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Reiten, Ingrid; Schlegel, Ulrike; Aasebø, Ida; van Swieten, Maaike M. H.; Davison, Andrew P. & Zehl, Lyuba
[Vis alle 11 forfattere av denne artikkelen]
(2022).
Share FAIR neuroscience data using the EBRAINS curation services.
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Reiten, Ingrid & Leergaard, Trygve Brauns
(2022).
Digitisation, sharing and atlas registration of tract tracing histological sections.
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Leergaard, Trygve B.; Bjerke, Ingvild Elise; Bjaalie, Jan G.; Palomero-Gallagher, Nicola; Puchades, Maja & Carey, Harry
[Vis alle 7 forfattere av denne artikkelen]
(2022).
Neuroscience data integration through use of digital brain atlases .
Vis sammendrag
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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Puchades, Maja ; Reiten, Ingrid; Destexhe, Alain; Palomero-Gallagher, Nicola; Ruikes, Thijs & Leergaard, Trygve B.
[Vis alle 7 forfattere av denne artikkelen]
(2022).
Workshop: Towards exploration of disease mechanisms in animal models: atlases, analysis, modelling and simulation.
Vis sammendrag
Researchers Maja A. Puchades and Prof Jan G. Bjaalie have organised a paralell session titled: "Towards exploration of disease mechanisms in animal models".
Agenda:
For the 90 min meeting:
· Presentation of speakers
· New directions in disease models research
· EBRAINS atlases enabling animal model research
· Tools and analytical workflows for data integration in atlases
· Use of animal brain atlases for modelling of brain function
· Connectivity rat data: tract-tracing use case
· Connectivity rat data: electrophysiological tetrodes use case
· Panel discussion
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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 ; Yates, Sharon Christine; Groeneboom, Nicolaas; Csucs, Gergely; Darine, Dmitri Aleksandrovitsj & Carey, Harry
[Vis alle 8 forfattere av denne artikkelen]
(2022).
EBRAINS tools for rodent brain atlasing.
Vis sammendrag
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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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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Leergaard, Trygve B.; Puchades, Maja ; Kleven, Heidi & Bjerke, Ingvild Elise
(2022).
HBP Brain Matters webinar #12 "Rodent Brain Atlasing".
Vis sammendrag
This episode featured the following HBP researchers:
Heidi Kleven who is a PhD student at the University of Oslo with a research focus on developing and expanding rat and mouse brain atlases. She presented her ongoing work on brain atlases, focusing on the Waxholm Space atlas of the Sprague Dawley rat brain.
Maja Puchades from the University of Oslo presented an overview of EBRAINS tools for analyses of Rodent brain images. A pipeline consisting of several tools allows users to register their images to a reference atlas and perform quantitative analysis in an atlas context. Both standalone and online versions of the tools are now available through the EBRAINS infrastructure.
Ingvild Bjerke from the University of Oslo is a postdoctoral researcher. Her research focuses on mapping cellular parameters in the rodent brain, using EBRAINS atlases and tools to perform brain-wide analyses. She presented her published research mapping calbindin- and parvalbumin-positive cells in the rat and mouse brain.
The session was moderated by Trygve Leergaard who is a professor of Anatomy at the Institute of Basic Medical Sciences, University of Oslo. His research has been focused on resolving fundamental architectonical principles underlying brain map transformations in major sensorimotor projection systems connecting the cerebral cortex with several brain stem nuclei.
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Bjerke, Ingvild Elise; Cullity, Ellen Rose; Kjelsberg, Kasper; Charan, Kristel; Leergaard, Trygve B. & Kim, Jee Hyun
(2022).
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
[Vis alle 16 forfattere av denne artikkelen]
(2022).
openMINDS - flexible metadata models for neuroscience.
Vis sammendrag
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
[Vis alle 8 forfattere av denne artikkelen]
(2022).
Mapping of parahippocampal and visual neural networks in mice: Preliminary evidence for feedback projections from perirhinal cortex to visual areas.
Vis sammendrag
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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Øyen, Ole Morten; Spurkland, Anne & Leergaard, Trygve B.
(2022).
Bedre etterutdanning i anatomi for norske klinikere.
Tidsskrift for Den norske legeforening.
ISSN 0029-2001.
142(11).
doi:
10.4045/tidsskr.22.0350.
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Leergaard, Trygve B.; Bjerke, Ingvild Elise; Palomero-Gallagher, Nicola; Witter, Menno; Dickscheid, Timo & Puchades, Maja
[Vis alle 7 forfattere av denne artikkelen]
(2021).
Neuroscience data integration through use of digital brain atlases.
Vis sammendrag
Kurs om data integrasjon og digitale hjerne atlas.
September 1:
INTRODUCTION (Trygve Leergaard & Ingvild Bjerke)
SESSION 1: Concepts for sharing and integration of neuroscience data (Jan Bjaalie)
SHORT BREAK
SESSION 2: Navigating the brain (Nicola Palomero-Gallagher, Menno P. Witter, Timo Dickscheid)
LUNCH BREAK
SESSION 2: continued
SHORT BREAK
SESSION 3: Introduction to EBRAINS digital brain atlas resources (Timo Dickscheid & Maja Puchades)
CLOSURE DAY 1
September 2:
SESSION 4: Sharing and assigning location parameters to murine data (Ingvild Bjerke & Maja Puchades)
SHORT BREAK
SESSION 4, continued
SHORT BREAK
SESSION 5: Automated quantification of experimental murine image data (Sharon Yates & Maja Puchades)
LUNCH BREAK
SESSION 5, continued (Sharon Yates)
BREAK
SESSION 6: Visions of data integration in the future (Jan Bjaalie)
CLOSURE DAY 2: Evaluation and preparation for exam task (Trygve Leergaard)
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Schlegel, Ulrike; Köhnen, Stefan; Davison, Andrew P.; Najafi, Peyman; Weyers, Benjamin & Gründling, Jan
[Vis alle 17 forfattere av denne artikkelen]
(2021).
openMINDS - flexible metadata models for neuroscience.
Vis sammendrag
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 ; Dickscheid, Timo & Leergaard, Trygve B.
(2021).
Hands-on Session V: Brain Atlases.
Vis sammendrag
The Virtual Master Class Series is a collaborative effort – between IBRO, IBI, INCF & HBP – to train Senior PIs on various EBRAINS Tools and Services throughout all regions of the world. This first edition focuses on the vast Brain Atlasing and Simulation Services available in EBRAINS. Expert speakers will present several tools and delve into practical usage in 6 hands-on sessions. The event is designed as a train-the-teacher” workshop. The target group of the event are mid-career researchers in the Asia-Pacific region, affiliated with and invited by IBRO.
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Puchades, Maja ; Yates, Sharon Christine; Bjerke, Ingvild Elise; Groeneboom, Nicolaas; Csucs, Gergely & Leergaard, Trygve B.
[Vis alle 7 forfattere av denne artikkelen]
(2021).
Implementing the QUINT workflow for spatial quantitative analysis of labelling in mice and rats.
Vis sammendrag
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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Yates, Sharon Christine; Groeneboom, Nicolaas; Csucs, Gergely; Leergaard, Trygve B.; Kreshuk, Anna & Kutra, Dominik
[Vis alle 8 forfattere av denne artikkelen]
(2020).
New functionalities i the QUINT workflow for brain atlas based image analysis.
Vis sammendrag
The novel QUINT workflow enables quantification and spatial analysis of labeling in series of histological section images from mouse or rat brains, that have been registered to 3D reference brain atlases (Allen Mouse Brain Atlas, CCFv3 and Waxholm Space atlas of the rat brain v2 and v3). The workflow utilizes several open source software developed with support from the Human Brain Project: QuickNII, ilastik, VisuAlign and Nutil.
Here we present new software functionalities: a tool for non-linear registration of 2D images to a reference atlas (VisuAlign) performed after the linear registration with QuickNII; an improved Nutil graphical user interface based on feedback from the community; and improved ilastik functionality allowing usage of masks, generated by tools like QuickNII or other software.
The workflow is exemplified by quantification of parvalbumin positive cells from an Allen mouse brain in situ hybridisation experiment, which is available in the EBRAINS data portal.
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Reiten, Ingrid; Schlegel, Ulrike; Aasebø, Ida Elisabeth Jørgensen; Blixhavn, Camilla Hagen; Zehl, Lyuba & Kjelsberg, Kasper
[Vis alle 13 forfattere av denne artikkelen]
(2020).
Neuroscience community-gains from data sharing through the EBRAINS infrastructure.
Vis sammendrag
EBRAINS provides tools and services which can be used to address challenges in brain research and brain-inspired technology development. EBRAINS assists scientists to collect, analyse, share and integrate brain data, and to perform modeling and simulation of brain function. EBRAINS is delivered by the EU Flagship Human Brain Project. All tools and services in EBRAINS are available for researchers in Europe and globally through https://ebrains.eu. Here we exemplify the use of the platform in different neuroscientific projects. In particular, the ‘Data & Knowledge’ services in EBRAINS offer one of the most comprehensive services for sharing brain research data ranging in type as well as spatial and temporal scale. An extensive metadata curation process ensures a robust presentation of datasets, models and software via the EBRAINS Knowledge Graph (https://kg.ebrains.eu/search/), making the data Findable, Accessible, Interoperable and Reusable (FAIR). By describing data across modalities in a standardised way and integrating it into the same reference space, data can be compared, combined and analysed with tools and analytical workflows embedded in the platform. New data, and data derived from the analytical workflows, can be submitted to curation and added to the existing EBRAINS datasets. The interplay between datasets, models and software makes EBRAINS attractive as a platform for discovery based and hypothesis driven research.
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Schlegel, Ulrike; Reiten, Ingrid; Aasebø, Ida Elisabeth Jørgensen; Blixhavn, Camilla Hagen; Zehl, Lyuba & Kjelsberg, Kasper
[Vis alle 13 forfattere av denne artikkelen]
(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
[Vis alle 12 forfattere av denne artikkelen]
(2020).
Data sharing through the online EBRAINS platform: a new service for brain research.
Vis sammendrag
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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Andersson, Krister Andreas; Blixhavn, Camilla Hagen; Zehl, Lyuba; Zarfarnia, Sara; Köhnen, Stefan & Hilverling, Anna
[Vis alle 19 forfattere av denne artikkelen]
(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
[Vis alle 17 forfattere av denne artikkelen]
(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
[Vis alle 10 forfattere av denne artikkelen]
(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.
Vis sammendrag
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.
[Vis alle 7 forfattere av denne artikkelen]
(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.
[Vis alle 7 forfattere av denne artikkelen]
(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
[Vis alle 11 forfattere av denne artikkelen]
(2019).
Integrating and analysing heterogeneous rodent neuroscience data using three-dimensional brain reference atlases.
Vis sammendrag
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
[Vis alle 15 forfattere av denne artikkelen]
(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
[Vis alle 20 forfattere av denne artikkelen]
(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
[Vis alle 20 forfattere av denne artikkelen]
(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.
[Vis alle 7 forfattere av denne artikkelen]
(2018).
HBP Analytic Workflow: from Microscopic Image Data to Extracted Features in Atlas Space.
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von Linstow, Christian Ulrich; Yates, Sharon Christine; Leergaard, Trygve B.; Plank, A-C; von Hörsten, S. & Puchades, Maja
[Vis alle 7 forfattere av denne artikkelen]
(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
[Vis alle 8 forfattere av denne artikkelen]
(2018).
Data integration through digital brain atlasing: Making diverse neuroscience data discoverable using Human Brain Project infrastructure.
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Leergaard, Trygve B.
(2017).
Building and using rodent brain reference atlases for integration of experimental data.
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Timonidis, Nestor; Bakker, Rembrandt; Øvsthus, Martin; Garcia-Amado, M; Porrero, C & Leergaard, Trygve B.
[Vis alle 8 forfattere av denne artikkelen]
(2017).
Towards predicting the full mesoconnectome based on data fusion.
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Leprince, Y; Coello, Sebastian Christopher; Csúcs, Gergely; Chervakov, P; Darine, Dmitri Aleksandrovitsj & Øvsthus, Martin
[Vis alle 12 forfattere av denne artikkelen]
(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
[Vis alle 11 forfattere av denne artikkelen]
(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
[Vis alle 18 forfattere av denne artikkelen]
(2017).
Submitting data and metadata through the HBP Data Workbench: Concepts, data flows, policies, and best practices.
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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.
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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.
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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 .
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Imad, Jala; Osen, Kirsten Kjelsberg; Clascá, Francisco; Wennberg, Arvind E.; Coello, Sebastian Christopher & Csúcs, Gergely
[Vis alle 9 forfattere av denne artikkelen]
(2017).
Enriching and Adding Detail to the Waxholm Space Rat Brain Reference Atlas.
Vis sammendrag
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.
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Yates, Sharon Christine; Puchades, Maja ; Coello, Sebastian Christopher; Kreshuk, Anna; Hartlage-Rübsamen, Maike & Rossner, Steffen
[Vis alle 8 forfattere av denne artikkelen]
(2017).
Workflow for automated quantification and spatial analysis
of labelling in microscoping rodent brain sections.
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Andersson, Krister; Øvsthus, Martin; Bjerke, Ingvild Elise; Puchades, Maja ; Telefont, Martin & Jeff, Muller
[Vis alle 9 forfattere av denne artikkelen]
(2017).
Data integration through digital brain atlasing: Human Brain Project infrastructure.
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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.
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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.
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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.
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Yates, Sharon Christine; Puchades, Maja ; Coello, Sebastian Christopher; Kreshuk, Anna; Hartlage-Rübsamen, Maike & Rossner, Steffen
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(2017).
Workflow for automated quantification and spatial analysis of labeling in microscoping rodent brain sections.
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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.
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Puchades, Maja ; Csúcs, Gergely; Checinska, Martyna; Øvsthus, Martin; Bjerke, Ingvild Elise & Andersson, Krister
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(2017).
Neuroinformatics tool and workflow for anchoring of serial histological images in rodent brain 3D space.
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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.
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Puchades, Maja ; Øvsthus, Martin; Bjerke, Ingvild Elise; Andersson, Krister; Csúcs, Gergely & Leergaard, Trygve Brauns
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(2017).
Data integration through digital brain atlasing: semiautomatic spatial registration of serial histological images to rodent brain 3D reference atlases.
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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.
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Leergaard, Trygve B.
(2016).
Navigating the rodent brain: An anatomist’s guide to assigning location to brain image data.
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Puchades, Maja ; Bjerke, Ingvild Elise; Øvsthus, Martin; Andersson, Krister; Csúcs, Gergely & Leergaard, Trygve Brauns
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(2016).
Spatial registration of serial histological image data to reference brain atlases.
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Leergaard, Trygve Brauns
(2015).
Hjerneforskning i den digitale tidsalder: Fremtidens hjerneatlas.
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Blackstad, Jan Sigurd Beddari; Osen, Kirsten Kjelsberg; Scharfman, HE; Storm-Mathisen, Jon; Blackstad, Theodor W. & Leergaard, Trygve Brauns
(2015).
Hippocampal mossy cells in mink (Neovison vison).
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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.
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Schubert, N; Axer, M; Graessel, D; Schober, M; Huynh, A & Huysegoms, M
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(2015).
3D reconstructed fiber-, cyto- and receptorarchitecture of the rat brain transferred into the Waxholm Space atlas.
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Papp, Eszter Agnes; Bjaalie, Jan G. & Leergaard, Trygve Brauns
(2018).
MR-basert atlas av rottehjernen for integrasjon av bildedata.
Universitetet i Oslo.
ISSN 978-82-8377-194-7.
Fulltekst i vitenarkiv
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Med økende levealder og en stadig eldre befolkning forventes det at hver tredje person vil kunne rammes av hjernesykdom i løpet av livet. En av de største utfordringene for å forstå hvordan hjernen fungerer under normale forhold og ved sykdom eller skade er knyttet til sammenstilling og analyse av informasjon om hjernen fra flere forskjellige kilder. Anatomisk lokalisering i hjernen er viktig for å kunne organisere og sammenligne innsamlede data. Eszter Agnes Papp og kolleger ved Universitetet i Oslo har etablert et anatomisk referanseatlas for rottehjernen og utviklet nye metoder for mer effektiv integrasjon og analyse av bildemateriale fra eksperimentelle studier.
I samarbeid med Duke University Medical Center har forskerne produsert høyoppløselige magnetresonansbilder for å skape et tredimensjonalt hjerneatlas. Basert på bildekontrast har de kartlagt grunnleggende anatomiske regioner og nervebaner gjennom hele rottehjernen. For å kunne navigere i atlaset har de introdusert et koordinatsystem i henhold til en internasjonal standard - Waxholm Space.
Forskerne har benyttet en samling av 2D og 3D hjernebilder for å identifisere anatomiske fellestrekk som er synlige i ulike typer bildedata fra rotte- og musehjernen, og derfor egner seg som landemerker for plassering av ny informasjon på riktig sted i atlaset. Videre har de utviklet analyseverktøy for automatisk deteksjon av regioner av interesse i mikroskopisk materiale og for registrering av bildene til atlaset.
Åpen tilgang til resultatene har gjort det mulig for andre forskere å bruke atlaset til å analysere bilder av både hjernestruktur og funksjon. Videre utvikling av atlaset er fokusert på å legge til mer detaljerte kart om flere hjerneområder, som for eksempel hippocampus og hørselssystemet.