Abstract
We developed software tools to download, extract features, and organize the Cell Types Database from the Allen Brain Institute (ABI) in order to integrate its whole cell patch clamp characterization data into the automated modeling/data analysis cycle. To expand the potential user base we employed both Python and MATLAB. The basic set of tools downloads selected raw data and extracts cell, sweep, and spike features, using ABI’s feature extraction code. To facilitate data manipulation we added a tool to build a local specialized database of raw data plus extracted features. Finally, to maximize automation, we extended our NeuroManager workflow automation suite to include these tools plus a separate investigation database. The extended suite allows the user to integrate ABI experimental and modeling data into an automated workflow deployed on heterogeneous computer infrastructures, from local servers, to high performance computing environments, to the cloud. Since our approach is focused on workflow procedures our tools can be modified to interact with the increasing number of neuroscience databases being developed to cover all scales and properties of the nervous system.
Similar content being viewed by others
Notes
REST: Representational State Transfer
HDF5: Hierarchical Data Format Version 5; NWB: Neurodata Without Borders; XML: Extended Meta Language; JSON: Javascript Object Notation
A SimSet is a set of input parameter vectors, each of which is associated with a single simulation. A SimSet may represent many simulations, which will be scheduled to run in parallel on the Simulators in the Simulator Pool. Please see the NeuroManager documents for more details.
Each row in a table has a unique key; a foreign key in a row associates that row with a row in another table. In this way tables support the various characteristics of a thing in a database.
References
Git (2017). Website. https://git-scm.com/.
ABI (2015a). Allen cell types database - overview. technical report, allen brain institute. http://help.brain-map.org/download/attachments/8323525/CellTypesOverview.pdf?version=1&modificationDate=1456188760121.
ABI (2015b). Allen cell types database - electrophysiology. technical report, allen brain institute. http://help.brain-map.org/download/attachments/8323525/EphysOverview.pdf?version=1&modificationDate=1456188786670.
ABI (2015c). Allen cell types database - morphology. technical report, allen brain institute. http://help.brain-map.org/download/attachments/8323525/MorphOverview.pdf?version=1&modificationDate=1456525256645.
ABI (2015d). Allen cell types database - glif models. technical report, allen brain institute. http://help.brain-map.org/download/attachments/8323525/GLIFModels.pdf?version=1&modificationDate=1456188812960.
ABI (2015e). Allen cell types database - biophysical modeling - perisomatic. technical report, allen brain institute. http://help.brain-map.org/download/attachments/8323525/BiophysModelPeri.pdf?version=1&modificationDate=1456188760131.
ABI (2017a). Allen brain institute cell types database application programmer’s interface. http://help.brain-map.org/display/celltypes/API.
ABI (2017b). Allen brain institute cell types webpage. http:/celltypes.brain-map.org.
ABI (2017c). Allen brain atlas portal - news and upyears. http://www.brain-map.org/announcements/index.
ABI (2017d). Allen brain institute restful model access (RMA). http://help.brain-map.org/pages/viewpage.action?pageId=5308449.
ABI (2017e). Allen brain institute allen brain atlas software development kit. http://alleninstitute.github.io/AllenSDK/.
ABI (2017f). Allen brain institute software development kit ephys code webpage. http://alleninstitute.github.io/AllenSDK/allensdk.ephys.html.
ABI (2017g). Allen brain institute SDK ephys features. http://help.brain-map.org/display/celltypes/API#API-ephys_features.
Antolík, J., & Davison, A.P. (2013). Integrated workflows for spiking neuronal network simulations. Frontiers in Neuroinformatics, 7(34), 1–15.
Autism Brain Imaging Data Exchange (2017). Autism brain imaging data exchange I – ABIDE I. http://fcon_1000.projects.nitrc.org/indi/abide/abide_I.html.
Baek, K., Shim, W.H., Jeong, J., Radhakrishnan, H., Rosen, B.R., Boas, D., Franceschini, M., Biswal, B.B., & Kim, Y.R. (2016). Layer-specific interhemispheric functional connectivity in the somatosensory cortex of rats: resting state electrophysiology and fMRI studies. Brain Structure and Function, 221(5), 2801–2815.
Bargmann, C., Newsome, W., Anderson, A., Brown, E., Deisseroth, K., Donoghue, J., MacLeish, P., Marder, E., Normann, R., Sanes, J., & et al (2014). Brain 2025: a scientific vision. Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Working Group Report to the Advisory Committee to the Director, NIH. https://www.braininitiative.nih.gov/2025/.
Chacon, S. (2014). Pro Git 2. Apress 2nd edn.
Davison, A. (2012). Automated capture of experiment context for easier reproducibility in computational research. Computing in Science & Engineering, 14(4), 48–56.
Davison, A.P., Hines, M.L., & Muller, E. (2009). Trends in programming languages for neuroscience simulations. Frontiers in Neuroscience, 3(3), 374–380. doi:10.3389/neuro.01.036.2009.
Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9–21. ISSN 0165-0270. doi:10.1016/j.jneumeth.2003.10.009.
Englitz, B., Sorenson, M.D., & Shamma, S.A. (2013). MANTA — an open-source, high density electrophysiology recording suite for MATLAB. Frontiers in Neural Circuits, 7, 69. doi:10.3389/fncir.2013.00069, http://journal.frontiersin.org/article/10.3389/fncir.2013.00069/full.
Felice, C.J., Albarracín, A.L., Farfán, F.D., Coletti, M.A., & Teruya, P.Y. (2016). Electrophysiology for biomedical engineering students. Advances in Physiology Education, 40, 402– 409.
Folk, M., Heber, G., Koziol, Q., Pourmal, E., & Robinson, D. (2011). An overview of the HDF5 technology suite and its applications. In Proceedings of the EDBT/ICDT 2011 Workshop on Array Databases, AD ’11. ISBN 978-1-4503-0614-0. doi:10.1145/1966895.1966900 (pp. 36–47). New York, NY, USA: ACM.
Fox, P., & Laird, A. (2017). BrainMap Website. http://brainmap.org/.
George Mason University (2017). NeuroMorpho.Org. http://neuromorpho.org/index.jsp.
Gleeson, P., Steuber, V., & Silver, R.A. (2007). neuroConstruct: A Tool for modeling networks of neurons in 3d space. Neuron, 54(2), 219–235.
Grillner, S., Ip, N., Koch, C., Koroshetz, W., Okano, H., Polachek, M., Poo, M.-m, & Sejnowski, T.J. (2016). Worldwide initiatives to advance brain research. Nature Neuroscience, 19(9), 1118– 1122.
Günay, C. (2007). PANDORA Neural Analysis Toolbox. https://senselab.med.yale.edu/simtooldb/.
Günay, C. (2012). Plotting and analysis for neural database-oriented research applications (PANDORA) toolbox — User’s and Programmer’s Manual Rev 1293. https://senselab.med.yale.edu/SimToolDB/showTool.cshtml?tool=112112&file=%5cpandora-1:3b%5cdoc%5cprog-manual:pdf.
Günay, C., Edgerton, J.R., Li, S., Sangrey, T., Prinz, A.A., & Jaeger, D. (2009). Database analysis of simulated and recorded electrophysiological datasets with PANDORA’s toolbox. Neuroinformatics, 7(2), 93–111. doi:10.1007/s12021-009-9048-z.
Hines, M.L., Davison, A.P., & Muller, E. (2009). NEURON and Python. Frontiers in Neuroinformatics, 3, 1. doi:10.3389/neuro.11.001.2009.
International Neuroinformatics Coordinating Facility (2017). INCF Website. https://www.incf.org/.
ISO (2017). ISO/IEC 9075-X:2016 SQL standards. https://www.iso.org/advanced-search/x/title/status/P/docNumber/9075/docPartNo/docType/10/langCode/en/ics/currentStage/true/stage/stageDateStart/stageDateEnd/committee.
Lawhern, V., Hairston, W.D., & Robbins, K. (2013). DETECT: A MATLAB toolbox for event detection and identification in time series, with applications to artifact detection in EEG signals. PLOS ONE, 8(4), 1–13.
Lytton, W.W. (2006). Neural query system. Neuroinformatics, 4(2), 163–175.
Mathworks (2017). MATLAB HDF5 files webpage. https://www.mathworks.com/help/matlab/hdf5-files.html.
MathWorks (2017). MATLAB database toolbox. https://www.mathworks.com/products/database.html.
Mattioni, M., Cohen, U., & Le Novere, N. (2012). Neuronvisio: a graphical user interface with 3d capabilities for neuron. Frontiers in Neuroinformatics, 6(20). ISSN 1662-5196. doi:10.3389/fninf.2012.00020. http://www.frontiersin.org/neuroinformatics/10.3389/fninf.2012.00020/abstract.
McDougal, R.A. , Morse, T.M. , Carnevale, T., Marenco, L., Wang, R., Migliore, M., Miller, P.L., Shepherd, G.M. , & Hines, M.L. (2017). Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience. Journal of Computational Neuroscience, 42(1), 1–10. ISSN 1573-6873. doi:10.1007/s10827-016-0623-7.
Miyasho, T., Takagi, H., Suzuki, H., Watanabe, S., Inoue, M., Kudo, Y., & Miyakawa, H. (2001). Low-threshold potassium channels and a low-threshold calcium channel regulate Ca2+ spike firing in the dendrites of cerebellar Purkinje neurons: a modeling study. Brain Research, 891(1–2), 106–115.
Muller, E., Bednar, J.A., Diesmann, M., Gewaltig, M.-O., Hines, M., & Davison, A.P. (2015). Python in neuroscience. Frontiers in Neuroinformatics, 9, 11.
MySQL (2017a). MySQL website. https://www.mysql.com/.
MySQL (2017b). MySQL Connector/Python Developer Guide. https://dev.mysql.com/doc/connector-python/en/.
MySQL (2017c). MySQL Workbench. https://www.mysql.com/products/workbench/.
NeurodataWithoutBorders (2016). NWB file format specification version 1.0.3. https://github.com/NeurodataWithoutBorders/specification.
NSG (2017a). Neuroscience gateway website. https://www.nsgportal.org/.
NSG (2017b). NSG REST Api (NSG-R) website. https://www.nsgportal.org/guide.html.
NWB-CN Project (2015). Neurodata without borders — computational neuroscience project. http://crcns.org/NWB.
Schrouff, J., Rosa, M.J., Rondina, J.M., Marquand, A.F., Chu, C., Ashburner, J., Phillips, C., Richiardi, J., & Mourão-miranda, J. (2013). PRoNTo Pattern recognition for neuroimaging toolbox. Neuroinformatics, 11(3), 319–337. doi:10.1007/s12021-013-9178-1.
SenseLab (2017). ModelDB Website. https://senselab.med.yale.edu/ModelDB/default.cshtml.
Shamlo, N., Mullen, T., Kothe, C., Su, K.M., & Robbins, K.A. (2015). The PREP Pipeline: Standardized preprocessing for large-scale EEG analysis. Frontiers in Neuroinformatics, 9(16), 1662–5196. ISSN 10.3389/fninf.2015.00016, http://www.frontiersin.org/neuroinformatics/10.3389/fninf.2015.00016/abstract.
Stockton, D., & Santamaria, F. (2017). NeuroManager Website. https://github.com/SantamariaLab/NeuroManager.
Stockton, D.B., & Santamaria, F. (2016). Automating NEURON simulation deployment in cloud resources. Neuroinformatics. ISSN 1559-0089. doi:10.1007/s12021-016-9315-8.
Stockton, D.B., & Santamaria, F. (2015). NeuroManager: A workflow analysis based simulation management engine for computational neuroscience. Frontiers in Neuroinformatics, 9(24). ISSN 1662-5196. doi:10.3389/fninf.2015.00024, http://www.frontiersin.org/neuroinformatics/10.3389/fninf.2015.00024/abstract.
Teka, W., Marinov, T.M., & Santamariam, F. (2014). Neuronal spike timing adaptation described with a fractional leaky integrate-and-fire model. PLos Computational Biology, 10 (3), e1003526. doi:10.1371/journal.pcbi.1003526.
Teka, W., Stockton, D.B., & Santamaria, F. (2016). Power-law dynamics of membrane conductances increase spiking diversity in a Hodgkin–Huxley model. PLos Computational Biology, 12(3). 1–23. doi:10.1371/journal.pcbi.1004776.
Tripathy, S.J., & Gerkin, R.C. (2015). NeuroElectro Project, (pp. 1915–1916). New York, NY: Springer New York. ISBN 978-1-4614-6675-8. doi:10.1007/978-1-4614-6675-8_477.
Tripathy, S.J., Savitskaya, J., Burton, S.D., Urban, N.N., & Gerkin, R.C. (2014). Neuroelectro: a window to the world’s neuron electrophysiology data. Frontiers in neuroinformatics, 8, 1–11.
Van Geit, W., Gevaert, M., Chindemi, G., Rössert, C., Courcol, J.-D., Muller, E.B., Schürmann, F., Segev, I., & Henry, M. (2016). BluePyOpt: Leveraging Open source software and cloud infrastructure to optimise model parameters in neuroscience. Frontiers in Neuroinformatics, 10.
Vidaurre, C., Sander, T.H., & Schlögl, A. (2011). BioSig: the free and open source software library for biomedical signal processing. Computational Intelligence and Neuroscience.
Acknowledgements
National Science Foundation (NSF-DBI1451032), National Institutes of Health (NIH-G12MD007591) (for use of computational facilities at UTSA).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interests
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Rights and permissions
About this article
Cite this article
Stockton, D., Santamaria, F. Integrating the Allen Brain Institute Cell Types Database into Automated Neuroscience Workflow. Neuroinform 15, 333–342 (2017). https://doi.org/10.1007/s12021-017-9337-x
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12021-017-9337-x