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Basodi S, Raja R, Gazula H, Romero JT, Panta S, Maullin-Sapey T, Nichols TE, Calhoun VD. Decentralized Mixed Effects Modeling in COINSTAC. Neuroinformatics 2024; 22:163-175. [PMID: 38424371 DOI: 10.1007/s12021-024-09657-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/15/2024] [Indexed: 03/02/2024]
Abstract
Performing group analysis on magnetic resonance imaging (MRI) data with linear mixed-effects (LME) models is challenging due to its large dimensionality and inherent multi-level covariance structure. In addition, as large-scale collaborative projects become commonplace in neuroimaging, data must increasingly be stored and analyzed from different locations. In such settings, substantial overhead can occur in terms of data transfer and coordination between participating research groups. In some cases, data cannot be pooled together due to privacy or regulatory concerns. In this work, we propose a decentralized LME model to perform a large-scale analysis of data from different collaborations without data pooling. This method is efficient as it overcomes the hurdles of data sharing and has lower bandwidth and memory requirements for analysis than the centralized modeling approach. We evaluate our model using features extracted from structural magnetic resonance imaging (sMRI) data. Results highlight gray matter reductions in the temporal lobe/insula and medial frontal regions in schizophrenia, consistent with prior studies. Our analysis also demonstrates that decentralized LME models achieve similar performance compared to the models trained with all the data in one location. We also implement the decentralized LME approach in COINSTAC, an open source, decentralized platform for federating neuroimaging analysis, providing an easy to use tool for dissemination to the neuroimaging community.
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Affiliation(s)
- Sunitha Basodi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Rajikha Raja
- St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Harshvardhan Gazula
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Javier Tomas Romero
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Sandeep Panta
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | | | - Thomas E Nichols
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.
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2
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Gazula H, Rootes-Murdy K, Holla B, Basodi S, Zhang Z, Verner E, Kelly R, Murthy P, Chakrabarti A, Basu D, Bhagyalakshmi Nanjayya S, Lenin Singh R, Lourembam Singh R, Kalyanram K, Kartik K, Kalyanaraman K, Ghattu K, Kuriyan R, Kurpad SS, Barker GJ, Bharath RD, Desrivieres S, Purushottam M, Orfanos DP, Sharma E, Hickman M, Toledano M, Vaidya N, Banaschewski T, Bokde ALW, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Brühl R, Martinot JL, Paillére Martinot ML, Artiges E, Nees F, Paus T, Poustka L, Fröhner JH, Robinson L, Smolka MN, Walter H, Winterer J, Whelan R, Turner JA, Sarwate AD, Plis SM, Benegal V, Schumann G, Calhoun VD. Federated Analysis in COINSTAC Reveals Functional Network Connectivity and Spectral Links to Smoking and Alcohol Consumption in Nearly 2,000 Adolescent Brains. Neuroinformatics 2023; 21:287-301. [PMID: 36434478 DOI: 10.1007/s12021-022-09604-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/29/2022] [Indexed: 11/27/2022]
Abstract
With the growth of decentralized/federated analysis approaches in neuroimaging, the opportunities to study brain disorders using data from multiple sites has grown multi-fold. One such initiative is the Neuromark, a fully automated spatially constrained independent component analysis (ICA) that is used to link brain network abnormalities among different datasets, studies, and disorders while leveraging subject-specific networks. In this study, we implement the neuromark pipeline in COINSTAC, an open-source neuroimaging framework for collaborative/decentralized analysis. Decentralized exploratory analysis of nearly 2000 resting-state functional magnetic resonance imaging datasets collected at different sites across two cohorts and co-located in different countries was performed to study the resting brain functional network connectivity changes in adolescents who smoke and consume alcohol. Results showed hypoconnectivity across the majority of networks including sensory, default mode, and subcortical domains, more for alcohol than smoking, and decreased low frequency power. These findings suggest that global reduced synchronization is associated with both tobacco and alcohol use. This proof-of-concept work demonstrates the utility and incentives associated with large-scale decentralized collaborations spanning multiple sites.
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Affiliation(s)
- Harshvardhan Gazula
- Martinos Center for Biomedical Imaging, MGH and Harvard Medical School, Boston, USA.
| | - Kelly Rootes-Murdy
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Bharath Holla
- Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India.
- Integrative Medicine, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India.
| | - Sunitha Basodi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Zuo Zhang
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology & Neuroscience, SGDP Centre, King's College London, London, SE5 8AF, United Kingdom
| | - Eric Verner
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Ross Kelly
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Pratima Murthy
- Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India
| | | | - Debasish Basu
- Department of Psychiatry, Postgraduate Institute of Medical Education & Research, Chandigarh, India
| | | | - Rajkumar Lenin Singh
- Department of Psychiatry, Regional Institute of Medical Sciences, Imphal, Manipur, India
| | - Roshan Lourembam Singh
- Department of Psychology, Regional Institute of Medical Sciences, Imphal, Manipur, India
| | | | | | | | - Krishnaveni Ghattu
- Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India
| | - Rebecca Kuriyan
- Division of Nutrition, St John's Research Institute, Bengaluru, India
| | - Sunita Simon Kurpad
- Department of Psychiatry & Department of Medical Ethics, St. John's Medical College & Hospital, Bangalore, India
| | - Gareth J Barker
- Department of Neuroimaging, Institute of Psychology, Psychiatry & Neuroscience, King's College London, London, UK
| | - Rose Dawn Bharath
- Department of Neuroimaging and Interventional Radiology, NIMHANS, Bengaluru, India
| | - Sylvane Desrivieres
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology & Neuroscience, SGDP Centre, King's College London, London, SE5 8AF, United Kingdom
| | | | | | - Eesha Sharma
- Department of Child & Adolescent Psychiatry, NIMHANS, Bengaluru, India
| | | | - Mireille Toledano
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Nilakshi Vaidya
- Centre for Addiction Medicine, NIMHANS, Bengaluru, India
- Centre for Population Neuroscience and Precision Medicine (PONS), Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, 68159, Germany
| | - Arun L W Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Herta Flor
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim, 68131, Germany
| | | | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, Burlington, Vermont, 05405, USA
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Rüdiger Brühl
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 "Trajectoires développementales en psychiatrie"; Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli;, Gif-sur-Yvette, France
| | - Marie-Laure Paillére Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 "Trajectoires développementales en psychiatrie"; Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli;, Gif-sur-Yvette, France
- AP-HP. Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 "Trajectoires développementales en psychiatrie"; Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli;, Gif-sur-Yvette, France
- Psychiatry Department, EPS Barthélémy Durand, Etampes, France
| | - Frauke Nees
- Centre for Population Neuroscience and Precision Medicine (PONS), Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, 68159, Germany
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
| | - Tomás Paus
- Department of Psychiatry, Faculty of Medicine and Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, Quebec, Canada
- Departments of Psychiatry and Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, von-Siebold-Str. 5, Göttingen, 37075, Germany
| | - Juliane H Fröhner
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Lauren Robinson
- Department of Psychological Medicine, Section for Eating Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK
| | - Michael N Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Jeanne Winterer
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Jessica A Turner
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - Anand D Sarwate
- Department of Electrical and Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Sergey M Plis
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Vivek Benegal
- Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India
| | - Gunter Schumann
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology & Neuroscience, SGDP Centre, King's College London, London, SE5 8AF, United Kingdom
- Centre for Population Neuroscience and Precision Medicine (PONS), Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Berlin, Germany
- Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, P.R. China
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Department of Psychology, Georgia State University, Atlanta, GA, USA
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3
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Basodi S, Raja R, Ray B, Gazula H, Sarwate AD, Plis S, Liu J, Verner E, Calhoun VD. Decentralized Brain Age Estimation Using MRI Data. Neuroinformatics 2022; 20:981-990. [PMID: 35380365 DOI: 10.1007/s12021-022-09570-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/27/2022] [Indexed: 12/31/2022]
Abstract
Recent studies have demonstrated that neuroimaging data can be used to estimate biological brain age, as it captures information about the neuroanatomical and functional changes the brain undergoes during development and the aging process. However, researchers often have limited access to neuroimaging data because of its challenging and expensive acquisition process, thereby limiting the effectiveness of the predictive model. Decentralized models provide a way to build more accurate and generalizable prediction models, bypassing the traditional data-sharing methodology. In this work, we propose a decentralized method for biological brain age estimation using support vector regression models and evaluate it on three different feature sets, including both volumetric and voxelwise structural MRI data as well as resting functional MRI data. The results demonstrate that our decentralized brain age regression models can achieve similar performance compared to the models trained with all the data in one location.
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Affiliation(s)
- Sunitha Basodi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.
| | - Rajikha Raja
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.,Department of Radiology, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Bhaskar Ray
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.,Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | | | - Anand D Sarwate
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, USA
| | - Sergey Plis
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Jingyu Liu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.,Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Eric Verner
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.,Department of Computer Science, Georgia State University, Atlanta, GA, USA.,Department of Psychology, Georgia State University, Atlanta, GA, USA
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4
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Li X, Liang H. Project, toolkit, and database of neuroinformatics ecosystem: A summary of previous studies on "Frontiers in Neuroinformatics". Front Neuroinform 2022; 16:902452. [PMID: 36225654 PMCID: PMC9549929 DOI: 10.3389/fninf.2022.902452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 08/29/2022] [Indexed: 11/13/2022] Open
Abstract
In the field of neuroscience, the core of the cohort study project consists of collection, analysis, and sharing of multi-modal data. Recent years have witnessed a host of efficient and high-quality toolkits published and employed to improve the quality of multi-modal data in the cohort study. In turn, gleaning answers to relevant questions from such a conglomeration of studies is a time-consuming task for cohort researchers. As part of our efforts to tackle this problem, we propose a hierarchical neuroscience knowledge base that consists of projects/organizations, multi-modal databases, and toolkits, so as to facilitate researchers' answer searching process. We first classified studies conducted for the topic "Frontiers in Neuroinformatics" according to the multi-modal data life cycle, and from these studies, information objects as projects/organizations, multi-modal databases, and toolkits have been extracted. Then, we map these information objects into our proposed knowledge base framework. A Python-based query tool has also been developed in tandem for quicker access to the knowledge base, (accessible at https://github.com/Romantic-Pumpkin/PDT_fninf). Finally, based on the constructed knowledge base, we discussed some key research issues and underlying trends in different stages of the multi-modal data life cycle.
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Affiliation(s)
- Xin Li
- School of Information Science and Technology, University of Science and Technology of China, Hefei, China
| | - Huadong Liang
- AI Research Institute, iFLYTEK Co., LTD, Hefei, China
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5
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NeuroCrypt: Machine Learning Over Encrypted Distributed Neuroimaging Data. Neuroinformatics 2022; 20:91-108. [PMID: 33948898 PMCID: PMC8566325 DOI: 10.1007/s12021-021-09525-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/04/2021] [Indexed: 01/05/2023]
Abstract
The field of neuroimaging can greatly benefit from building machine learning models to detect and predict diseases, and discover novel biomarkers, but much of the data collected at various organizations and research centers is unable to be shared due to privacy or regulatory concerns (especially for clinical data or rare disorders). In addition, aggregating data across multiple large studies results in a huge amount of duplicated technical debt and the resources required can be challenging or impossible for an individual site to build. Training on the data distributed across organizations can result in models that generalize much better than models trained on data from any of organizations alone. While there are approaches for decentralized sharing, these often do not provide the highest possible guarantees of sample privacy that only cryptography can provide. In addition, such approaches are often focused on probabilistic solutions. In this paper, we propose an approach that leverages the potential of datasets spread among a number of data collecting organizations by performing joint analyses in a secure and deterministic manner when only encrypted data is shared and manipulated. The approach is based on secure multiparty computation which refers to cryptographic protocols that enable distributed computation of a function over distributed inputs without revealing additional information about the inputs. It enables multiple organizations to train machine learning models on their joint data and apply the trained models to encrypted data without revealing their sensitive data to the other parties. In our proposed approach, organizations (or sites) securely collaborate to build a machine learning model as it would have been trained on the aggregated data of all the organizations combined. Importantly, the approach does not require a trusted party (i.e. aggregator), each contributing site plays an equal role in the process, and no site can learn individual data of any other site. We demonstrate effectiveness of the proposed approach, in a range of empirical evaluations using different machine learning algorithms including logistic regression and convolutional neural network models on human structural and functional magnetic resonance imaging datasets.
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6
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Turner JA, Calhoun VD, Thompson PM, Jahanshad N, Ching CRK, Thomopoulos SI, Verner E, Strauss GP, Ahmed AO, Turner MD, Basodi S, Ford JM, Mathalon DH, Preda A, Belger A, Mueller BA, Lim KO, van Erp TGM. ENIGMA + COINSTAC: Improving Findability, Accessibility, Interoperability, and Re-usability. Neuroinformatics 2022; 20:261-275. [PMID: 34846691 PMCID: PMC9149142 DOI: 10.1007/s12021-021-09559-y] [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] [Accepted: 11/19/2021] [Indexed: 01/07/2023]
Abstract
The FAIR principles, as applied to clinical and neuroimaging data, reflect the goal of making research products Findable, Accessible, Interoperable, and Reusable. The use of the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymized Computation (COINSTAC) platform in the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium combines the technological approach of decentralized analyses with the sociological approach of sharing data. In addition, ENIGMA + COINSTAC provides a platform to facilitate the use of machine-actionable data objects. We first present how ENIGMA and COINSTAC support the FAIR principles, and then showcase their integration with a decentralized meta-analysis of sex differences in negative symptom severity in schizophrenia, and finally present ongoing activities and plans to advance FAIR principles in ENIGMA + COINSTAC. ENIGMA and COINSTAC currently represent efforts toward improved Access, Interoperability, and Reusability. We highlight additional improvements needed in these areas, as well as future connections to other resources for expanded Findability.
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Affiliation(s)
- Jessica A Turner
- Psychology Department, Georgia State University, Atlanta, GA, USA.
| | - Vince D Calhoun
- Psychology Department, Georgia State University, Atlanta, GA, USA
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, 30303, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Christopher R K Ching
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Eric Verner
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, 30303, USA
| | - Gregory P Strauss
- Departments of Psychology and Neuroscience, University of Georgia, Athens, GA, USA
| | - Anthony O Ahmed
- Weill Cornell Medicine, Department of Psychiatry, White Plains, NY, 10605, USA
| | - Matthew D Turner
- Psychology Department, Georgia State University, Atlanta, GA, USA
| | - Sunitha Basodi
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, 30303, USA
| | - Judith M Ford
- Veterans Affairs San Francisco Healthcare System, San Francisco, CA, 94121, USA
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, 94121, USA
| | - Daniel H Mathalon
- Veterans Affairs San Francisco Healthcare System, San Francisco, CA, 94121, USA
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, 94121, USA
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, University of California Irvine Medical Center, 101 The City Drive S, Orange, CA, 92868, USA
| | - Aysenil Belger
- Department of Psychiatry and Frank Porter Graham Child Development Institute, University of North Carolina at Chapel Hill, 105 Smith Level Road, Chapel Hill, NC, 27599-8180, USA
| | - Bryon A Mueller
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, 55414, USA
| | - Kelvin O Lim
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, 55414, 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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7
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White T, Blok E, Calhoun VD. Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed. Hum Brain Mapp 2022; 43:278-291. [PMID: 32621651 PMCID: PMC8675413 DOI: 10.1002/hbm.25120] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 05/12/2020] [Accepted: 06/22/2020] [Indexed: 12/19/2022] Open
Abstract
Collaborative networks and data sharing initiatives are broadening the opportunities for the advancement of science. These initiatives offer greater transparency in science, with the opportunity for external research groups to reproduce, replicate, and extend research findings. Further, larger datasets offer the opportunity to identify homogeneous patterns within subgroups of individuals, where these patterns may be obscured by the heterogeneity of the neurobiological measure in smaller samples. However, data sharing and data pooling initiatives are not without their challenges, especially with new laws that may at first glance appear quite restrictive for open science initiatives. Interestingly, what is key to some of these new laws (i.e, the European Union's general data protection regulation) is that they provide greater control of data to those who "give" their data for research purposes. Thus, the most important element in data sharing is allowing the participants to make informed decisions about how they want their data to be used, and, within the law of the specific country, to follow the participants' wishes. This framework encompasses obtaining thorough informed consent and allowing the participant to determine the extent that they want their data shared, many of the ethical and legal obstacles are reduced to just monsters under the bed. In this manuscript we discuss the many options and obstacles for data sharing, from fully open, to federated learning, to fully closed. Importantly, we highlight the intersection of data sharing, privacy, and data ownership and highlight specific examples that we believe are informative to the neuroimaging community.
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Affiliation(s)
- Tonya White
- Department of Child and Adolescent Psychiatry/PsychologyErasmus University Medical CenterRotterdamThe Netherlands
- Department of RadiologyErasmus University Medical CenterRotterdamThe Netherlands
| | - Elisabet Blok
- Department of Child and Adolescent Psychiatry/PsychologyErasmus University Medical CenterRotterdamThe Netherlands
| | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
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8
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Federated Analysis of Neuroimaging Data: A Review of the Field. Neuroinformatics 2021; 20:377-390. [PMID: 34807353 PMCID: PMC9124226 DOI: 10.1007/s12021-021-09550-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/21/2021] [Indexed: 10/19/2022]
Abstract
The field of neuroimaging has embraced sharing data to collaboratively advance our understanding of the brain. However, data sharing, especially across sites with large amounts of protected health information (PHI), can be cumbersome and time intensive. Recently, there has been a greater push towards collaborative frameworks that enable large-scale federated analysis of neuroimaging data without the data having to leave its original location. However, there still remains a need for a standardized federated approach that not only allows for data sharing adhering to the FAIR (Findability, Accessibility, Interoperability, Reusability) data principles, but also streamlines analyses and communication while maintaining subject privacy. In this paper, we review a non-exhaustive list of neuroimaging analytic tools and frameworks currently in use. We then provide an update on our federated neuroimaging analysis software system, the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC). In the end, we share insights on future research directions for federated analysis of neuroimaging data.
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9
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Basodi S, Raja R, Ray B, Gazula H, Liu J, Verner E, Calhoun VD. Federation of Brain Age Estimation in Structural Neuroimaging Data. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3854-3857. [PMID: 34892075 DOI: 10.1109/embc46164.2021.9629865] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Brain age estimation is a widely used approach to evaluate the impact of various neurological or psychiatric brain disorders on the brain developmental or aging process. Current studies show that neuroimaging data can be used to predict brain age, as it captures structural and functional changes that the brain undergoes during development and the aging process. A robust brain age prediction model not only has the potential in assisting early diagnosis of brain disorders but also helps in monitoring and evaluating effects of a treatment. Although access to large amounts of data helps build better models and validate their effectiveness, researchers often have limited access to brain data because of its challenging and expensive acquisition process. This data is not always sharable due to privacy restrictions. Decentralized models provide a way which does not require data exchange between the multiple involved groups. In this work, we propose a decentralized approach for brain age prediction and evaluate our models using features extracted from structural MRI data. Results demonstrate that our decentralized brain age model achieves similar performance compared to the models trained with all the data in one location.
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10
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A differential privacy protection query language for medical data: a proof-of-concept system validation. JOURNAL OF BIO-X RESEARCH 2021. [DOI: 10.1097/jbr.0000000000000099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
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11
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Beauvais MJS, Knoppers BM, Illes J. A marathon, not a sprint - neuroimaging, Open Science and ethics. Neuroimage 2021; 236:118041. [PMID: 33848622 DOI: 10.1016/j.neuroimage.2021.118041] [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: 12/23/2020] [Revised: 03/10/2021] [Accepted: 03/31/2021] [Indexed: 01/10/2023] Open
Abstract
Open Science is calling for a radical re-thinking of existing scientific practices. Within the neuroimaging community, Open Science practices are taking the form of open data repositories and open lab notebooks. The broad sharing of data that accompanies Open Science, however, raises some difficult ethical and legal issues. With neuroethics as a focusing lens, we explore eight central concerns posed by open data with regard to human brain imaging studies: respect for individuals and communities, concern for marginalized communities, consent, privacy protections, participatory research designs, contextual integrity, fusions of clinical and research goals, and incidental findings. Each consideration assists in bringing nuance to the potential benefits for open data sharing against associated challenges. We combine current understandings with forward-looking solutions to key issues. We conclude by underscoring the need for new policy tools to enhance the potential for responsible open data.
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Affiliation(s)
| | | | - Judy Illes
- Neuroethics Canada, Division of Neurology, Department of Medicine, University of British Columbia, Canada.
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12
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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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13
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McCarthy N, Dahlan A, Cook TS, Hare NO, Ryan ML, St John B, Lawlor A, Curran KM. Enterprise imaging and big data: A review from a medical physics perspective. Phys Med 2021; 83:206-220. [DOI: 10.1016/j.ejmp.2021.04.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 03/24/2021] [Accepted: 04/06/2021] [Indexed: 02/04/2023] Open
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14
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Decentralized Multisite VBM Analysis During Adolescence Shows Structural Changes Linked to Age, Body Mass Index, and Smoking: a COINSTAC Analysis. Neuroinformatics 2021; 19:553-566. [PMID: 33462781 DOI: 10.1007/s12021-020-09502-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/17/2020] [Indexed: 12/23/2022]
Abstract
There has been an upward trend in developing frameworks that enable neuroimaging researchers to address challenging questions by leveraging data across multiple sites all over the world. One such open-source framework is the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC) that works on Windows, macOS, and Linux operating systems and leverages containerized analysis pipelines to analyze neuroimaging data stored locally across multiple physical locations without the need for pooling the data at any point during the analysis. In this paper, the COINSTAC team partnered with a data collection consortium to implement the first-ever decentralized voxelwise analysis of brain imaging data performed outside the COINSTAC development group. Decentralized voxel-based morphometry analysis of over 2000 structural magnetic resonance imaging data sets collected at 14 different sites across two cohorts and co-located in different countries was performed to study the structural changes in brain gray matter which linked to age, body mass index (BMI), and smoking. Results produced by the decentralized analysis were consistent with and extended previous findings in the literature. In particular, a widespread cortical gray matter reduction (resembling a 'default mode network' pattern) and hippocampal increase with age, bilateral increases in the hypothalamus and basal ganglia with BMI, and cingulate and thalamic decreases with smoking. This work provides a critical real-world test of the COINSTAC framework in a "Large-N" study. It showcases the potential benefits of performing multivoxel and multivariate analyses of large-scale neuroimaging data located at multiple sites.
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15
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Palk A, Illes J, Thompson PM, Stein DJ. Ethical issues in global neuroimaging genetics collaborations. Neuroimage 2020; 221:117208. [PMID: 32736000 DOI: 10.1016/j.neuroimage.2020.117208] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Revised: 06/09/2020] [Accepted: 07/24/2020] [Indexed: 10/23/2022] Open
Abstract
Neuroimaging genetics is a rapidly developing field that combines neuropsychiatric genetics studies with imaging modalities to investigate how genetic variation influences brain structure and function. As both genetic and imaging technologies improve further, their combined power may hold translational potential in terms of improving psychiatric nosology, diagnosis, and treatment. While neuroimaging genetics studies offer a number of scientific advantages, they also face challenges. In response to some of these challenges, global neuroimaging genetics collaborations have been created to pool and compare brain data and replicate study findings. Attention has been paid to ethical issues in genetics, neuroimaging, and multi-site collaborative research, respectively, but there have been few substantive discussions of the ethical issues generated by the confluence of these areas in global neuroimaging genetics collaborations. Our discussion focuses on two areas: benefits and risks of global neuroimaging genetics collaborations and the potential impact of neuroimaging genetics research findings in low- and middle-income countries. Global neuroimaging genetics collaborations have the potential to enhance relations between countries and address global mental health challenges, however there are risks regarding inequity, exploitation and data sharing. Moreover, neuroimaging genetics research in low- and middle-income countries must address the issue of feedback of findings and the risk of essentializing and stigmatizing interpretations of mental disorders. We conclude by examining how the notion of solidarity, informed by an African Ethics framework, may justify some of the suggestions made in our discussion.
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Affiliation(s)
- Andrea Palk
- Department of Philosophy, Stellenbosch University, Bag X1, Matieland, Stellenbosch, 7602, South Africa.
| | - Judy Illes
- Neuroethics Canada, Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Dan J Stein
- SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry & Neuroscience Institute, University of Cape Town, Groote Schuur Hospital, Cape Town 7925, South Africa
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16
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17
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Baker BT, Damaraju E, Silva RF, Plis SM, Calhoun VD. Decentralized dynamic functional network connectivity: State analysis in collaborative settings. Hum Brain Mapp 2020; 41:2909-2925. [PMID: 32319193 PMCID: PMC7336163 DOI: 10.1002/hbm.24986] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 02/17/2020] [Accepted: 03/04/2020] [Indexed: 11/11/2022] Open
Abstract
As neuroimaging data increase in complexity and related analytical problems follow suite, more researchers are drawn to collaborative frameworks that leverage data sets from multiple data‐collection sites to balance out the complexity with an increased sample size. Although centralized data‐collection approaches have dominated the collaborative scene, a number of decentralized approaches—those that avoid gathering data at a shared central store—have grown in popularity. We expect the prevalence of decentralized approaches to continue as privacy risks and communication overhead become increasingly important for researchers. In this article, we develop, implement and evaluate a decentralized version of one such widely used tool: dynamic functional network connectivity. Our resulting algorithm, decentralized dynamic functional network connectivity (ddFNC), synthesizes a new, decentralized group independent component analysis algorithm (dgICA) with algorithms for decentralized k‐means clustering. We compare both individual decentralized components and the full resulting decentralized analysis pipeline against centralized counterparts on the same data, and show that both provide comparable performance. Additionally, we perform several experiments which evaluate the communication overhead and convergence behavior of various decentralization strategies and decentralized clustering algorithms. Our analysis indicates that ddFNC is a fine candidate for facilitating decentralized collaboration between neuroimaging researchers, and stands ready for the inclusion of privacy‐enabling modifications, such as differential privacy.
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Affiliation(s)
- Bradley T Baker
- Tri-Institutional Research Center in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA.,Mind Research Network, Albuquerque, New Mexico, USA.,University of New Mexico, Albuquerque, New Mexico, USA
| | - Eswar Damaraju
- Tri-Institutional Research Center in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA.,Mind Research Network, Albuquerque, New Mexico, USA.,University of New Mexico, Albuquerque, New Mexico, USA
| | - Rogers F Silva
- Tri-Institutional Research Center in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA.,Mind Research Network, Albuquerque, New Mexico, USA
| | - Sergey M Plis
- Tri-Institutional Research Center in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA.,Mind Research Network, Albuquerque, New Mexico, USA.,University of New Mexico, Albuquerque, New Mexico, USA
| | - Vince D Calhoun
- Tri-Institutional Research Center in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA.,Mind Research Network, Albuquerque, New Mexico, USA.,University of New Mexico, Albuquerque, New Mexico, USA
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18
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Crutzen R, Ygram Peters GJ, Mondschein C. Why and how we should care about the General Data Protection Regulation. Psychol Health 2019; 34:1347-1357. [PMID: 31111730 DOI: 10.1080/08870446.2019.1606222] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
The General Data Protection Regulation (GDPR) is the new European Union-wide (EU) law on data protection, which is a great step towards more comprehensive and more far-reaching protection of individuals' personal data. In this editorial, we describe why and how we - as researchers within the field of health psychology - should care about the GDPR. In the first part, we explain when the GDPR is applicable, who is accountable for data protection, and what is covered by the notions of personal data and processing. In the second part, we explain aspects of the GDPR that are relevant for researchers within the field of health psychology (e.g., obtaining informed consent, data minimisation, and open science). We focus on questions that researchers may ask themselves in their daily practice. Compliance with the GDPR requires adopting research practices (e.g., data minimisation and anonymization procedures) that are not yet commonly used, but serve the fundamental right to protection of personal data of study participants.
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Affiliation(s)
- Rik Crutzen
- Department of Health Promotion, Maastricht University/CAPHRI , Maastricht , The Netherlands
| | - Gjalt-Jorn Ygram Peters
- Faculty of Psychology and Education Science, Open University of the Netherlands , Heerlen , The Netherlands.,Department of Work & Social Psychology, Faculty of Psychology and Neuroscience, Maastricht University , Maastricht , The Netherlands
| | - Christopher Mondschein
- Maastricht European Centre on Privacy and Cybersecurity, Maastricht University , Maastricht , The Netherlands
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19
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Keshavan A, Poline JB. From the Wet Lab to the Web Lab: A Paradigm Shift in Brain Imaging Research. Front Neuroinform 2019; 13:3. [PMID: 30881299 PMCID: PMC6405692 DOI: 10.3389/fninf.2019.00003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Accepted: 01/22/2019] [Indexed: 01/08/2023] Open
Abstract
Web technology has transformed our lives, and has led to a paradigm shift in the computational sciences. As the neuroimaging informatics research community amasses large datasets to answer complex neuroscience questions, we find that the web is the best medium to facilitate novel insights by way of improved collaboration and communication. Here, we review the landscape of web technologies used in neuroimaging research, and discuss future applications, areas for improvement, and the limitations of using web technology in research. Fully incorporating web technology in our research lifecycle requires not only technical skill, but a widespread culture change; a shift from the small, focused "wet lab" to a multidisciplinary and largely collaborative "web lab."
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Affiliation(s)
- Anisha Keshavan
- Department of Speech and Hearing, Institute for Neuroengineering, eScience Institute, University of Washington, Seattle, WA, United States
| | - Jean-Baptiste Poline
- Faculty of Medicine, McConnell Brain Imaging Centre, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
- Henry H. Wheeler Jr. Brain Imaging Center, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
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20
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Imtiaz H, Sarwate AD. Distributed Differentially-Private Algorithms for Matrix and Tensor Factorization. IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2018; 12:1449-1464. [PMID: 31595179 PMCID: PMC6782067 DOI: 10.1109/jstsp.2018.2877842] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
In many signal processing and machine learning applications, datasets containing private information are held at different locations, requiring the development of distributed privacy-preserving algorithms. Tensor and matrix factorizations are key components of many processing pipelines. In the distributed setting, differentially private algorithms suffer because they introduce noise to guarantee privacy. This paper designs new and improved distributed and differentially private algorithms for two popular matrix and tensor factorization methods: principal component analysis (PCA) and orthogonal tensor decomposition (OTD). The new algorithms employ a correlated noise design scheme to alleviate the effects of noise and can achieve the same noise level as the centralized scenario. Experiments on synthetic and real data illustrate the regimes in which the correlated noise allows performance matching with the centralized setting, outperforming previous methods and demonstrating that meaningful utility is possible while guaranteeing differential privacy.
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Affiliation(s)
- Hafiz Imtiaz
- Department of Electrical and Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA. ,
| | - Anand D Sarwate
- Department of Electrical and Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 USA. ,
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21
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Heinze-Deml C, McWilliams B, Meinshausen N. Preserving privacy between features in distributed estimation. Stat (Int Stat Inst) 2018. [DOI: 10.1002/sta4.189] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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22
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Klein O, Hardwicke TE, Aust F, Breuer J, Danielsson H, Mohr AH, IJzerman H, Nilsonne G, Vanpaemel W, Frank MC. A Practical Guide for Transparency in Psychological Science. COLLABRA-PSYCHOLOGY 2018. [DOI: 10.1525/collabra.158] [Citation(s) in RCA: 82] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The credibility of scientific claims depends upon the transparency of the research products upon which they are based (e.g., study protocols, data, materials, and analysis scripts). As psychology navigates a period of unprecedented introspection, user-friendly tools and services that support open science have flourished. However, the plethora of decisions and choices involved can be bewildering. Here we provide a practical guide to help researchers navigate the process of preparing and sharing the products of their research (e.g., choosing a repository, preparing their research products for sharing, structuring folders, etc.). Being an open scientist means adopting a few straightforward research management practices, which lead to less error prone, reproducible research workflows. Further, this adoption can be piecemeal – each incremental step towards complete transparency adds positive value. Transparent research practices not only improve the efficiency of individual researchers, they enhance the credibility of the knowledge generated by the scientific community.
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Affiliation(s)
| | | | | | | | - Henrik Danielsson
- Linköping University, SE
- The Swedish Institute for Disability Research, SE
| | | | | | - Gustav Nilsonne
- Stanford University, US
- Karolinska Institutet and Stockholm University, SE
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23
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Abstract
The revolution in neuroscientific data acquisition is creating an analysis challenge. We propose leveraging cloud-computing technologies to enable large-scale neurodata storing, exploring, analyzing, and modeling. This utility will empower scientists globally to generate and test theories of brain function and dysfunction.
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24
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Ming J, Verner E, Sarwate A, Kelly R, Reed C, Kahleck T, Silva R, Panta S, Turner J, Plis S, Calhoun V. COINSTAC: Decentralizing the future of brain imaging analysis. F1000Res 2017; 6:1512. [PMID: 29123643 PMCID: PMC5657031 DOI: 10.12688/f1000research.12353.1] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/17/2017] [Indexed: 11/20/2022] Open
Abstract
In the era of Big Data, sharing neuroimaging data across multiple sites has become increasingly important. However, researchers who want to engage in centralized, large-scale data sharing and analysis must often contend with problems such as high database cost, long data transfer time, extensive manual effort, and privacy issues for sensitive data. To remove these barriers to enable easier data sharing and analysis, we introduced a new, decentralized, privacy-enabled infrastructure model for brain imaging data called COINSTAC in 2016. We have continued development of COINSTAC since this model was first introduced. One of the challenges with such a model is adapting the required algorithms to function within a decentralized framework. In this paper, we report on how we are solving this problem, along with our progress on several fronts, including additional decentralized algorithms implementation, user interface enhancement, decentralized regression statistic calculation, and complete pipeline specifications.
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Affiliation(s)
- Jing Ming
- Datalytic Solutions, Albuquerque, NM, 87106, USA.,The Mind Research Network, Albuquerque, NM, 87106, USA
| | - Eric Verner
- Datalytic Solutions, Albuquerque, NM, 87106, USA.,The Mind Research Network, Albuquerque, NM, 87106, USA
| | - Anand Sarwate
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA
| | - Ross Kelly
- The Mind Research Network, Albuquerque, NM, 87106, USA
| | - Cory Reed
- The Mind Research Network, Albuquerque, NM, 87106, USA
| | | | - Rogers Silva
- Datalytic Solutions, Albuquerque, NM, 87106, USA.,The Mind Research Network, Albuquerque, NM, 87106, USA
| | - Sandeep Panta
- Datalytic Solutions, Albuquerque, NM, 87106, USA.,The Mind Research Network, Albuquerque, NM, 87106, USA
| | - Jessica Turner
- The Mind Research Network, Albuquerque, NM, 87106, USA.,Department of Psychology and Neuroscience Institute, Georgia State University, Atlanta, GA, 30303, USA
| | - Sergey Plis
- The Mind Research Network, Albuquerque, NM, 87106, USA
| | - Vince Calhoun
- Datalytic Solutions, Albuquerque, NM, 87106, USA.,The Mind Research Network, Albuquerque, NM, 87106, USA.,Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, 87131, USA
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25
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Dluhoš P, Schwarz D, Cahn W, van Haren N, Kahn R, Španiel F, Horáček J, Kašpárek T, Schnack H. Multi-center machine learning in imaging psychiatry: A meta-model approach. Neuroimage 2017; 155:10-24. [PMID: 28428048 DOI: 10.1016/j.neuroimage.2017.03.027] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Revised: 03/06/2017] [Accepted: 03/14/2017] [Indexed: 01/17/2023] Open
Abstract
One of the biggest problems in automated diagnosis of psychiatric disorders from medical images is the lack of sufficiently large samples for training. Sample size is especially important in the case of highly heterogeneous disorders such as schizophrenia, where machine learning models built on relatively low numbers of subjects may suffer from poor generalizability. Via multicenter studies and consortium initiatives researchers have tried to solve this problem by combining data sets from multiple sites. The necessary sharing of (raw) data is, however, often hindered by legal and ethical issues. Moreover, in the case of very large samples, the computational complexity might become too large. The solution to this problem could be distributed learning. In this paper we investigated the possibility to create a meta-model by combining support vector machines (SVM) classifiers trained on the local datasets, without the need for sharing medical images or any other personal data. Validation was done in a 4-center setup comprising of 480 first-episode schizophrenia patients and healthy controls in total. We built SVM models to separate patients from controls based on three different kinds of imaging features derived from structural MRI scans, and compared models built on the joint multicenter data to the meta-models. The results showed that the combined meta-model had high similarity to the model built on all data pooled together and comparable classification performance on all three imaging features. Both similarity and performance was superior to that of the local models. We conclude that combining models is thus a viable alternative that facilitates data sharing and creating bigger and more informative models.
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Affiliation(s)
- Petr Dluhoš
- Behavioural and Social Neuroscience Group, CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic; Department of Psychiatry, University Hospital Brno and Masaryk University, Brno, Czech Republic.
| | - Daniel Schwarz
- Institute of Biostatistics and Analyses, Masaryk University, Brno, Czech Republic
| | - Wiepke Cahn
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
| | - Neeltje van Haren
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
| | - René Kahn
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
| | - Filip Španiel
- National Institute of Mental Health, Klecany, Czech Republic
| | - Jiří Horáček
- National Institute of Mental Health, Klecany, Czech Republic
| | - Tomáš Kašpárek
- Behavioural and Social Neuroscience Group, CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic; Department of Psychiatry, University Hospital Brno and Masaryk University, Brno, Czech Republic
| | - Hugo Schnack
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
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Arbabshirani MR, Plis S, Sui J, Calhoun VD. Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. Neuroimage 2017; 145:137-165. [PMID: 27012503 PMCID: PMC5031516 DOI: 10.1016/j.neuroimage.2016.02.079] [Citation(s) in RCA: 513] [Impact Index Per Article: 73.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2015] [Revised: 02/03/2016] [Accepted: 02/25/2016] [Indexed: 01/18/2023] Open
Abstract
Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hundreds of studies have been carried out for accurate classification of patients with heterogeneous mental and neurodegenerative disorders such as schizophrenia and Alzheimer's disease. More than 500 studies have been published during the past quarter century on single subject prediction focused on a multiple brain disorders. In the first part of this study, we provide a survey of more than 200 reports in this field with a focus on schizophrenia, mild cognitive impairment (MCI), Alzheimer's disease (AD), depressive disorders, autism spectrum disease (ASD) and attention-deficit hyperactivity disorder (ADHD). Detailed information about those studies such as sample size, type and number of extracted features and reported accuracy are summarized and discussed. To our knowledge, this is by far the most comprehensive review of neuroimaging-based single subject prediction of brain disorders. In the second part, we present our opinion on major pitfalls of those studies from a machine learning point of view. Common biases are discussed and suggestions are provided. Moreover, emerging trends such as decentralized data sharing, multimodal brain imaging, differential diagnosis, disease subtype classification and deep learning are also discussed. Based on this survey, there is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders. However, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper. Emerging big data technologies and advanced data-intensive machine learning methodologies such as deep learning have coincided with an increasing need for accurate, robust and generalizable single subject prediction of brain disorders during an exciting time. In this report, we survey the past and offer some opinions regarding the road ahead.
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Affiliation(s)
- Mohammad R Arbabshirani
- The Mind Research Network, Albuquerque, NM 87106, USA; Geisinger Health System, Danville, PA 17822, USA
| | - Sergey Plis
- The Mind Research Network, Albuquerque, NM 87106, USA
| | - Jing Sui
- The Mind Research Network, Albuquerque, NM 87106, USA; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM 87106, USA; Department of ECE, University of New Mexico, Albuquerque, NM, USA
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Goldstein ND, Sarwate AD. Privacy, security, and the public health researcher in the era of electronic health record research. Online J Public Health Inform 2016; 8:e207. [PMID: 28210428 PMCID: PMC5302472 DOI: 10.5210/ojphi.v8i3.7251] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Health data derived from electronic health records are increasingly utilized in large-scale population health analyses. Going hand in hand with this increase in data is an increasing number of data breaches. Ensuring privacy and security of these data is a shared responsibility between the public health researcher, collaborators, and their institutions. In this article, we review the requirements of data privacy and security and discuss epidemiologic implications of emerging technologies from the computer science community that can be used for health data. In order to ensure that our needs as researchers are captured in these technologies, we must engage in the dialogue surrounding the development of these tools.
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Affiliation(s)
- Neal D. Goldstein
- Christiana Care Health System, Department of
Pediatrics, 4745 Ogletown-Stanton Road, MAP 1, Suite 116, Newark, DE 19713
USA
- Drexel University Dornsife School of Public Health,
Department of Epidemiology and Biostatistics, 3215 Market Street, Philadelphia,
PA 19104 USA
| | - Anand D. Sarwate
- Rutgers, The State University of New Jersey,
Department of Electrical and Computer Engineering, 94 Brett Road, Piscataway, NJ
08854 USA
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Plis SM, Sarwate AD, Wood D, Dieringer C, Landis D, Reed C, Panta SR, Turner JA, Shoemaker JM, Carter KW, Thompson P, Hutchison K, Calhoun VD. COINSTAC: A Privacy Enabled Model and Prototype for Leveraging and Processing Decentralized Brain Imaging Data. Front Neurosci 2016; 10:365. [PMID: 27594820 PMCID: PMC4990563 DOI: 10.3389/fnins.2016.00365] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Accepted: 07/22/2016] [Indexed: 01/17/2023] Open
Abstract
The field of neuroimaging has embraced the need for sharing and collaboration. Data sharing mandates from public funding agencies and major journal publishers have spurred the development of data repositories and neuroinformatics consortia. However, efficient and effective data sharing still faces several hurdles. For example, open data sharing is on the rise but is not suitable for sensitive data that are not easily shared, such as genetics. Current approaches can be cumbersome (such as negotiating multiple data sharing agreements). There are also significant data transfer, organization and computational challenges. Centralized repositories only partially address the issues. We propose a dynamic, decentralized platform for large scale analyses called the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC). The COINSTAC solution can include data missing from central repositories, allows pooling of both open and "closed" repositories by developing privacy-preserving versions of widely-used algorithms, and incorporates the tools within an easy-to-use platform enabling distributed computation. We present an initial prototype system which we demonstrate on two multi-site data sets, without aggregating the data. In addition, by iterating across sites, the COINSTAC model enables meta-analytic solutions to converge to "pooled-data" solutions (i.e., as if the entire data were in hand). More advanced approaches such as feature generation, matrix factorization models, and preprocessing can be incorporated into such a model. In sum, COINSTAC enables access to the many currently unavailable data sets, a user friendly privacy enabled interface for decentralized analysis, and a powerful solution that complements existing data sharing solutions.
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Affiliation(s)
- Sergey M. Plis
- The Mind Research Network, Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA
| | - Anand D. Sarwate
- Department of Electrical and Computer Engineering, Rutgers, The State University of New JerseyPiscataway, NJ, USA
| | - Dylan Wood
- The Mind Research Network, Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA
| | - Christopher Dieringer
- The Mind Research Network, Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA
| | - Drew Landis
- The Mind Research Network, Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA
| | - Cory Reed
- The Mind Research Network, Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA
| | - Sandeep R. Panta
- The Mind Research Network, Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA
| | - Jessica A. Turner
- The Mind Research Network, Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA
- Department of Psychology and Neuroscience Institute, Georgia State UniversityAtlanta, GA, USA
| | - Jody M. Shoemaker
- The Mind Research Network, Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA
| | - Kim W. Carter
- Telethon Kids Institute, The University of Western AustraliaSubiaco, WA, Australia
| | - Paul Thompson
- Departments of Neurology, Psychiatry, Engineering, Radiology, and Pediatrics, Imaging Genetics Center, Enhancing Neuroimaging and Genetics through Meta-Analysis Center for Worldwide Medicine, Imaging, and Genomics, University of Southern CaliforniaMarina del Rey, CA, USA
| | - Kent Hutchison
- Department of Psychology and Neuroscience, University of Colorado BoulderBoulder, CO, USA
| | - Vince D. Calhoun
- The Mind Research Network, Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA
- Department of Electrical and Computer Engineering, University of New MexicoAlbuquerque, NM, USA
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29
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Quintana DS, Alvares GA, Heathers JAJ. Guidelines for Reporting Articles on Psychiatry and Heart rate variability (GRAPH): recommendations to advance research communication. Transl Psychiatry 2016; 6:e803. [PMID: 27163204 PMCID: PMC5070064 DOI: 10.1038/tp.2016.73] [Citation(s) in RCA: 222] [Impact Index Per Article: 27.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2015] [Revised: 03/18/2016] [Accepted: 03/23/2016] [Indexed: 12/11/2022] Open
Abstract
The number of publications investigating heart rate variability (HRV) in psychiatry and the behavioral sciences has increased markedly in the last decade. In addition to the significant debates surrounding ideal methods to collect and interpret measures of HRV, standardized reporting of methodology in this field is lacking. Commonly cited recommendations were designed well before recent calls to improve research communication and reproducibility across disciplines. In an effort to standardize reporting, we propose the Guidelines for Reporting Articles on Psychiatry and Heart rate variability (GRAPH), a checklist with four domains: participant selection, interbeat interval collection, data preparation and HRV calculation. This paper provides an overview of these four domains and why their standardized reporting is necessary to suitably evaluate HRV research in psychiatry and related disciplines. Adherence to these communication guidelines will help expedite the translation of HRV research into a potential psychiatric biomarker by improving interpretation, reproducibility and future meta-analyses.
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Affiliation(s)
- D S Quintana
- Division of Mental Health and Addiction, NORMENT, KG Jebsen Centre for Psychosis Research, University of Oslo, Oslo University Hospital, Oslo, Norway,Division of Mental Health and Addiction, NORMENT, KG Jebsen Centre for Psychosis Research, University of Oslo, Oslo University Hospital, Building 49, Oslo University Hospital, Ullevål, Kirkeveien 166, PO Box 4956, Nydalen, Oslo N-0424, Norway. E-mail:
| | - G A Alvares
- Telethon Kids Institute, University of Western Australia, Perth, WA, Australia,Cooperative Research Centre for Living with Autism (Autism CRC), Brisbane, QLD, Australia
| | - J A J Heathers
- School of Psychology, University of Sydney, Sydney, NSW, Australia,Department of Cardiology and Intensive Therapy, Poznań University of Medical Sciences, Poznań, Poland
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30
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Panta SR, Wang R, Fries J, Kalyanam R, Speer N, Banich M, Kiehl K, King M, Milham M, Wager TD, Turner JA, Plis SM, Calhoun VD. A Tool for Interactive Data Visualization: Application to Over 10,000 Brain Imaging and Phantom MRI Data Sets. Front Neuroinform 2016; 10:9. [PMID: 27014049 PMCID: PMC4791544 DOI: 10.3389/fninf.2016.00009] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2015] [Accepted: 02/22/2016] [Indexed: 11/21/2022] Open
Abstract
In this paper we propose a web-based approach for quick visualization of big data from brain magnetic resonance imaging (MRI) scans using a combination of an automated image capture and processing system, nonlinear embedding, and interactive data visualization tools. We draw upon thousands of MRI scans captured via the COllaborative Imaging and Neuroinformatics Suite (COINS). We then interface the output of several analysis pipelines based on structural and functional data to a t-distributed stochastic neighbor embedding (t-SNE) algorithm which reduces the number of dimensions for each scan in the input data set to two dimensions while preserving the local structure of data sets. Finally, we interactively display the output of this approach via a web-page, based on data driven documents (D3) JavaScript library. Two distinct approaches were used to visualize the data. In the first approach, we computed multiple quality control (QC) values from pre-processed data, which were used as inputs to the t-SNE algorithm. This approach helps in assessing the quality of each data set relative to others. In the second case, computed variables of interest (e.g., brain volume or voxel values from segmented gray matter images) were used as inputs to the t-SNE algorithm. This approach helps in identifying interesting patterns in the data sets. We demonstrate these approaches using multiple examples from over 10,000 data sets including (1) quality control measures calculated from phantom data over time, (2) quality control data from human functional MRI data across various studies, scanners, sites, (3) volumetric and density measures from human structural MRI data across various studies, scanners and sites. Results from (1) and (2) show the potential of our approach to combine t-SNE data reduction with interactive color coding of variables of interest to quickly identify visually unique clusters of data (i.e., data sets with poor QC, clustering of data by site) quickly. Results from (3) demonstrate interesting patterns of gray matter and volume, and evaluate how they map onto variables including scanners, age, and gender. In sum, the proposed approach allows researchers to rapidly identify and extract meaningful information from big data sets. Such tools are becoming increasingly important as datasets grow larger.
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Affiliation(s)
- Sandeep R Panta
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute Albuquerque, NM, USA
| | - Runtang Wang
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute Albuquerque, NM, USA
| | - Jill Fries
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute Albuquerque, NM, USA
| | - Ravi Kalyanam
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute Albuquerque, NM, USA
| | - Nicole Speer
- Intermountain Neuroimaging Consortium, University of Boulder Colorado Boulder, CO, USA
| | - Marie Banich
- Intermountain Neuroimaging Consortium, University of Boulder Colorado Boulder, CO, USA
| | - Kent Kiehl
- The Mind Research Network and Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA; Department of Psychology, University of New MexicoAlbuquerque, NM, USA
| | - Margaret King
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute Albuquerque, NM, USA
| | - Michael Milham
- The Child Mind Institute and The Nathan Kline Institute New York, NY, USA
| | - Tor D Wager
- Intermountain Neuroimaging Consortium, University of Boulder Colorado Boulder, CO, USA
| | - Jessica A Turner
- Department of Psychology, Georgia Tech University Atlanta, GA, USA
| | - Sergey M Plis
- The Mind Research Network and Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA; Department of Electrical & Computer Engineering, University of New MexicoAlbuquerque, NM, USA
| | - Vince D Calhoun
- The Mind Research Network and Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA; Department of Electrical & Computer Engineering, University of New MexicoAlbuquerque, NM, USA
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31
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Open Data: Can It Prevent Research Fraud, Promote Reproducibility, and Enable Big Data Analytics In Clinical Research? Ann Thorac Surg 2015; 100:1539-40. [PMID: 26522522 DOI: 10.1016/j.athoracsur.2015.08.041] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2015] [Revised: 08/13/2015] [Accepted: 08/17/2015] [Indexed: 11/22/2022]
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32
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Cheng X, Marcus D, Van Horn JD, Luo Q, Mattay VS, Weinberger DR. Going beyond the current neuroinformatics infrastructure. Front Neuroinform 2015; 9:15. [PMID: 26136680 PMCID: PMC4468376 DOI: 10.3389/fninf.2015.00015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2015] [Accepted: 05/27/2015] [Indexed: 11/19/2022] Open
Affiliation(s)
- Xi Cheng
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus Baltimore, MD, USA
| | - Daniel Marcus
- Department of Radiology, Washington University School of Medicine St. Louis, MO, USA
| | - John D Van Horn
- Laboratory of Neuro Imaging, Department of Neurology, Institute of Neuroimaging and Informatics, University of Southern California Los Angeles, CA, USA
| | - Qian Luo
- Center for Military Psychiatry and Neuroscience Research, Walter Reed Army Research Institute Silver Spring, MD, USA †
| | - Venkata S Mattay
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus Baltimore, MD, USA
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus Baltimore, MD, USA
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33
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Joly Y, Dyke SO, Cheung WA, Rothstein MA, Pastinen T. Risk of re-identification of epigenetic methylation data: a more nuanced response is needed. Clin Epigenetics 2015; 7:45. [PMID: 25904991 PMCID: PMC4405848 DOI: 10.1186/s13148-015-0079-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2015] [Accepted: 04/06/2015] [Indexed: 11/10/2022] Open
Abstract
In this letter to the editor, we respond to the recent publication by Philibert et al. Methylation array data can simultaneously identify individuals and convey protected health information: an unrecognized ethical concern (Clinical Epigenetics 2014, 6:28). Further discussion of the issues raised by the risk of re-identification of epigenetic methylation data is needed, and a more nuanced approach should be taken with respect to its implications for data sharing policy than the one provided.
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Affiliation(s)
- Yann Joly
- McGill Epigenome Mapping Centre, Centre of Genomics and Policy, Faculty of Medicine, McGill University, 740 Dr. Penfield Avenue, Montreal, Quebec H3A 0G1 Canada
| | - Stephanie Om Dyke
- McGill Epigenome Mapping Centre, Centre of Genomics and Policy, Faculty of Medicine, McGill University, 740 Dr. Penfield Avenue, Montreal, Quebec H3A 0G1 Canada
| | - Warren A Cheung
- McGill Epigenome Mapping Centre, Faculty of Medicine, McGill University and Genome Quebec Innovation Centre, 740 Dr. Penfield Avenue, Montreal, Quebec H3A 0G1 Canada
| | - Mark A Rothstein
- Institute for Bioethics, Health Policy and Law, University of Louisville School of Medicine, 501 East Broadway, Suite #310, Louisville, KY 40292 USA
| | - Tomi Pastinen
- McGill Epigenome Mapping Centre, Faculty of Medicine, McGill University and Genome Quebec Innovation Centre, 740 Dr. Penfield Avenue, Montreal, Quebec H3A 0G1 Canada
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34
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Calhoun VD. A spectrum of sharing: maximization of information content for brain imaging data. Gigascience 2015; 4:2. [PMID: 25653850 PMCID: PMC4316396 DOI: 10.1186/s13742-014-0042-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2014] [Accepted: 12/17/2014] [Indexed: 12/14/2022] Open
Abstract
Efforts to expand sharing of neuroimaging data have been growing exponentially in recent years. There are several different types of data sharing which can be considered to fall along a spectrum, ranging from simpler and less informative to more complex and more informative. In this paper we consider this spectrum for three domains: data capture, data density, and data analysis. Here the focus is on the right end of the spectrum, that is, how to maximize the information content while addressing the challenges. A summary of associated challenges of and possible solutions is presented in this review and includes: 1) a discussion of tools to monitor quality of data as it is collected and encourage adoption of data mapping standards; 2) sharing of time-series data (not just summary maps or regions); and 3) the use of analytic approaches which maximize sharing potential as much as possible. Examples of existing solutions for each of these points, which we developed in our lab, are also discussed including the use of a comprehensive beginning-to-end neuroinformatics platform and the use of flexible analytic approaches, such as independent component analysis and multivariate classification approaches, such as deep learning.
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Affiliation(s)
- Vince D Calhoun
- />The Mind Research Network & LBERI, 1101 Yale Blvd NE, Albuquerque, New Mexico 87106 USA
- />Department of ECE, University of New Mexico, Albuquerque, New Mexico USA
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