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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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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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Li X, Gu Y, Dvornek N, Staib LH, Ventola P, Duncan JS. Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results. Med Image Anal 2020; 65:101765. [PMID: 32679533 DOI: 10.1016/j.media.2020.101765] [Citation(s) in RCA: 107] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 04/16/2020] [Accepted: 06/22/2020] [Indexed: 12/17/2022]
Abstract
Deep learning models have shown their advantage in many different tasks, including neuroimage analysis. However, to effectively train a high-quality deep learning model, the aggregation of a significant amount of patient information is required. The time and cost for acquisition and annotation in assembling, for example, large fMRI datasets make it difficult to acquire large numbers at a single site. However, due to the need to protect the privacy of patient data, it is hard to assemble a central database from multiple institutions. Federated learning allows for population-level models to be trained without centralizing entities' data by transmitting the global model to local entities, training the model locally, and then averaging the gradients or weights in the global model. However, some studies suggest that private information can be recovered from the model gradients or weights. In this work, we address the problem of multi-site fMRI classification with a privacy-preserving strategy. To solve the problem, we propose a federated learning approach, where a decentralized iterative optimization algorithm is implemented and shared local model weights are altered by a randomization mechanism. Considering the systemic differences of fMRI distributions from different sites, we further propose two domain adaptation methods in this federated learning formulation. We investigate various practical aspects of federated model optimization and compare federated learning with alternative training strategies. Overall, our results demonstrate that it is promising to utilize multi-site data without data sharing to boost neuroimage analysis performance and find reliable disease-related biomarkers. Our proposed pipeline can be generalized to other privacy-sensitive medical data analysis problems. Our code is publicly available at: https://github.com/xxlya/Fed_ABIDE/.
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Affiliation(s)
- Xiaoxiao Li
- Biomedical Engineering, Yale University, New Haven, CT, 06511, USA.
| | - Yufeng Gu
- College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, 310058, China
| | - Nicha Dvornek
- Biomedical Engineering, Yale University, New Haven, CT, 06511, USA; Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06511, USA
| | - Lawrence H Staib
- Biomedical Engineering, Yale University, New Haven, CT, 06511, USA; Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06511, USA; Electrical Engineering, Yale University, New Haven, CT, 06511, USA
| | - Pamela Ventola
- Child Study Center, Yale School of Medicine, New Have, CT, 06511, USA
| | - James S Duncan
- Biomedical Engineering, Yale University, New Haven, CT, 06511, USA; Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06511, USA; Electrical Engineering, Yale University, New Haven, CT, 06511, USA; Statistics & Data Science, Yale University New Haven, CT, 06511, USA.
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Sui J, Liu M, Lee JH, Zhang J, Calhoun V. Deep learning methods and applications in neuroimaging. J Neurosci Methods 2020; 339:108718. [PMID: 32272117 DOI: 10.1016/j.jneumeth.2020.108718] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Affiliation(s)
- Jing Sui
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, CAS Center for Excellence in Brain Science and Intelligence Technology, Beijing, 100049, China; Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, 30303, USA.
| | - MingXia Liu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, NC, USA
| | - Jong-Hwan Lee
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
| | - Jun Zhang
- Department of Electrical and Computer Engineering (ECE), Duke University, Durham, NC, USA
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, 30303, USA
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