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Rimehaug AE, Dale AM, Arkhipov A, Einevoll GT. Uncovering population contributions to the extracellular potential in the mouse visual system using Laminar Population Analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.15.575805. [PMID: 38293236 PMCID: PMC10827114 DOI: 10.1101/2024.01.15.575805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
The local field potential (LFP), the low-frequency part of the extracellular potential, reflects transmembrane currents in the vicinity of the recording electrode. Thought mainly to stem from currents caused by synaptic input, it provides information about neural activity complementary to that of spikes, the output of neurons. However, the many neural sources contributing to the LFP, and likewise the derived current source density (CSD), can often make it challenging to interpret. Efforts to improve its interpretability have included the application of statistical decomposition tools like principal component analysis (PCA) and independent component analysis (ICA) to disentangle the contributions from different neural sources. However, their underlying assumptions of, respectively, orthogonality and statistical independence are not always valid for the various processes or pathways generating LFP. Here, we expand upon and validate a decomposition algorithm named Laminar Population Analysis (LPA), which is based on physiological rather than statistical assumptions. LPA utilizes the multiunit activity (MUA) and LFP jointly to uncover the contributions of different populations to the LFP. To perform the validation of LPA, we used data simulated with the large-scale, biophysically detailed model of mouse V1 developed by the Allen Institute. We find that LPA can identify laminar positions within V1 and the temporal profiles of laminar population firing rates from the MUA. We also find that LPA can estimate the salient current sinks and sources generated by feedforward input from the lateral geniculate nucleus (LGN), recurrent activity in V1, and feedback input from the lateromedial (LM) area of visual cortex. LPA identifies and distinguishes these contributions with a greater accuracy than the alternative statistical decomposition methods, PCA and ICA. Lastly, we also demonstrate the application of LPA on experimentally recorded MUA and LFP from 24 animals in the publicly available Visual Coding dataset. Our results suggest that LPA can be used both as a method to estimate positions of laminar populations and to uncover salient features in LFP/CSD contributions from different populations.
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Affiliation(s)
| | - Anders M. Dale
- Department of Neuroscience, University of California San Diego, San Diego, California, USA
| | | | - Gaute T. Einevoll
- Department of Physics, Norwegian University of Life Sciences, Ås, Norway
- Department of Physics, University of Oslo, Oslo, Norway
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2
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Abe T, Kinsella I, Saxena S, Buchanan EK, Couto J, Briggs J, Kitt SL, Glassman R, Zhou J, Paninski L, Cunningham JP. Neuroscience Cloud Analysis As a Service: An open-source platform for scalable, reproducible data analysis. Neuron 2022; 110:2771-2789.e7. [PMID: 35870448 PMCID: PMC9464703 DOI: 10.1016/j.neuron.2022.06.018] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 05/06/2022] [Accepted: 06/22/2022] [Indexed: 10/17/2022]
Abstract
A key aspect of neuroscience research is the development of powerful, general-purpose data analyses that process large datasets. Unfortunately, modern data analyses have a hidden dependence upon complex computing infrastructure (e.g., software and hardware), which acts as an unaddressed deterrent to analysis users. Although existing analyses are increasingly shared as open-source software, the infrastructure and knowledge needed to deploy these analyses efficiently still pose significant barriers to use. In this work, we develop Neuroscience Cloud Analysis As a Service (NeuroCAAS): a fully automated open-source analysis platform offering automatic infrastructure reproducibility for any data analysis. We show how NeuroCAAS supports the design of simpler, more powerful data analyses and that many popular data analysis tools offered through NeuroCAAS outperform counterparts on typical infrastructure. Pairing rigorous infrastructure management with cloud resources, NeuroCAAS dramatically accelerates the dissemination and use of new data analyses for neuroscientific discovery.
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Affiliation(s)
- Taiga Abe
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Department of Neuroscience, Columbia University Medical Center, Columbia University, New York, NY 10027, USA
| | - Ian Kinsella
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Department of Statistics, Columbia University, New York, NY 10027, USA
| | - Shreya Saxena
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA; Department of Statistics, Columbia University, New York, NY 10027, USA; Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32607, USA
| | - E Kelly Buchanan
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Department of Neuroscience, Columbia University Medical Center, Columbia University, New York, NY 10027, USA
| | - Joao Couto
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - John Briggs
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA
| | - Sian Lee Kitt
- Department of Computer Science, Columbia University, New York, NY 10027, USA
| | - Ryan Glassman
- Department of Computer Science, Columbia University, New York, NY 10027, USA
| | - John Zhou
- Department of Computer Science, Columbia University, New York, NY 10027, USA
| | - Liam Paninski
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA; Department of Neuroscience, Columbia University Medical Center, Columbia University, New York, NY 10027, USA; Department of Statistics, Columbia University, New York, NY 10027, USA
| | - John P Cunningham
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA; Department of Statistics, Columbia University, New York, NY 10027, USA.
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3
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Linking Brain Structure, Activity, and Cognitive Function through Computation. eNeuro 2022; 9:ENEURO.0316-21.2022. [PMID: 35217544 PMCID: PMC8925650 DOI: 10.1523/eneuro.0316-21.2022] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 01/11/2022] [Accepted: 01/17/2022] [Indexed: 01/19/2023] Open
Abstract
Understanding the human brain is a “Grand Challenge” for 21st century research. Computational approaches enable large and complex datasets to be addressed efficiently, supported by artificial neural networks, modeling and simulation. Dynamic generative multiscale models, which enable the investigation of causation across scales and are guided by principles and theories of brain function, are instrumental for linking brain structure and function. An example of a resource enabling such an integrated approach to neuroscientific discovery is the BigBrain, which spatially anchors tissue models and data across different scales and ensures that multiscale models are supported by the data, making the bridge to both basic neuroscience and medicine. Research at the intersection of neuroscience, computing and robotics has the potential to advance neuro-inspired technologies by taking advantage of a growing body of insights into perception, plasticity and learning. To render data, tools and methods, theories, basic principles and concepts interoperable, the Human Brain Project (HBP) has launched EBRAINS, a digital neuroscience research infrastructure, which brings together a transdisciplinary community of researchers united by the quest to understand the brain, with fascinating insights and perspectives for societal benefits.
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4
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Hider R, Kleissas D, Gion T, Xenes D, Matelsky J, Pryor D, Rodriguez L, Johnson EC, Gray-Roncal W, Wester B. The Brain Observatory Storage Service and Database (BossDB): A Cloud-Native Approach for Petascale Neuroscience Discovery. Front Neuroinform 2022; 16:828787. [PMID: 35242021 PMCID: PMC8885591 DOI: 10.3389/fninf.2022.828787] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Accepted: 01/10/2022] [Indexed: 12/04/2022] Open
Abstract
Technological advances in imaging and data acquisition are leading to the development of petabyte-scale neuroscience image datasets. These large-scale volumetric datasets pose unique challenges since analyses often span the entire volume, requiring a unified platform to access it. In this paper, we describe the Brain Observatory Storage Service and Database (BossDB), a cloud-based solution for storing and accessing petascale image datasets. BossDB provides support for data ingest, storage, visualization, and sharing through a RESTful Application Programming Interface (API). A key feature is the scalable indexing of spatial data and automatic and manual annotations to facilitate data discovery. Our project is open source and can be easily and cost effectively used for a variety of modalities and applications, and has effectively worked with datasets over a petabyte in size.
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WeBrain: A web-based brainformatics platform of computational ecosystem for EEG big data analysis. Neuroimage 2021; 245:118713. [PMID: 34798231 DOI: 10.1016/j.neuroimage.2021.118713] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 10/25/2021] [Accepted: 11/04/2021] [Indexed: 01/06/2023] Open
Abstract
The current evolution of 'cloud neuroscience' leads to more efforts with the large-scale EEG applications, by using EEG pipelines to handle the rapidly accumulating EEG data. However, there are a few specific cloud platforms that seek to address the cloud computational challenges of EEG big data analysis to benefit the EEG community. In response to the challenges, a WeBrain cloud platform (https://webrain.uestc.edu.cn/) is designed as a web-based brainformatics platform and computational ecosystem to enable large-scale EEG data storage, exploration and analysis using cloud high-performance computing (HPC) facilities. WeBrain connects researchers from different fields to EEG and multimodal tools that have become the norm in the field and the cloud processing power required to handle those large EEG datasets. This platform provides an easy-to-use system for novice users (even no computer programming skills) and provides satisfactory maintainability, sustainability and flexibility for IT administrators and tool developers. A range of resources are also available on https://webrain.uestc.edu.cn/, including documents, manuals, example datasets related to WeBrain, and collected links to open EEG datasets and tools. It is not necessary for users or administrators to install any software or system, and all that is needed is a modern web browser, which reduces the technical expertise required to use or manage WeBrain. The WeBrain platform is sponsored and driven by the China-Canada-Cuba international brain cooperation project (CCC-Axis, http://ccc-axis.org/), and we hope that WeBrain will be a promising cloud brainformatics platform for exploring brain information in large-scale EEG applications in the EEG community.
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Abstract
Brain scientists are now capable of collecting more data in a single experiment than researchers a generation ago might have collected over an entire career. Indeed, the brain itself seems to thirst for more and more data. Such digital information not only comprises individual studies but is also increasingly shared and made openly available for secondary, confirmatory, and/or combined analyses. Numerous web resources now exist containing data across spatiotemporal scales. Data processing workflow technologies running via cloud-enabled computing infrastructures allow for large-scale processing. Such a move toward greater openness is fundamentally changing how brain science results are communicated and linked to available raw data and processed results. Ethical, professional, and motivational issues challenge the whole-scale commitment to data-driven neuroscience. Nevertheless, fueled by government investments into primary brain data collection coupled with increased sharing and community pressure challenging the dominant publishing model, large-scale brain and data science is here to stay.
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Affiliation(s)
- John Darrell Van Horn
- Department of Psychology, University of Virginia, Charlottesville, Virginia, USA
- School of Data Science, University of Virginia, Charlottesville, Virginia, USA
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7
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Constructing the rodent stereotaxic brain atlas: a survey. SCIENCE CHINA-LIFE SCIENCES 2021; 65:93-106. [PMID: 33860452 DOI: 10.1007/s11427-020-1911-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 02/03/2021] [Indexed: 12/22/2022]
Abstract
The stereotaxic brain atlas is a fundamental reference tool commonly used in the field of neuroscience. Here we provide a brief history of brain atlas development and clarify three key conceptual elements of stereotaxic brain atlasing: brain image, atlas, and stereotaxis. We also refine four technical indices for evaluating the construction of atlases: the quality of staining and labeling, the granularity of delineation, spatial resolution, and the precision of spatial location and orientation. Additionally, we discuss state-of-the-art technologies and their trends in the fields of image acquisition, stereotaxic coordinate construction, image processing, anatomical structure recognition, and publishing: the procedures of brain atlas illustration. We believe that the use of single-cell resolution and micron-level location precision will become a future trend in the study of the stereotaxic brain atlas, which will greatly benefit the development of neuroscience.
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Lei B, Wu F, Zhou J, Xiong D, Wang K, Kong L, Ke P, Chen J, Ning Y, Li X, Xiang Z, Wu K. NEURO-LEARN: a Solution for Collaborative Pattern Analysis of Neuroimaging Data. Neuroinformatics 2021; 19:79-91. [PMID: 32524429 DOI: 10.1007/s12021-020-09468-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The development of neuroimaging instrumentation has boosted neuroscience researches. Consequently, both the fineness and the cost of data acquisition have profoundly increased, leading to the main bottleneck of this field: limited sample size and high dimensionality of neuroimaging data. Therefore, the emphasis of ideas of data pooling and research collaboration has increased over the past decade. Collaborative analysis techniques emerge as the idea developed. In this paper, we present NEURO-LEARN, a solution for collaborative pattern analysis of neuroimaging data. Its collaboration scheme consists of four parts: projects, data, analysis, and reports. While data preparation workflows defined in projects reduce the high dimensionality of neuroimaging data by collaborative computation, pooling of derived data and sharing of pattern analysis workflows along with generated reports on the Web enlarge the sample size and ensure the reliability and reproducibility of pattern analysis. Incorporating this scheme, NEURO-LEARN provides an easy-to-use Web application that allows users from different sites to share projects and processed data, perform pattern analysis, and obtain result reports. We anticipate that this solution will help neuroscientists to enlarge sample size, conquer the curse of dimensionality and conduct reproducible studies on neuroimaging data with efficiency and validity.
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Affiliation(s)
- Bingye Lei
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, 510006, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, 510370, China
- National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, 510006, China
| | - Fengchun Wu
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, 510370, China
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, 510370, China
| | - Jing Zhou
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, 510006, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, 510370, China
| | - Dongsheng Xiong
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, 510006, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, 510370, China
| | - Kaixi Wang
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Lingyin Kong
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Pengfei Ke
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Jun Chen
- Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, 510500, China
- National Engineering Research Center for Healthcare Devices, Guangzhou, 510500, China
| | - Yuping Ning
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, 510370, China
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, 510370, China
| | - Xiaobo Li
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, 510370, China
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | - Zhiming Xiang
- Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, 510500, China
- Department of Radiology, Panyu Central Hospital of Guangzhou, Guangzhou, 511400, China
| | - Kai Wu
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, 510006, China.
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, 510370, China.
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, 510370, China.
- Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, 510500, China.
- National Engineering Research Center for Healthcare Devices, Guangzhou, 510500, China.
- National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, 510006, China.
- Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai, 980-8575, Japan.
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9
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Du M, Di ZW, Gürsoy D, Xian RP, Kozorovitskiy Y, Jacobsen C. Upscaling X-ray nanoimaging to macroscopic specimens. J Appl Crystallogr 2021; 54:386-401. [PMID: 33953650 PMCID: PMC8056767 DOI: 10.1107/s1600576721000194] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 01/06/2021] [Indexed: 11/10/2022] Open
Abstract
Upscaling X-ray nanoimaging to macroscopic specimens has the potential for providing insights across multiple length scales, but its feasibility has long been an open question. By combining the imaging requirements and existing proof-of-principle examples in large-specimen preparation, data acquisition and reconstruction algorithms, the authors provide imaging time estimates for howX-ray nanoimaging can be scaled to macroscopic specimens. To arrive at this estimate, a phase contrast imaging model that includes plural scattering effects is used to calculate the required exposure and corresponding radiation dose. The coherent X-ray flux anticipated from upcoming diffraction-limited light sources is then considered. This imaging time estimation is in particular applied to the case of the connectomes of whole mouse brains. To image the connectome of the whole mouse brain, electron microscopy connectomics might require years, whereas optimized X-ray microscopy connectomics could reduce this to one week. Furthermore, this analysis points to challenges that need to be overcome (such as increased X-ray detector frame rate) and opportunities that advances in artificial-intelligence-based 'smart' scanning might provide. While the technical advances required are daunting, it is shown that X-ray microscopy is indeed potentially applicable to nanoimaging of millimetre- or even centimetre-size specimens.
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Affiliation(s)
- Ming Du
- Advanced Photon Source, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Zichao Wendy Di
- Advanced Photon Source, Argonne National Laboratory, Argonne, IL 60439, USA.,Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Doǧa Gürsoy
- Advanced Photon Source, Argonne National Laboratory, Argonne, IL 60439, USA.,Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL 60208, USA
| | - R Patrick Xian
- Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA
| | - Yevgenia Kozorovitskiy
- Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA.,Chemistry of Life Processes Institute, Northwestern University, Evanston, IL 60208, USA
| | - Chris Jacobsen
- Advanced Photon Source, Argonne National Laboratory, Argonne, IL 60439, USA.,Chemistry of Life Processes Institute, Northwestern University, Evanston, IL 60208, USA.,Department of Physics and Astronomy, Northwestern University, Evanston, IL 60208, USA
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10
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Dai K, Gratiy SL, Billeh YN, Xu R, Cai B, Cain N, Rimehaug AE, Stasik AJ, Einevoll GT, Mihalas S, Koch C, Arkhipov A. Brain Modeling ToolKit: An open source software suite for multiscale modeling of brain circuits. PLoS Comput Biol 2020; 16:e1008386. [PMID: 33253147 PMCID: PMC7728187 DOI: 10.1371/journal.pcbi.1008386] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 12/10/2020] [Accepted: 09/16/2020] [Indexed: 11/26/2022] Open
Abstract
Experimental studies in neuroscience are producing data at a rapidly increasing rate, providing exciting opportunities and formidable challenges to existing theoretical and modeling approaches. To turn massive datasets into predictive quantitative frameworks, the field needs software solutions for systematic integration of data into realistic, multiscale models. Here we describe the Brain Modeling ToolKit (BMTK), a software suite for building models and performing simulations at multiple levels of resolution, from biophysically detailed multi-compartmental, to point-neuron, to population-statistical approaches. Leveraging the SONATA file format and existing software such as NEURON, NEST, and others, BMTK offers a consistent user experience across multiple levels of resolution. It permits highly sophisticated simulations to be set up with little coding required, thus lowering entry barriers to new users. We illustrate successful applications of BMTK to large-scale simulations of a cortical area. BMTK is an open-source package provided as a resource supporting modeling-based discovery in the community.
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Affiliation(s)
- Kael Dai
- Allen Institute, Seattle, Washington, United States of America
| | | | - Yazan N. Billeh
- Allen Institute, Seattle, Washington, United States of America
| | - Richard Xu
- Allen Institute, Seattle, Washington, United States of America
| | - Binghuang Cai
- Allen Institute, Seattle, Washington, United States of America
| | - Nicholas Cain
- Allen Institute, Seattle, Washington, United States of America
| | - Atle E. Rimehaug
- Norwegian University of Life Sciences & University of Oslo, Oslo, Norway
| | | | - Gaute T. Einevoll
- Norwegian University of Life Sciences & University of Oslo, Oslo, Norway
| | - Stefan Mihalas
- Allen Institute, Seattle, Washington, United States of America
| | - Christof Koch
- Allen Institute, Seattle, Washington, United States of America
| | - Anton Arkhipov
- Allen Institute, Seattle, Washington, United States of America
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11
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Ježek P, Teeters JL, Sommer FT. NWB Query Engines: Tools to Search Data Stored in Neurodata Without Borders Format. Front Neuroinform 2020; 14:27. [PMID: 33041776 PMCID: PMC7526650 DOI: 10.3389/fninf.2020.00027] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Accepted: 05/20/2020] [Indexed: 11/19/2022] Open
Abstract
The Neurodata Without Borders (abbreviation NWB) format is a current technology for storing neurophysiology data along with the associated metadata. Data stored in the format is organized into separate HDF5 files, each file usually storing the data associated with a single recording session. While the NWB format provides a structured method for storing data, so far there have not been tools which enable searching a collection of NWB files in order to find data of interest for a particular purpose. We describe here three tools to enable searching NWB files. The tools have different features making each of them most useful for a particular task. The first tool, called the NWB Query Engine, is written in Java. It allows searching the complete content of NWB files. It was designed for the first version of NWB (NWB 1) and supports most (but not all) features of the most recent version (NWB 2). For some searches, it is the fastest tool. The second tool, called “search_nwb” is written in Python and also allow searching the complete contents of NWB files. It works with both NWB 1 and NWB 2, as does the third tool. The third tool, called “nwbindexer” enables searching a collection of NWB files using a two-step process. In the first step, a utility is run which creates an SQLite database containing the metadata in a collection of NWB files. This database is then searched in the second step, using another utility. Once the index is built, this two-step processes allows faster searches than are done by the other tools, but does not enable as complete of searches. All three tools use a simple query language which was developed for this project. Software integrating the three tools into a web-interface is provided which enables searching NWB files by submitting a web form.
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Affiliation(s)
- Petr Ježek
- Faculty of Applied Sciences, New Technologies for the Information Society, University of West Bohemia, Plzeň, Czechia
| | - Jeffery L Teeters
- Redwood Center for Theoretical Neuroscience & Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
| | - Friedrich T Sommer
- Redwood Center for Theoretical Neuroscience & Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
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12
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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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13
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Dai K, Hernando J, Billeh YN, Gratiy SL, Planas J, Davison AP, Dura-Bernal S, Gleeson P, Devresse A, Dichter BK, Gevaert M, King JG, Van Geit WAH, Povolotsky AV, Muller E, Courcol JD, Arkhipov A. The SONATA data format for efficient description of large-scale network models. PLoS Comput Biol 2020; 16:e1007696. [PMID: 32092054 PMCID: PMC7058350 DOI: 10.1371/journal.pcbi.1007696] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 03/05/2020] [Accepted: 01/28/2020] [Indexed: 12/04/2022] Open
Abstract
Increasing availability of comprehensive experimental datasets and of high-performance computing resources are driving rapid growth in scale, complexity, and biological realism of computational models in neuroscience. To support construction and simulation, as well as sharing of such large-scale models, a broadly applicable, flexible, and high-performance data format is necessary. To address this need, we have developed the Scalable Open Network Architecture TemplAte (SONATA) data format. It is designed for memory and computational efficiency and works across multiple platforms. The format represents neuronal circuits and simulation inputs and outputs via standardized files and provides much flexibility for adding new conventions or extensions. SONATA is used in multiple modeling and visualization tools, and we also provide reference Application Programming Interfaces and model examples to catalyze further adoption. SONATA format is free and open for the community to use and build upon with the goal of enabling efficient model building, sharing, and reproducibility. Neuroscience is experiencing a rapid growth of data streams characterizing composition, connectivity, and activity of brain networks in ever increasing details. Data-driven modeling will be essential to integrate these multimodal and complex data into predictive simulations to advance our understanding of brain function and mechanisms. To enable efficient development and sharing of such large-scale models utilizing diverse data types, we have developed the Scalable Open Network Architecture TemplAte (SONATA) data format. The format represents neuronal circuits and simulation inputs and outputs via standardized files and provides much flexibility for adding new conventions or extensions. SONATA is already supported by several popular tools for model building, simulations, and visualization. It is free and open for everyone to use and build upon and will enable increased efficiency, reproducibility, and scientific exchange in the community.
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Affiliation(s)
- Kael Dai
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Juan Hernando
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Yazan N. Billeh
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Sergey L. Gratiy
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Judit Planas
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Andrew P. Davison
- Paris-Saclay Institute of Neuroscience UMR, Centre National de la Recherche Scientifique/Université Paris-Saclay, Gif-sur-Yvette, France
| | - Salvador Dura-Bernal
- State University of New York Downstate Medical Center, Brooklyn, New York, United States of America
- Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, United States of America
| | - Padraig Gleeson
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, United Kingdom
| | - Adrien Devresse
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Benjamin K. Dichter
- Department of Neurosurgery, Stanford University, Stanford, California, United States of America
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
| | - Michael Gevaert
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - James G. King
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Werner A. H. Van Geit
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Arseny V. Povolotsky
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Eilif Muller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Jean-Denis Courcol
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Anton Arkhipov
- Allen Institute for Brain Science, Seattle, Washington, United States of America
- * E-mail:
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14
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Oschwald J, Guye S, Liem F. Brain structure and cognitive ability in healthy aging: a review on longitudinal correlated change. Rev Neurosci 2019; 31:1-57. [PMID: 31194693 PMCID: PMC8572130 DOI: 10.1515/revneuro-2018-0096] [Citation(s) in RCA: 115] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 03/02/2019] [Indexed: 12/20/2022]
Abstract
Little is still known about the neuroanatomical substrates related to changes in specific cognitive abilities in the course of healthy aging, and the existing evidence is predominantly based on cross-sectional studies. However, to understand the intricate dynamics between developmental changes in brain structure and changes in cognitive ability, longitudinal studies are needed. In the present article, we review the current longitudinal evidence on correlated changes between magnetic resonance imaging-derived measures of brain structure (e.g. gray matter/white matter volume, cortical thickness), and laboratory-based measures of fluid cognitive ability (e.g. intelligence, memory, processing speed) in healthy older adults. To theoretically embed the discussion, we refer to the revised Scaffolding Theory of Aging and Cognition. We found 31 eligible articles, with sample sizes ranging from n = 25 to n = 731 (median n = 104), and participant age ranging from 19 to 103. Several of these studies report positive correlated changes for specific regions and specific cognitive abilities (e.g. between structures of the medial temporal lobe and episodic memory). However, the number of studies presenting converging evidence is small, and the large methodological variability between studies precludes general conclusions. Methodological and theoretical limitations are discussed. Clearly, more empirical evidence is needed to advance the field. Therefore, we provide guidance for future researchers by presenting ideas to stimulate theory and methods for development.
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Affiliation(s)
- Jessica Oschwald
- University Research Priority Program ‘Dynamics of Healthy Aging’, University of Zurich, Andreasstrasse 15, CH-8050 Zurich, Switzerland
| | - Sabrina Guye
- University Research Priority Program ‘Dynamics of Healthy Aging’, University of Zurich, Andreasstrasse 15, CH-8050 Zurich, Switzerland
| | - Franziskus Liem
- University Research Priority Program ‘Dynamics of Healthy Aging’, University of Zurich, Andreasstrasse 15, CH-8050 Zurich, Switzerland
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15
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Kepecs A. Summary: Order and Disorder in Brains and Behavior. COLD SPRING HARBOR SYMPOSIA ON QUANTITATIVE BIOLOGY 2019; 83:219-225. [PMID: 31358660 DOI: 10.1101/sqb.2018.83.038885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
The 83rd Cold Spring Harbor Symposium on Quantitative Biology on Brains and Behavior: Order and Disorder in the Nervous System explored the tremendous recent progress in neuroscience and how these advances may be used to improve brain health and address psychiatric and neurological disorders. The Symposium explored a vast array of topics from cell types to cognition. My summary focuses on a few emerging themes. Innovative techniques were ever-present, opening up new experimental possibilities. The commoditization of many state-of-the-art technologies is pushing neuroscience beyond its artisanal ways. Another important theme was "circuits in the middle": Numerous presentations dissected cell type-specific circuits that connect different levels of analysis from molecules to behavior. These new technologies have enabled curiosity-driven investigations in animals to connect more directly with preclinical and clinical studies of human brain disorders. Numerous emerging approaches were presented in human neuroscience, bolstering the hope that circuit-specific manipulations will soon provide improved treatments for brain disorders.
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Affiliation(s)
- Adam Kepecs
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
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16
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Khalili-Mahani N, De Schutter B. Affective Game Planning for Health Applications: Quantitative Extension of Gerontoludic Design Based on the Appraisal Theory of Stress and Coping. JMIR Serious Games 2019; 7:e13303. [PMID: 31172966 PMCID: PMC6592517 DOI: 10.2196/13303] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Revised: 04/06/2019] [Accepted: 04/24/2019] [Indexed: 12/12/2022] Open
Abstract
User retention is the first challenge in introducing any information and communication technologies (ICT) for health applications, particularly for seniors who are increasingly targeted as beneficiaries of such technologies. Interaction with digital technologies may be too stressful to older adults to guarantee their adoption in their routine selfcare. The second challenge, which also relates to adoption, is to supply empirical evidence that support the expectations of their beneficial outcomes. To address the first challenge, persuasive technologies such as serious games (SGs) are increasingly promoted as ludic approaches to deliver assistive care to older adults. However, there are no standards yet to assess the efficacy of different genres of games across populations, or compare and contrast variations in health outcomes arising from user interface design and user experience. For the past 3 decades, research has focused either on qualitative assessment of the appeal of digital games for seniors (by game designers) or on the quantitative evaluation of their clinical efficacy (by clinical researchers). The consensus is that interindividual differences play a key role in whether games can be useful or not for different individuals. Our challenge is to design SGs that retain their users long enough to sustain beneficial transfer effects. We propose to add a neuropsychological experimental framework (based on the appraisal theory of stress and coping) to a Gerontoludic design framework (that emphasizes designing positive and meaningful gaming experience over benefit-centric ones) in order to capture data to guide SG game development. Affective Game Planning for Health Applications (AGPHA) adds a model-driven mixed-methods experimental stage to a user-centered mechanics-dynamics-aesthetics game-design cycle. This intersectoral framework is inspired by latest trends in the fields of neuroimaging and neuroinformatics that grapple with similar challenges related to the psychobiological context of an individual's behaviors. AGPHA aims to bring users, designers, clinicians, and researchers together to generate a common data repository that consists of 4 components to define, design, evaluate, and document SGs. By unifying efforts under a standard approach, we will accelerate innovations in persuasive and efficacious ICTs for the aging population.
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Affiliation(s)
- Najmeh Khalili-Mahani
- PERFORM Centre, Concordia University, Montreal, QC, Canada.,McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.,Department of Design and Computation Arts, Concordia University, Montreal, QC, Canada
| | - Bob De Schutter
- Armstrong Institute for Interactive Media Studies, Miami University, Oxford, OH, United States
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17
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Abstract
Few would argue that science is better done in silos, with no transparency or sharing of methods and resources. Yet scientists and scientific stakeholders (e.g., academic institutions, funding agencies, journals) alike continue to find themselves at a relative impasse in the implementation of open science practices, slowing advancement and inadvertently perpetuating ongoing crises surrounding reproducibility. The present commentary draws attention to critical gaps in the current scientific ecosystem that perpetuate closed science practices and divide the community on how to best move forward. It also challenges scientists as individuals to improve the quality of their science by incorporating open practices in their everyday work, and provides a starter list of steps that any researcher can take to be the change they seek.
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Affiliation(s)
- Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA. .,Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, New York, NY, USA.
| | - Arno Klein
- MATTER Lab, Child Mind Institute, New York, NY, USA
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18
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Coggan JS, Calì C, Keller D, Agus M, Boges D, Abdellah M, Kare K, Lehväslaiho H, Eilemann S, Jolivet RB, Hadwiger M, Markram H, Schürmann F, Magistretti PJ. A Process for Digitizing and Simulating Biologically Realistic Oligocellular Networks Demonstrated for the Neuro-Glio-Vascular Ensemble. Front Neurosci 2018; 12:664. [PMID: 30319342 PMCID: PMC6171468 DOI: 10.3389/fnins.2018.00664] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 09/04/2018] [Indexed: 01/01/2023] Open
Abstract
One will not understand the brain without an integrated exploration of structure and function, these attributes being two sides of the same coin: together they form the currency of biological computation. Accordingly, biologically realistic models require the re-creation of the architecture of the cellular components in which biochemical reactions are contained. We describe here a process of reconstructing a functional oligocellular assembly that is responsible for energy supply management in the brain and creating a computational model of the associated biochemical and biophysical processes. The reactions that underwrite thought are both constrained by and take advantage of brain morphologies pertaining to neurons, astrocytes and the blood vessels that deliver oxygen, glucose and other nutrients. Each component of this neuro-glio-vasculature ensemble (NGV) carries-out delegated tasks, as the dynamics of this system provide for each cell-type its own energy requirements while including mechanisms that allow cooperative energy transfers. Our process for recreating the ultrastructure of cellular components and modeling the reactions that describe energy flow uses an amalgam of state-of the-art techniques, including digital reconstructions of electron micrographs, advanced data analysis tools, computational simulations and in silico visualization software. While we demonstrate this process with the NGV, it is equally well adapted to any cellular system for integrating multimodal cellular data in a coherent framework.
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Affiliation(s)
- Jay S Coggan
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Corrado Calì
- Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Daniel Keller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Marco Agus
- Visual Computing Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.,CRS4, Center of Research and Advanced Studies in Sardinia, Visual Computing, Pula, Italy
| | - Daniya Boges
- Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Marwan Abdellah
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Kalpana Kare
- Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Heikki Lehväslaiho
- Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.,CSC - IT Center for Science, Espoo, Finland
| | - Stefan Eilemann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Renaud Blaise Jolivet
- Département de Physique Nucléaire et Corpusculaire, University of Geneva, Geneva, Switzerland.,The European Organization for Nuclear Research, Geneva, Switzerland
| | - Markus Hadwiger
- Visual Computing Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Felix Schürmann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Pierre J Magistretti
- Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
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19
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Abstract
Array tomography encompasses light and electron microscopy modalities that offer unparalleled opportunities to explore three-dimensional cellular architectures in extremely fine structural and molecular detail. Fluorescence array tomography achieves much higher resolution and molecular multiplexing than most other fluorescence microscopy methods, while electron array tomography can capture three-dimensional ultrastructure much more easily and rapidly than traditional serial-section electron microscopy methods. A correlative fluorescence/electron microscopy mode of array tomography furthermore offers a unique capacity to merge the molecular discrimination strengths of multichannel fluorescence microscopy with the ultrastructural imaging strengths of electron microscopy. This essay samples the first decade of array tomography, highlighting applications in neuroscience.
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20
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Sosa PV. T66. Reinstating electrophysiology into global brain projects via CBRAIN and LORIS. Clin Neurophysiol 2018. [DOI: 10.1016/j.clinph.2018.04.067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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21
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A Brain-Inspired Trust Management Model to Assure Security in a Cloud Based IoT Framework for Neuroscience Applications. Cognit Comput 2018. [DOI: 10.1007/s12559-018-9543-3] [Citation(s) in RCA: 103] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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22
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Madhyastha TM, Koh N, Day TKM, Hernández-Fernández M, Kelley A, Peterson DJ, Rajan S, Woelfer KA, Wolf J, Grabowski TJ. Running Neuroimaging Applications on Amazon Web Services: How, When, and at What Cost? Front Neuroinform 2017; 11:63. [PMID: 29163119 PMCID: PMC5675877 DOI: 10.3389/fninf.2017.00063] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Accepted: 10/18/2017] [Indexed: 01/07/2023] Open
Abstract
The contribution of this paper is to identify and describe current best practices for using Amazon Web Services (AWS) to execute neuroimaging workflows “in the cloud.” Neuroimaging offers a vast set of techniques by which to interrogate the structure and function of the living brain. However, many of the scientists for whom neuroimaging is an extremely important tool have limited training in parallel computation. At the same time, the field is experiencing a surge in computational demands, driven by a combination of data-sharing efforts, improvements in scanner technology that allow acquisition of images with higher image resolution, and by the desire to use statistical techniques that stress processing requirements. Most neuroimaging workflows can be executed as independent parallel jobs and are therefore excellent candidates for running on AWS, but the overhead of learning to do so and determining whether it is worth the cost can be prohibitive. In this paper we describe how to identify neuroimaging workloads that are appropriate for running on AWS, how to benchmark execution time, and how to estimate cost of running on AWS. By benchmarking common neuroimaging applications, we show that cloud computing can be a viable alternative to on-premises hardware. We present guidelines that neuroimaging labs can use to provide a cluster-on-demand type of service that should be familiar to users, and scripts to estimate cost and create such a cluster.
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Affiliation(s)
- Tara M Madhyastha
- Department of Radiology, University of Washington, Seattle, WA, United States
| | - Natalie Koh
- Department of Radiology, University of Washington, Seattle, WA, United States
| | - Trevor K M Day
- Department of Radiology, University of Washington, Seattle, WA, United States
| | - Moises Hernández-Fernández
- Centre for Functional Magnetic Resonance Imaging of the Brain, University of Oxford, Oxford, United Kingdom
| | - Austin Kelley
- Department of Radiology, University of Washington, Seattle, WA, United States
| | - Daniel J Peterson
- Department of Radiology, University of Washington, Seattle, WA, United States
| | - Sabreena Rajan
- Department of Radiology, University of Washington, Seattle, WA, United States
| | - Karl A Woelfer
- Department of Radiology, University of Washington, Seattle, WA, United States
| | - Jonathan Wolf
- Department of Radiology, University of Washington, Seattle, WA, United States
| | - Thomas J Grabowski
- Department of Radiology, University of Washington, Seattle, WA, United States.,Department of Neurology, University of Washington, Seattle, WA, United States
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23
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Abstract
Open scholarship, such as the sharing of articles, code, data, and educational resources, has the potential to improve university research and education as well as increase the impact universities can have beyond their own walls. To support this perspective, I present evidence from case studies, published literature, and personal experiences as a practicing open scholar. I describe some of the challenges inherent to practicing open scholarship and some of the tensions created by incompatibilities between institutional policies and personal practice. To address this, I propose several concrete actions universities could take to support open scholarship and outline ways in which such initiatives could benefit the public as well as institutions. Importantly, I do not think most of these actions would require new funding but rather a redistribution of existing funds and a rewriting of internal policies to better align with university missions of knowledge dissemination and societal impact.
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Affiliation(s)
- Erin C. McKiernan
- Departamento de Física, Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
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24
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Amunts K, Ebell C, Muller J, Telefont M, Knoll A, Lippert T. The Human Brain Project: Creating a European Research Infrastructure to Decode the Human Brain. Neuron 2017; 92:574-581. [PMID: 27809997 DOI: 10.1016/j.neuron.2016.10.046] [Citation(s) in RCA: 128] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Decoding the human brain is perhaps the most fascinating scientific challenge in the 21st century. The Human Brain Project (HBP), a 10-year European Flagship, targets the reconstruction of the brain's multi-scale organization. It uses productive loops of experiments, medical, data, data analytics, and simulation on all levels that will eventually bridge the scales. The HBP IT architecture is unique, utilizing cloud-based collaboration and development platforms with databases, workflow systems, petabyte storage, and supercomputers. The HBP is developing toward a European research infrastructure advancing brain research, medicine, and brain-inspired information technology.
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Affiliation(s)
- Katrin Amunts
- Institute for Neuroscience and Medicine, 52425 Forschungszentrum Jülich, Germany; C. and O. Vogt Institute for Brain Research, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany.
| | - Christoph Ebell
- Human Brain Project École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Batiment B1, Chemin des Mines 9, CH-1202 Geneva, Switzerland
| | - Jeff Muller
- Human Brain Project École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Batiment B1, Chemin des Mines 9, CH-1202 Geneva, Switzerland
| | - Martin Telefont
- Human Brain Project École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Batiment B1, Chemin des Mines 9, CH-1202 Geneva, Switzerland
| | - Alois Knoll
- Institut für Informatik VI, Technische Universität München, Boltzmannstraße 3, 85748 Garching bei München, Germany
| | - Thomas Lippert
- Jülich Supercomputing Centre, Institute for Advanced Simulation, 52425 Forschungszentrum Jülich, Germany
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25
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Madan CR. Advances in Studying Brain Morphology: The Benefits of Open-Access Data. Front Hum Neurosci 2017; 11:405. [PMID: 28824407 PMCID: PMC5543094 DOI: 10.3389/fnhum.2017.00405] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Accepted: 07/21/2017] [Indexed: 12/20/2022] Open
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26
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Milham MP, Craddock RC, Klein A. Clinically useful brain imaging for neuropsychiatry: How can we get there? Depress Anxiety 2017; 34:578-587. [PMID: 28426908 DOI: 10.1002/da.22627] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2016] [Revised: 03/09/2017] [Accepted: 03/14/2017] [Indexed: 11/10/2022] Open
Abstract
Despite decades of research, visions of transforming neuropsychiatry through the development of brain imaging-based "growth charts" or "lab tests" have remained out of reach. In recent years, there is renewed enthusiasm about the prospect of achieving clinically useful tools capable of aiding the diagnosis and management of neuropsychiatric disorders. The present work explores the basis for this enthusiasm. We assert that there is no single advance that currently has the potential to drive the field of clinical brain imaging forward. Instead, there has been a constellation of advances that, if combined, could lead to the identification of objective brain imaging-based markers of illness. In particular, we focus on advances that are helping to (1) elucidate the research agenda for biological psychiatry (e.g., neuroscience focus, precision medicine), (2) shift research models for clinical brain imaging (e.g., big data exploration, standardization), (3) break down research silos (e.g., open science, calls for reproducibility and transparency), and (4) improve imaging technologies and methods. Although an arduous road remains ahead, these advances are repositioning the brain imaging community for long-term success.
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Affiliation(s)
- Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, New York.,Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, New York, New York
| | - R Cameron Craddock
- Center for the Developing Brain, Child Mind Institute, New York, New York.,Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, New York, New York
| | - Arno Klein
- Center for the Developing Brain, Child Mind Institute, New York, New York
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27
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Inference in the age of big data: Future perspectives on neuroscience. Neuroimage 2017; 155:549-564. [PMID: 28456584 DOI: 10.1016/j.neuroimage.2017.04.061] [Citation(s) in RCA: 113] [Impact Index Per Article: 16.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2016] [Revised: 04/25/2017] [Accepted: 04/25/2017] [Indexed: 11/23/2022] Open
Abstract
Neuroscience is undergoing faster changes than ever before. Over 100 years our field qualitatively described and invasively manipulated single or few organisms to gain anatomical, physiological, and pharmacological insights. In the last 10 years neuroscience spawned quantitative datasets of unprecedented breadth (e.g., microanatomy, synaptic connections, and optogenetic brain-behavior assays) and size (e.g., cognition, brain imaging, and genetics). While growing data availability and information granularity have been amply discussed, we direct attention to a less explored question: How will the unprecedented data richness shape data analysis practices? Statistical reasoning is becoming more important to distill neurobiological knowledge from healthy and pathological brain measurements. We argue that large-scale data analysis will use more statistical models that are non-parametric, generative, and mixing frequentist and Bayesian aspects, while supplementing classical hypothesis testing with out-of-sample predictions.
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28
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29
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Sumari P, Idris Z, Abdullah JM. We Must Invest in Applied Knowledge of Computational Neurosciences and Neuroinformatics as an Important Future in Malaysia: The Malaysian Brain Mapping Project. Malays J Med Sci 2017; 24:1-9. [PMID: 28381924 PMCID: PMC5345998 DOI: 10.21315/mjms2017.24.1.1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Accepted: 12/05/2016] [Indexed: 10/20/2022] Open
Abstract
The Academy of Sciences Malaysia and the Malaysian Industry-Government group for High Technology has been working hard to project the future of big data and neurotechnology usage up to the year 2050. On the 19 September 2016, the International Brain Initiative was announced by US Under Secretary of State Thomas Shannon at a meeting that accompanied the United Nations' General Assembly in New York City. This initiative was seen as an important effort but deemed costly for developing countries. At a concurrent meeting hosted by the US National Science Foundation at Rockefeller University, numerous countries discussed this massive project, which would require genuine collaboration between investigators in the realms of neuroethics. Malaysia's readiness to embark on using big data in the field of brain, mind and neurosciences is to prepare for the 4th Industrial Revolution which is an important investment for the country's future. The development of new strategies has also been encouraged by the involvement of the Society of Brain Mapping and Therapeutics, USA and the International Neuroinformatics Coordinating Facility.
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Affiliation(s)
- Putra Sumari
- School of Computer Sciences, Universiti Sains Malaysia, 11800 USM Pulau Pinang, Malaysia
- Center for Neuroscience Services and Research, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia
| | - Zamzuri Idris
- Center for Neuroscience Services and Research, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia
| | - Jafri Malin Abdullah
- Chief Editor, Malaysian Journal of Medical Sciences, USM Press, Universiti Sains Malaysia, 11800 USM Pulau Pinang, Malaysia
- Center for Neuroscience Services and Research, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia
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