 0362-4331. Full text in Research Archive
• Amunts, Katrin; Defelipe, Javier; Pennartz, Cyriel M.A.; Destexhe, Alain; Migliore, Michele & Ryvlin, Philippe [Show all 15 contributors for this article] (2022). Linking Brain Structure, Activity, and Cognitive Function through Computation. eNeuro. 9(2). doi: 10.1523/ENEURO.0316-21.2022. Full text in Research Archive Show summary

  Understanding the human brain is a “Grand Challenge” for 21st century research. Computational approaches enable large and complex datasets to be addressed efficiently, supported by artificial neural networks, modeling and simulation. Dynamic generative multiscale models, which enable the investigation of causation across scales and are guided by principles and theories of brain function, are instrumental for linking brain structure and function. An example of a resource enabling such an integrated approach to neuroscientific discovery is the BigBrain, which spatially anchors tissue models and data across different scales and ensures that multiscale models are supported by the data, making the bridge to both basic neuroscience and medicine. Research at the intersection of neuroscience, computing and robotics has the potential to advance neuro-inspired technologies by taking advantage of a growing body of insights into perception, plasticity and learning. To render data, tools and methods, theories, basic principles and concepts interoperable, the Human Brain Project (HBP) has launched EBRAINS, a digital neuroscience research infrastructure, which brings together a transdisciplinary community of researchers united by the quest to understand the brain, with fascinating insights and perspectives for societal benefits.

• Leergaard, Trygve B. & Bjaalie, Jan G. (2022). Atlas-based data integration for mapping the connections and architecture of the brain. Science. ISSN 0036-8075. 378(6619), p. 488–492. doi: 10.1126/science.abq2594. Full text in Research Archive Show summary

  Detailed knowledge about the neural connections among regions of the brain is key for advancing our understanding of normal brain function and changes that occur with aging and disease. Researchers use a range of experimental techniques to map connections at different levels of granularity in rodent animal models, but the results are often challenging to compare and integrate. Three-dimensional reference atlases of the brain provide new opportunities for cumulating, integrating, and reinterpreting research findings across studies. Here, we review approaches for integrating data describing neural connections and other modalities in rodent brain atlases and discuss how atlas-based workflows can facilitate brainwide analyses of neural network organization in relation to other facets of neuroarchitecture.

• Tocco, Chiara; Øvsthus, Martin; Bjaalie, Jan G.; Leergaard, Trygve B. & Studer, Michèle (2022). The topography of corticopontine projections is controlled by postmitotic expression of the area-mapping gene Nr2f1. Development. ISSN 0950-1991. 149(5). doi: 10.1242/dev.200026. Full text in Research Archive Show summary

  ABSTRACT Axonal projections from layer V neurons of distinct neocortical areas are topographically organized into discrete clusters within the pontine nuclei during the establishment of voluntary movements. However, the molecular determinants controlling corticopontine connectivity are insufficiently understood. Here, we show that an intrinsic cortical genetic program driven by Nr2f1 graded expression is directly implicated in the organization of corticopontine topographic mapping. Transgenic mice lacking cortical expression of Nr2f1 and exhibiting areal organization defects were used as model systems to investigate the arrangement of corticopontine projections. By combining three-dimensional digital brain atlas tools, Cre-dependent mouse lines and axonal tracing, we show that Nr2f1 expression in postmitotic neurons spatially and temporally controls somatosensory topographic projections, whereas expression in progenitor cells influences the ratio between corticopontine and corticospinal fibres passing the pontine nuclei. We conclude that cortical gradients of area-patterning genes are directly implicated in the establishment of a topographic somatotopic mapping from the cortex onto pontine nuclei.

• Schirner, Michael; Domide, Lia; Perdikis, Dionysios; Triebkorn, Paul; Stefanovski, Leon & Pai, Roopa [Show all 44 contributors for this article] (2022). Brain simulation as a cloud service: The Virtual Brain on EBRAINS. NeuroImage. ISSN 1053-8119. 251. doi: 10.1016/j.neuroimage.2022.118973. Full text in Research Archive Show summary

  The Virtual Brain (TVB) is now available as open-source services on the cloud research platform EBRAINS (ebrains.eu). It offers software for constructing, simulating and analysing brain network models including the TVB simulator; magnetic resonance imaging (MRI) processing pipelines to extract structural and functional brain networks; combined simulation of large-scale brain networks with small-scale spiking networks; automatic conversion of user-specified model equations into fast simulation code; simulation-ready brain models of patients and healthy volunteers; Bayesian parameter optimization in epilepsy patient models; data and software for mouse brain simulation; and extensive educational material. TVB cloud services facilitate reproducible online collaboration and discovery of data assets, models, and software embedded in scalable and secure workflows, a precondition for research on large cohort data sets, better generalizability, and clinical translation.

• Carey, Harry; Pegios, Michael; Martin, Lewis; Saleeba, Christine; Turner, Anita & Everett, Nicholas [Show all 9 contributors for this article] (2022). DeepSlice: rapid fully automatic registration of mouse brain imaging to a volumetric atlas. bioRxiv. ISSN 2692-8205. doi: 10.1101/2022.04.28.489953. Full text in Research Archive Show summary

  Registration of data to a common frame of reference is an essential step in the analysis and integration of diverse neuroscientific data modalities. To this end, volumetric brain atlases enable histological datasets to be spatially registered and analysed, yet accurate registration remains expertise-dependent and slow. We have trained a neural network, DeepSlice, to register mouse brain histology to the Allen Brain Atlas, retaining accuracy while improving speed by >1000 fold.

• Eke, Damian Okaibedi; Bernard, Amy; Bjaalie, Jan G.; Chavarriaga, Ricardo; Hanakawa, Takashi & Hannan, Anthony J [Show all 13 contributors for this article] (2021). International data governance for neuroscience. Neuron. ISSN 0896-6273. 110. doi: 10.1016/j.neuron.2021.11.017. Full text in Research Archive Show summary

  As neuroscience projects increase in scale and cross international borders, different ethical principles, national and international laws, regulations, and policies for data sharing must be considered. These concerns are part of what is collectively called data governance. Whereas neuroscience data transcend borders, data governance is typically constrained within geopolitical boundaries. An international data governance framework and accompanying infrastructure can assist investigators, institutions, data repositories, and funders with navigating disparate policies. Here, we propose principles and operational considerations for how data governance in neuroscience can be navigated at an international scale and highlight gaps, challenges, and opportunities in a global brain data ecosystem. We consider how to approach data governance in a way that balances data protection requirements and the need for open science, so as to promote international collaboration through federated constructs such as the International Brain Initiative (IBI).

• Abrams, Mathew Birdsall; Bjaalie, Jan G.; Das, Samir; Egan, Gary F.; Ghosh, Satrajit S. & Goscinski, Wojtek J. [Show all 23 contributors for this article] (2021). A Standards Organization for Open and FAIR Neuroscience: the International Neuroinformatics Coordinating Facility. Neuroinformatics. ISSN 1539-2791. doi: 10.1007/s12021-020-09509-0. Full text in Research Archive Show summary

  Abstract There is great need for coordination around standards and best practices in neuroscience to support efforts to make neuroscience a data-centric discipline. Major brain initiatives launched around the world are poised to generate huge stores of neuroscience data. At the same time, neuroscience, like many domains in biomedicine, is confronting the issues of transparency, rigor, and reproducibility. Widely used, validated standards and best practices are key to addressing the challenges in both big and small data science, as they are essential for integrating diverse data and for developing a robust, effective, and sustainable infrastructure to support open and reproducible neuroscience. However, developing community standards and gaining their adoption is difficult. The current landscape is characterized both by a lack of robust, validated standards and a plethora of overlapping, underdeveloped, untested and underutilized standards and best practices. The International Neuroinformatics Coordinating Facility (INCF), an independent organization dedicated to promoting data sharing through the coordination of infrastructure and standards, has recently implemented a formal procedure for evaluating and endorsing community standards and best practices in support of the FAIR principles. By formally serving as a standards organization dedicated to open and FAIR neuroscience, INCF helps evaluate, promulgate, and coordinate standards and best practices across neuroscience. Here, we provide an overview of the process and discuss how neuroscience can benefit from having a dedicated standards body.

• Dojat, Michel; Bjaalie, Jan G. & Barbier, Emmanuel L (2021). Editorial: APPNING: Animal Population Imaging. Frontiers in Neuroinformatics. 15, p. 1–3. doi: 10.3389/fninf.2021.676603. Full text in Research Archive Show summary

  This editorial review of the Research Topic Appning describes several solutions to support the sharing of animal imaging data and processing tools. Appning promotes the federation of multiple sources of information, processing tools and shows how this contributes to the diffusion of knowledge distributed in various preclinical imaging centers.

• Royan, Muhammad Rahmad; Siddique, Khadeeja; Csucs, Gergely; Puchades, Maja; Nourizadeh-Lillabadi, Rasoul & Bjaalie, Jan G. [Show all 9 contributors for this article] (2021). 3D Atlas of the Pituitary Gland of the Model Fish Medaka (Oryzias latipes). Frontiers in Endocrinology. 12. doi: 10.3389/fendo.2021.719843. Full text in Research Archive Show summary

  In vertebrates, the anterior pituitary plays a crucial role in regulating several essential physiological processes via the secretion of at least seven peptide hormones by different endocrine cell types. Comparative and comprehensive knowledge of the spatial distribution of those endocrine cell types is required to better understand their physiological functions. Using medaka as a model and several combinations of multicolor fluorescence in situ hybridization, we present the first 3D atlas revealing the glandwide distribution of seven endocrine cell populations: lactotropes, thyrotropes, Lh and Fsh gonadotropes, somatotropes, and pomca-expressing cells (corticotropes and melanotropes) in the anterior pituitary of a teleost fish. By combining in situ hybridization and immunofluorescence techniques, we deciphered the location of corticotropes and melanotropes within the pomca-expressing cell population. The 3D localization approach reveals sexual dimorphism of tshba-, pomca-, and lhb-expressing cells in the adult medaka pituitary. Finally, we show the existence of bi-hormonal cells coexpressing lhb-fshb, fshb-tshba and lhb-sl using single-cell transcriptomics analysis and in situ hybridization. This study offers a solid basis for future comparative studies of the teleost pituitary and its functional plasticity.

• Adams, Amy; Albin, Stephanie; Amunts, Katrin; Asakawa, Tasia; Bernard, Amy & Bjaalie, Jan G. [Show all 41 contributors for this article] (2020). International Brain Initiative: An Innovative Framework for Coordinated Global Brain Research Efforts. Neuron. ISSN 0896-6273. 105(2), p. 212–216. doi: 10.1016/j.neuron.2020.01.002. Full text in Research Archive Show summary

  The International Brain Initiative (IBI) has been established to coordinate efforts across existing and emerging national and regional brain initiatives. This NeuroView describes how to be involved and the new opportunities for global collaboration that are emerging between scientists, scientific societies, funders, industry, government, and society.

• Aarestrup, FM; Albeyatti, A; Armitage, W; Auffray, C; Augello, L & Balling, Rudi [Show all 52 contributors for this article] (2020). Towards a European health research and innovation cloud (HRIC). Genome Medicine. 12(18). doi: 10.1186/s13073-020-0713-z. Full text in Research Archive Show summary

  The European Union (EU) initiative on the Digital Transformation of Health and Care (Digicare) aims to provide the conditions necessary for building a secure, flexible, and decentralized digital health infrastructure. Creating a European Health Research and Innovation Cloud (HRIC) within this environment should enable data sharing and analysis for health research across the EU, in compliance with data protection legislation while preserving the full trust of the participants. Such a HRIC should learn from and build on existing data infrastructures, integrate best practices, and focus on the concrete needs of the community in terms of technologies, governance, management, regulation, and ethics requirements. Here, we describe the vision and expected benefits of digital data sharing in health research activities and present a roadmap that fosters the opportunities while answering the challenges of implementing a HRIC. For this, we put forward five specific recommendations and action points to ensure that a European HRIC: i) is built on established standards and guidelines, providing cloud technologies through an open and decentralized infrastructure; ii) is developed and certified to the highest standards of interoperability and data security that can be trusted by all stakeholders; iii) is supported by a robust ethical and legal framework that is compliant with the EU General Data Protection Regulation (GDPR); iv) establishes a proper environment for the training of new generations of data and medical scientists; and v) stimulates research and innovation in transnational collaborations through public and private initiatives and partnerships funded by the EU through Horizon 2020 and Horizon Europe.

• Bjerke, Ingvild Elise; Yates, Sharon Christine; Laja, Arthur; Witter, Menno; Puchades, Maja & Bjaalie, Jan G. [Show all 7 contributors for this article] (2020). Densities and numbers of calbindin and parvalbumin positive neurons across the rat and mouse brain. iScience. 24:101906(1), p. 1–52. doi: 10.1016/j.isci.2020.101906. Full text in Research Archive Show summary

  The calcium-binding proteins parvalbumin and calbindin are expressed in neuronal populations regulating brain networks involved in spatial navigation, memory processes, and social interactions. Information about the numbers of these neurons across brain regions is required to understand their functional roles but is scarcely available. Employing semi-automated image analysis, we performed brain-wide analysis of immunohistochemically stained parvalbumin and calbindin sections and show that these neurons distribute in complementary patterns across the mouse brain. Parvalbumin neurons dominate in areas related to sensorimotor processing and navigation, whereas calbindin neurons prevail in regions reflecting behavioral states. We also find that parvalbumin neurons distribute according to similar principles in the hippocampal region of the rat and mouse brain. We validated our results against manual counts and evaluated variability of results among researchers. Comparison of our results to previous reports showed that neuron numbers vary, whereas patterns of relative densities and numbers are consistent.

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• Sand, Olav; Sjaastad, Øystein; Haug, Egil & Bjaalie, Jan G. (2021). Människokroppen - Fysiologi och anatomi, 3. upplagan. Liber. ISBN 9789147142873. 668 p. Full text in Research Archive

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• Golla, Archana; Pieschnik, Sophia; Kobchenko, Maya; Reiten, Ingrid; Chatzopoulos, Serafeim & Rojas, César Parra [Show all 16 contributors for this article] (2026). Building an Integrated Scientific Knowledge Graph for Neuroscience: Enhancing Research Impact Analysis and Optimizing Data Curation. doi: 10.5281/ZENODO.18864403. Full text in Research Archive Show summary

  The Neuroscience pilot of the EU-funded SciLake project seeks to tackle the complexities of fragmented and disorganized open research data. This initiative has united partners with expertise in knowledge management with curators from EBRAINS (RRID: SCR_019260), an open research infrastructure focused on brain-related studies. The innovative Scientific Knowledge Graph (SKG) (https://bip.imsi.athenarc.gr/search/neuroscience-pilot) created through this pilot serves as a cohesive framework for data integration while enabling the use of value-added services provided by SciLake. The developed SKG combines curated datasets from EBRAINS with extensive neuroscience research products from the OpenAIRE neuroscience gateway (https://neuroscience.openaire.eu/). The SKG incorporates controlled terms from openMINDS (RRID: SCR_023173) as nodes and utilizes an entity recognition model to classify and map these terms in relevant texts, enriching the connections among research products. Additionally, the SciLake services deploy language models to extract mentions of research artifacts (RAs) and enhance the interlinking of research products. Fully integrated with impact-driven discovery and reproducibility tools and leveraging the analytical capabilities of SciLake, the SKG serves as a powerful resource for contextualizing curation efforts and exploring research trends, ultimately optimizing curation processes in the neuroscientific landscape.

• Pieschnik, Sophia; Benediktsdottir, Signy; Karadag, Naz; Papp, Eszter Agnes; Puchades, Maja & Leergaard, Trygve B. [Show all 7 contributors for this article] (2026). Digital data collections for discovery-driven neuroscience. doi: https:/fens2026.abstractserver.com/program/#%2Fdetails%2Fpresentations%2F4724. Full text in Research Archive Show summary

  The transition to open science has led to a growing volume of shared neuroscience data in different data repositories. While the amount and diversity of research data available is extensive, data reusability is often limited by a lack of appropriate metadata annotations according to the FAIR guiding principles (Wilkinson et al., 2016). The EBRAINS Research Infrastructure (RRID:SCR_019260) shares well-annotated datasets to facilitate data discovery and reuse across studies and modalities via the EBRAINS Knowledge Graph (KG; RRID:SCR_017612). The EBRAINS KG currently indexes 1100+ heterogeneous datasets, annotated with standardised metadata following the openMINDS metadata framework (RRID:SCR_023173). We present two examples for building data collections via the KG Search functionality using a combination of multi-term text queries and faceted filtering to exemplify the potential of data driven workflows. First, we explore the brain-wide distribution of calbindin- and parvalbumin-positive neurons in rodent brains, identifying a comprehensive data collection generated using the QUINT workflow (Yates et al., 2019). Related datasets are connected through metadata including species and techniques. Second, we collect BIDS-compliant macaque datasets, containing NIfTI files, enabling the combination of neuroimaging data from different laboratories. Filtering and query options for experimental methods, content types and data organisation standards enable the selection of data for viewing and analysis using EBRAINS tools, highlighting their interoperability. These examples demonstrate how discovery-driven neuroscience is facilitated by FAIR annotated data across a range of studies.

• Balkir, Arda; Yates, Sharon Christine; Csucs, Gergely; Leergaard, Trygve B.; Puchades, Maja & Bjaalie, Jan G. (2026). QUINT-ONLINE WORKFLOW: PERSONALISED WORKING ENVIRONMENT FOR ATLAS-BASED ANALYSES OF BRAIN SECTION IMAGES IN RODENTS. doi: https:/fens2026.abstractserver.com/program/#%2Fdetails%2Fpresentations%2F4213. Full text in Research Archive Show summary

  Studies of the cellular architecture and neural connections in the brain often rely on histology-based methods in rodent wild type and disease models. Registration of brain section images to a reference brain atlas has become the standard for obtaining high quality and precise data, allowing comparison and integration of disparate data within a standardised coordinate system. While a few analytical tools are available to researchers, barriers to uptake, such as coding skill requirements, data type limitations, and insufficient documentation are still present. We have developed an intuitive, reliable and versatile solution for enabling such analyses. The QUINT-online workflow combines the use of a series of web-applications (CreateZoom, WebAlign, WebWarp, Webilastik, WebNutil and MeshView) integrated in an online platform, accessible with an EBRAINS account. It builds on the QUINT workflow (Yates et al. 2019, doi: 10.3389/fninf.2019.00075) using desktop applications, which has been used widely by the research community for cell counting, tract tracing studies and for analysis of tissue clearing data. This new online solution allows researchers to register heterogeneous image series to reference brain atlases, to identify features to quantify, and to perform brain-wide mapping and quantification, on a user-friendly platform. The QUINT-online workflow benefits from simplified file management steps and new features such as microscopy viewer links and automated mapping of selected objects. EBRAINS users can already access a beta version at https://quint-online.apps.ebrains.eu/.

• Pieschnik, Sophia; Najafi, Peyman; Pernet, Cyril; Bjaalie, Jan G.; Leergaard, Trygve B. & Davison, Andrew [Show all 7 contributors for this article] (2025). bids2openminds: facilitating automated conversion between neuroscience data standards. Full text in Research Archive Show summary

  BIDS and openMINDS are expert- and community-driven standards that facilitate FAIR data management in the field of neuroscience. BIDS is a commonly adopted data model that standardizes the file organization and metadata annotations per dataset within a data repository, such as openNeuro.org or publicneuro.eu. The openMINDS metadata framework provides integrated metadata models for Linked Data, libraries of ontology-based metadata instances and supports rich search capabilities in graph database systems, such as the EBRAINS Knowledge Graph. To facilitate ingestion of metadata from BIDS datasets into such graph database systems, we developed the bids2openminds converter. bids2openminds is an open-source project, provided as a python package with a command line interface (https://github.com/openMetadataInitiative/bids2openminds). It builds on the functionalities of, pyBIDS and openMINDS_Python. However semantic mapping between BIDS and openMINDS terminologies had to be hard coded. Currently, the converter supports the BIDS modalities structural and functional Magnetic Resonance Imaging (MRI), Electroencephalography (EEG), and Intracranial Electroencephalography (iEEG). We demonstrate the metadata conversion using bids2openminds on BIDS compliant, open access datasets. Converted to openMINDS and registered in the EBRAINS Knowledge Graph, data and metadata have an increased findability via the search user interface and advanced query options. Through the semantic integration data are linked to other datasets within and beyond the EBRAINS database, increasing their interoperability and reusability. The bids2openminds converter facilitates automatic registration of BIDS compliant datasets in openMINDS-based graph databases, fostering interoperability and alliances across different data management systems (e.g., publicneuro.eu and EBRAINS).

• Dyb, Grete Anita; Løberg, Magnus; Feiring, Eli; Bjaalie, Jan G. & Harbo, Hanne-Cathrin Flinstad (2025). Revidert regelverk for ph.d.utdanningen. Tidsskrift for Den norske legeforening (Tidsskriftet). ISSN 0029-2001. 14(14). doi: 10.4045/tidsskr.25.0652. Full text in Research Archive
• Bjaalie, Jan G.; Løberg, Magnus; Haugaa, Kristina Ingrid Helena Hermann; Nebb, Hilde Irene & Harbo, Hanne-Cathrin Flinstad (2025). Innovation – the final leg in the research relay race. Tidsskrift for Den norske legeforening (Tidsskriftet). ISSN 0029-2001. doi: 10.4045/tidsskr.25.0553. Full text in Research Archive
• Puchades, Maja; Bjerke, Ingvild Elise; Carey, Harry Alexander Chandler; Kleven, Heidi; Øvsthus, Martin & Yates, Sharon Christine [Show all 10 contributors for this article] (2025). QUINT workflow now supports whole brain analysis using the Developmental Mouse Brain Atlas (DeMBA). Full text in Research Archive Show summary

  INTRODUCTION: Three-dimensional, open access digital brain atlases are important resources in neuroscience research, opening new possibilities for automated analyses in atlas defined regions of interest. The EBRAINS QUINT workflow [1,2] has been developed to support standardized atlas-based analysis of sectioned tissue without the need for coding ability. It features integrated brain atlases for the adult rat and mouse brain and is increasingly used in the neuroscience community. However, a recurrent request from many QUINT users interested in understanding brain development, has been to include brain atlases representing different developmental ages, to so achieve better spatial registration for brains from young individuals. METHODS: To meet this need, we have now integrated the novel Developmental Mouse Brain Atlas (DeMBA), a four-dimensional atlas covering every postnatal day from 4 to 56 [3]. This atlas features segmentations from the widely adopted Allen Mouse Brain CCF and the Kim developmental atlases, that have been transformed and interpolated to the same ages, ensuring comparable region definitions across ages [4]. RESULTS AND DISCUSSION: The DeMBA atlas was implemented in the registration software QuickNII and VisuAlign and shared as specific packages together with the atlas itself on EBRAINS [4]. Those versions require usage of the specific Filebuilder utility shared within the packages. Next, the DeMBA atlas was implemented in a new Nutil Quantifier version (v1.1) and thereby the complete QUINT analysis can now be performed on young mouse brains. All the software tools and the DeMBA atlas are shared through the EBRAINS Knowledge graph [5]. We here present the updated QUINT workflow and give examples of preliminary developmental data analysis. References: 1.Yates, S. et al. (2019) QUINT: Workflow for Quantification and Spatial Analysis of Features in Histological Images From Rodent Brain. Front. Neuroinform. 13, 1–14. https://doi.org/10.3389/fninf.2019.00075 2. Puchades MA, Yates SC, Csucs G, Carey H, Balkir A, Leergaard TB and Bjaalie JG (2025) Software and pipelines for registration and analyses of rodent brain image data in reference atlas space. Front. Neuroinform. 19:1629388. https://doi.org/10.3389/fninf.2025.1629388 3.Carey, H., Ovsthus, M., Kleven, H., Yates, S. C., Csucs, G., Puchades, M. A., Bjaalie, J. G., Leergaard, T. B., & Bjerke, I. E. (2024). Developmental mouse brain atlas (DeMBA) with continuous coverage of postnatal day 4 to 56 (v1) [Data set]. EBRAINS. https://doi.org/10.25493/V3AH-HK7 4.Carey, H., Kleven, H., Øvsthus, M. et al. DeMBA: a developmental atlas for navigating the mouse brain in space and time. Nat Commun 16, 8108 (2025). https://doi.org/10.1038/s41467-025-63177-9 5.https://search.kg.ebrains.eu Acknowledgeme

• Benediktsdottir, Signy; Golla, Archana; Blixhavn, Camilla Hagen; Hennestad, Eivind; Kleven, Heidi & Najafi, Peyman [Show all 16 contributors for this article] (2024). Towards FAIR neuroscience: an efficient workflow for sharing and integrating data. Full text in Research Archive
• Pieschnik, Sophia; Najafi, Peyman; Bjaalie, Jan G.; Leergaard, Trygve Brauns; Davison, Andrew P. & Zehl, Lyuba (2024). Automated conversion of BIDS datasets to Linked Data using openMINDS. Full text in Research Archive
• Zehl, Lyuba; Pieschnik, Sophia; Najafi, Peyman; Bjaalie, Jan G.; Leergaard, Trygve Brauns & Davison, Andrew P. (2024). bids2openminds: extracting linked data from brain imaging data structures. Full text in Research Archive
• Kleven, Heidi; Øvsthus, Martin; Carey, Harry Alexander Chandler; Puchades, Maja; Bjaalie, Jan G. & Leergaard, Trygve B. [Show all 7 contributors for this article] (2024). Three-dimensional reference atlases for integration and analysis of data from the postnatal mouse brain. Full text in Research Archive
• Reiten, Ingrid; Olsen, Grete; Bjaalie, Jan G.; Witter, Menno Peter & Leergaard, Trygve B. (2023). The efferent connections of the orbitofrontal, posterior parietal and insular cortices in the rat: FAIR tract tracing data enabled by EBRAINS. Full text in Research Archive Show summary

  Poster presenterer et datasett som er delt via EBRAINS (ebrains.eu). Datasettet inneholder bildemateriale av koronale rottehjernesnitt som er merket for anterograd tracer, samt en registerering av disse bildene til Waxholm Space Rat Brain v4 atlas. Dataene er delt i følge FAIR-prinsippene og er open access.

• Yates, Sharon Christine; Gurdon, Brianna; Csucs, Gergely; Groeneboom, Nicolaas Ervik; Puchades, Maja Amedjkouh & Kaczorowski, Catherine [Show all 7 contributors for this article] (2023). High-throughput method for brain-wide characterisation of genetically diverse AD mice. Full text in Research Archive
• Blixhavn, Camilla Hagen; Bjerke, Ingvild Elise; Reiten, Ingrid; Kleven, Heidi; Schlegel, Ulrike & Puchades, Maja [Show all 8 contributors for this article] (2023). A conceptual framework for positioning and integrating brain data in murine 3D brain atlases. Full text in Research Archive
• Feiring, Eli; Løberg, Magnus; Bjaalie, Jan G.; Dyb, Grete Anita & Harbo, Hanne-Cathrin Flinstad (2023). Kjønn og helse, utdanning og forskning. 143(11). doi: 10.4045/tidsskr.23.0408. Full text in Research Archive
• Pieschnik, Sophia; Golla, Archana; Reiten, Ingrid; Schlegel, Ulrike; Hennestad, Eivind & Hübenette, Saira Jameela [Show all 20 contributors for this article] (2023). Pioneering open neuroscience - the role of the curation services. Full text in Research Archive
• Swieten, Maaike van; Schmid, Oliver; Dénervaud, Gilles; Tsanaktsidis, Ioannis; Weyers, Benjamin & Davison, Andrew P. [Show all 9 contributors for this article] (2022). A Practical Guide to Using the EBRAINS Knowledge Graph in (your) Research. Full text in Research Archive Show summary

  Scientific articles are mostly published as text files containing unstructured and semi-structured information. Consequently, important information is typically deeply hidden in documents which severely limits the possibilities to automatically process and reuse scholarly knowledge. One approach to make information explicit and directly usable is to transform this into a standardised format and store it in a knowledge graph. This allows scholarly knowledge to be represented in a structured, machine-actionable, interlinked and semantically rich manner. The EBRAINS Knowledge Graph was developed to facilitate the search and information exchange in research, so that research results across different domains become directly comparable and easier to retrieve and reuse. Here, we provide a practical guide to extracting information from the EBRAINS Knowledge Graph using a user-friendly interface as well as a more advanced programmatic route via an Application Programming Interface (API). We also provide concrete examples on how the extracted information can be leveraged in order to develop new research objectives as well as validate ongoing research. The EBRAINS Knowledge Graph is integrated in the wider EBRAINS research infrastructure as a part of the EBRAINS Data and Knowledge services for sharing and finding research data and models. Data found through these services can be directly used and analysed via the various integrated tools and analysis workflows. The EBRAINS Knowledge Graph is a valuable machine-actionable and FAIR (Findable, Accessible, Interoperable and Reusable) resource for discovery-based and hypothesis-driven research as it already contains a wide variety of neuroscience data types, models and software.

• Puchades, Maja; Carey, Harry & Bjaalie, Jan G. (2022). Neuroinformatics tools for performing feature extraction and advanced brain-wide distribution analysis in an atlas context. Full text in Research Archive Show summary

  Workshop organised by University of Oslo during INCF assembly 2022. This workshop will present and compare a series of neuroinformatics tools for performing feature extraction and advanced brain-wide distribution analysis in an atlas context. The speakers will be recruited from different projects and institutions developing complementary tools and services for brain-wide analysis of data originating from small animal experimental Workshop neuroscience. They will discuss alternative approaches for registration of images, segmentation, analyses, reuse of data, and smart combinations of new neuroinformatics tools originating from different laboratories and projects. Attendees will be guided through use cases demonstrating how the tools can be used in different contexts and for different purposes. Topic: Neuroinformatics tools for performing feature extraction and advanced brain-wide distribution analysis in an atlas context. International invited speakers: Lydia Ng (Allen Brain Institute); Daniel Furth (Uppsala University); Nicolas Chiaruttini (EPFL); Adam Tyson (UCL).

• Bjerke, Ingvild Elise; Imad, Jala; Clascá, Francisco; Groenewegen, Henk J.; Bjaalie, Jan G. & Leergaard, Trygve Brauns (2022). Waxholm Space atlas of the Sprague Dawley rat brain version 4: A volumetric atlas enabling data integration and analysis. Full text in Research Archive
• Carey, Harry; Csucs, Gergely; McMullan, Simon; Puchades, Maja & Bjaalie, Jan G. (2022). DeepSlice Rat: A Deep Neural Network for automatic registration of rat brain images to the Waxholm Space atlas of the rat brain. Full text in Research Archive Show summary

  The rat is widely used in experimental neuroscience and with the increasing availability of many genetic tools in rat, its popularity in neuroscientific research is likely to increase. In order to properly leverage the magnitude of rat data that the neuroscientific community generates, brain derived data must be mapped to a common spatial reference space. To this end, the Waxholm Space atlas of the rat brain (RRID:SCR_017124) has been created. While there exist tools to register rat brain data to the Waxholm Space, including the QuickNII (RRID:SCR_016854) and VisuAlign (RRID:SCR_017978) tools provided by the EBRAINS research infrastructure (https://ebrains.eu/services/atlases), these tools are relatively demanding of time and expertise, often requiring hours per whole brain. If we are to comprehensively map the massive amount of brain data currently being generated, we will require automated methods capable of registering data to a common reference space. Here we present DeepSlice Rat, a deep neural network for the automated registration of rat brain images to the Waxholm Space atlas of the rat brain. We show that DeepSlice Rat is equal in performance to an expert neuroanatomist whilst being thousands of times faster. DeepSlice Rat can be used in combination with QuickNII for verification of the registration, and VisuAlign for non-linear adjustments, creating an AI assisted workflow that will greatly enhance productivity. DeepSlice Rat will be freely available on EBRAINS (alongside a mouse implementation) as both a Python package and web-application.

• Puchades, Maja & Bjaalie, Jan G. (2022). Introduction to the EBRAINS Research Infrastructure: Data Knowledge services and Data analysis. Full text in Research Archive
• Reiten, Ingrid; Schlegel, Ulrike; Aasebø, Ida; Davison, Andrew P.; Zehl, Lyuba & Schmid, Oliver [Show all 10 contributors for this article] (2022). Share FAIR neuroscience data using the EBRAINS curation services. Full text in Research Archive
• Reiten, Ingrid; Schlegel, Ulrike; Aasebø, Ida; Swieten, Maaike M. H. van; Davison, Andrew P. & Zehl, Lyuba [Show all 11 contributors for this article] (2022). Share FAIR neuroscience data using the EBRAINS curation services. Full text in Research Archive
• Bjaalie, Jan G. (2022). Comment on the astonishing complexity of working with brain health data. Full text in Research Archive
• Bjaalie, Jan G. (2022). How the EBRAINS research infrastructure can have a transformative impact on research on brain structure, activity and cognitive function. Full text in Research Archive
• Bjaalie, Jan G. (2022). The EBRAINS Research Platform. Full text in Research Archive
• Bjaalie, Jan G. (2022). Managing, sharing, and publishing research data through EBRAINS. Full text in Research Archive
• Carey, Harry; Csucs, Gergely; Puchades, Maja & Bjaalie, Jan G. (2022). AutoAlign Rat: Spatially anchoring rat brain histology to the Waxholm rat brain atlas using deep neural networks. Full text in Research Archive Show summary

  The rat offers many benefits over the mouse for experimental neuroscience, though the popularity of the mouse has surged in recent years due to an ever-growing genetic toolbox. This toolbox is now being expanded to rats and will surely lead to an explosion in rat brain data. To prepare for this we must build tools to spatially anchor rat brain data into a common reference space, enabling comparison and meta-analysis of anchored data. To this end we have created AutoAlign, a deep learning toolbox which spatially anchors rat brain histology to the Waxholm Space atlas of the rat brain (RRID: SCR_017124). AutoAlign can anchor rat brain histology cut coronally, sagitally, and horizontally, here we compare the accuracy of the algorithm to humans across each of these planes. While anchoring a whole-brain dataset would take an anatomist many hours, AutoAlign achieves this in seconds. AutoAlign is compatible with the QUINT workflow, including QuickNII (RRID: SCR_016854), allowing predictions to be modified by users, and VisuAlign (RRID: SCR_017978) enabling the atlas to be warped and deformed to anchored sections. AutoAlign Rat is freely available as both a Python package and web application.

• Puchades, Maja; Reiten, Ingrid; Destexhe, Alain; Palomero-Gallagher, Nicola; Ruikes, Thijs & Leergaard, Trygve B. [Show all 7 contributors for this article] (2022). Workshop: Towards exploration of disease mechanisms in animal models: atlases, analysis, modelling and simulation. Full text in Research Archive Show summary

  Researchers Maja A. Puchades and Prof Jan G. Bjaalie have organised a paralell session titled: "Towards exploration of disease mechanisms in animal models". Agenda: For the 90 min meeting: · Presentation of speakers · New directions in disease models research · EBRAINS atlases enabling animal model research · Tools and analytical workflows for data integration in atlases · Use of animal brain atlases for modelling of brain function · Connectivity rat data: tract-tracing use case · Connectivity rat data: electrophysiological tetrodes use case · Panel discussion

• Puchades, Maja; Yates, Sharon Christine; Groeneboom, Nicolaas; Csucs, Gergely; Darine, Dmitri Aleksandrovitsj & Carey, Harry [Show all 8 contributors for this article] (2022). EBRAINS tools for rodent brain atlasing. Full text in Research Archive Show summary

  Research in small animals depends on comparisons of cellular and molecular measures in large groups, requiring efficient and reproducible methods for registration of data to brain atlases followed by quantitative analysis. While numerous methodologies and increasing amounts of data are available, they are difficult to combine into coherent and reproducible workflows suitable for large-scale analyses. The EBRAINS research infrastructure enables neuroscientists to conduct their mouse and rat brain data analyses in an open, robust and user-friendly environment. It provides tools and workflows for viewing images; automatic and user guided registration of brain section image data to an atlas; feature extraction with machine learning, whole brain distribution analysis, and metadata management according to the FAIR principles. We present examples of typical use cases, illustrating how the available suite of tools can be used and the results obtained, with an emphasis on the reproducibility of the methods applied and the sharing of the data and metadata. The tools are part of the EBRAINS Atlases services (https://ebrains.eu/services/atlases) and offer one comprehensive solutions for organizing, analyzing and sharing brain research data. These and other EBRAINS research infrastructure tools and workflows are developed in close interaction with users in the neuroscience community. Funded from EU Horizon 2020, Specific Grant Agreement No. 945539 (Human Brain Project SGA3).

• Schlegel, Ulrike; Köhnen, Stefan; Davison, Andrew; Najafi, Peyman; Weyers, Benjamin & Gründling, Jan [Show all 16 contributors for this article] (2022). openMINDS - flexible metadata models for neuroscience. Full text in Research Archive Show summary

  Enhancing transparency and findability of research data is an emerging theme across scientific disciplines. More and more journals and funders, such as the Research Council of Norway, require the sharing of data in accordance with the FAIR guiding principles (Wilkinson et al., Scientific Data 3:160018, 2016), but few publicly accessible databases achieve this requirement. Their suitability is determined by attributes and constraints of the underlying metadata model. In neuroscience, the heterogeneity of data is particularly challenging. The multimodal nature of the research data as well as the wide range in spatial and temporal scales need to be adequately captured. Therefore, a suitable metadata model for neuroscience data has to balance flexibility and restrictiveness to accommodate the individuality of research products, without diminishing comparability across them. Powered by the Human Brain Project (HBP) and EBRAINS, we present the open Metadata Initiative for Neuroscience Data Structures (openMINDS). This novel initiative develops and maintains interlinked metadata models tailored to describe neuroscience research products in graph databases, such as the EBRAINS Knowledge Graph. The openMINDS research products cover data originating from human, animal or simulation studies (datasets), computational models (models), software tools (software), formal specifications for structuring metadata and/or data (metaDataModels), and reference brain atlases (brainAtlas). To illustrate the power of openMINDS, we present selected features describing these research products. We highlight how respective data and their provenance as well as studied specimens can be captured with user-defined granularity, and how the various integration of data via openMINDS can enhance its comparability and findability within and beyond the EBRAINS Knowledge Graph.

• Yates, Sharon Christine; Gurdon, Brianna; Csucs, Gergely; Groeneboom, Nicolaas Ervik; Puchades, Maja Amedjkouh & Kaczorowski, Catherine [Show all 7 contributors for this article] (2022). Quint workflow for brain-wide quantification of rodent models: new functionality for high-throughput studies. Full text in Research Archive Show summary

  Sectioned brain tissue of rodent models is used to explore the cellular and molecular composition of the brain in health and disease. The QUINT workflow was developed to support standardized atlas-based analysis of sectioned tissue without the need for coding ability. It is shared on the EBRAINS Atlas Services as a suite of open-source tools that can be flexibly combined to meet the needs of diverse projects (ebrains.eu). This includes tools for atlas-registration, feature extraction and quantification in regions defined by a reference atlas of the brain. New functionality has been added to the QUINT workflow to meet the needs of a novel Alzheimer’s disease project involving a large population of genetically diverse mice, the AD-BDX panel (Neuner et al, Neuron 2019, PMID: 30595332). This includes QCAlign: a tool for assessing the quality of the section images, as well as the quality of the atlas-registration to the sections as performed with the registration tools. By supporting systematic assessment of the material, QCAlign allows the removal of damaged sections, and the post-processing of results according to strict criteria that are transparent and reproducible. As a proof-of-concept, the QCAlign tool was applied to data from the Alzheimer’s project to detect and remove sections with more than 30% damage. It was also used to assess the quality of the atlas-registration before and after application of nonlinear refinements to the registration. To summarize, QCAlign tackles challenges posed by high-throughput studies and expands the scope of the QUINT workflow for comprehensive analysis.

• Blixhavn, Camilla Hagen; S., Haug Finn-Mogens; Kleven, Heidi; Puchades, Maja; Bjaalie, Jan G. & Leergaard, Trygve B. (2022). Multiplane microscopic atlas of rat brain zincergic terminal fields and metal-containing glia stained with Timm's sulphide silver method. Full text in Research Archive
• Schlegel, Ulrike; Hjallar, Viktor A.; Lensjø, Kristian; Uggerud, Ida Margrethe; Bjaalie, Jan G. & Hvoslef-Eide, Martha [Show all 8 contributors for this article] (2022). Mapping of parahippocampal and visual neural networks in mice: Preliminary evidence for feedback projections from perirhinal cortex to visual areas. Full text in Research Archive Show summary

  Feedback projections from higher-order processing areas to lower-order areas within the visual system havebeen observed in several species. While much is known about the function of feedforward connections in thevisual processing pathway, feedback connections are not as well studied. They are thought to cause behaviorssuch as (selective) attention or expectations, but the exact mechanism and underlying neural connectivity isstill unknown. The perirhinal cortex (PRH) forms the intersection between perceptual and mnemonic areas ofthe visual processing pathway. Evidence from rats, monkeys and humans suggests that it is involved in bothobject memory and perceptual tasks, which is reflected in its connectivity. PRH forms numerous feedforwardand feedback projections with other areas of the visual processing pathway. However, little is known about thefunction and organization of feedback projections from PRH, and other parahippocampal regions, to visualareas in mice. Given the widespread availability of genetic and molecular tools, novel opportunities toinvestigate the function of these feedback projections would be enabled by mapping them in mice. Here wepresent preliminary data from anterograde and retrograde tract tracing experiments showing feedbackprojections from PRH, and other parahippocampal regions, to visual areas. We have found first evidence forfeedback projections from PRH to lower-order visual areas that were assumed to be exclusive to higher-developed animals such as monkeys or humans. Further, we have identified feedback projections from otherparahippocampal regions with specific subregional distribution.

• Bjerke, Ingvild Elise; Leergaard, Trygve B.; Bjaalie, Jan G. & Kim, Jee Hyun (2022). Quantitative map of dopamine 1- and 2-receptor positive cells in the developing mouse forebrain. Full text in Research Archive
• Reiten, Ingrid & Bjaalie, Jan G. (2022). EBRAINS Data & Knowledge Services: perspectives on open data from within EBRAINS. Full text in Research Archive
• Leergaard, Trygve B.; Bjerke, Ingvild Elise; Bjaalie, Jan G.; Palomero-Gallagher, Nicola; Puchades, Maja & Carey, Harry [Show all 7 contributors for this article] (2022). Neuroscience data integration through use of digital brain atlases. Full text in Research Archive Show summary

  This two-day course will provide a hands-on introduction to three-dimensional reference atlases for the rat and mouse brain, and demonstrate how such atlases can be utilized to integrate and analyze heterogeneous neuroscience data. Students will gain updated knowledge about current approaches to assigning anatomical location to experimental data from the brain, and acquire basic skills in associated analytic tools. Invited speaker Nicola Palomero-Gallagher will give a lecture on neuroanatomy. Jan Bjaalie, Trygve Leergaard and co-workers Camilla Blixhavn, Ingvild Bjerke, Maja Puchades, Sharon Yates and Harry Carey from the Neural Systems Laboratory (University of Oslo) will introduce new concepts for data integration and development of murine brain atlas resources established in context of the European Human Brain Project.

• Schlegel, Ulrike; Köhnen, Stefan; Davison, Andrew P.; Najafi, Peyman; Weyers, Benjamin & Gründling, Jan [Show all 17 contributors for this article] (2021). openMINDS - flexible metadata models for neuroscience. Full text in Research Archive Show summary

  Enhancing transparency and findability of research data is an emerging theme across scientific disciplines. While publicly accessible databases aid in achieving this goal, their suitability is determined by attributes and constraints of the underlying metadata model. In neuroscience, the heterogeneity of data is particularly challenging. The multimodal nature of the research data as well as the wide range in spatial and temporal scales need to be adequately captured . Therefore, a suitable metadata model for neuroscience data has to balance flexibility and restrictiveness to accommodate the individuality of research products, without diminishing comparability across them. Powered by the Human Brain Project (HBP) and EBRAINS, we present the open Metadata Initiative for Neuroscience Data Structures (openMINDS). This novel initiative develops and maintains interlinked metadata models tailored to describe neuroscience research products in graph databases, such as the EBRAINS Knowledge Graph. The openMINDS research products cover data originating from human, animal or simulation studies (datasets), computational models (models), software tools (software), formal specifications for structuring metadata and/or data (metaDataModels), and reference brain atlases (brainAtlas). To illustrate the power of openMINDS, we present selected features describing these research products. We highlight how respective data and their provenance as well as studied specimens can be captured with user-defined granularity, and how the various integration of data via openMINDS can enhance its comparability and findability within and beyond the EBRAINS Knowledge Graph.

• Quaglio, GianLuca; Toia, Patrizia; Moser, Edvard Ingjald; Karapiperis, Theodoros; Amunts, Katrin & Okabe, Shigeo [Show all 12 contributors for this article] (2021). The International Brain Initiative: enabling collaborative science. 20(12), p. 985–986. doi: 10.1016/S1474-4422(21)00389-6. Full text in Research Archive
• Bjaalie, Jan G. (2021). Panel 255: A Canadian BRAIN initiative? Full text in Research Archive
• Bjaalie, Jan G. (2021). Talk at the EAN-EBRAINS Workshop: The power of the EBRAINS Knowledge Graph. Full text in Research Archive
• Bjaalie, Jan G. (2021). Forskningsdagene 2020.Hjernepodcast live med Gaute Einevoll https://www.forskningsdagene.no/arrangementer/t-9443. [Radio]. Litteraturhuset, Wergeland. Full text in Research Archive
• Bjaalie, Jan G. (2021). Value and Costs of Managing and Sharing Data: Reflections from the field. Full text in Research Archive
• Bjaalie, Jan G. (2021). Livestream session from the European Parliament STOA Panel: Enabling International Collaboration: IBI overview. Full text in Research Archive
• Bjaalie, Jan G. (2021). NeuroHub Seminar - A global perspective on large-scale digital research infrastructures: The role of EBRAINS in international collaborations. Full text in Research Archive
• Abrams, Mathew Birdsall; Bjaalie, Jan G.; Das, Samir; Egan, Gary F.; Ghosh, Satrajit S. & Goscinski, Wojtek J. [Show all 23 contributors for this article] (2021). Correction to: A Standards Organization for Open and FAIR Neuroscience: the International Neuroinformatics Coordinating Facility (Neuroinformatics, (2021), 10.1007/s12021-020-09509-0). Neuroinformatics. ISSN 1539-2791. doi: 10.1007/s12021-021-09522-x. Full text in Research Archive
• Bjaalie, Jan G. (2021). Chair at Parallel sessions at FLAG-ERA Project Workshop. Full text in Research Archive
• Bjaalie, Jan G. (2021). Talk: "Reproducible and transparent neuroscience: EBRAINS services for publishing research data". Full text in Research Archive
• Bjaalie, Jan G. (2021). Presentation Session 6: Tools and infrastructure showcase: EBRAINS. Full text in Research Archive
• Bjaalie, Jan G. (2021). Chair session 11:"Towards neuroscience-centered selection criteria for data repositories and scientific gateways". Full text in Research Archive
• Bjaalie, Jan G. (2021). Chair session 9 and Talk: Introduction to the International Brain Initiative. Full text in Research Archive
• Bjaalie, Jan G. (2021). SESSION IV: Value and costs of managing and sharing Data: Reflections from the field and panel Discussion. Full text in Research Archive
• Swieten, Maaike van & Bjaalie, Jan G. (2021). How to make data public. Full text in Research Archive
• Puchades, Maja; Yates, Sharon Christine; Bjerke, Ingvild Elise; Groeneboom, Nicolaas; Csucs, Gergely & Leergaard, Trygve B. [Show all 7 contributors for this article] (2021). Implementing the QUINT workflow for spatial quantitative analysis of labelling in mice and rats. Full text in Research Archive Show summary

  Research in small animal disease models and simulation depend on quantitative comparisons of cellular and molecular measures in large groups of specimens, requiring efficient and reproducible methods. The EBRAINS QUINT workflow combines 3D atlas registration tools (QuickNII and VisuAlign) with machine learning based image segmentation (ilastik), and a tool for quantitative analysis on whole brain and regional level (Nutil Quantifier). Workflow descriptions, video tutorials, courses and email sup

• Reiten, Ingrid; Schlegel, Ulrike; Aasebø, Ida; Zehl, Lyuba; Blixhavn, Camilla Hagen & Davison, Andrew [Show all 17 contributors for this article] (2021). Data sharing via EBRAINS: why and how. Full text in Research Archive
• Bjaalie, Jan G. (2020). Presentation: Overview of Human Brain Project at 6th Internation Brain Intitiative Investigators Meeting. Full text in Research Archive
• Einevoll, Gaute; Bjaalie, Jan G. & Plesser, Hans Ekkehard (2020). Om hjerneforskning og Human Brain Project. [Internet]. Podcast: Vett og vitenskap med Gaute Einevoll. Full text in Research Archive
• Reiten, Ingrid; Schlegel, Ulrike; Aasebø, Ida Elisabeth Jørgensen; Blixhavn, Camilla Hagen; Zehl, Lyuba & Kjelsberg, Kasper [Show all 13 contributors for this article] (2020). Neuroscience community-gains from data sharing through the EBRAINS infrastructure. Full text in Research Archive Show summary

  EBRAINS provides tools and services which can be used to address challenges in brain research and brain-inspired technology development. EBRAINS assists scientists to collect, analyse, share and integrate brain data, and to perform modeling and simulation of brain function. EBRAINS is delivered by the EU Flagship Human Brain Project. All tools and services in EBRAINS are available for researchers in Europe and globally through https://ebrains.eu. Here we exemplify the use of the platform in different neuroscientific projects. In particular, the ‘Data & Knowledge’ services in EBRAINS offer one of the most comprehensive services for sharing brain research data ranging in type as well as spatial and temporal scale. An extensive metadata curation process ensures a robust presentation of datasets, models and software via the EBRAINS Knowledge Graph (https://kg.ebrains.eu/search/), making the data Findable, Accessible, Interoperable and Reusable (FAIR). By describing data across modalities in a standardised way and integrating it into the same reference space, data can be compared, combined and analysed with tools and analytical workflows embedded in the platform. New data, and data derived from the analytical workflows, can be submitted to curation and added to the existing EBRAINS datasets. The interplay between datasets, models and software makes EBRAINS attractive as a platform for discovery based and hypothesis driven research.

• Gurdon, Brianna; Hadad, Niran; Telpoukhovskaia, Maria; Yates, Sharon Christine; Puchades, Maja & Bjaalie, Jan G. [Show all 7 contributors for this article] (2020). Brain-wide spatial analysis to identify region-specific changes in cell composition associated with resilience to Alzheimer’s disease in the AD-BXD mouse population. Full text in Research Archive
• Bjaalie, Jan G. (2020). EBRAINS: New enabling infrastructure for brain research (FENS 2020 Virtual Forum: Open Theatre talk). Full text in Research Archive
• Bjaalie, Jan G. (2020). HBP / EBRAINS Overview at Human Brain Project 2020 Summit. Full text in Research Archive
• Bjaalie, Jan G. (2020). Sharing and publishing human brain data through the EBRAINS research infrastructure (GDPR impact conference). Full text in Research Archive Show summary

  Sharing and publishing human brain data through the EBRAINS research infrastructure

• Bjaalie, Jan G. (2020). International Brain Initiative: Facilitating Global Neuroscience Engagement. Full text in Research Archive
• Bjaalie, Jan G. (2020). Introduction to EBRAINS at Code Jam # 11. Full text in Research Archive
• Schlegel, Ulrike; Reiten, Ingrid; Aasebø, Ida Elisabeth Jørgensen; Blixhavn, Camilla Hagen; Zehl, Lyuba & Kjelsberg, Kasper [Show all 13 contributors for this article] (2020). EBRAINS data sharing: benefits and workflow. Full text in Research Archive

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