| 1. |
- Abrams, M. B., et al.
(författare)
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A Standards Organization for Open and FAIR Neuroscience : the International Neuroinformatics Coordinating Facility
- 2021
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Ingår i: Neuroinformatics. - : Springer Nature. - 1539-2791 .- 1559-0089.
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Tidskriftsartikel (refereegranskat)abstract
- There is great need for coordination around standards and best practices in neuroscience to support efforts to make neuroscience a data-centric discipline. Major brain initiatives launched around the world are poised to generate huge stores of neuroscience data. At the same time, neuroscience, like many domains in biomedicine, is confronting the issues of transparency, rigor, and reproducibility. Widely used, validated standards and best practices are key to addressing the challenges in both big and small data science, as they are essential for integrating diverse data and for developing a robust, effective, and sustainable infrastructure to support open and reproducible neuroscience. However, developing community standards and gaining their adoption is difficult. The current landscape is characterized both by a lack of robust, validated standards and a plethora of overlapping, underdeveloped, untested and underutilized standards and best practices. The International Neuroinformatics Coordinating Facility (INCF), an independent organization dedicated to promoting data sharing through the coordination of infrastructure and standards, has recently implemented a formal procedure for evaluating and endorsing community standards and best practices in support of the FAIR principles. By formally serving as a standards organization dedicated to open and FAIR neuroscience, INCF helps evaluate, promulgate, and coordinate standards and best practices across neuroscience. Here, we provide an overview of the process and discuss how neuroscience can benefit from having a dedicated standards body.
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| 4. |
- Djurfeldt, Mikael, 1967-, et al.
(författare)
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Run-Time Interoperability Between Neuronal Network Simulators Based on the MUSIC Framework
- 2010
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Ingår i: Neuroinformatics. - : Springer Science and Business Media LLC. - 1539-2791 .- 1559-0089. ; 8:1, s. 43-60
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Tidskriftsartikel (refereegranskat)abstract
- MUSIC is an API allowing large scale neuron simulators using MPI internally to exchange data during runtime. We provide experiences from the adaptation of two neuronal network simulators of different kinds, NEST and MOOSE, to this API. A multi-simulation of a cortico-striatal network model involving both simulators is performed, demonstrating how MUSIC can promote inter-operability between models written for different simulators and how these can be re-used to build a larger model system. We conclude that MUSIC fulfills the design goals of being portable and simple to adapt to existing simulators. In addition, since the MUSIC API enforces independence between the applications, the multi-simulationcould be built from pluggable component modules without adaptation of the components to each other in terms of simulation time-step or topology of connections between the modules.
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| 5. |
- Djurfeldt, Mikael
(författare)
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The Connection-set Algebra-A Novel Formalism for the Representation of Connectivity Structure in Neuronal Network Models
- 2012
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Ingår i: Neuroinformatics. - : Springer Science and Business Media LLC. - 1539-2791 .- 1559-0089. ; 10:3, s. 287-304
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Tidskriftsartikel (refereegranskat)abstract
- The connection-set algebra (CSA) is a novel and general formalism for the description of connectivity in neuronal network models, from small-scale to large-scale structure. The algebra provides operators to form more complex sets of connections from simpler ones and also provides parameterization of such sets. CSA is expressive enough to describe a wide range of connection patterns, including multiple types of random and/or geometrically dependent connectivity, and can serve as a concise notation for network structure in scientific writing. CSA implementations allow for scalable and efficient representation of connectivity in parallel neuronal network simulators and could even allow for avoiding explicit representation of connections in computer memory. The expressiveness of CSA makes prototyping of network structure easy. A C+ + version of the algebra has been implemented and used in a large-scale neuronal network simulation (Djurfeldt et al., IBM J Res Dev 52(1/2):31-42, 2008b) and an implementation in Python has been publicly released.
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| 6. |
- Dunås, Tora, et al.
(författare)
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A Stereotactic Probabilistic Atlas for the Major Cerebral Arteries
- 2017
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Ingår i: Neuroinformatics. - : Springer Science and Business Media LLC. - 1539-2791 .- 1559-0089. ; 15:1, s. 101-110
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Tidskriftsartikel (refereegranskat)abstract
- Improved whole brain angiographic and velocity-sensitive MRI is pushing the boundaries of noninvasively obtained cerebral vascular flow information. The complexity of the information contained in such datasets calls for automated algorithms and pipelines, thus reducing the need of manual analyses by trained radiologists. The objective of this work was to lay the foundation for such automated pipelining by constructing and evaluating a probabilistic atlas describing the shape and location of the major cerebral arteries. Specifically, we investigated how the implementation of a non-linear normalization into Montreal Neurological Institute (MNI) space improved the alignment of individual arterial branches. In a population-based cohort of 167 subjects, age 64-68 years, we performed 4D flow MRI with whole brain volumetric coverage, yielding both angiographic and anatomical data. For each subject, sixteen cerebral arteries were manually labeled to construct the atlas. Angiographic data were normalized to MNI space using both rigid-body and non-linear transformations obtained from anatomical images. The alignment of arterial branches was significantly improved by the non-linear normalization (p < 0.001). Validation of the atlas was based on its applicability in automatic arterial labeling. A leave-one-out validation scheme revealed a labeling accuracy of 96 %. Arterial labeling was also performed in a separate clinical sample (n = 10) with an accuracy of 92.5 %. In conclusion, using non-linear spatial normalization we constructed an artery-specific probabilistic atlas, useful for cerebral arterial labeling.
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| 8. |
- Hjorth, J. J. Johannes, et al.
(författare)
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Predicting Synaptic Connectivity for Large-Scale Microcircuit Simulations Using Snudda
- 2021
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Ingår i: Neuroinformatics. - : Springer Nature. - 1539-2791 .- 1559-0089. ; 19:4, s. 685-701
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Tidskriftsartikel (refereegranskat)abstract
- Simulation of large-scale networks of neurons is an important approach to understanding and interpreting experimental data from healthy and diseased brains. Owing to the rapid development of simulation software and the accumulation of quantitative data of different neuronal types, it is possible to predict both computational and dynamical properties of local microcircuits in a ‘bottom-up’ manner. Simulated data from these models can be compared with experiments and ‘top-down’ modelling approaches, successively bridging the scales. Here we describe an open source pipeline, using the software Snudda, for predicting microcircuit connectivity and for setting up simulations using the NEURON simulation environment in a reproducible way. We also illustrate how to further ‘curate’ data on single neuron morphologies acquired from public databases. This model building pipeline was used to set up a first version of a full-scale cellular level model of mouse dorsal striatum. Model components from that work are here used to illustrate the different steps that are needed when modelling subcortical nuclei, such as the basal ganglia.
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| 9. |
- Ljungquist, Bengt, et al.
(författare)
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A Bit-Encoding Based New Data Structure for Time and Memory Efficient Handling of Spike Times in an Electrophysiological Setup
- 2018
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Ingår i: Neuroinformatics. - : Springer. - 1539-2791 .- 1559-0089. ; 16:2, s. 217-229
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Tidskriftsartikel (refereegranskat)abstract
- Recent neuroscientific and technical developments of brain machine interfaces have put increasing demands on neuroinformatic databases and data handling software, especially when managing data in real time from large numbers of neurons. Extrapolating these developments we here set out to construct a scalable software architecture that would enable near-future massive parallel recording, organization and analysis of neurophysiological data on a standard computer. To this end we combined, for the first time in the present context, bit-encoding of spike data with a specific communication format for real time transfer and storage of neuronal data, synchronized by a common time base across all unit sources. We demonstrate that our architecture can simultaneously handle data from more than one million neurons and provide, in real time (< 25 ms), feedback based on analysis of previously recorded data. In addition to managing recordings from very large numbers of neurons in real time, it also has the capacity to handle the extensive periods of recording time necessary in certain scientific and clinical applications. Furthermore, the bit-encoding proposed has the additional advantage of allowing an extremely fast analysis of spatiotemporal spike patterns in a large number of neurons. Thus, we conclude that this architecture is well suited to support current and near-future Brain Machine Interface requirements.
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| 10. |
- Santos, J. P. G., et al.
(författare)
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A Modular Workflow for Model Building, Analysis, and Parameter Estimation in Systems Biology and Neuroscience
- 2021
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Ingår i: Neuroinformatics. - : Springer Nature. - 1539-2791 .- 1559-0089.
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Tidskriftsartikel (refereegranskat)abstract
- Neuroscience incorporates knowledge from a range of scales, from single molecules to brain wide neural networks. Modeling is a valuable tool in understanding processes at a single scale or the interactions between two adjacent scales and researchers use a variety of different software tools in the model building and analysis process. Here we focus on the scale of biochemical pathways, which is one of the main objects of study in systems biology. While systems biology is among the more standardized fields, conversion between different model formats and interoperability between various tools is still somewhat problematic. To offer our take on tackling these shortcomings and by keeping in mind the FAIR (findability, accessibility, interoperability, reusability) data principles, we have developed a workflow for building and analyzing biochemical pathway models, using pre-existing tools that could be utilized for the storage and refinement of models in all phases of development. We have chosen the SBtab format which allows the storage of biochemical models and associated data in a single file and provides a human readable set of syntax rules. Next, we implemented custom-made MATLAB® scripts to perform parameter estimation and global sensitivity analysis used in model refinement. Additionally, we have developed a web-based application for biochemical models that allows simulations with either a network free solver or stochastic solvers and incorporating geometry. Finally, we illustrate convertibility and use of a biochemical model in a biophysically detailed single neuron model by running multiscale simulations in NEURON. Using this workflow, we can simulate the same model in three different simulators, with a smooth conversion between the different model formats, enhancing the characterization of different aspects of the model.
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