Jan G. Bjaalie

• 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.
• Tocco, Chiara; Øvsthus, Martin; Bjaalie, Jan G. & Leergaard, Trygve B. (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
• 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. ISSN 2373-2822. 9(2). doi: 10.1523/ENEURO.0316-21.2022.
• 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
• Dojat, Michel; Bjaalie, Jan G. & Barbier, Emmanuel L (2021). Editorial: APPNING: Animal Population Imaging. Frontiers in Neuroinformatics. ISSN 1662-5196. 15, p. 1–3. doi: 10.3389/fninf.2021.676603. Full text in Research Archive
• 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. ISSN 1664-2392. 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.

• 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
• Groeneboom, Nicolaas Ervik; Yates, Sharon Christine; Puchades, Maja & Bjaalie, Jan G. (2020). Nutil: A Pre- and Post-processing Toolbox for Histological Rodent Brain Section Images. Frontiers in Neuroinformatics. ISSN 1662-5196. doi: 10.3389/fninf.2020.00037.
• 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. ISSN 2589-0042. 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.

• Bjerke, Ingvild Elise; Puchades, Maja ; Bjaalie, Jan G. & Leergaard, Trygve B. (2020). Database of literature derived cellular measurements from the murine basal ganglia. Scientific Data. ISSN 2052-4463. 7. doi: 10.6084/m9.figshare.12199088.
• 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.
• 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. ISSN 1756-994X. 12(18). doi: 10.1186/s13073-020-0713-z. Full text in Research Archive
• Yates, Sharon Christine; Groeneboom, Nicolaas Ervik; Coello, Christopher; Lichtenthaler, Stefan F.; Kuhn, Peer-Hendrik & Demuth, Hans-Ulrich [Show all 12 contributors for this article] (2019). QUINT: Workflow for Quantification and Spatial Analysis of Features in Histological Images From Rodent Brain. Frontiers in Neuroinformatics. ISSN 1662-5196. 13. doi: 10.3389/fninf.2019.00075. Full text in Research Archive
• Amunts, Katrin; Knoll, Alois C; Lippert, Thomas; Pennartz, Cyriel M.A.; Ryvlin, Philippe & Destexhe, Alain [Show all 9 contributors for this article] (2019). The Human Brain Project?Synergy between neuroscience, computing, informatics, and brain-inspired technologies. PLoS Biology. ISSN 1544-9173. 17(7). doi: 10.1371/journal.pbio.3000344. Full text in Research Archive
• Lillehaug, Sveinung; Yetman, Michael J.; Puchades, Maja ; Checinska, Martyna; Kleven, Heidi & Jankowsky, Joanna L. [Show all 8 contributors for this article] (2019). Data Descriptor: Brain-wide distribution of reporter expression in five transgenic tetracyclinetransactivator mouse lines. Scientific Data. ISSN 2052-4463. 6, p. 1–15. doi: 10.1038/sdata.2019.28.
• Puchades, Maja ; Csúcs, Gergely; Ledergerber, Debora; Leergaard, Trygve B. & Bjaalie, Jan G. (2019). Spatial registration of serial microscopic brain images to three-dimensional reference atlases with the QuickNII tool. PLOS ONE. ISSN 1932-6203. 14(5). doi: 10.1371/journal.pone.0216796. Full text in Research Archive
• Heiland, Tina; Zeitschel, Ulrike; Puchades, Maja ; Kuhn, Peer-Hendrik; Lichtenthaler, Stefan F. & Bjaalie, Jan G. [Show all 9 contributors for this article] (2018). Defined astrocytic expression of human amyloid precursor protein in Tg2576 mouse brain. Glia. ISSN 0894-1491. 67(2), p. 393–403. doi: 10.1002/glia.23550. Full text in Research Archive
• Bjerke, Ingvild Elise; Øvsthus, Martin; Andersson, Krister Andreas; Blixhavn, Camilla Hagen; Kleven, Heidi & Yates, Sharon Christine [Show all 9 contributors for this article] (2018). Navigating the Murine Brain: Toward Best Practices for Determining and Documenting Neuroanatomical Locations in Experimental Studies. Frontiers in Neuroanatomy. ISSN 1662-5129. 12. doi: 10.3389/fnana.2018.00082. Full text in Research Archive
• Plank, Anne-Christine; Canneva, Fabio; Raber, Kerstin; Urbach, Yvonne K; Dobner, Julia & Puchades, Maja [Show all 12 contributors for this article] (2018). Early Alterations in Operant Performance and Prominent Huntingtin Aggregation in a Congenic F344 Rat Line of the Classical CAGn51trunc Model of Huntington Disease. Frontiers in Neuroscience. ISSN 1662-4548. 12. doi: 10.3389/fnins.2018.00011. Full text in Research Archive
• Bjerke, Ingvild Elise; Øvsthus, Martin; Papp, Eszter Agnes; Yates, Sharon Christine; Silvestri, L & Fiorilli, J [Show all 11 contributors for this article] (2018). Data integration through brain atlasing: Human Brain Project tools and strategies. European psychiatry. ISSN 0924-9338. 50, p. 70–76. doi: 10.1016/j.eurpsy.2018.02.004. Full text in Research Archive
• Dempsey, Bowen; Le, Sheng; Turner, Anita; Bokiniec, Phil; Ramadas, Radhika & Bjaalie, Jan G. [Show all 11 contributors for this article] (2017). Mapping and analysis of the connectome of sympathetic premotor neurons in the rostral ventrolateral medulla of the rat using a volumetric brain atlas. Frontiers in Neural Circuits. ISSN 1662-5110. 11. doi: 10.3389/fncir.2017.00009.
• Schubert, N; Axer, M; Schober, M; Huynh, A; Huysegoms, M & Palomero-Gallagher, N [Show all 11 contributors for this article] (2016). 3D reconstructed cyto-, muscarinic M<sub>2</sub> receptor, and fiber architecture of the rat brain registered to the waxholm space atlas. Frontiers in Neuroanatomy. ISSN 1662-5129. 10(MAY). doi: 10.3389/fnana.2016.00051.
• Papp, Eszter Agnes; Leergaard, Trygve Brauns; Csúcs, Gergely & Bjaalie, Jan G. (2016). Brain-wide mapping of axonal connections: Workflow for automated detection and spatial analysis of labeling in microscopic sections. Frontiers in Neuroinformatics. ISSN 1662-5196. 10(11). doi: 10.3389/fninf.2016.00011. Full text in Research Archive
• Yetman, Michael J.; Lillehaug, Sveinung; Bjaalie, Jan G.; Leergaard, Trygve Brauns & Jankowsky, Joanna L. (2016). Transgene expression in the Nop-tTA driver line is not inherently restricted to the entorhinal cortex. Brain Structure and Function. ISSN 1863-2653. 221(4), p. 2231–2249. doi: 10.1007/s00429-015-1040-9.
• Zakiewicz, Izabela Maria; Majka, Piotr; Wòjcik, Daniel K.; Bjaalie, Jan G. & Leergaard, Trygve Brauns (2015). Three-Dimensional Histology Volume Reconstruction of Axonal Tract Tracing Data: Exploring Topographical Organization in Subcortical Projections from Rat Barrel Cortex. PLOS ONE. ISSN 1932-6203. 10(9). doi: 10.1371/journal.pone.0137571.
• Boccara, Charlotte Nina; Kjønigsen, Lisa Jannicke; Hammer, Ingvild Marie F.; Bjaalie, Jan G.; Leergaard, Trygve Brauns & Witter, Menno (2015). A three-plane architectonic atlas of the rat hippocampal region. Hippocampus. ISSN 1050-9631. 25(7), p. 838–857. doi: 10.1002/hipo.22407.

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• Sand, Olav; Sjaastad, Øystein; Haug, Egil & Bjaalie, Jan G. (2019). Anatomi og fysiologi - Menneskekroppen, 4. utgave. Gads forlag. ISBN 9788712058328. 666 p.
• Sjaastad, Øystein; Toverud, Kari C.; Sand, Olav; Bjaalie, Jan G. & Egil, Haug (2018). Arbeidsbok til Menneskekroppen - Fysiologi og anatomi, 3. utgave. Gyldendal Akademisk. ISBN 978-82-05-51403-4. 224 p.
• Sand, Olav; Sjaastad, Øystein; Haug, Egil & Bjaalie, Jan G. (2018). Menneskekroppen - Fysiologi og anatomi, 3. utgave. Gyldendal Akademisk. ISBN 978-82-05-50482-0. 666 p.
• Sand, Olav; Sjaastad, Øystein; Haug, Egil & Bjaalie, Jan G. (2015). Menneskets anatomi og fysiologi 3. utgave. Gads Forlag. ISBN 978-87-12-05107-7. 538 p.
• Sjaastad, Øystein; Toverud, Kari C.; Sand, Olav; Bjaalie, Jan G. & Egil, Haug (2015). Studieoppgaver til Menneskets anatomi og fysiologi. Gads forlag. ISBN 9788712050353. 203 p.

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• van Swieten, Maaike; 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. 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.

• van Swieten, Maaike & Bjaalie, Jan G. (2021). How to make data public.
• 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.
• 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. Lancet Neurology. ISSN 1474-4422. 20(12), p. 985–986. doi: 10.1016/S1474-4422%2821%2900389-6.
• 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 .
• Bjaalie, Jan G. (2021). SESSION IV: Value and costs of managing and sharing Data: Reflections from the field and panel Discussion.
• Bjaalie, Jan G. (2021). Chair session 9 and Talk: Introduction to the International Brain Initiative.
• Bjaalie, Jan G. (2021). Chair session 11:"Towards neuroscience-centered selection criteria for data repositories and scientific gateways".
• Bjaalie, Jan G. (2021). Presentation Session 6: Tools and infrastructure showcase: EBRAINS.
• Bjaalie, Jan G. (2021). Chair at Parallel sessions at FLAG-ERA Project Workshop.
• Bjaalie, Jan G. (2021). Talk: "Reproducible and transparent neuroscience: EBRAINS services for publishing research data" .
• Bjaalie, Jan G. (2021). Talk at the EAN-EBRAINS Workshop: The power of the EBRAINS Knowledge Graph.
• Bjaalie, Jan G. (2021). Panel 255: A Canadian BRAIN initiative?
• Bjaalie, Jan G. (2021). Forskningsdagene 2020.Hjernepodcast live med Gaute Einevoll https://www.forskningsdagene.no/arrangementer/t-9443. [Radio]. Litteraturhuset, Wergeland.
• 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. 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.

• 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. 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

• Bjaalie, Jan G. (2020). Global Neuroethic Summit Cross IBI workshop.
• Bjaalie, Jan G. (2020). EBRA-EPICLUSTER Consensus Workshop. Overview Human Brain Project and General view on research infrastructures and research clusters. .
• Bjaalie, Jan G. (2020). Presentation: Overview of Human Brain Project at 6th Internation Brain Intitiative Investigators Meeting .
• Bjaalie, Jan G. (2020). Introduction to EBRAINS at Code Jam # 11.
• Yates, Sharon Christine; Groeneboom, Nicolaas; Csucs, Gergely; Leergaard, Trygve B.; Kreshuk, Anna & Kutra, Dominik [Show all 8 contributors for this article] (2020). New functionalities i the QUINT workflow for brain atlas based image analysis.
• Einevoll, Gaute; Bjaalie, Jan G. & Plesser, Hans Ekkehard (2020). Om hjerneforskning og Human Brain Project . [Internet]. Podcast: Vett og vitenskap med Gaute Einevoll.
• 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.
• 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.
• 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.
• Reiten, Ingrid; Schlegel, Ulrike; Blixhavn, Camilla Hagen; Andersson, Krister Andreas; Aasebø, Ida Elisabeth Jørgensen & Yates, Sharon Christine [Show all 12 contributors for this article] (2020). Data sharing through the online EBRAINS platform: a new service for brain research. Show summary

  Enhancing the reproducibility and transparency of research is an emerging theme across scientific disciplines, driven by new technological advances, and captured by the Open Science concept. The heterogeneity of research data, which often hinders direct comparisons of findings, adds a layer of complexity to this effort. To address these challenges in neuroscience, the Human Brain Project has developed a new research infrastructure, EBRAINS, providing tools and services to the neuroscientific community. The EBRAINS data curation service offers comprehensive stewardship for sharing experimental and computational data. New workflows and standards for neuroscience data and metadata management have been developed to make the research results discoverable, comparable across modalities, and possible to reanalyse and reuse in new combinations. Here we present our workflows and curation services tailored for sharing heterogeneous neuroscience data. We demonstrate the integration of such data in the infrastructure, and highlight some practicalities for researchers who want to share their neuroscience data through EBRAINS.

• Bjaalie, Jan G. (2020). International Brain Initiative: Facilitating Global Neuroscience Engagement.
• Bjaalie, Jan G. (2020). HBP / EBRAINS Overview at Human Brain Project 2020 Summit.
• Bjaalie, Jan G. (2020). EBRAINS: New enabling infrastructure for brain research (FENS 2020 Virtual Forum: Open Theatre talk).
• Bjaalie, Jan G. (2020). Sharing and publishing human brain data through the EBRAINS research infrastructure (GDPR impact conference). Show summary

  Sharing and publishing human brain data through the EBRAINS research infrastructure

• Andersson, Krister Andreas; Blixhavn, Camilla Hagen; Zehl, Lyuba; Zarfarnia, Sara; Köhnen, Stefan & Hilverling, Anna [Show all 19 contributors for this article] (2019). Resources for making neuroscience data FAIR. The Human Brain Project invites researchers to share, find, and use data via the new EBRAINS infrastructure.
• Andersson, Krister Andreas; Blixhavn, Camilla Hagen; Kleven, Heidi; Zehl, Lyuba; Bjerke, Ingvild Elise & Schmid, Oliver [Show all 17 contributors for this article] (2019). Neuroinformatics platform for making neuroscience data Findable, Accessible, Interoperable, and Reuseable.
• Andersson, Krister Andreas; Blixhavn, Camilla Hagen; Kleven, Heidi; Schlegel, Ulrike; Oliver, Schmid & Puchades, Maja [Show all 10 contributors for this article] (2019). EBRAINS fair data service: A novel infrastructure for making neuroscience data findable, accessible, interoperable, and reuseable.
• Puchades, Maja ; Yates, Sharon Christine; Groeneboom, Nicolaas; Csúcs, Gergely; Leergaard, Trygve B. & Bjaalie, Jan G. (2019). Workflow for quantification and spatial analysis of labeling in large series of histological images from murine brains. Show summary

  We present a novel workflow - QUINT- for quantification and spatial analysis of labeling in series of histological section images from mouse or rat brains, using Human Brain Project (HBP) tools and procedures. The workflow can be used to detect and localise any distinct feature in the brain sections. The workflow is therefore transferable to different transgenic disease models and other types of mouse or rat studies.

• Blixhavn, Camilla Hagen; Andersson, Krister Andreas; Kleven, Heidi; Schlegel, Ulrike; Puchades, Maja & Bjaalie, Jan G. [Show all 7 contributors for this article] (2019). Find and explore rodent brain data using 3D atlases in the new EBRAINS infrastructure.
• Yates, Sharon Christine; Groeneboom, Nicolaas; Csúcs, Gergely; Leergaard, Trygve B.; Puchades, Maja & Bjaalie, Jan G. (2019). Batch quantification and spatial analysis of labelling in microscopic rodent brain sections .
• Blixhavn, Camilla Hagen; Andersson, Krister Andreas; Kleven, Heidi; Schlegel, Ulrike; Puchades, Maja & Bjaalie, Jan G. [Show all 7 contributors for this article] (2019). Infrastructure and workflow for integrating and navigating multi-scale and multi-modal murine neuroscience data using 3D digital brain reference atlases.
• Schlegel, Ulrike; Blixhavn, Camilla Hagen; Andersson, Krister Andreas; Yates, Sharon Christine; Øvsthus, Martin & Bjerke, Ingvild Elise [Show all 11 contributors for this article] (2019). Integrating and analysing heterogeneous rodent neuroscience data using three-dimensional brain reference atlases. Show summary

  Achieving advances in the field of neuroscience with its rapidly growing number of published data requires integration across many scales and levels of investigation. Such integration is challenging due to the heterogeneous nature of the data, and the difficulty of comparing data from different studies. Key aspects include lack of standards for presentation of data and experimental parameters (metadata), and variable practices for assigning and reporting anatomical location in the brain. The EU Human Brain Project (HBP) is addressing these challenges by establishing an infrastructure of neuroinformatic tools and data curation services through which disparate neuroscience data can be shared, used and analysed. Three-dimensional (3D) open access brain reference atlases provide anatomical context for all the shared data, easing comparison and interpretation of findings. We here present HBP workflows for assigning metadata describing anatomical locations to different types of neuroscience data, and workflows for extracting, quantifying and co-visualizing morphological features from multiple datasets in 3D anatomical brain atlas viewers. We highlight the added value of mapping data to a common atlas framework in example studies, and demonstrate new analytic opportunities enabled by combining image analysis tools with information from a 3D brain reference atlas. The HBP now invites the community to use the new research infrastructure established.

• Andersson, Krister Andreas; Blixhavn, Camilla Hagen; Zehl, Lyuba; Markovic, Milica; Kleven, Heidi & Zafarnia, Sara [Show all 15 contributors for this article] (2018). HBP Curation service: Share your data via the Neuroinformatics platform.
• Zehl, Lyuba; Zafarnia, Sara; Köhnen, Stefan; Andersson, Krister Andreas; Markovic, Milica & Legouée, Elodie [Show all 20 contributors for this article] (2018). Integrating neuroscientific data into a unified database: from individual experiments to a standardized metadata collection using the Human Brain Project Neuroinformatics Platform.
• Zehl, Lyuba; Zafarnia, Sara; Köhnen, Stefan; Andersson, Krister Andreas; Markovic, Milica & Legouée, Elodie [Show all 20 contributors for this article] (2018). Integrating neuroscientific data into a unified database: from individual experiments to a standardized metadata collection using the Human Brain Project Neuroinformatics Platform.
• Yates, Sharon Christine; Puchades, Maja ; kreshuk, anna; Kutra, Dominik; Leergaard, Trygve B. & Bjaalie, Jan G. [Show all 7 contributors for this article] (2018). HBP Analytic Workflow: from Microscopic Image Data to Extracted Features in Atlas Space.
• Bjaalie, Jan G. (2018). Data sharing in neuroscience: traditions, standards, and transformations.
• von Linstow, Christian Ulrich; Yates, Sharon Christine; Leergaard, Trygve B.; Plank, A-C; von Hörsten, S. & Puchades, Maja [Show all 7 contributors for this article] (2018). Whole brain analysis of Huntingtin aggregates in the F344 transgenic rat model of Huntington’s disease.
• Bjerke, Ingvild Elise; Øvsthus, Martin; Andersson, Krister Andreas; Bjaalie, Jan G. & Leergaard, Trygve B. (2018). Best practices for determining and documenting neuroanatomical locations in the rodent brain.
• Øvsthus, Martin; Bjerke, Ingvild Elise; Yates, Sharon Christine; Bjaalie, Jan G. & Leergaard, Trygve Brauns (2018). Mapping topographical organization in mouse brain subcortical axonal projections using Human Brain Project infrastructure.
• Blixhavn, Camilla Hagen; Andersson, Krister; Øvsthus, Martin; Bjerke, Ingvild Elise; Kleven, Heidi & Puchades, Maja [Show all 8 contributors for this article] (2018). Data integration through digital brain atlasing: Making diverse neuroscience data discoverable using Human Brain Project infrastructure.
• Leprince, Y; Coello, Sebastian Christopher; Csúcs, Gergely; Chervakov, P; Darine, Dmitri Aleksandrovitsj & Øvsthus, Martin [Show all 12 contributors for this article] (2017). Interactive tools for registering of 2D and 3D images to reference atlases.
• Imad, Jala; Kleven, Heidi; Clascá, Francisco; Wennberg, Arvind Eirik; Osen, Kirsten Kjelsberg & Coello, Sebastian Christopher [Show all 11 contributors for this article] (2017). Enriching and adding detail to the Waxholm Space rat brain reference atlas.
• Andersson, Krister Andreas; Bell, Simon; Bjerke, Ingvild Elise; Øvsthus, Martin; Blixhavn, Camilla Hagen & Kleven, Heidi [Show all 18 contributors for this article] (2017). Submitting data and metadata through the HBP Data Workbench: Concepts, data flows, policies, and best practices.
• Coello, Sebastian Christopher; Leergaard, Trygve B. & Bjaalie, Jan G. (2017). Tackling the normalization of 2-D rodent histology sections in a 3-D coordinate space.
• Coello, Sebastian Christopher; Leergaard, Trygve B. & Bjaalie, Jan G. (2017). Tackling the normalization of 2-D rodent histology sections in a 3-D coordinate space.
• Bjerke, Ingvild Elise; Andersson, Krister Andreas; Øvsthus, Martin; Puchades, Maja ; Bjaalie, Jan G. & Leergaard, Trygve B. (2017). Navigating the rodent brain: Best practice recommendations for determining and documenting spatial location for neuroscience data .
• Imad, Jala; Osen, Kirsten Kjelsberg; Clascá, Francisco; Wennberg, Arvind E.; Coello, Sebastian Christopher & Csúcs, Gergely [Show all 9 contributors for this article] (2017). Enriching and Adding Detail to the Waxholm Space Rat Brain Reference Atlas. Show summary

  Brain reference atlases are important resources for assigning anatomical location to experimental data, and for planning and interpreting experimental results. Three-dimensional atlases can also serve as templates for spatial co-registration (integration) and comparison of different types of brain images. The Waxholm Space atlas of the rat brain is a public resource based on high resolution magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) data. It features a coordinate system based on internal landmarks (Waxholm Space) and delineations of so far 79 brain structures (Papp et al., Neuroimage 97:374-386, 2015; Kjonigsen et al., Neuroimage 108:441-449, 2015). The atlas is shared via the Neuroimaging Informatics Tools and Resources Clearinghouse (www.nitrc.org), and has since its release been widely adopted by the community. A limitation is, however, that detailed anatomical delineations of several major brain regions are missing. In some instances, delineations are not easily introduced due to low contrast in the underlying MRI / DTI templates, or complex features that are challenging to interpret in MRI / DTI data. To amend this, we have spatially registered images of serial histological sections stained for cyto-, chemo- and myeloarchitecture to the Waxholm Space rat brain template, and used these to aid the interpretation of boundaries of structures. We here outline our approach to defining brain structures in the Waxholm Space rat brain template, and present new delineations of the ascending auditory system, the thalamus, as well as several regions in the cerebral cortex and basal forebrain. The anatomical criteria underlying the delineations will be published, and the next version of the atlas delineations will be shared from www.nitrc.org.

• Yates, Sharon Christine; Puchades, Maja ; Coello, Sebastian Christopher; Kreshuk, Anna; Hartlage-Rübsamen, Maike & Rossner, Steffen [Show all 8 contributors for this article] (2017). Workflow for automated quantification and spatial analysis of labelling in microscoping rodent brain sections.
• Andersson, Krister; Øvsthus, Martin; Bjerke, Ingvild Elise; Puchades, Maja ; Telefont, Martin & Jeff, Muller [Show all 9 contributors for this article] (2017). Data integration through digital brain atlasing: Human Brain Project infrastructure. Show summary

  The Human Brain Project is building an ICT-based scientific research infrastructure that will permit researchers to advance our knowledge in the fields of neuroscience through data exploration, analytics and simulation at multiple levels of brain organization. Experimental neuroscience is connected to the infrastructure through systems for organizing and managing heterogeneous research data. These data systems are initially tested by data producing laboratories in the HBP, and will ultimately be opened for the community. HBP data curation services support users in elevating the level of data consistency and in the migration of data to the open domain. The starting point for research projects that will use HBP resources is the HBP Collaboratory, a rich collaborative workspace which is open to the community. The Collaboratory provides guidance and access to resources, including storage for data and a workbench for entering and organizing metadata. As a central element, the Collaboratory provides high-quality reference atlases of the rodent and human brain, together with appropriate tools and workflows that allow users to register data to the atlases for their study, and to perform initial analysis of data. It also links to important external data repositories and services. Here we present an overview of currently available reference atlases, tools and workflows. We exemplify the use of these resources in a range of neuroscience projects, ranging from brain-wide mapping of molecular level information to identification of precise location of electrophysiology recording sites. With coordinates corresponding to reference atlas space, harvested through the workflow, valuable metadata for future search and analysis of data are captured. Furthermore, with data aligned to reference atlases, analysis of the spatial distribution of events, labeled elements, and regions of interest in image material is strongly supported. Following registration to reference atlas, subsequent image processing and analysis steps delivers lists of extracted features corresponding to atlas structures, enabling quantitative regional analysis. We exemplify analytical workflows producing automated quantification and spatial analysis of labeling in series of histological section images from whole rodent brain. These and other atlas related workflows will be made available as HBP software services.

• Bjerke, Ingvild Elise; Andersson, Krister; Øvsthus, Martin; Puchades, Maja ; Bjaalie, Jan G. & Leergaard, Trygve Brauns (2017). Determining and documenting the anatomical location of experimental neuroscience data: Best practice recommendations. Show summary

  Anatomical location is a key parameter for interpretation and comparison of neuroscientific data. Location is typically determined by looking up diagrams in anatomical reference atlases, communicated using anatomical terms, and shown in representative images. But the documentation provided varies considerably among scientific publications. Essential information about nomenclature and reference atlases, or criteria used to define boundaries of structures is often missing. This lack of accuracy limits the opportunities for comparing and integrating data from different publications, and could lead to failure in replicating scientific experiments. To clarify and address this challenge, we have investigated current practice for assigning and documenting anatomical location for different categories of experimental neuroscience data reported in > 120 articles investigating the rodent brain. Our findings show that the specificity and accuracy of anatomical documentation in most cases can be considerably improved with relatively simple procedures. We here suggest some general and method-specific recommendations for such improvements, and discuss how these steps may contribute to increase the accuracy of anatomical descriptions and data interpretation. We demonstrate how new three-dimensional rodent brain reference atlases, and associated software tools for spatial registration of brain image data to a common anatomical space, offer new opportunities for efficient integration and comparison of neuroscience data.

• Yates, Sharon Christine; Puchades, Maja ; Coello, Sebastian Christopher; Kreshuk, Anna; Hartlage-Rübsamen, Maike & Rossner, Steffen [Show all 8 contributors for this article] (2017). Workflow for automated quantification and spatial analysis of labeling in microscoping rodent brain sections. Show summary

  Introduction: Automated analysis of series of microscopic images from rodent brains can to advantage be used in many neuroscience research projects. To this end, we present a workflow for the automated quantification and spatial analysis of labeling in large series of section images from rodent brain. As an example, we present the quantification of Alzheimer’s disease plaques across a whole mouse brain series immunohistochemically stained for the APP N-terminus (1D1). Method: As a first step, the whole brain image series was anchored to the Allen mouse brain atlas, using an in-house software tool (QuickNII), to produce accurate anatomical maps adapted to the orientation of the images. Subsequently, the section images were classified by use of the ilastik software tool, based on supervised random forest learning algorithms. The classifier relies on input from manual user annotations of selected training images and the image features intensity, edge and texture. In a separate process, the class corresponding to plaque staining was cleaned, to remove edge artifact and background noise. The classified images were subsequently analyzed on a whole brain and regional level with input from the atlas maps. Thereby, a list of individual plaque features (area, location, etc.), region level features (number of plaques, area of plaques, etc.) and whole brain features were derived, enabling quantitative regional analysis as a semi-automatic pipeline. Results: With limited user interaction, we identified and localized according to brain region, individual amyloid plaques across a whole brain image series (Figure 1). Further validation of the workflow is on-going.

• Puchades, Maja ; Csúcs, Gergely; Checinska, Martyna; Øvsthus, Martin; Bjerke, Ingvild Elise & Andersson, Krister [Show all 8 contributors for this article] (2017). Neuroinformatics tool and workflow for anchoring of serial histological images in rodent brain 3D space. Show summary

  Reference atlases of the brain are important tools for assigning location to data captured in neuroscience experiments. Spatial alignment of sectional images to reference atlases is, however, challenging to perform for several reasons. Manual approaches applied to large series of sectional images are time consuming and, moreover, histological sections are often cut at angles deviating from the principal anatomical planes presented in conventional reference atlases. Novel 3D reference atlases and accompanying tools provide new opportunities for rapid and accurate spatial registration and integration of data in common atlas space. We here present QuickNII which is new tools for use with the Waxholm Space atlas for the rat brain and the Allen Mouse brain atlas for the mouse brain, and a workflow that allows users to 1) interactively generate customized atlas images (slices of the 3D reference atlas) corresponding to the plane of sectioning of any experimental image series, 2) superimpose atlas images onto experimental images using affine transformations to match key anatomical landmarks, 3) propagate the transformations across a series of images, 4) assign spatial reference atlas coordinates to the experimental images, and 5) allow viewing and analysis of the experimental data integrated in the reference atlas. We exemplify the workflow and use of our methods with a range of experimental data from neuroanatomical and neurophysiological investigations.

• Puchades, Maja ; Øvsthus, Martin; Bjerke, Ingvild Elise; Andersson, Krister; Csúcs, Gergely & Leergaard, Trygve Brauns [Show all 7 contributors for this article] (2017). Data integration through digital brain atlasing: semiautomatic spatial registration of serial histological images to rodent brain 3D reference atlases. Show summary

  Reference atlases of the brain are important tools for assigning location to data captured in neuroscience experiments. Spatial alignment of sectional images to reference atlases is, however, challenging to perform for several reasons. Manual approaches applied to large series of sectional images are time consuming and, moreover, histological sections are often cut at angles deviating from the principal anatomical planes presented in conventional reference atlases. Novel 3D reference atlases and accompanying tools provide new opportunities for rapid and accurate spatial registration and integration of data in common atlas space. We here present new tools for use with the Waxholm Space atlas for the rat brain and the Allen Mouse brain atlas, and workflow that allows users to 1) interactively generate customized atlas images (slices of the 3D reference atlas) corresponding to the plane of sectioning of any experimental image series, 2) superimpose atlas images onto experimental images using affine transformations to match key anatomical landmarks, 3) propagate the transformations across a series of images, 4) assign spatial reference atlas coordinates to the experimental images, and 5) allow viewing and analysis of the experimental data integrated in the reference atlas. We exemplify the work flow and use of our methods with a range of experimental data from neuroanatomical and neurophysiological investigations.

• Bjaalie, Jan G. (2017). Infrastructure for digital brain atlasing: storing, organizing, visualizing and analyzing large quantities of heterogeneous neuroscience research data.
• Puchades, Maja ; Bjerke, Ingvild Elise; Øvsthus, Martin; Andersson, Krister; Csúcs, Gergely & Leergaard, Trygve Brauns [Show all 7 contributors for this article] (2016). Spatial registration of serial histological image data to reference brain atlases.
• Puchades, Maja Amedjkouh; Darine, Dmitri Aleksandrovitsj; Csúcs, Gergely; Lillehaug, Sveinung; Leergaard, Trygve Brauns & Bjaalie, Jan G. (2015). Feeding rodent brain data to computational models through HBP spatial atlas templates and knowledge graph portal.
• Schubert, N; Axer, M; Graessel, D; Schober, M; Huynh, A & Huysegoms, M [Show all 11 contributors for this article] (2015). 3D reconstructed fiber-, cyto- and receptorarchitecture of the rat brain transferred into the Waxholm Space atlas.
• Bjaalie, Jan G. (2015). Mouse and rat brain atlasing: tools and templates for web-­‐enabled data integration.
• Bjaalie, Jan G. (2015). Brain atlasing: rodent to human.
• Bjaalie, Jan G. (2015). Global collaborative brain research: the International Neuroinformatics Coordinating Facility. Show summary

  The International Neuroinformatics Coordinating Facility (INCF) is an international network of national nodes with neuroinformatics expertise and infrastructure to support collaboration throughout the global brain research community. The mission of the INCF is to accelerate advances in understanding and treating the brain through the development of neuroinformatics - applying the best practices of data science to challenges in basic and clinical brain research. The major scientific output of the organisation has over the recent years been through 4 scientific programs, focusing on Digital Brain Atlasing (Waxholm Space atlases and Scalable Brain Atlas), Ontologies of Neural Structures (NeuroLex and KnowledgeSpace), Standards for Data Sharing (federated infrastructure for storage - DataSpace, Neuroimaging Analysis Pipeline – Nipype, and standard for storing basic electrophysiology data), and Multiscale Modeling (Network Interchange Format for Neuroscience - NineML, and Multi-Simulation Coordinator - MUSIC). All programs have strong engagement from the neuroinformatics community worldwide. The organization is introducing a new agile model for future work which will allow activities of the programs to continue while responding rapidly to new developments and community needs also in other areas of neuroinformatics, including training and education. Overall, INCF strategic objectives include 1) partnering with international stakeholders to promote and prioritise neuroinformatics at global, national and local levels, 2) advancing data reuse and reproducibility in brain research through the development of global standards, best practices, tools and infrastructure, 3) catalyzing community-driven collaborative brain research projects by engaging scientific, clinical, technical, industry and funding partners, and 4) serving as a globally recognized leader for the development and provision of training and educational resources in neuroinformatics.

• Bjaalie, Jan G. (2015). Brain atlasing and the role of the International Neuroinformatics Coordinating Facility (INCF) in global neuroscience research . Show summary

  With a new generation of three-dimensional digital brain atlases, new solutions for analyzing and integrating brain data are being developed. Digital brain atlases will serve as frameworks for services similar to current online geographical atlases, such as Google Maps and Google Earth, which provide interactive access to huge amounts of high resolution image data, together with additional information and detailed visualizations. The key aim of the INCF program on digital atlasing is to coordinate and improve the impact of atlasing projects, with a focus on the rodent brain. Several of the INCF national nodes contribute to development of infrastructures for brain atlasing in a combined effort with the European Union ICT Future Emerging Technologies Flagship project, the Human Brain Project. The templates used for digital brain atlases can be high resolution 3-D histology data or other volumetric data sets acquired with, e.g., magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). The templates can re-sliced and viewed in arbitrary angles without loss of image quality. Additional data modalities required to delineate brain regions can be added by registering high resolution microscopic images from slices through the brain to the templates. With these new atlas frameworks, and a set of tools to interact with the frameworks, research groups can connect their data to atlas space, share the data through online data systems, and search and find other relevant data through the same systems. The main role of the INCF in these efforts is to continue to support the establishment of standards for nomenclatures and spatial coordinate systems (Waxholm Space), and coordinate the development of data systems delivering services to the community. Examples of efforts at the national level include the Scalable Brain Atlas (Dutch node of the INCF) and the Rodent Brain WorkBench (Norwegian node of the INCF). New INCF special interest groups are being established, aiming at reaching out to the broader science community.

• Papp, Eszter Agnes; Bjaalie, Jan G. & Leergaard, Trygve Brauns (2018). Volumetric atlasing in the rodent brain: connecting experimental image data to standard anatomical space. Faculty of Medicine, University of Oslo. ISSN 978-82-8377-194-7. Full text in Research Archive Show summary

  A major challenge for understanding the brain in health and disease is related to combining information about the brain from many different sources. Spatial analysis and comparison of 2D and 3D brain images with the aim to discover new structural and functional features in the data requires mapping the images to a common anatomical space. For rodent models, widely used anatomical reference atlases have until recently been published in a book format that hinders effective comparison across images acquired at different angles and dimensions. The ambition of this study is to advance digital atlasing of the rodent brain towards a new generation of volumetric atlases that facilitate integration of whole-brain image data from multiple modalities. We present the first comprehensive anatomical reference atlas of the Sprague Dawley rat brain based on magnetic resonance imaging. To ensure interoperability with other atlases, we applied the Waxholm Space standard in the rat brain for the first time, and created a mapping against the widely used stereotaxic space. For connecting experimental data to the new reference atlas, we established standard anatomical landmarks for image registration across different levels of structural detail, and created a workflow for section to volume alignment and spatial analysis of brain-wide microscopic image series. Open access to the results of this work has enabled localization of signal in functional and structural image volumes, integration of 2D image series, and development of new atlases of various features of the brain.

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