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Amunts K, Mohlberg H, Bludau S, Zilles K. Julich-Brain: A 3D probabilistic atlas of the human brain’s
cytoarchitecture. Science 2020; 369:988-992. [DOI: 10.1126/science.abb4588] [Citation(s) in RCA: 117] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 06/24/2020] [Indexed: 12/13/2022]
Abstract
Cytoarchitecture is a basic principle of microstructural brain parcellation.
We introduce Julich-Brain, a three-dimensional atlas containing cytoarchitectonic
maps of cortical areas and subcortical nuclei. The atlas is probabilistic, which
enables it to account for variations between individual brains. Building such an
atlas was highly data- and labor-intensive and required the development of nested,
interdependent workflows for detecting borders between brain areas, data
processing, provenance tracking, and flexible execution of processing chains to
handle large amounts of data at different spatial scales. Full cortical coverage
was achieved by the inclusion of gap maps to complement cortical maps. The atlas
is dynamic and will be adapted as mapping progresses; it is openly available to
support neuroimaging studies as well as modeling and simulation; and it is
interoperable, enabling connection to other atlases and resources.
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Affiliation(s)
- Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- C. and O. Vogt Institute for Brain Research, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Hartmut Mohlberg
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Sebastian Bludau
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Karl Zilles
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
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2
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Eglen SJ, Marwick B, Halchenko YO, Hanke M, Sufi S, Gleeson P, Silver RA, Davison AP, Lanyon L, Abrams M, Wachtler T, Willshaw DJ, Pouzat C, Poline JB. Toward standard practices for sharing computer code and programs in neuroscience. Nat Neurosci 2017; 20:770-773. [PMID: 28542156 PMCID: PMC6386137 DOI: 10.1038/nn.4550] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Computational techniques are central in many areas of neuroscience, and are relatively easy to share. This paper describes why computer programs underlying scientific publications should be shared, and lists simple steps for sharing. Together with ongoing efforts in data sharing, this should aid reproducibility of research.
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Affiliation(s)
- Stephen J. Eglen
- Cambridge Computational Biology Institute, Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK
| | - Ben Marwick
- Department of Anthropology, University of Washington, Seattle, WA 98195-3100 USA
| | - Yaroslav O. Halchenko
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755 USA
| | - Michael Hanke
- Institute of Psychology II, Otto-von-Guericke-University Magdeburg, 39106 Magdeburg, Germany
- Center for Behavioral Brain Sciences, 39106 Magdeburg, Germany
| | - Shoaib Sufi
- Software Sustainability Institute, University of Manchester, UK
| | - Padraig Gleeson
- Department of Neuroscience, Physiology and Pharmacology, University College London, UK
| | - R. Angus Silver
- Department of Neuroscience, Physiology and Pharmacology, University College London, UK
| | - Andrew P. Davison
- Unité de Neurosciences, Information et Complexité, CNRS, Gif sur Yvette, France
| | - Linda Lanyon
- International Neuroinformatics Coordinating Facility, Karolinska Institutet, Stockholm, Sweden
| | - Mathew Abrams
- International Neuroinformatics Coordinating Facility, Karolinska Institutet, Stockholm, Sweden
| | - Thomas Wachtler
- Department of Biology II, Ludwig-Maximilians-Universität Muünchen, Germany
| | - David J. Willshaw
- Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, UK
| | - Christophe Pouzat
- MAP5 Paris-Descartes University and CNRS UMR 8145, 75006 Paris, France
| | - Jean-Baptiste Poline
- Henry H. Wheeler, Jr. Brain Imaging Center, Helen Wills Neuroscience Institute, University of California, Berkeley, USA
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3
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Workflows for Ultra-High Resolution 3D Models of the Human Brain on Massively Parallel Supercomputers. ACTA ACUST UNITED AC 2016. [DOI: 10.1007/978-3-319-50862-7_2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
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Rosenberg DM, Horn CC. Neurophysiological analytics for all! Free open-source software tools for documenting, analyzing, visualizing, and sharing using electronic notebooks. J Neurophysiol 2016; 116:252-62. [PMID: 27098025 DOI: 10.1152/jn.00137.2016] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2016] [Accepted: 04/04/2016] [Indexed: 12/18/2022] Open
Abstract
Neurophysiology requires an extensive workflow of information analysis routines, which often includes incompatible proprietary software, introducing limitations based on financial costs, transfer of data between platforms, and the ability to share. An ecosystem of free open-source software exists to fill these gaps, including thousands of analysis and plotting packages written in Python and R, which can be implemented in a sharable and reproducible format, such as the Jupyter electronic notebook. This tool chain can largely replace current routines by importing data, producing analyses, and generating publication-quality graphics. An electronic notebook like Jupyter allows these analyses, along with documentation of procedures, to display locally or remotely in an internet browser, which can be saved as an HTML, PDF, or other file format for sharing with team members and the scientific community. The present report illustrates these methods using data from electrophysiological recordings of the musk shrew vagus-a model system to investigate gut-brain communication, for example, in cancer chemotherapy-induced emesis. We show methods for spike sorting (including statistical validation), spike train analysis, and analysis of compound action potentials in notebooks. Raw data and code are available from notebooks in data supplements or from an executable online version, which replicates all analyses without installing software-an implementation of reproducible research. This demonstrates the promise of combining disparate analyses into one platform, along with the ease of sharing this work. In an age of diverse, high-throughput computational workflows, this methodology can increase efficiency, transparency, and the collaborative potential of neurophysiological research.
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Affiliation(s)
- David M Rosenberg
- Biobehavioral Oncology Program, University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania; Department of Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Charles C Horn
- Biobehavioral Oncology Program, University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania; Division of Gastroenterology, Hepatology, and Nutrition, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; Department of Anesthesiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; and Center for Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania
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Lawrence TJ, Kauffman KT, Amrine KCH, Carper DL, Lee RS, Becich PJ, Canales CJ, Ardell DH. FAST: FAST Analysis of Sequences Toolbox. Front Genet 2015; 6:172. [PMID: 26042145 PMCID: PMC4437040 DOI: 10.3389/fgene.2015.00172] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2015] [Accepted: 04/20/2015] [Indexed: 11/13/2022] Open
Abstract
FAST (FAST Analysis of Sequences Toolbox) provides simple, powerful open source command-line tools to filter, transform, annotate and analyze biological sequence data. Modeled after the GNU (GNU's Not Unix) Textutils such as grep, cut, and tr, FAST tools such as fasgrep, fascut, and fastr make it easy to rapidly prototype expressive bioinformatic workflows in a compact and generic command vocabulary. Compact combinatorial encoding of data workflows with FAST commands can simplify the documentation and reproducibility of bioinformatic protocols, supporting better transparency in biological data science. Interface self-consistency and conformity with conventions of GNU, Matlab, Perl, BioPerl, R, and GenBank help make FAST easy and rewarding to learn. FAST automates numerical, taxonomic, and text-based sorting, selection and transformation of sequence records and alignment sites based on content, index ranges, descriptive tags, annotated features, and in-line calculated analytics, including composition and codon usage. Automated content- and feature-based extraction of sites and support for molecular population genetic statistics make FAST useful for molecular evolutionary analysis. FAST is portable, easy to install and secure thanks to the relative maturity of its Perl and BioPerl foundations, with stable releases posted to CPAN. Development as well as a publicly accessible Cookbook and Wiki are available on the FAST GitHub repository at https://github.com/tlawrence3/FAST. The default data exchange format in FAST is Multi-FastA (specifically, a restriction of BioPerl FastA format). Sanger and Illumina 1.8+ FastQ formatted files are also supported. FAST makes it easier for non-programmer biologists to interactively investigate and control biological data at the speed of thought.
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Affiliation(s)
- Travis J Lawrence
- Quantitative and Systems Biology Program, University of California, Merced Merced, CA, USA
| | - Kyle T Kauffman
- Molecular Cell Biology Unit, School of Natural Sciences, University of California, Merced Merced, CA, USA
| | - Katherine C H Amrine
- Quantitative and Systems Biology Program, University of California, Merced Merced, CA, USA ; Department of Viticulture and Enology, University of California, Davis Davis, CA, USA
| | - Dana L Carper
- Quantitative and Systems Biology Program, University of California, Merced Merced, CA, USA
| | - Raymond S Lee
- School of Engineering, University of California, Merced Merced, CA, USA
| | - Peter J Becich
- Molecular Cell Biology Unit, School of Natural Sciences, University of California, Merced Merced, CA, USA
| | - Claudia J Canales
- School of Engineering, University of California, Merced Merced, CA, USA
| | - David H Ardell
- Quantitative and Systems Biology Program, University of California, Merced Merced, CA, USA ; Molecular Cell Biology Unit, School of Natural Sciences, University of California, Merced Merced, CA, USA
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6
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Eglen SJ, Weeks M, Jessop M, Simonotto J, Jackson T, Sernagor E. A data repository and analysis framework for spontaneous neural activity recordings in developing retina. Gigascience 2014; 3:3. [PMID: 24666584 PMCID: PMC4076503 DOI: 10.1186/2047-217x-3-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2013] [Accepted: 03/13/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND During early development, neural circuits fire spontaneously, generating activity episodes with complex spatiotemporal patterns. Recordings of spontaneous activity have been made in many parts of the nervous system over the last 25 years, reporting developmental changes in activity patterns and the effects of various genetic perturbations. RESULTS We present a curated repository of multielectrode array recordings of spontaneous activity in developing mouse and ferret retina. The data have been annotated with minimal metadata and converted into HDF5. This paper describes the structure of the data, along with examples of reproducible research using these data files. We also demonstrate how these data can be analysed in the CARMEN workflow system. This article is written as a literate programming document; all programs and data described here are freely available. CONCLUSIONS 1. We hope this repository will lead to novel analysis of spontaneous activity recorded in different laboratories. 2. We encourage published data to be added to the repository. 3. This repository serves as an example of how multielectrode array recordings can be stored for long-term reuse.
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Affiliation(s)
- Stephen John Eglen
- Cambridge Computational Biology Institute, University of Cambridge, Wilberforce Road, CB3 0WA Cambridge, UK.
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Estimating background-subtracted fluorescence transients in calcium imaging experiments: a quantitative approach. Cell Calcium 2013; 54:71-85. [PMID: 23787148 DOI: 10.1016/j.ceca.2013.04.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2012] [Revised: 04/14/2013] [Accepted: 04/15/2013] [Indexed: 01/31/2023]
Abstract
Calcium imaging has become a routine technique in neuroscience for subcellular to network level investigations. The fast progresses in the development of new indicators and imaging techniques call for dedicated reliable analysis methods. In particular, efficient and quantitative background fluorescence subtraction routines would be beneficial to most of the calcium imaging research field. A background-subtracted fluorescence transients estimation method that does not require any independent background measurement is therefore developed. This method is based on a fluorescence model fitted to single-trial data using a classical nonlinear regression approach. The model includes an appropriate probabilistic description of the acquisition system's noise leading to accurate confidence intervals on all quantities of interest (background fluorescence, normalized background-subtracted fluorescence time course) when background fluorescence is homogeneous. An automatic procedure detecting background inhomogeneities inside the region of interest is also developed and is shown to be efficient on simulated data. The implementation and performances of the proposed method on experimental recordings from the mouse hypothalamus are presented in details. This method, which applies to both single-cell and bulk-stained tissues recordings, should help improving the statistical comparison of fluorescence calcium signals between experiments and studies.
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