Jan G. Bjaalie

• Aarestrup, FM; Albeyatti, A; Armitage, W; Auffray, C; Augello, L; Balling, Rudi; Benhabiles, N; Bertolini, Guido; Bjaalie, Jan G.; Black, Merja; Blomberg, N.; Bogaert, P; Bubak, M; Claerhout, B; Clarke, Leon; De Meulder, B; D'Errico, G; Di Meglio, A; Forgo, N; Gans-Combe, C; Gray, AE; Gut, I; Gyllenberg, Alexandra; Hemmrich-Stanisak, G; Hjorth, L; Ioannidis, Yannis; Jarmalaite, S; Kel, A; Kherif, F; Korbel, Jan O.; Larue, C; Laszlo, Marton; Maas, Andrew; Magalhaes, Livia; Manneh-Vangramberen, I; Morley-Fletcher, E; Ohmann, Christian; Oksvold, P; Oxtoby, NP; Perseil, Isabelle; Pezoulas, V; Riess, O; Riper, Heleen; Roca, J; Rosenstiel, P; Sabatier, Pierre; Sanz, F.; Tayeb, M; Thomassen, Gard O Sundby; Van Bussel, J; Van den Bulcke, Marc & Van Oyen, Herman (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. 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.

• Adams, Amy; Albin, Stephanie; Amunts, Katrin; Asakawa, Tasia; Bernard, Amy; Bjaalie, Jan G.; Chakli, Khaled; Deshler, James; De Koninck, Yves; Ebell, Christoph; Egan, Gary; Hale, Melina E; Häusser, Michael; Jeong, Sung-Jin; lles, Judy; Lanyon, Linda; Li, Pingping; Li, Yan; Magistretti, Pierre; McMahon, Agnes; Montojo, Caroline; Ohtsuka, Toshihisa; Okabe, Shigeo; Okano, Hideyuki; Pei, Gang; Pouget, Alexandre; Reindorp, Jason; Richards, Linda J.; Rommelfanger, Karen S.; Sajda, Paul; Scobie, Kimberly N.; Suh, Pann-Ghill; Tanaka, Keiji; Thiels, Edda; Valdes-Sosa, Pedro; Welchman, Andrew E.; White, Samantha; Wilson, Gary; Yuste, Rafael; Zhang, Xu & Zheng, Jialin (2020). International Brain Initiative: An Innovative Framework for Coordinated Global Brain Research Efforts.. Neuron.  ISSN 0896-6273.  105(2), s 212- 216 . doi: https://doi.org/10.1016/j.neuron.2020.01.002 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.

• 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: https://doi.org/10.6084/m9.figshare.12199088
• Bjerke, Ingvild Elise; Yates, Sharon Christine; Laja, Arthur; Witter, Menno; Puchades, Maja; Bjaalie, Jan G. & Leergaard, Trygve B. (2020). Densities and numbers of calbindin andparvalbumin positive neurons across the rat and mouse brain. iScience.  ISSN 2589-0042.
• Groeneboom, Nicolaas; 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
• Amunts, Katrin; Knoll, Alois C; Lippert, Thomas; Pennartz, Cyriel M.A.; Ryvlin, Philippe; Destexhe, Alain; Jirsa, Viktor K; D'Angelo, Egidio & Bjaalie, Jan G. (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.; Bjaalie, Jan G. & Leergaard, Trygve B. (2019). Data Descriptor: Brain-wide distribution of reporter expression in five transgenic tetracyclinetransactivator mouse lines. Scientific Data.  ISSN 2052-4463.  6, s 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.
• Yates, Sharon Christine; Groeneboom, Nicolaas Ervik; Coello, Christopher; Lichtenthaler, Stefan F.; Kuhn, Peer-Hendrik; Demuth, Hans-Ulrich; Hartlage-Rübsamen, Maike; Rossner, Steffen; Leergaard, Trygve B.; Kreshuk, Anna; Puchades, Maja & Bjaalie, Jan G. (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. Show summary

  Transgenic animal models are invaluable research tools for elucidating the pathways and mechanisms involved in the development of neurodegenerative diseases. Mechanistic clues can be revealed by applying labelling techniques such as immunohistochemistry or in situ hybridisation to brain tissue sections. Precision in both assigning anatomical location to the sections and quantifying labelled features is crucial for output validity, with a stereological approach or image-based feature extraction typically used. However, both approaches are restricted by the need to manually delineate anatomical regions. To circumvent this limitation, we present the QUINT workflow for quantification and spatial analysis of labelling in series of rodent brain section images based on available 3D reference atlases. The workflow is semi-automated, combining three open source software that can be operated without scripting knowledge, making it accessible to most researchers. As an example, a brain region-specific quantification of amyloid plaques across whole transgenic Tg2576 mouse brain series, immunohistochemically labelled for three amyloid-related antigens is demonstrated. First, the whole brain image series were registered to the Allen Mouse Brain Atlas to produce customised atlas maps adapted to match the cutting plan and proportions of the sections (QuickNII software). Second, the labelling was segmented from the original images by the Random Forest Algorithm for supervised classification (ilastik software). Finally, the segmented images and atlas maps were used to generate plaque quantifications for each region in the reference atlas (Nutil software). The method yielded comparable results to manual delineations and to the output of a stereological method. While the use case demonstrates the QUINT workflow for quantification of amyloid plaques only, the workflow is suited to all mouse or rat brain series with labelling that is visually distinct from the background, for example for the quantification of cells or labelled proteins.

• Bjerke, Ingvild Elise; Øvsthus, Martin; Papp, Eszter Agnes; Yates, Sharon Christine; Silvestri, L; Fiorilli, J; Pennartz, CMA; Pavone, FS; Puchades, Maja; Leergaard, Trygve Brauns & Bjaalie, Jan G. (2018). Data integration through brain atlasing: Human Brain Project tools and strategies. European psychiatry.  ISSN 0924-9338.  50, s 70- 76 . doi: 10.1016/j.eurpsy.2018.02.0 Full text in Research Archive.
• Bjerke, Ingvild Elise; Øvsthus, Martin; Andersson, Krister Andreas; Blixhavn, Camilla Hagen; Kleven, Heidi; Yates, Sharon Christine; Puchades, Maja; Bjaalie, Jan G. & Leergaard, Trygve B. (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.
• Heiland, Tina; Zeitschel, Ulrike; Puchades, Maja; Kuhn, Peer-Hendrik; Lichtenthaler, Stefan F.; Bjaalie, Jan G.; Hartlage-Rübsamen, Maike; Roßner, Steffen & Höfling, Corinna (2018). Defined astrocytic expression of human amyloid precursor protein in Tg2576 mouse brain. Glia.  ISSN 0894-1491.  67(2), s 393- 403 . doi: 10.1002/glia.23550 Full text in Research Archive.
• Plank, Anne-Christine; Canneva, Fabio; Raber, Kerstin; Urbach, Yvonne K; Dobner, Julia; Puchades, Maja; Bjaalie, Jan G.; Gillmann, Clarissa; Bäuerle, Tobias; Riess, Olaf; Nguyen, Hoa H.P. & von Hörsten, Stephan (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.
• Dempsey, Bowen; Le, Sheng; Turner, Anita; Bokiniec, Phil; Ramadas, Radhika; Bjaalie, Jan G.; Menuet, Clement; Neve, Rachael; Allen, Andrew M; Goodchild, Ann K & McMullan, Simon (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
• 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.
• Schubert, N; Axer, M; Schober, M; Huynh, A; Huysegoms, M; Palomero-Gallagher, N; Bjaalie, Jan G.; Leergaard, Trygve Brauns; Kirlangic, Mehmet E.; Amunts, K & Zilles, Karl (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
• 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), s 2231- 2249 . doi: 10.1007/s00429-015-1040-9
• 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), s 838- 857 . doi: 10.1002/hipo.22407
• Kjønigsen, Lisa Jannicke; Lillehaug, Sveinung; Bjaalie, Jan G.; Witter, Menno & Leergaard, Trygve Brauns (2015). Waxholm Space atlas of the rat brain hippocampal region: Three-dimensional delineations based on magnetic resonance and diffusion tensor imaging. NeuroImage.  ISSN 1053-8119.  108, s 441- 449 . doi: 10.1016/j.neuroimage.2014.12.080 Full text in Research Archive.
• Papp, Eszter Agnes; Leergaard, Trygve Brauns; Calabrese, Evan; Johnson, G. Allan & Bjaalie, Jan G. (2015). Addendum to “Waxholm Space atlas of the Sprague Dawley rat brain” [NeuroImage 97 (2014) 374-386]. NeuroImage.  ISSN 1053-8119.  105, s 561- 562 . doi: 10.1016/j.neuroimage.2014.10.017
• Sergejeva, Marina; Papp, Eszter Agnes; Bakker, Rembrandt; Gaudnek, Manuel A.; Okamura-Oho, Yuko; Boline, Jyl; Bjaalie, Jan G. & Hess, Andreas (2015). Anatomical landmarks for registration of experimental image data to volumetric rodent brain atlasing templates. Journal of Neuroscience Methods.  ISSN 0165-0270.  240, s 161- 169 . doi: 10.1016/j.jneumeth.2014.11.005
• Tiesinga, Paul; Bakker, Rembrandt; Hill, Sean & Bjaalie, Jan G. (2015). Feeding the human brain model. Current Opinion in Neurobiology.  ISSN 0959-4388.  32, s 107- 114 . doi: 10.1016/j.conb.2015.02.003
• 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
• Lillehaug, Sveinung; Syverstad, Gry-Helen Enger; Nilsson, Lars; Bjaalie, Jan G.; Leergaard, Trygve Brauns & Torp, Reidun (2014). Brainwide distribution and variance of amyloid-beta deposits in tg-ArcSwe mice. Neurobiology of Aging.  ISSN 0197-4580.  35(3), s 556- 564 . doi: 10.1016/j.neurobiolaging.2013.09.013
• Papp, Eszter Agnes; Leergaard, Trygve Brauns; Calabrese, Evan; Johnson, G. Allan & Bjaalie, Jan G. (2014). Waxholm Space atlas of the Sprague Dawley rat brain. NeuroImage.  ISSN 1053-8119.  97, s 374- 386 . doi: 10.1016/j.neuroimage.2014.04.001
• Zakiewicz, Izabela Maria; Bjaalie, Jan G. & Leergaard, Trygve Brauns (2014). Brain-wide map of efferent projections from rat barrel cortex. Frontiers in Neuroinformatics.  ISSN 1662-5196.  8 . doi: 10.3389/fninf.2014.00005
• Hines, Kim Eigard; Bolle, Erlend; Rissi, Michael; Volgyes, David; Dorholt, Ole; Røhne, Ole Myren; Stapnes, Steinar; Bjaalie, Jan G. & Skretting, Arne (2013). COMPET: High resolution high sensitivity MRI compatible pre-clinical PET scanner. Nuclear Instruments and Methods in Physics Research Section A : Accelerators, Spectrometers, Detectors and Associated Equipment.  ISSN 0168-9002.  732, s 581- 585 . doi: 10.1016/j.nima.2013.05.148
• Nagelhus, Erlend Arnulf; Amiry-Moghaddam, Mahmood; Bergersen, Linda Hildegard; Bjaalie, Jan G.; Eriksson, Jens; Gundersen, Vidar; Leergaard, Trygve Brauns; Morth, Jens Preben; Storm-Mathisen, Jon; Torp, Reidun; Walhovd, Kristine B & Tønjum, Tone (2013). The glia doctrine: Addressing the role of glial cells in healthy brain ageing. Mechanisms of Ageing and Development.  ISSN 0047-6374.  134(10), s 449- 459 . doi: 10.1016/j.mad.2013.10.001
• Stensrud, Mats Julius; Chaudhry, Farrukh Abbas; Leergaard, Trygve Brauns; Bjaalie, Jan G. & Gundersen, Vidar (2013). Vesicular glutamate transporter-3 in the rodent brain: Vesicular colocalization with vesicular -aminobutyric acid transporter. Journal of Comparative Neurology.  ISSN 0021-9967.  521(13), s 3042- 3056 . doi: 10.1002/cne.23331
• White, NS; Leergaard, Trygve Brauns; D'Arceuil, Helen; Bjaalie, Jan G. & Dale, AM (2013). Probing tissue microstructure with Restriction Spectrum Imaging: Histological and theoretical validation. Human Brain Mapping.  ISSN 1065-9471.  34(2), s 327- 346 . doi: 10.1002/hbm.21454
• Rissi, Michael; Bolle, Erlend; Volgyes, David; Bjaalie, Jan G.; Dorholt, Ole; Hines, Kim Eigard; Røhne, Ole Myren; Skretting, Arne & Stapnes, Steinar (2012). Characterization of a high resolution and high sensitivity pre-clinical PET scanner with 3D event reconstruction. Nuclear Instruments and Methods in Physics Research Section A : Accelerators, Spectrometers, Detectors and Associated Equipment.  ISSN 0168-9002.  695, s 135- 138 . doi: 10.1016/j.nima.2011.10.042
• Kjønigsen, Lisa Jannicke; Leergaard, Trygve Brauns; Witter, Menno & Bjaalie, Jan G. (2011). Digital atlas of anatomical subdivisions and boundaries of the rat hippocampal region. Frontiers in Neuroinformatics.  ISSN 1662-5196.  8(5) . doi: 10.3389/fninf.2011.00002 Full text in Research Archive.
• Odeh, U. R. Francis; Leergaard, Trygve Brauns; Boy, J.; Schmidt, T.; Riess, O. & Bjaalie, Jan G. (2011). Atlas of transgenic Tet-Off Ca2+/calmodulin-dependent protein kinase II and prion protein promoter activity in the mouse brain. NeuroImage.  ISSN 1053-8119.  54(4), s 2603- 2611 . doi: 10.1016/j.neuroimage.2010.11.032

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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 s.
• 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 s.
• 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 s.
• 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 s.
• Sjaastad, Øystein; Toverud, Kari C.; Sand, Olav; Bjaalie, Jan G. & Egil, Haug (2015). Studieoppgaver til Menneskets anatomi og fysiologi. Gads forlag.  ISBN 9788712050353.  203 s.

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• Bjaalie, Jan G. (2020). EBRAINS: New enabling infrastructure for brain research (FENS 2020 Virtual Forum: Open Theatre talk).
• Bjaalie, Jan G. (2020). HBP / EBRAINS Overview at Human Brain Project 2020 Summit.
• Bjaalie, Jan G. (2020). International Brain Initiative: Facilitating Global Neuroscience Engagement.
• 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

• Gurdon, Brianna; Hadad, Niran; Telpoukhovskaia, Maria; Yates, Sharon Christine; Puchades, Maja; Bjaalie, Jan G. & Kaczorowski, Catherine (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; Schmid, Oliver; Puchades, Maja; Weyers, Benjamin; Davison, Andrew P.; Dicksheid, Timo; Leergaard, Trygve B. & Bjaalie, Jan G. (2020). Neuroscience community-gains from data sharing through the EBRAINS infrastructure.
• Reiten, Ingrid; Schlegel, Ulrike; Blixhavn, Camilla Hagen; Andersson, Krister Andreas; Aasebø, Ida Elisabeth Jørgensen; Yates, Sharon Christine; Kleven, Heidi; Øvsthus, Martin; Bjerke, Ingvild Elise; Puchades, Maja; Leergaard, Trygve B. & Bjaalie, Jan G. (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.

• Schlegel, Ulrike; Reiten, Ingrid; Aasebø, Ida Elisabeth Jørgensen; Blixhavn, Camilla Hagen; Zehl, Lyuba; Kjelsberg, Kasper; Schmid, Oliver; Puchades, Maja; Weyers, Benjamin; Davison, Andrew P.; Dickscheid, Timo; Leergaard, Trygve B. & Bjaalie, Jan G. (2020). EBRAINS data sharing: benefits and workflow.
• Andersson, Krister Andreas; Blixhavn, Camilla Hagen; Kleven, Heidi; Schlegel, Ulrike; Oliver, Schmid; Puchades, Maja; Davison, Andrew; Dickscheid, Timo; Leergaard, Trygve B. & Bjaalie, Jan G. (2019). EBRAINS fair data service: A novel infrastructure for making neuroscience data findable, accessible, interoperable, and reuseable.
• Andersson, Krister Andreas; Blixhavn, Camilla Hagen; Kleven, Heidi; Zehl, Lyuba; Bjerke, Ingvild Elise; Schmid, Oliver; Øvsthus, Martin; Aasebø, Ida Elisabeth Jørgensen; Yates, Sharon Christine; Schlegel, Ulrike; Legouée, Elodie; Abrams, Mathew; Puchades, Maja; Davison, Andrew; Dickscheid, Timo; Leergaard, Trygve B. & Bjaalie, Jan G. (2019). Neuroinformatics platform for making neuroscience data Findable, Accessible, Interoperable, and Reuseable.
• Andersson, Krister Andreas; Blixhavn, Camilla Hagen; Zehl, Lyuba; Zarfarnia, Sara; Köhnen, Stefan; Hilverling, Anna; Kleven, Heidi; Bjerke, Ingvild Elise; Schmid, Oliver; Øvsthus, Martin; Yates, Sharon Christine; Schlegel, Ulrike; Legouée, Elodie; Abrams, Mathew; Puchades, Maja; Davison, Andrew; Dickscheid, Timo; Leergaard, Trygve B. & Bjaalie, Jan G. (2019). Resources for making neuroscience data FAIR. The Human Brain Project invites researchers to share, find, and use data via the new EBRAINS infrastructure.
• Blixhavn, Camilla Hagen; Andersson, Krister Andreas; Kleven, Heidi; Schlegel, Ulrike; Puchades, Maja; Bjaalie, Jan G. & Leergaard, Trygve B. (2019). Find and explore rodent brain data using 3D atlases in the new EBRAINS infrastructure.
• Blixhavn, Camilla Hagen; Andersson, Krister Andreas; Kleven, Heidi; Schlegel, Ulrike; Puchades, Maja; Bjaalie, Jan G. & Leergaard, Trygve B. (2019). Infrastructure and workflow for integrating and navigating multi-scale and multi-modal murine neuroscience data using 3D digital brain reference atlases.
• 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.

• Schlegel, Ulrike; Blixhavn, Camilla Hagen; Andersson, Krister Andreas; Yates, Sharon Christine; Øvsthus, Martin; Bjerke, Ingvild Elise; Kleven, Heidi; Groeneboom, Nicolaas Ervik; Puchades, Maja; Bjaalie, Jan G. & Leergaard, Trygve B. (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.

• 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.
• Andersson, Krister Andreas; Blixhavn, Camilla Hagen; Zehl, Lyuba; Markovic, Milica; Kleven, Heidi; Zafarnia, Sara; Köhnen, Stefan; Legouée, Elodie; Puchades, Maja; Schmid, Oliver; Davison, Andrew P.; Muller, Jeff; Dicksheid, Timo; Leergaard, Trygve B. & Bjaalie, Jan G. (2018). HBP Curation service: Share your data via the Neuroinformatics platform.
• Bjaalie, Jan G. (2018). Data sharing in neuroscience: traditions, standards, and transformations.
• 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.
• Blixhavn, Camilla Hagen; Andersson, Krister; Øvsthus, Martin; Bjerke, Ingvild Elise; Kleven, Heidi; Puchades, Maja; Leergaard, Trygve Brauns & Bjaalie, Jan G. (2018). Data integration through digital brain atlasing: Making diverse neuroscience data discoverable using Human Brain Project infrastructure.
• Papp, Eszter Agnes; Bjaalie, Jan G. & Leergaard, Trygve Brauns (2018). Volumetric atlasing in the rodent brain: connecting experimental image data to standard anatomical space. 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.

• von Linstow, Christian Ulrich; Yates, Sharon Christine; Leergaard, Trygve B.; Plank, A-C; von Hörsten, S.; Puchades, Maja & Bjaalie, Jan G. (2018). Whole brain analysis of Huntingtin aggregates in the F344 transgenic rat model of Huntington’s disease.
• Yates, Sharon Christine; Puchades, Maja; kreshuk, anna; Kutra, Dominik; Leergaard, Trygve B.; Bjaalie, Jan G. & Groeneboom, Nicolaas (2018). HBP Analytic Workflow: from Microscopic Image Data to Extracted Features in Atlas Space.
• Zehl, Lyuba; Zafarnia, Sara; Köhnen, Stefan; Andersson, Krister Andreas; Markovic, Milica; Legouée, Elodie; Blixhavn, Camilla Hagen; Kleven, Heidi; Gui, Xiaoyun; Chervakov, Pavel; Schmid, Oliver; Bell, Simon; Gillespie, Tom; Abrams, Matthew; Davison, Andrew P.; Muller, Jeff; Leergaard, Trygve B.; Amunts, K; Bjaalie, Jan G. & Dicksheid, Timo (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; Blixhavn, Camilla Hagen; Kleven, Heidi; Gui, Xiaoyun; Chervakov, Pavel; Schmid, Oliver; Bell, Simon; Gillespie, Tom; Abrams, Matthew; Davison, Andrew P.; Muller, Jeff; Leergaard, Trygve B.; Amunts, K; Bjaalie, Jan G. & Dicksheid, Timo (2018). Integrating neuroscientific data into a unified database: from individual experiments to a standardized metadata collection using the Human Brain Project Neuroinformatics Platform.
• 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.
• Imad, Jala; Kleven, Heidi; Clascá, Francisco; Wennberg, Arvind Eirik; Osen, Kirsten Kjelsberg; Coello, Sebastian Christopher; Csúcs, Gergely; Papp, Eszter Agnes; Witter, Menno; Bjaalie, Jan G. & Leergaard, Trygve B. (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; Puchades, Maja; Suzen, M; Denker, Michael; Davison, A.; Grün, Sonja; Leprince, Y; Dickscheid, Timo; McMurtrie, C; Telefont, Martin; Muller, Jeff; Leergaard, Trygve B. & Bjaalie, Jan G. (2017). Submitting data and metadata through the HBP Data Workbench: Concepts, data flows, policies, and best practices.
• Andersson, Krister; Øvsthus, Martin; Bjerke, Ingvild Elise; Puchades, Maja; Telefont, Martin; Jeff, Muller; Dickscheid, Timo; Leergaard, Trygve Brauns & Bjaalie, Jan G. (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.

• Bjaalie, Jan G. (2017). Infrastructure for digital brain atlasing: storing, organizing, visualizing and analyzing large quantities of heterogeneous neuroscience research data.
• 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.
• 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.

• 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.
• Imad, Jala; Osen, Kirsten Kjelsberg; Clascá, Francisco; Wennberg, Arvind E.; Coello, Sebastian Christopher; Csúcs, Gergely; Papp, Eszter Agnes; Bjaalie, Jan G. & Leergaard, Trygve Brauns (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.

• Leprince, Y; Coello, Sebastian Christopher; Csúcs, Gergely; Chervakov, P; Darine, Dmitri Aleksandrovitsj; Øvsthus, Martin; Bjerke, Ingvild Elise; Yates, Sharon Christine; Puchades, Maja; Leergaard, Trygve B.; Dickscheid, Timo & Bjaalie, Jan G. (2017). Interactive tools for registering of 2D and 3D images to reference atlases.
• Puchades, Maja; Csúcs, Gergely; Checinska, Martyna; Øvsthus, Martin; Bjerke, Ingvild Elise; Andersson, Krister; Leergaard, Trygve Brauns & Bjaalie, Jan G. (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 & Bjaalie, Jan G. (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.

• Yates, Sharon Christine; Puchades, Maja; Coello, Sebastian Christopher; Kreshuk, Anna; Hartlage-Rübsamen, Maike; Rossner, Steffen; Leergaard, Trygve Brauns & Bjaalie, Jan G. (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.

• Yates, Sharon Christine; Puchades, Maja; Coello, Sebastian Christopher; Kreshuk, Anna; Hartlage-Rübsamen, Maike; Rossner, Steffen; Leergaard, Trygve Brauns & Bjaalie, Jan G. (2017). Workflow for automated quantification and spatial analysis of labelling in microscoping rodent brain sections.
• Puchades, Maja; Bjerke, Ingvild Elise; Øvsthus, Martin; Andersson, Krister; Csúcs, Gergely; Leergaard, Trygve Brauns & Bjaalie, Jan G. (2016). Spatial registration of serial histological image data to reference brain atlases.
• 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.

• 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). Mouse and rat brain atlasing: tools and templates for web-­‐enabled data integration.
• 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; Palomero-Gallagher, N; Bjaalie, Jan G.; Leergaard, Trygve Brauns; Zilles, K & Amunts, K (2015). 3D reconstructed fiber-, cyto- and receptorarchitecture of the rat brain transferred into the Waxholm Space atlas.
• Bjaalie, Jan G. & Leergaard, Trygve Brauns (2014). Neuroscience in the information ages: Brain atlasing goes digital. META.  ISSN 1890-1956.  (2), s 14- 17
• Leergaard, Trygve Brauns & Bjaalie, Jan G. (2014). Infrastructure for digital atlasing of mouse and rat brain: Rodent Brain WorkBench.
• Leergaard, Trygve Brauns; Coello, Christopher; Papp, Eszter Agnes; Ragan, Tim & Bjaalie, Jan G. (2014). Registration of serial two-photon data to rodent brain Waxholm Space. Frontiers in Neuroinformatics.  ISSN 1662-5196. . doi: 10.3389/conf.fninf.2014.18.00059
• Leergaard, Trygve Brauns; Coello, Christopher; Papp, Eszter Agnes; Ragan, Tim & Bjaalie, Jan G. (2014). Registration of serial two-photon data to rodent brain Waxholm Space.
• Leergaard, Trygve Brauns; Lillehaug, Sveinung; Papp, Eszter Agnes; Coello, Christopher; Ragan, Tim & Bjaalie, Jan G. (2014). Waxholm Space atlas template enriched with serial two-photon and histological data.
• Leergaard, Trygve Brauns; Lillehaug, Sveinung; Yetman, Michael J.; Jankowsky, Joanna L. & Bjaalie, Jan G. (2014). Interactive brainwide atlas of tetracycline responsive trans-activator promoter mouse lines.
• Leergaard, Trygve Brauns; Yetman, Michael J.; Lillehaug, Sveinung; Bjaalie, Jan G. & Jankowsky, Joanna L. (2014). Transgene expression in the neuropsin tTA driver line is not restricted to the entorhinal cortex.
• Papp, Eszter Agnes; Leergaard, Trygve Brauns; Csucs, Gergely; Darine, Dmitri Aleksandrovitsj & Bjaalie, Jan G. (2014). Workflow for integration and analysis of histological data in rodent brain Waxholm Space.
• Papp, Eszter Agnes; Leergaard, Trygve Brauns; Csucs, Gergely; Darine, Dmitri Aleksandrovitsj & Bjaalie, Jan G. (2014). Workflow for integration and analysis of histological data in rodent brain Waxholm Space. Frontiers in Neuroinformatics.  ISSN 1662-5196. . doi: 10.3389/conf.fninf.2014.18.00060
• Bjaalie, Jan G.; Papp, Eszter Agnes; Kjonigsen, LJ; Johnson, GA; Witter, Menno & Leergaard, Trygve Brauns (2013). Waxholm Space Atlas of the rat brain with detailed hippocampal subdivisions. Show summary

  Volumetric brain atlases are typically built on top of template data sets acquired with Magnetic Resonance Imaging. A relatively high level of structural details is provided combined with the advantage of allowing the entire brain to be viewed interactively in any plane. With appropriate tools for interactive viewing, volumetric atlases are useful for planning of experiments and data analysis by registration of other data sets to the atlas. We provide a volumetric atlas of the rat brain based on ex vivo MRI anatomical (T2*) images with 39 μm isotropic voxels and diffusion tensor imaging (DTI) volumes with 78 μm isotropic voxels acquired from an 80 day old male Sprague-Dawley rat. The atlas covers 83 anatomical structures (areas, nuclei, regions, and fiber tracts) across the brain. The structures were delineated using ITK-SNAP software based on multiple types of image contrasts and anatomical reference information. White matter structures were readily distinguished from grey matter, and some structural differences could also be observed within gray matter. Delineations were aided by information from classical 2D atlases and collections of histological images from brains of comparable animals, and delineation criteria were defined for all structures. Cyto- and chemoarchitectonic features from an online 2D histological atlas (Kjonigsen et al., Front Neuroinform. 2011) were used for delineation of 16 structures in the hippocampal region. The atlas and the underlying MRI and DTI image volumes are to be made open access through the INCF Software Center. Spatial reference is provided by Waxholm Space, a standard atlas space recently defined by the International Neuroinformatics Coordinating Facility (INCF). Waxholm Space connects the atlas to a growing infrastructure of interoperable resources and services for multi-level data integration and analysis across reference spaces.

• Syverstad, Gry-Helen Enger; Lillehaug, Sveinung; Nilsson, Lars N. G.; Bjaalie, Jan G.; Leergaard, Trygve B. & Torp, Reidun (2013). Spatial distribution and variance of amyloid-beta load in middle-aged transgenic Alzheimer’s disease mice with both the Artic and Swedish AβPP mutations.
• Jacobsen, Line Melå; Gjerstad, Johannes & Bjaalie, Jan G. (2012). Spinal nociceptive signalling mechanisms to long-lasting low back pain and sciatica. Series of dissertations submitted to the Faculty of Medicine, University of Oslo. No.1387.
• Papp, Eszter Agnes; Leergaard, Trygve Brauns; Johnson, G. Allan & Bjaalie, Jan G. (2012). First generation Waxholm atlas of the Sprague-Dawley rat brain.
• Kjønigsen, Lisa Jannicke; Leergaard, Trygve Brauns; Witter, Menno & Bjaalie, Jan G. (2011). Three-dimensional atlas framework for mapping of projections from the entorhinal cortex to the hippocampal formation in rat. Frontiers in Neuroinformatics.  ISSN 1662-5196. . doi: 10.3389/conf.fninf.2011.08.00129
• Moene, Ivar Andre; Darine, Dmitri Aleksandrovitsj; Ramachandran, Muthuraja; Leergaard, Trygve Brauns & Bjaalie, Jan G. (2011). Rodent Brain Navigator: database and atlas system for microscopy and imaging data. Frontiers in Neuroinformatics.  ISSN 1662-5196. . doi: 10.3389/conf.fninf.2011.08.00159

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