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Bjaalie, Jan G.
(2022).
Comment on the astonishing complexity of working with brain health data
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Bjaalie, Jan G.
(2022).
Managing, sharing, and publishing
research data through EBRAINS
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Reiten, Ingrid & Bjaalie, Jan G.
(2022).
EBRAINS Data & Knowledge Services: perspectives on open data from within EBRAINS.
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Leergaard, Trygve B.; Bjerke, Ingvild Elise; Bjaalie, Jan G.; Palomero-Gallagher, Nicola; Puchades, Maja & Carey, Harry
[Show all 7 contributors for this article]
(2022).
Neuroscience data integration through use of digital brain atlases .
Show summary
This two-day course will provide a hands-on introduction to three-dimensional reference atlases for the rat and mouse brain, and demonstrate how such atlases can be utilized to integrate and analyze heterogeneous neuroscience data. Students will gain updated knowledge about current approaches to assigning anatomical location to experimental data from the brain, and acquire basic skills in associated analytic tools. Invited speaker Nicola Palomero-Gallagher will give a lecture on neuroanatomy. Jan Bjaalie, Trygve Leergaard and co-workers Camilla Blixhavn, Ingvild Bjerke, Maja Puchades, Sharon Yates and Harry Carey from the Neural Systems Laboratory (University of Oslo) will introduce new concepts for data integration and development of murine brain atlas resources established in context of the European Human Brain Project.
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Carey, Harry; Csucs, Gergely; Puchades, Maja & Bjaalie, Jan G.
(2022).
AutoAlign Rat: Spatially anchoring rat brain histology to the Waxholm rat brain atlas using deep neural networks. .
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Puchades, Maja ; Reiten, Ingrid; Destexhe, Alain; Palomero-Gallagher, Nicola; Ruikes, Thijs & Leergaard, Trygve B.
[Show all 7 contributors for this article]
(2022).
Workshop: Towards exploration of disease mechanisms in animal models: atlases, analysis, modelling and simulation.
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Bjerke, Ingvild Elise; Imad, Jala; Clascá, Francisco; Groenewegen, Henk J.; Bjaalie, Jan G. & Leergaard, Trygve Brauns
(2022).
Waxholm Space atlas of the Sprague Dawley rat brain version 4: A volumetric atlas enabling data integration and analysis.
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Puchades, Maja ; Carey, Harry & Bjaalie, Jan G.
(2022).
Neuroinformatics tools for performing feature extraction and advanced brain-wide distribution analysis in an atlas context.
Show summary
Workshop organised by University of Oslo during INCF assembly 2022.
This workshop will present and compare a series of neuroinformatics tools for performing feature extraction and advanced brain-wide distribution analysis in an atlas context. The speakers will be recruited from different projects and institutions developing complementary tools and services for brain-wide analysis of data originating from small animal experimental Workshop neuroscience. They will discuss alternative approaches for registration of images, segmentation, analyses, reuse of data, and smart combinations of new neuroinformatics tools originating from different laboratories and projects.
Attendees will be guided through use cases demonstrating how the tools can be used in different contexts and for different purposes.
Topic: Neuroinformatics tools for performing feature extraction and advanced brain-wide distribution analysis in an atlas context.
International invited speakers: Lydia Ng (Allen Brain Institute); Daniel Furth (Uppsala University); Nicolas Chiaruttini (EPFL); Adam Tyson (UCL).
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Carey, Harry; Csucs, Gergely; McMullan, Simon; Puchades, Maja & Bjaalie, Jan G.
(2022).
DeepSlice Rat: A Deep Neural Network for automatic registration of rat brain images to the Waxholm Space atlas of the rat brain.
Show summary
The rat is widely used in experimental neuroscience and with the increasing availability of many genetic tools in rat, its popularity in neuroscientific research is likely to increase. In order to properly leverage the magnitude of rat data that the neuroscientific community generates, brain derived data must be mapped to a common spatial reference space. To this end, the Waxholm Space atlas of the rat brain (RRID:SCR_017124) has been created. While there exist tools to register rat brain data to the Waxholm Space, including the QuickNII (RRID:SCR_016854) and VisuAlign (RRID:SCR_017978) tools provided by the EBRAINS research infrastructure (https://ebrains.eu/services/atlases), these tools are relatively demanding of time and expertise, often requiring hours per whole brain. If we are to comprehensively map the massive amount of brain data currently being generated, we will require automated methods capable of registering data to a common reference space. Here we present DeepSlice Rat, a deep neural network for the automated registration of rat brain images to the Waxholm Space atlas of the rat brain. We show that DeepSlice Rat is equal in performance to an expert neuroanatomist whilst being thousands of times faster. DeepSlice Rat can be used in combination with QuickNII for verification of the registration, and VisuAlign for non-linear adjustments, creating an AI assisted workflow that will greatly enhance productivity. DeepSlice Rat will be freely available on EBRAINS (alongside a mouse implementation) as both a Python package and web-application.
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Puchades, Maja ; Yates, Sharon Christine; Groeneboom, Nicolaas; Csucs, Gergely; Darine, Dmitri Aleksandrovitsj & Carey, Harry
[Show all 8 contributors for this article]
(2022).
EBRAINS tools for rodent brain atlasing.
Show summary
Research in small animals depends on comparisons of cellular and molecular measures in large groups, requiring efficient and reproducible methods for registration of data to brain atlases followed by quantitative analysis. While numerous methodologies and increasing amounts of data are available, they are difficult to combine into coherent and reproducible workflows suitable for large-scale analyses. The EBRAINS research infrastructure enables neuroscientists to conduct their mouse and rat brain data analyses in an open, robust and user-friendly environment. It provides tools and workflows for viewing images; automatic and user guided registration of brain section image data to an atlas; feature extraction with machine learning, whole brain distribution analysis, and metadata management according to the FAIR principles. We present examples of typical use cases, illustrating how the available suite of tools can be used and the results obtained, with an emphasis on the reproducibility of the methods applied and the sharing of the data and metadata. The tools are part of the EBRAINS Atlases services (https://ebrains.eu/services/atlases) and offer one comprehensive solutions for organizing, analyzing and sharing brain research data. These and other EBRAINS research infrastructure tools and workflows are developed in close interaction with users in the neuroscience community. Funded from EU Horizon 2020, Specific Grant Agreement No. 945539 (Human Brain Project SGA3).
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Yates, Sharon Christine; Gurdon, Brianna; Csucs, Gergely; Groeneboom, Nicolaas Ervik; Puchades, Maja Amedjkouh & Kaczorowski, Catherine
[Show all 7 contributors for this article]
(2022).
Quint workflow for brain-wide quantification of rodent models: new functionality for high-throughput studies.
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Bjerke, Ingvild Elise; Leergaard, Trygve B.; Bjaalie, Jan G. & Kim, Jee Hyun
(2022).
Quantitative map of dopamine 1- and 2-receptor positive cells in the developing mouse forebrain.
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Schlegel, Ulrike; Köhnen, Stefan; Davison, Andrew; Najafi, Peyman; Weyers, Benjamin & Gründling, Jan
[Show all 16 contributors for this article]
(2022).
openMINDS - flexible metadata models for neuroscience.
Show summary
Enhancing transparency and findability of research data is an emerging theme across scientific disciplines. More and more journals and funders, such as the Research Council of Norway, require the sharing of data in accordance with the FAIR guiding principles (Wilkinson et al., Scientific Data 3:160018, 2016), but few publicly accessible databases achieve this requirement. Their suitability is determined by attributes and constraints of the underlying metadata model. In neuroscience, the heterogeneity of data is particularly challenging. The multimodal nature of the research data as well as the wide range in spatial and temporal scales need to be adequately captured. Therefore, a suitable metadata model for neuroscience data has to balance flexibility and restrictiveness to accommodate the individuality of research products, without diminishing comparability across them. Powered by the Human Brain Project (HBP) and EBRAINS, we present the open Metadata Initiative for Neuroscience Data Structures (openMINDS). This novel initiative develops and maintains interlinked metadata models tailored to describe neuroscience research products in graph databases, such as the EBRAINS Knowledge Graph. The openMINDS research products cover data originating from human, animal or simulation studies (datasets), computational models (models), software tools (software), formal specifications for structuring metadata and/or data (metaDataModels), and reference brain atlases (brainAtlas). To illustrate the power of openMINDS, we present selected features describing these research products. We highlight how respective data and their provenance as well as studied specimens can be captured with user-defined granularity, and how the various integration of data via openMINDS can enhance its comparability and findability within and beyond the EBRAINS Knowledge Graph.
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Blixhavn, Camilla Hagen; Finn-Mogens S., Haug; Kleven, Heidi; Puchades, Maja ; Bjaalie, Jan G. & Leergaard, Trygve B.
(2022).
Multiplane microscopic atlas of rat brain zincergic terminal fields and metal-containing glia stained with Timm's sulphide silver method.
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Schlegel, Ulrike; Hjallar, Viktor A.; Lensjø, Kristian; Uggerud, Ida Margrethe; Bjaalie, Jan G. & Hvoslef-Eide, Martha
[Show all 8 contributors for this article]
(2022).
Mapping of parahippocampal and visual neural networks in mice: Preliminary evidence for feedback projections from perirhinal cortex to visual areas.
Show summary
Feedback projections from higher-order processing areas to lower-order areas within the visual system havebeen observed in several species. While much is known about the function of feedforward connections in thevisual processing pathway, feedback connections are not as well studied. They are thought to cause behaviorssuch as (selective) attention or expectations, but the exact mechanism and underlying neural connectivity isstill unknown. The perirhinal cortex (PRH) forms the intersection between perceptual and mnemonic areas ofthe visual processing pathway. Evidence from rats, monkeys and humans suggests that it is involved in bothobject memory and perceptual tasks, which is reflected in its connectivity. PRH forms numerous feedforwardand feedback projections with other areas of the visual processing pathway. However, little is known about thefunction and organization of feedback projections from PRH, and other parahippocampal regions, to visualareas in mice. Given the widespread availability of genetic and molecular tools, novel opportunities toinvestigate the function of these feedback projections would be enabled by mapping them in mice. Here wepresent preliminary data from anterograde and retrograde tract tracing experiments showing feedbackprojections from PRH, and other parahippocampal regions, to visual areas. We have found first evidence forfeedback projections from PRH to lower-order visual areas that were assumed to be exclusive to higher-developed animals such as monkeys or humans. Further, we have identified feedback projections from otherparahippocampal regions with specific subregional distribution.
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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.
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Bjaalie, Jan G.
(2021).
Value and Costs of Managing and Sharing Data: Reflections from the field.
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van Swieten, Maaike & Bjaalie, Jan G.
(2021).
How to make data public.
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Bjaalie, Jan G.
(2021).
SESSION IV: Value and costs of managing and sharing Data: Reflections from the field and panel Discussion.
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Bjaalie, Jan G.
(2021).
Chair session 9 and Talk: Introduction to the International Brain Initiative.
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Bjaalie, Jan G.
(2021).
Chair session 11:"Towards neuroscience-centered selection criteria for data repositories and scientific gateways".
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Bjaalie, Jan G.
(2021).
Presentation Session 6: Tools and infrastructure showcase: EBRAINS.
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Bjaalie, Jan G.
(2021).
Chair at Parallel sessions at FLAG-ERA Project Workshop.
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Bjaalie, Jan G.
(2021).
Talk: "Reproducible and transparent neuroscience: EBRAINS services for publishing research data" .
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Bjaalie, Jan G.
(2021).
Panel 255: A Canadian BRAIN initiative?
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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.
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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
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Bjaalie, Jan G.
(2020).
Global Neuroethic Summit Cross IBI workshop.
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Bjaalie, Jan G.
(2020).
EBRA-EPICLUSTER Consensus Workshop. Overview Human Brain Project and General view on research infrastructures and research clusters. .
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Bjaalie, Jan G.
(2020).
Presentation: Overview of Human Brain Project at 6th Internation Brain Intitiative Investigators Meeting .
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Bjaalie, Jan G.
(2020).
Introduction to EBRAINS at Code Jam # 11.
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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.
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Einevoll, Gaute; Bjaalie, Jan G. & Plesser, Hans Ekkehard
(2020).
Om hjerneforskning og Human Brain Project .
[Internet].
Podcast: Vett og vitenskap med Gaute Einevoll.
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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.
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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.
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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.
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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.
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Bjaalie, Jan G.
(2020).
International Brain Initiative: Facilitating Global Neuroscience Engagement.
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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
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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.
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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.
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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.
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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.
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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.
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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 .
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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.
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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.
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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.
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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.
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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.
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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.
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Bjaalie, Jan G.
(2018).
Data sharing in neuroscience: traditions, standards, and transformations.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
