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Rokem A, Benson NC. Hands-On Neuroinformatics Education at the Crossroads of Online and In-Person: Lessons Learned from NeuroHackademy. Neuroinformatics 2024:10.1007/s12021-024-09666-6. [PMID: 38763989 DOI: 10.1007/s12021-024-09666-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/27/2024] [Indexed: 05/21/2024]
Abstract
NeuroHackademy ( https://neurohackademy.org ) is a two-week event designed to train early-career neuroscience researchers in data science methods and their application to neuroimaging. The event seeks to bridge the big data skills gap by introducing participants to data science methods and skills that are often ignored in traditional curricula. Such skills are needed for the analysis and interpretation of the kinds of large and complex datasets that have become increasingly important to neuroimaging research due to concerted data collection efforts. In 2020, the event rapidly pivoted from an in-person event to an online event that included hundreds of participants from all over the world. This experience and those of the participants substantially changed our valuation of large online-accessible events. In subsequent events held in 2022 and 2023, we have developed a "hybrid" format that includes both online and in-person participants. We discuss the technical and sociotechnical elements of hybrid events and discuss some of the lessons we have learned while organizing them. We emphasize in particular the role that these events can play in creating a global and inclusive community of practice in the intersection of neuroimaging and data science.
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Affiliation(s)
- Ariel Rokem
- Department of Psychology, University of Washington, 119 Guthrie Hall, Seattle, 98195, Washington, USA.
- eScience Institute, University of Washington, 3910 15th Ave NE, Seattle, 98195, Washington, USA.
| | - Noah C Benson
- eScience Institute, University of Washington, 3910 15th Ave NE, Seattle, 98195, Washington, USA
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2
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Renton AI, Dao TT, Johnstone T, Civier O, Sullivan RP, White DJ, Lyons P, Slade BM, Abbott DF, Amos TJ, Bollmann S, Botting A, Campbell MEJ, Chang J, Close TG, Dörig M, Eckstein K, Egan GF, Evas S, Flandin G, Garner KG, Garrido MI, Ghosh SS, Grignard M, Halchenko YO, Hannan AJ, Heinsfeld AS, Huber L, Hughes ME, Kaczmarzyk JR, Kasper L, Kuhlmann L, Lou K, Mantilla-Ramos YJ, Mattingley JB, Meier ML, Morris J, Narayanan A, Pestilli F, Puce A, Ribeiro FL, Rogasch NC, Rorden C, Schira MM, Shaw TB, Sowman PF, Spitz G, Stewart AW, Ye X, Zhu JD, Narayanan A, Bollmann S. Neurodesk: an accessible, flexible and portable data analysis environment for reproducible neuroimaging. Nat Methods 2024; 21:804-808. [PMID: 38191935 DOI: 10.1038/s41592-023-02145-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 11/27/2023] [Indexed: 01/10/2024]
Abstract
Neuroimaging research requires purpose-built analysis software, which is challenging to install and may produce different results across computing environments. The community-oriented, open-source Neurodesk platform ( https://www.neurodesk.org/ ) harnesses a comprehensive and growing suite of neuroimaging software containers. Neurodesk includes a browser-accessible virtual desktop, command-line interface and computational notebook compatibility, allowing for accessible, flexible, portable and fully reproducible neuroimaging analysis on personal workstations, high-performance computers and the cloud.
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Affiliation(s)
- Angela I Renton
- The University of Queensland, Queensland Brain Institute, St Lucia, Brisbane, Queensland, Australia.
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia.
| | - Thuy T Dao
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
| | - Tom Johnstone
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Oren Civier
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Ryan P Sullivan
- The University of Sydney, School of Biomedical Engineering, Sydney, New South Wales, Australia
| | - David J White
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Paris Lyons
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Benjamin M Slade
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - David F Abbott
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Toluwani J Amos
- School of Life Science and Technology, University of Electronic Science and Technology, Chengdu, China
| | - Saskia Bollmann
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
| | - Andy Botting
- Australian Research Data Commons (ARDC), Sydney, New South Wales, Australia
| | - Megan E J Campbell
- School of Psychological Sciences, University of Newcastle, Newcastle, New South Wales, Australia
- Hunter Medical Research Institute Imaging Centre, Newcastle, New South Wales, Australia
| | - Jeryn Chang
- The University of Queensland, School of Biomedical Sciences, St Lucia, Brisbane, Queensland, Australia
| | - Thomas G Close
- The University of Sydney, School of Biomedical Engineering, Sydney, New South Wales, Australia
| | - Monika Dörig
- Integrative Spinal Research, Department of Chiropractic Medicine, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Korbinian Eckstein
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
| | - Gary F Egan
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
| | - Stefanie Evas
- School of Psychology, University of Adelaide, Adelaide, South Australia, Australia
- Human Health, Health & Biosecurity, CSIRO, Adelaide, South Australia, Australia
| | - Guillaume Flandin
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Kelly G Garner
- School of Psychology, University of New South Wales, Sydney, New South Wales, Australia
- The University of Queensland, School of Psychology, St Lucia, Brisbane, Queensland, Australia
| | - Marta I Garrido
- Melbourne School of Psychological Sciences, he University of Melbourne, Melbourne, Victoria, Australia
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia
| | - Satrajit S Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Otolaryngology - Head and Neck Surgery, Harvard Medical School, Boston, MA, USA
| | - Martin Grignard
- GIGA CRC In-Vivo Imaging, University of Liège, Liège, Belgium
| | - Yaroslav O Halchenko
- Center for Open Neuroscience, Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Anthony J Hannan
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Anibal S Heinsfeld
- Department of Psychology, Center for Perceptual Systems, Institute for Neuroscience, Center For Learning and Memory, The University of Texas at Austin, Austin, TX, USA
| | - Laurentius Huber
- National Institute of Mental Health (NIMH), National Institutes Health, Bethesda, MD, USA
| | - Matthew E Hughes
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Jakub R Kaczmarzyk
- Department of Biomedical Informatics, Stony Brook University, New York, NY, USA
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, New York, NY, USA
| | - Lars Kasper
- BRAIN-TO Lab, Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Levin Kuhlmann
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Melbourne, Victoria, Australia
| | - Kexin Lou
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Yorguin-Jose Mantilla-Ramos
- Grupo Neuropsicología y Conducta (GRUNECO), Facultad de Medicina, Universidad de Antioquia, Medellín, Colombia
| | - Jason B Mattingley
- The University of Queensland, Queensland Brain Institute, St Lucia, Brisbane, Queensland, Australia
- The University of Queensland, School of Psychology, St Lucia, Brisbane, Queensland, Australia
| | - Michael L Meier
- Integrative Spinal Research, Department of Chiropractic Medicine, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Jo Morris
- Australian Research Data Commons (ARDC), Sydney, New South Wales, Australia
| | - Akshaiy Narayanan
- School of Computer Science, The University of Auckland, Auckland, New Zealand
| | - Franco Pestilli
- Department of Psychology, Center for Perceptual Systems, Institute for Neuroscience, Center For Learning and Memory, The University of Texas at Austin, Austin, TX, USA
| | - Aina Puce
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Fernanda L Ribeiro
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
| | - Nigel C Rogasch
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
- Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia
- Hopwood Centre for Neurobiology, Lifelong Health Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, South Australia, Australia
| | - Chris Rorden
- McCausland Center for Brain Imaging, Department of Psychology, University of South Carolina, Columbia, SC, USA
| | - Mark M Schira
- School of Psychology, University of Wollongong, Wollongong, New South Wales, Australia
| | - Thomas B Shaw
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
- The University of Queensland, Centre for Advanced Imaging, St Lucia, Brisbane, Queensland, Australia
- Department of Neurology, Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia
| | - Paul F Sowman
- Macquarie University, School of Psychological Sciences, Sydney, New South Wales, Australia
| | - Gershon Spitz
- Department of Neuroscience, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia
- Monash-Epworth Rehabilitation Research Centre, Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Ashley W Stewart
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia
| | - Xincheng Ye
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
| | - Judy D Zhu
- Macquarie University, School of Psychological Sciences, Sydney, New South Wales, Australia
| | - Aswin Narayanan
- The University of Queensland, Centre for Advanced Imaging, St Lucia, Brisbane, Queensland, Australia
| | - Steffen Bollmann
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia.
- The University of Queensland, Centre for Advanced Imaging, St Lucia, Brisbane, Queensland, Australia.
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia.
- Queensland Digital Health Centre, The University of Queensland, Brisbane, Queensland, Australia.
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3
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Ishida S, Nishitsutsumi Y, Kashioka H, Taguchi T, Shineha R. A comparative review on neuroethical issues in neuroscientific and neuroethical journals. Front Neurosci 2023; 17:1160611. [PMID: 37781239 PMCID: PMC10536163 DOI: 10.3389/fnins.2023.1160611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 08/28/2023] [Indexed: 10/03/2023] Open
Abstract
This study is a pilot literature review that compares the interest of neuroethicists and neuroscientists. It aims to determine whether there is a significant gap between the neuroethical issues addressed in philosophical neuroethics journals and neuroscience journals. We retrieved 614 articles from two specialist neuroethics journals (Neuroethics and AJOB Neuroscience) and 82 neuroethics-focused articles from three specialist neuroscience journals (Neuron, Nature Neuroscience, and Nature Reviews Neuroscience). We classified these articles in light of the neuroethical issue in question before we compared the neuroethical issues addressed in philosophical neuroethics with those addressed by neuroscientists. A notable result is a parallelism between them as a general tendency. Neuroscientific articles cover most neuroethical issues discussed by philosophical ethicists and vice versa. Subsequently, there are notable discrepancies between the two bodies of neuroethics literature. For instance, theoretical questions, such as the ethics of moral enhancement and the philosophical implications of neuroscientific findings on our conception of personhood, are more intensely discussed in philosophical-neuroethical articles. Conversely, neuroscientific articles tend to emphasize practical questions, such as how to successfully integrate ethical perspectives into scientific research projects and justifiable practices of animal-involving neuroscientific research. These observations will help us settle the common starting point of the attempt at "ethics integration" in emerging neuroscience, contributing to better governance design and neuroethical practice.
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Affiliation(s)
- Shu Ishida
- Graduate School of Life Sciences, Tohoku University, Sendai, Japan
| | - Yu Nishitsutsumi
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita, Japan
| | - Hideki Kashioka
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita, Japan
| | - Takahisa Taguchi
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita, Japan
| | - Ryuma Shineha
- Research Center on Ethical, Legal, and Social Issues, Osaka University, Suita, Japan
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4
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Hayashi S, Caron BA, Heinsfeld AS, Vinci-Booher S, McPherson B, Bullock DN, Bertò G, Niso G, Hanekamp S, Levitas D, Ray K, MacKenzie A, Kitchell L, Leong JK, Nascimento-Silva F, Koudoro S, Willis H, Jolly JK, Pisner D, Zuidema TR, Kurzawski JW, Mikellidou K, Bussalb A, Rorden C, Victory C, Bhatia D, Baran Aydogan D, Yeh FCF, Delogu F, Guaje J, Veraart J, Bollman S, Stewart A, Fischer J, Faskowitz J, Chaumon M, Fabrega R, Hunt D, McKee S, Brown ST, Heyman S, Iacovella V, Mejia AF, Marinazzo D, Craddock RC, Olivetti E, Hanson JL, Avesani P, Garyfallidis E, Stanzione D, Carson J, Henschel R, Hancock DY, Stewart CA, Schnyer D, Eke DO, Poldrack RA, George N, Bridge H, Sani I, Freiwald WA, Puce A, Port NL, Pestilli F. brainlife.io: A decentralized and open source cloud platform to support neuroscience research. ARXIV 2023:arXiv:2306.02183v3. [PMID: 37332566 PMCID: PMC10274934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research.
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5
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Renton AI, Dao TT, Johnstone T, Civier O, Sullivan RP, White DJ, Lyons P, Slade BM, Abbott DF, Amos TJ, Bollmann S, Botting A, Campbell MEJ, Chang J, Close TG, Eckstein K, Egan GF, Evas S, Flandin G, Garner KG, Garrido MI, Ghosh SS, Grignard M, Hannan AJ, Huber R, Kaczmarzyk JR, Kasper L, Kuhlmann L, Lou K, Mantilla-Ramos YJ, Mattingley JB, Morris J, Narayanan A, Pestilli F, Puce A, Ribeiro FL, Rogasch NC, Rorden C, Schira M, Shaw TB, Sowman PF, Spitz G, Stewart A, Ye X, Zhu JD, Hughes ME, Narayanan A, Bollmann S. Neurodesk: An accessible, flexible, and portable data analysis environment for reproducible neuroimaging. RESEARCH SQUARE 2023:rs.3.rs-2649734. [PMID: 36993557 PMCID: PMC10055538 DOI: 10.21203/rs.3.rs-2649734/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Neuroimaging data analysis often requires purpose-built software, which can be challenging to install and may produce different results across computing environments. Beyond being a roadblock to neuroscientists, these issues of accessibility and portability can hamper the reproducibility of neuroimaging data analysis pipelines. Here, we introduce the Neurodesk platform, which harnesses software containers to support a comprehensive and growing suite of neuroimaging software (https://www.neurodesk.org/). Neurodesk includes a browser-accessible virtual desktop environment and a command line interface, mediating access to containerized neuroimaging software libraries on various computing platforms, including personal and high-performance computers, cloud computing and Jupyter Notebooks. This community-oriented, open-source platform enables a paradigm shift for neuroimaging data analysis, allowing for accessible, flexible, fully reproducible, and portable data analysis pipelines.
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Affiliation(s)
- Angela I. Renton
- The University of Queensland, Queensland Brain Institute, St Lucia 4072, Australia
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Thuy T. Dao
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Tom Johnstone
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn 3122, Australia
| | - Oren Civier
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn 3122, Australia
| | - Ryan P. Sullivan
- The University of Sydney, School of Biomedical Engineering, Sydney, Australia
| | - David J. White
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn 3122, Australia
| | - Paris Lyons
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn 3122, Australia
| | - Benjamin M. Slade
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn 3122, Australia
| | - David F. Abbott
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Victoria, Australia
| | - Toluwani J. Amos
- School of Life Science and Technology, University of Electronic Science and Technology, China
| | - Saskia Bollmann
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Andy Botting
- Australian Research Data Commons (ARDC), Australia
| | - Megan E. J. Campbell
- School of Psychological Sciences, University of Newcastle, Australia
- Hunter Medical Research Institute Imaging Centre, Newcastle, Australia
| | - Jeryn Chang
- The University of Queensland, School of Biomedical Sciences, St Lucia 4072, Australia
| | - Thomas G. Close
- The University of Sydney, School of Biomedical Engineering, Sydney, Australia
| | - Korbinian Eckstein
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Gary F. Egan
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Stefanie Evas
- School of Psychology, University of Adelaide, Adelaide, 5000, Australia
- Human Health, Health & Biosecurity, CSIRO, Adelaide, 5000, Australia
| | - Guillaume Flandin
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Kelly G. Garner
- The University of Queensland, Queensland Brain Institute, St Lucia 4072, Australia
- The University of Queensland, School of Psychology, St Lucia 4072, Australia
| | - Marta I. Garrido
- Melbourne School of Psychological Sciences, The University of Melbourne
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne
| | - Satrajit S. Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Otolaryngology - Head and Neck Surgery, Harvard Medical School, Boston, MA, USA
| | - Martin Grignard
- GIGA CRC In-Vivo Imaging, University of Liege, Liege, Belgium
| | - Anthony J. Hannan
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Victoria, Australia
| | - Renzo Huber
- Functional Magnetic Resonance Imaging Core Facility (FMRIF), National Institute of Mental Health (NIMH), USA
| | - Jakub R. Kaczmarzyk
- Medical Scientist Training Program, Stony Brook University, Stony Brook, NY, United States of America
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, United States of America
| | - Lars Kasper
- Techna Institute, University Health Network, Toronto, Canada
| | - Levin Kuhlmann
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia
| | - Kexin Lou
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | | | - Jason B. Mattingley
- The University of Queensland, Queensland Brain Institute, St Lucia 4072, Australia
- The University of Queensland, School of Psychology, St Lucia 4072, Australia
| | - Jo Morris
- Australian Research Data Commons (ARDC), Australia
| | | | - Franco Pestilli
- Department of Psychology, Center for Perceptual Systems, Center for Theoretical and Computational Neuroscience, Center on Aging and Population Sciences, Center for Learning and Memory, The University of Texas at Austin, 108 E Dean Keeton St, Austin, TX 78712, USA
| | - Aina Puce
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Fernanda L. Ribeiro
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Nigel C. Rogasch
- Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Australia
- Hopwood Centre for Neurobiology, Lifelong Health Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA, Australia
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Victoria, Australia
| | - Chris Rorden
- McCausland Center for Brain Imaging, Department of Psychology, University of South Carolina, Columbia SC, 29208, USA
| | - Mark Schira
- School of Psychology, University of Wollongong, Wollongong, 2522, Australia
| | - Thomas B. Shaw
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
- The University of Queensland, Centre for Advanced Imaging, St Lucia 4072, Australia
- Department of Neurology, Royal Brisbane and Women's Hospital, Brisbane, Australia
| | - Paul F. Sowman
- Macquarie University, School of Psychological Sciences, North Ryde 2112, Australia
| | - Gershon Spitz
- Department of Neuroscience, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Australia
- Monash-Epworth Rehabilitation Research Centre, Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, 3168, Australia
| | - Ashley Stewart
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Xincheng Ye
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Judy D. Zhu
- Macquarie University, School of Psychological Sciences, North Ryde 2112, Australia
| | - Matthew E. Hughes
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn 3122, Australia
| | - Aswin Narayanan
- The University of Queensland, Centre for Advanced Imaging, St Lucia 4072, Australia
| | - Steffen Bollmann
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
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A framework and resource for global collaboration in non-human primate neuroscience. CURRENT RESEARCH IN NEUROBIOLOGY 2023. [DOI: 10.1016/j.crneur.2023.100079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/19/2023] Open
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7
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Jin H, Wang Q, Yang YF, Zhang H, Gao M(M, Jin S, Chen Y(S, Xu T, Zheng YR, Chen J, Xiao Q, Yang J, Wang X, Geng H, Ge J, Wang WW, Chen X, Zhang L, Zuo XN, Chuang-Peng H. The Chinese Open Science Network (COSN): Building an Open Science Community From Scratch. ADVANCES IN METHODS AND PRACTICES IN PSYCHOLOGICAL SCIENCE 2023. [DOI: 10.1177/25152459221144986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
Open Science is becoming a mainstream scientific ideology in psychology and related fields. However, researchers, especially early-career researchers (ECRs) in developing countries, are facing significant hurdles in engaging in Open Science and moving it forward. In China, various societal and cultural factors discourage ECRs from participating in Open Science, such as the lack of dedicated communication channels and the norm of modesty. To make the voice of Open Science heard by Chinese-speaking ECRs and scholars at large, the Chinese Open Science Network (COSN) was initiated in 2016. With its core values being grassroots-oriented, diversity, and inclusivity, COSN has grown from a small Open Science interest group to a recognized network both in the Chinese-speaking research community and the international Open Science community. So far, COSN has organized three in-person workshops, 12 tutorials, 48 talks, and 55 journal club sessions and translated 15 Open Science-related articles and blogs from English to Chinese. Currently, the main social media account of COSN (i.e., the WeChat Official Account) has more than 23,000 subscribers, and more than 1,000 researchers/students actively participate in the discussions on Open Science. In this article, we share our experience in building such a network to encourage ECRs in developing countries to start their own Open Science initiatives and engage in the global Open Science movement. We foresee great collaborative efforts of COSN together with all other local and international networks to further accelerate the Open Science movement.
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8
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Niso G, Botvinik-Nezer R, Appelhoff S, De La Vega A, Esteban O, Etzel JA, Finc K, Ganz M, Gau R, Halchenko YO, Herholz P, Karakuzu A, Keator DB, Markiewicz CJ, Maumet C, Pernet CR, Pestilli F, Queder N, Schmitt T, Sójka W, Wagner AS, Whitaker KJ, Rieger JW. Open and reproducible neuroimaging: From study inception to publication. Neuroimage 2022; 263:119623. [PMID: 36100172 PMCID: PMC10008521 DOI: 10.1016/j.neuroimage.2022.119623] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 08/17/2022] [Accepted: 09/09/2022] [Indexed: 10/31/2022] Open
Abstract
Empirical observations of how labs conduct research indicate that the adoption rate of open practices for transparent, reproducible, and collaborative science remains in its infancy. This is at odds with the overwhelming evidence for the necessity of these practices and their benefits for individual researchers, scientific progress, and society in general. To date, information required for implementing open science practices throughout the different steps of a research project is scattered among many different sources. Even experienced researchers in the topic find it hard to navigate the ecosystem of tools and to make sustainable choices. Here, we provide an integrated overview of community-developed resources that can support collaborative, open, reproducible, replicable, robust and generalizable neuroimaging throughout the entire research cycle from inception to publication and across different neuroimaging modalities. We review tools and practices supporting study inception and planning, data acquisition, research data management, data processing and analysis, and research dissemination. An online version of this resource can be found at https://oreoni.github.io. We believe it will prove helpful for researchers and institutions to make a successful and sustainable move towards open and reproducible science and to eventually take an active role in its future development.
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Affiliation(s)
- Guiomar Niso
- Psychological & Brain Sciences, Indiana University, Bloomington, IN, USA; Universidad Politecnica de Madrid, Madrid and CIBER-BBN, Spain; Instituto Cajal, CSIC, Madrid, Spain.
| | - Rotem Botvinik-Nezer
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA.
| | - Stefan Appelhoff
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany
| | | | - Oscar Esteban
- Dept. of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Department of Psychology, Stanford University, Stanford, CA, USA
| | - Joset A Etzel
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Karolina Finc
- Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University, Toruń, Poland
| | - Melanie Ganz
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Rémi Gau
- Institute of Psychology, Université catholique de Louvain, Louvain la Neuve, Belgium
| | - Yaroslav O Halchenko
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Peer Herholz
- Montreal Neurological Institute-Hospital, McGill University, Montréal, Quebec, Canada
| | - Agah Karakuzu
- Biomedical Engineering Institute, Polytechnique Montréal, Montréal, Quebec, Canada; Montréal Heart Institute, Montréal, Quebec, Canada
| | - David B Keator
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA
| | | | - Camille Maumet
- Inria, Univ Rennes, CNRS, Inserm - IRISA UMR 6074, Empenn ERL U 1228, Rennes, France
| | - Cyril R Pernet
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark
| | - Franco Pestilli
- Psychological & Brain Sciences, Indiana University, Bloomington, IN, USA; Department of Psychology, The University of Texas at Austin, Austin, TX, USA
| | - Nazek Queder
- Montreal Neurological Institute-Hospital, McGill University, Montréal, Quebec, Canada; Department of Neurobiology and Behavior, University of California, Irvine, CA, USA
| | - Tina Schmitt
- Neuroimaging Unit, Carl-von-Ossietzky Universität, Oldenburg, Germany
| | - Weronika Sójka
- Faculty of Philosophy and Social Sciences, Nicolaus Copernicus University, Toruń, Poland
| | - Adina S Wagner
- Institute for Neuroscience and Medicine, Research Centre Juelich, Germany
| | | | - Jochem W Rieger
- Neuroimaging Unit, Carl-von-Ossietzky Universität, Oldenburg, Germany; Department of Psychology, Carl-von-Ossietzky Universität, Oldenburg, Germany.
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9
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Rutherford S, Kia SM, Wolfers T, Fraza C, Zabihi M, Dinga R, Berthet P, Worker A, Verdi S, Ruhe HG, Beckmann CF, Marquand AF. The normative modeling framework for computational psychiatry. Nat Protoc 2022; 17:1711-1734. [PMID: 35650452 PMCID: PMC7613648 DOI: 10.1038/s41596-022-00696-5] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 03/17/2022] [Indexed: 11/09/2022]
Abstract
Normative modeling is an emerging and innovative framework for mapping individual differences at the level of a single subject or observation in relation to a reference model. It involves charting centiles of variation across a population in terms of mappings between biology and behavior, which can then be used to make statistical inferences at the level of the individual. The fields of computational psychiatry and clinical neuroscience have been slow to transition away from patient versus 'healthy' control analytic approaches, probably owing to a lack of tools designed to properly model biological heterogeneity of mental disorders. Normative modeling provides a solution to address this issue and moves analysis away from case-control comparisons that rely on potentially noisy clinical labels. Here we define a standardized protocol to guide users through, from start to finish, normative modeling analysis using the Predictive Clinical Neuroscience toolkit (PCNtoolkit). We describe the input data selection process, provide intuition behind the various modeling choices and conclude by demonstrating several examples of downstream analyses that the normative model may facilitate, such as stratification of high-risk individuals, subtyping and behavioral predictive modeling. The protocol takes ~1-3 h to complete.
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Affiliation(s)
- Saige Rutherford
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands.
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands.
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA.
| | - Seyed Mostafa Kia
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Psychiatry, Utrecht University Medical Center, Utrecht, the Netherlands
| | - Thomas Wolfers
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research, University of Oslo, Oslo, Norway
| | - Charlotte Fraza
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Mariam Zabihi
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Richard Dinga
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Pierre Berthet
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research, University of Oslo, Oslo, Norway
| | - Amanda Worker
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Serena Verdi
- Centre for Medical Image Computing, Medical Physics and Biomedical Engineering, University College London, London, UK
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Henricus G Ruhe
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Christian F Beckmann
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
- Centre for Functional MRI of the Brain, University of Oxford, Oxford, UK
| | - Andre F Marquand
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
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10
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Liew SL, Lo BP, Donnelly MR, Zavaliangos-Petropulu A, Jeong JN, Barisano G, Hutton A, Simon JP, Juliano JM, Suri A, Wang Z, Abdullah A, Kim J, Ard T, Banaj N, Borich MR, Boyd LA, Brodtmann A, Buetefisch CM, Cao L, Cassidy JM, Ciullo V, Conforto AB, Cramer SC, Dacosta-Aguayo R, de la Rosa E, Domin M, Dula AN, Feng W, Franco AR, Geranmayeh F, Gramfort A, Gregory CM, Hanlon CA, Hordacre BG, Kautz SA, Khlif MS, Kim H, Kirschke JS, Liu J, Lotze M, MacIntosh BJ, Mataró M, Mohamed FB, Nordvik JE, Park G, Pienta A, Piras F, Redman SM, Revill KP, Reyes M, Robertson AD, Seo NJ, Soekadar SR, Spalletta G, Sweet A, Telenczuk M, Thielman G, Westlye LT, Winstein CJ, Wittenberg GF, Wong KA, Yu C. A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms. Sci Data 2022; 9:320. [PMID: 35710678 PMCID: PMC9203460 DOI: 10.1038/s41597-022-01401-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 05/19/2022] [Indexed: 01/16/2023] Open
Abstract
Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in stroke research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires neuroanatomical expertise. We previously released an open-source dataset of stroke T1w MRIs and manually-segmented lesion masks (ATLAS v1.2, N = 304) to encourage the development of better algorithms. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N = 1271), a larger dataset of T1w MRIs and manually segmented lesion masks that includes training (n = 655), test (hidden masks, n = 300), and generalizability (hidden MRIs and masks, n = 316) datasets. Algorithm development using this larger sample should lead to more robust solutions; the hidden datasets allow for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke research.
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Affiliation(s)
- Sook-Lei Liew
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA.
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
| | - Bethany P Lo
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA
| | - Miranda R Donnelly
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA
| | - Artemis Zavaliangos-Petropulu
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jessica N Jeong
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA
| | - Giuseppe Barisano
- Laboratory of Neuroimaging, Mark and Mary Stevens Neuroimaging and Informatics Institutes, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
| | - Alexandre Hutton
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA
| | - Julia P Simon
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Julia M Juliano
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
| | - Anisha Suri
- Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Zhizhuo Wang
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA
| | - Aisha Abdullah
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA
| | - Jun Kim
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Tyler Ard
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Nerisa Banaj
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Michael R Borich
- Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Lara A Boyd
- Department of Physical Therapy & Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada
| | - Amy Brodtmann
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Cathrin M Buetefisch
- Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, Emory University, Atlanta, GA, USA
| | - Lei Cao
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA
| | - Jessica M Cassidy
- Department of Allied Health Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Valentina Ciullo
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Adriana B Conforto
- Hospital das Clínicas, São Paulo University, Sao Paulo, SP, Brazil
- Hospital Israelita Albert Einstein, Sao Paulo, SP, Brazil
| | - Steven C Cramer
- Department of Neurology, University of California Los Angeles and California Rehabilitation Institute, Los Angeles, CA, USA
| | - Rosalia Dacosta-Aguayo
- Department of Psychiatry and Clinical Psychobiology, University of Barcelona, Barcelona, Spain
| | - Ezequiel de la Rosa
- icometrix, Leuven, Belgium
- Department of Computer Science, Technical University of Munich, Munich, Germany
| | - Martin Domin
- Functional Imaging Unit, Department of Diagnostic Radiology and Neuroradiology, University of Greifswald, Greifswald, Germany
| | - Adrienne N Dula
- Departments of Neurology and Diagnostic Medicine, Dell Medical School at The University of Texas Austin, Austin, TX, USA
| | - Wuwei Feng
- Department of Neurology, Duke University School of Medicine, Durham, NC, USA
| | - Alexandre R Franco
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, USA
| | | | - Alexandre Gramfort
- Center for Data Science, Université Paris-Saclay, Inria, Palaiseau, France
| | - Chris M Gregory
- Department of Health Sciences & Research, Medical University of South Carolina, Charleston, SC, USA
| | - Colleen A Hanlon
- Cancer Biology, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Brenton G Hordacre
- Innovation, Implementation and Clinical Translation (IIMPACT) in Health, Allied Health and Human Performance, University of South Australia, Adelaide, South Australia, Australia
| | - Steven A Kautz
- Department of Health Sciences & Research, Medical University of South Carolina, Charleston, SC, USA
- Ralph H Johnson VA Medical Center, Charleston, SC, USA
| | - Mohamed Salah Khlif
- The Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC, Australia
| | - Hosung Kim
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jan S Kirschke
- Neuroradiology, School of Medicine, Technical University Munich, München, Germany
| | - Jingchun Liu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Martin Lotze
- Functional Imaging Unit, Department of Diagnostic Radiology and Neuroradiology, University of Greifswald, Greifswald, Germany
| | - Bradley J MacIntosh
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Hurvitz Brain Sciences Program, Toronto, Ontario, Canada
| | - Maria Mataró
- Department of Clinical Psychology and Psychobiology, Institut de Neurociències, Universitat de Barcelona, Barcelona, Spain
- Institut de Recerca Sant Joan de Déu, 08950, Esplugues de Llobregat, Spain
| | - Feroze B Mohamed
- Jefferson Magnetic Resonance Imaging Center, Philadelphia, PA, USA
| | - Jan E Nordvik
- CatoSenteret Rehabilitation Center, SON, Norway
- Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway
| | - Gilsoon Park
- Laboratory of Neuroimaging, Mark and Mary Stevens Neuroimaging and Informatics Institutes, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Amy Pienta
- Inter-university Consortium for Political and Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Shane M Redman
- Inter-university Consortium for Political and Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Kate P Revill
- Facility for Education and Research in Neuroscience, Emory University, Atlanta, GA, USA
| | - Mauricio Reyes
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Andrew D Robertson
- Schlegel-University of Waterloo Research Institute for Aging, University of Waterloo, Waterloo, Ontario, Canada
- Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Na Jin Seo
- Department of Health Sciences & Research, Medical University of South Carolina, Charleston, SC, USA
- Ralph H Johnson VA Medical Center, Charleston, SC, USA
- Department of Rehabilitation Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Surjo R Soekadar
- Clinical Neurotechnology Laboratory, Dept. of Psychiatry and Neurosciences (CCM), Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Gianfranco Spalletta
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
- Menninger Department of Psychiatry and Behavioral Sciences, Division of Neuropsychiatry, Baylor College of Medicine, Houston, TX, USA
| | - Alison Sweet
- Inter-university Consortium for Political and Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Maria Telenczuk
- Center for Data Science, Université Paris-Saclay, Inria, Palaiseau, France
| | - Gregory Thielman
- Department of Physical Therapy and Neuroscience, Samson College of Health Sciences, St. Joseph's University, Philadelphia, PA, USA
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway
- NORMENT, Department of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Carolee J Winstein
- Division of Biokinesiology and Physical Therapy of the Herman Ostrow School of Dentistry, University of Southern California, Los Angeles, CA, USA
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - George F Wittenberg
- Geriatrics Research, Education and Clinical Center, HERL, Department of Veterans Affairs, Pittsburgh, PA, USA
- Departments of Neurology, PM&R, RNEL, CNBC, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kristin A Wong
- Department of Physical Medicine & Rehabilitation, Dell Medical School, University of Texas at Austin, Austin, TX, USA
| | - Chunshui Yu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
- Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
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11
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Combrisson E, Allegra M, Basanisi R, Ince RAA, Giordano B, Bastin J, Brovelli A. Group-level inference of information-based measures for the analyses of cognitive brain networks from neurophysiological data. Neuroimage 2022; 258:119347. [PMID: 35660460 DOI: 10.1016/j.neuroimage.2022.119347] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Revised: 05/24/2022] [Accepted: 05/30/2022] [Indexed: 12/30/2022] Open
Abstract
The reproducibility crisis in neuroimaging and in particular in the case of underpowered studies has introduced doubts on our ability to reproduce, replicate and generalize findings. As a response, we have seen the emergence of suggested guidelines and principles for neuroscientists known as Good Scientific Practice for conducting more reliable research. Still, every study remains almost unique in its combination of analytical and statistical approaches. While it is understandable considering the diversity of designs and brain data recording, it also represents a striking point against reproducibility. Here, we propose a non-parametric permutation-based statistical framework, primarily designed for neurophysiological data, in order to perform group-level inferences on non-negative measures of information encompassing metrics from information-theory, machine-learning or measures of distances. The framework supports both fixed- and random-effect models to adapt to inter-individuals and inter-sessions variability. Using numerical simulations, we compared the accuracy in ground-truth retrieving of both group models, such as test- and cluster-wise corrections for multiple comparisons. We then reproduced and extended existing results using both spatially uniform MEG and non-uniform intracranial neurophysiological data. We showed how the framework can be used to extract stereotypical task- and behavior-related effects across the population covering scales from the local level of brain regions, inter-areal functional connectivity to measures summarizing network properties. We also present an open-source Python toolbox called Frites1 that includes the proposed statistical pipeline using information-theoretic metrics such as single-trial functional connectivity estimations for the extraction of cognitive brain networks. Taken together, we believe that this framework deserves careful attention as its robustness and flexibility could be the starting point toward the uniformization of statistical approaches.
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Affiliation(s)
- Etienne Combrisson
- Institut de Neurosciences de la Timone, Aix Marseille Université, UMR 7289 CNRS, 13005, Marseille, France.
| | - Michele Allegra
- Institut de Neurosciences de la Timone, Aix Marseille Université, UMR 7289 CNRS, 13005, Marseille, France; Dipartimento di Fisica e Astronomia "Galileo Galilei", Università di Padova, via Marzolo 8, 35131 Padova, Italy; Padua Neuroscience Center, Università di Padova, via Orus 2, 35131 Padova, Italy
| | - Ruggero Basanisi
- Institut de Neurosciences de la Timone, Aix Marseille Université, UMR 7289 CNRS, 13005, Marseille, France
| | - Robin A A Ince
- School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
| | - Bruno Giordano
- Institut de Neurosciences de la Timone, Aix Marseille Université, UMR 7289 CNRS, 13005, Marseille, France
| | - Julien Bastin
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
| | - Andrea Brovelli
- Institut de Neurosciences de la Timone, Aix Marseille Université, UMR 7289 CNRS, 13005, Marseille, France.
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12
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Cheon EJ, Bearden CE, Sun D, Ching CRK, Andreassen OA, Schmaal L, Veltman DJ, Thomopoulos SI, Kochunov P, Jahanshad N, Thompson PM, Turner JA, van Erp TG. Cross disorder comparisons of brain structure in schizophrenia, bipolar disorder, major depressive disorder, and 22q11.2 deletion syndrome: A review of ENIGMA findings. Psychiatry Clin Neurosci 2022; 76:140-161. [PMID: 35119167 PMCID: PMC9098675 DOI: 10.1111/pcn.13337] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Revised: 11/29/2021] [Accepted: 01/21/2022] [Indexed: 12/25/2022]
Abstract
This review compares the main brain abnormalities in schizophrenia (SZ), bipolar disorder (BD), major depressive disorder (MDD), and 22q11.2 Deletion Syndrome (22q11DS) determined by ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) consortium investigations. We obtained ranked effect sizes for subcortical volumes, regional cortical thickness, cortical surface area, and diffusion tensor imaging abnormalities, comparing each of these disorders relative to healthy controls. In addition, the studies report on significant associations between brain imaging metrics and disorder-related factors such as symptom severity and treatments. Visual comparison of effect size profiles shows that effect sizes are generally in the same direction and scale in severity with the disorders (in the order SZ > BD > MDD). The effect sizes for 22q11DS, a rare genetic syndrome that increases the risk for psychiatric disorders, appear to be much larger than for either of the complex psychiatric disorders. This is consistent with the idea of generally larger effects on the brain of rare compared to common genetic variants. Cortical thickness and surface area effect sizes for 22q11DS with psychosis compared to 22q11DS without psychosis are more similar to those of SZ and BD than those of MDD; a pattern not observed for subcortical brain structures and fractional anisotropy effect sizes. The observed similarities in effect size profiles for cortical measures across the psychiatric disorders mimic those observed for shared genetic variance between these disorders reported based on family and genetic studies and are consistent with shared genetic risk for SZ and BD and structural brain phenotypes.
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Affiliation(s)
- Eun-Jin Cheon
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, 5251 California Ave, Irvine, CA, 92617, USA
- Department of Psychiatry, Yeungnam University College of Medicine, Yeungnam University Medical Center, Daegu, Republic of Korea
| | - Carrie E. Bearden
- Departments of Psychiatry and Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles
| | - Daqiang Sun
- Departments of Psychiatry and Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles
- Department of Mental Health, Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA
| | - Christopher R. K. Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ole A. Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Lianne Schmaal
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia
- Orygen, Parkville, Australia
| | - Dick J. Veltman
- Department of Psychiatry, Amsterdam UMC, location VUMC, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Sophia I. Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jessica A. Turner
- Psychology Department and Neuroscience Institute, Georgia State University, Atlant, GA, USA
| | - Theo G.M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, 5251 California Ave, Irvine, CA, 92617, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, 309 Qureshey Research Lab, Irvine, CA, 92697, USA
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13
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Ren W, Ji B, Guan Y, Cao L, Ni R. Recent Technical Advances in Accelerating the Clinical Translation of Small Animal Brain Imaging: Hybrid Imaging, Deep Learning, and Transcriptomics. Front Med (Lausanne) 2022; 9:771982. [PMID: 35402436 PMCID: PMC8987112 DOI: 10.3389/fmed.2022.771982] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 02/16/2022] [Indexed: 12/26/2022] Open
Abstract
Small animal models play a fundamental role in brain research by deepening the understanding of the physiological functions and mechanisms underlying brain disorders and are thus essential in the development of therapeutic and diagnostic imaging tracers targeting the central nervous system. Advances in structural, functional, and molecular imaging using MRI, PET, fluorescence imaging, and optoacoustic imaging have enabled the interrogation of the rodent brain across a large temporal and spatial resolution scale in a non-invasively manner. However, there are still several major gaps in translating from preclinical brain imaging to the clinical setting. The hindering factors include the following: (1) intrinsic differences between biological species regarding brain size, cell type, protein expression level, and metabolism level and (2) imaging technical barriers regarding the interpretation of image contrast and limited spatiotemporal resolution. To mitigate these factors, single-cell transcriptomics and measures to identify the cellular source of PET tracers have been developed. Meanwhile, hybrid imaging techniques that provide highly complementary anatomical and molecular information are emerging. Furthermore, deep learning-based image analysis has been developed to enhance the quantification and optimization of the imaging protocol. In this mini-review, we summarize the recent developments in small animal neuroimaging toward improved translational power, with a focus on technical improvement including hybrid imaging, data processing, transcriptomics, awake animal imaging, and on-chip pharmacokinetics. We also discuss outstanding challenges in standardization and considerations toward increasing translational power and propose future outlooks.
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Affiliation(s)
- Wuwei Ren
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
- Shanghai Engineering Research Center of Energy Efficient and Custom AI IC, Shanghai, China
| | - Bin Ji
- Department of Radiopharmacy and Molecular Imaging, School of Pharmacy, Fudan University, Shanghai, China
| | - Yihui Guan
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Lei Cao
- Shanghai Changes Tech, Ltd., Shanghai, China
| | - Ruiqing Ni
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
- Institute for Biomedical Engineering, ETH Zürich and University of Zurich, Zurich, Switzerland
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14
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Horien C, Lee K, Westwater ML, Noble S, Tejavibulya L, Kayani T, Constable RT, Scheinost D. A protocol for working with open-source neuroimaging datasets. STAR Protoc 2022; 3:101077. [PMID: 35036958 PMCID: PMC8749295 DOI: 10.1016/j.xpro.2021.101077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Large, publicly available neuroimaging datasets are becoming increasingly common, but their use presents challenges because of insufficient knowledge of the tool options for data processing and proper data organization. Here, we describe a protocol to lessen these barriers. We describe the steps for the search and download of the open-source dataset. We detail the steps for proper data management and practical guidelines for data analysis. Finally, we give instructions for data and result sharing on public repositories and preprint services. For complete details on the use and execution of this profile, please refer to Horien et al. (2021).
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Affiliation(s)
- Corey Horien
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA
- MD-PhD Program, Yale School of Medicine, New Haven, CT 06510, USA
| | - Kangjoo Lee
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA
| | - Margaret L. Westwater
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA
| | - Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA
| | - Teimur Kayani
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA
| | - R. Todd Constable
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT 06510, USA
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA
- Yale Child Study Center, New Haven, CT 06510, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT 06520, USA
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15
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Large, open datasets for human connectomics research: Considerations for reproducible and responsible data use. Neuroimage 2021; 244:118579. [PMID: 34536537 DOI: 10.1016/j.neuroimage.2021.118579] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 08/27/2021] [Accepted: 09/14/2021] [Indexed: 12/19/2022] Open
Abstract
Large, open datasets have emerged as important resources in the field of human connectomics. In this review, the evolution of data sharing involving magnetic resonance imaging is described. A summary of the challenges and progress in conducting reproducible data analyses is provided, including description of recent progress made in the development of community guidelines and recommendations, software and data management tools, and initiatives to enhance training and education. Finally, this review concludes with a discussion of ethical conduct relevant to analyses of large, open datasets and a researcher's responsibility to prevent further stigmatization of historically marginalized racial and ethnic groups. Moving forward, future work should include an enhanced emphasis on the social determinants of health, which may further contextualize findings among diverse population-based samples. Leveraging the progress to date and guided by interdisciplinary collaborations, the future of connectomics promises to be an impressive era of innovative research, yielding a more inclusive understanding of brain structure and function.
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16
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Joseph K, Kirsch M, Johnston M, Münkel C, Stieglitz T, Haas CA, Hofmann UG. Transcriptional characterization of the glial response due to chronic neural implantation of flexible microprobes. Biomaterials 2021; 279:121230. [PMID: 34736153 DOI: 10.1016/j.biomaterials.2021.121230] [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] [Received: 05/31/2021] [Revised: 10/20/2021] [Accepted: 10/24/2021] [Indexed: 01/13/2023]
Abstract
Long term implantation of (micro-)probes into neural tissue causes unique and disruptive responses. In this study, we investigate the transcriptional trajectory of glial cells responding to chronic implantation of 380 μm flexible micro-probes for up to 18 weeks. Transcriptomic analysis shows a rapid activation of microglial cells and a strong reactive astrocytic polarization, both of which are lost over the chronic of the implant duration. Animals that were implanted for 18 weeks show a transcriptional profile similar to non-implanted controls, with increased expression of genes associated with wound healing and angiogenesis, which raises hope of a normalization of the neuropil to the pre-injury state when using flexible probes. Nevertheless, our data shows that a subset of genes upregulated after 18 weeks belong to the family of immediate early genes, which indicates that structural and functional remodeling is not complete at this time point. Our results confirm and extend previous work on the molecular changes resulting from the presence of neural probes and provide a rational basis for developing interventional strategies to control them.
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Affiliation(s)
- Kevin Joseph
- Neuroelectronic Systems, Department of Neurosurgery, Medical Center, University of Freiburg, Germany; Department of Neurosurgery, Medical Center University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany; BrainLinks-BrainTools, University of Freiburg, Germany.
| | - Matthias Kirsch
- BrainLinks-BrainTools, University of Freiburg, Germany; Department of Neuroanatomy, Institute of Anatomy and Cell Biology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Midori Johnston
- Faculty of Medicine, University of Freiburg, Germany; BrainLinks-BrainTools, University of Freiburg, Germany; Experimental Epilepsy Research, Dept. of Neurosurgery, Medical Center- University of Freiburg, Germany
| | - Christian Münkel
- Neuroelectronic Systems, Department of Neurosurgery, Medical Center, University of Freiburg, Germany; Department of Neurosurgery, Medical Center University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany
| | - Thomas Stieglitz
- BrainLinks-BrainTools, University of Freiburg, Germany; Laboratory for Biomedical Microtechnology, Department of Microsystems Engineering-IMTEK, Faculty of Engineering, University of Freiburg, Germany
| | - Carola A Haas
- Faculty of Medicine, University of Freiburg, Germany; Experimental Epilepsy Research, Dept. of Neurosurgery, Medical Center- University of Freiburg, Germany
| | - Ulrich G Hofmann
- Neuroelectronic Systems, Department of Neurosurgery, Medical Center, University of Freiburg, Germany; Department of Neurosurgery, Medical Center University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany; BrainLinks-BrainTools, University of Freiburg, Germany
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17
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Rutherford S, Kia SM, Wolfers T, Fraza C, Zabihi M, Dinga R, Berthet P, Worker A, Verdi S, Ruhe HG, Beckmann CF, Marquand AF. The Normative Modeling Framework for Computational Psychiatry.. [PMID: 35650452 PMCID: PMC7613648 DOI: 10.1101/2021.08.08.455583] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Normative modeling is an emerging and innovative framework for mapping individual differences at the level of a single subject or observation in relation to a reference model. It involves charting centiles of variation across a population in terms of mappings between biology and behavior which can then be used to make statistical inferences at the level of the individual. The fields of computational psychiatry and clinical neuroscience have been slow to transition away from patient versus “healthy” control analytic approaches, likely due to a lack of tools designed to properly model biological heterogeneity of mental disorders. Normative modeling provides a solution to address this issue and moves analysis away from case-control comparisons that rely on potentially noisy clinical labels. In this article, we define a standardized protocol to guide users through, from start to finish, normative modeling analysis using the Predictive Clinical Neuroscience toolkit (PCNtoolkit). We describe the input data selection process, provide intuition behind the various modeling choices, and conclude by demonstrating several examples of down-stream analyses the normative model results may facilitate, such as stratification of high-risk individuals, subtyping, and behavioral predictive modeling. The protocol takes approximately 1-3 hours to complete.
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18
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Affiliation(s)
- Francisco Xavier Castellanos
- Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine, and Nathan Kline Institute for Psychiatric Research, New York
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