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Bhandari R, Phatangare RV, Eckert MA, Vaden KI, Wang JZ. Dyslexia Data Consortium Repository: A Data Sharing and Delivery Platform for Research. BRAIN INFORMATICS : 16TH INTERNATIONAL CONFERENCE, BI 2023, HOBOKEN, NJ, USA, AUGUST 1-3, 2023, PROCEEDINGS. INTERNATIONAL CONFERENCE ON BRAIN INFORMATICS (16TH : 2023 : HOBOKEN, N.J.) 2023; 13974:167-178. [PMID: 38352916 PMCID: PMC10859776 DOI: 10.1007/978-3-031-43075-6_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2024]
Abstract
Specific learning disability of reading, or dyslexia, affects 5-17% of the population in the United States. Research on the neurobiology of dyslexia has included studies with relatively small sample sizes across research sites, thus limiting inference and the application of novel methods, such as deep learning. To address these issues and facilitate open science, we developed an online platform for data-sharing and advanced research programs to enhance opportunities for replication by providing researchers with secondary data that can be used in their research (https://www.dyslexiadata.org). This platform integrates a set of well-designed machine learning algorithms and tools to generate secondary datasets, such as cortical thickness, as well as regional brain volume metrics that have been consistently associated with dyslexia. Researchers can access shared data to address fundamental questions about dyslexia and development, replicate research findings, apply new methods, and educate the next generation of researchers. The overarching goal of this platform is to advance our understanding of a disorder that has significant academic, social, and economic impacts on children, their families, and society.
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Affiliation(s)
| | | | - Mark A Eckert
- Department of Otolaryngology - Head and Neck Surgery, Medical University of South Carolina, Charleston, SC, USA
| | - Kenneth I Vaden
- Department of Otolaryngology - Head and Neck Surgery, Medical University of South Carolina, Charleston, SC, USA
| | - James Z Wang
- School of Computing, Clemson University, Clemson, SC, USA
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Eke DO, Bernard A, Bjaalie JG, Chavarriaga R, Hanakawa T, Hannan AJ, Hill SL, Martone ME, McMahon A, Ruebel O, Crook S, Thiels E, Pestilli F. International data governance for neuroscience. Neuron 2022; 110:600-612. [PMID: 34914921 PMCID: PMC8857067 DOI: 10.1016/j.neuron.2021.11.017] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 06/16/2021] [Accepted: 11/15/2021] [Indexed: 12/19/2022]
Abstract
As neuroscience projects increase in scale and cross international borders, different ethical principles, national and international laws, regulations, and policies for data sharing must be considered. These concerns are part of what is collectively called data governance. Whereas neuroscience data transcend borders, data governance is typically constrained within geopolitical boundaries. An international data governance framework and accompanying infrastructure can assist investigators, institutions, data repositories, and funders with navigating disparate policies. Here, we propose principles and operational considerations for how data governance in neuroscience can be navigated at an international scale and highlight gaps, challenges, and opportunities in a global brain data ecosystem. We consider how to approach data governance in a way that balances data protection requirements and the need for open science, so as to promote international collaboration through federated constructs such as the International Brain Initiative (IBI).
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Affiliation(s)
- Damian O Eke
- Centre for Computing and Social Responsibility, De Montfort University, Leicester, UK; Human Brain Project
| | | | | | - Ricardo Chavarriaga
- Center for Artificial Intelligence, School of Engineering, Zurich University of Applied Sciences, Zurich, Switzerland
| | | | - Anthony J Hannan
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia
| | - Sean L Hill
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | | | | | - Oliver Ruebel
- Scientific Data Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Sharon Crook
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, USA
| | - Edda Thiels
- National Science Foundation, Alexandria, VA, USA
| | - Franco Pestilli
- Department of Psychology, Center for Perceptual Systems, Center for Theoretical and Computational Neuroscience, and Institute for Neuroscience, University of Texas, Austin, TX, USA.
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Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools. Phys Med 2021; 83:25-37. [DOI: 10.1016/j.ejmp.2021.02.007] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 01/27/2021] [Accepted: 02/15/2021] [Indexed: 02/06/2023] Open
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Vaden KI, Gebregziabher M, Dyslexia Data Consortium, Eckert MA. Fully synthetic neuroimaging data for replication and exploration. Neuroimage 2020; 223:117284. [PMID: 32828925 PMCID: PMC7688496 DOI: 10.1016/j.neuroimage.2020.117284] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 08/12/2020] [Accepted: 08/16/2020] [Indexed: 11/19/2022] Open
Abstract
Scientific transparency, data exploration, and education are advanced through data sharing. However, risk for disclosure of personal information and institutional data sharing regulations can impede human subject/patient data sharing and thus limit open science initiatives. Sharing fully synthetic data is an alternative when it is not possible to share real or observed data. Here we describe a data sharing approach that borrows principles and methods from multiple imputation to replace observed values with synthetic values, thereby creating a fully synthetic neuroimaging dataset that accurately represents the covariance structure of the observed dataset. Predictor tables composed of demographic, site, behavioral and total intracranial volume (ICV) variables from 264 pediatric cases were used to create synthetic predictor tables, which were then used to synthesize gray matter images derived from T1-weighted data. The synthetic predictor tables demonstrated pooled variance and statistical estimates that closely approximated the observed data, as reflected in measures of efficiency and statistical bias. Similarly, the synthetic gray matter data accurately represented the variance and voxel-level associations with predictor variables (age, sex, verbal IQ, and ICV). The magnitude and spatial distribution of gray matter effects in the observed imaging data were replicated in the pooled results from the synthetic datasets. This approach for generating fully synthetic neuroimaging data has widespread potential for data sharing, including replication, new discovery, and education. Fully synthetic neuroimaging datasets can enable data-sharing because it accurately represents patterns of variance in the original data, while diminishing the risk of privacy disclosures that can accompany neuroimaging data sharing.
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Affiliation(s)
- Kenneth I Vaden
- Department of Otolaryngology-Head and Neck Surgery, Medical University of South Carolina, 135 Rutledge Avenue, MSC 550, Charleston, SC, Unites States.
| | - Mulugeta Gebregziabher
- Division of Biostatistics and Epidemiology, Medical University of South Carolina, Unites States
| | | | - Mark A Eckert
- Department of Otolaryngology-Head and Neck Surgery, Medical University of South Carolina, 135 Rutledge Avenue, MSC 550, Charleston, SC, Unites States.
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de Sitter A, Visser M, Brouwer I, Cover KS, van Schijndel RA, Eijgelaar RS, Müller DMJ, Ropele S, Kappos L, Rovira Á, Filippi M, Enzinger C, Frederiksen J, Ciccarelli O, Guttmann CRG, Wattjes MP, Witte MG, de Witt Hamer PC, Barkhof F, Vrenken H. Facing privacy in neuroimaging: removing facial features degrades performance of image analysis methods. Eur Radiol 2019; 30:1062-1074. [PMID: 31691120 PMCID: PMC6957560 DOI: 10.1007/s00330-019-06459-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 08/16/2019] [Accepted: 09/13/2019] [Indexed: 11/30/2022]
Abstract
Background Recent studies have created awareness that facial features can be reconstructed from high-resolution MRI. Therefore, data sharing in neuroimaging requires special attention to protect participants’ privacy. Facial features removal (FFR) could alleviate these concerns. We assessed the impact of three FFR methods on subsequent automated image analysis to obtain clinically relevant outcome measurements in three clinical groups. Methods FFR was performed using QuickShear, FaceMasking, and Defacing. In 110 subjects of Alzheimer’s Disease Neuroimaging Initiative, normalized brain volumes (NBV) were measured by SIENAX. In 70 multiple sclerosis patients of the MAGNIMS Study Group, lesion volumes (WMLV) were measured by lesion prediction algorithm in lesion segmentation toolbox. In 84 glioblastoma patients of the PICTURE Study Group, tumor volumes (GBV) were measured by BraTumIA. Failed analyses on FFR-processed images were recorded. Only cases in which all image analyses completed successfully were analyzed. Differences between outcomes obtained from FFR-processed and full images were assessed, by quantifying the intra-class correlation coefficient (ICC) for absolute agreement and by testing for systematic differences using paired t tests. Results Automated analysis methods failed in 0–19% of cases in FFR-processed images versus 0–2% of cases in full images. ICC for absolute agreement ranged from 0.312 (GBV after FaceMasking) to 0.998 (WMLV after Defacing). FaceMasking yielded higher NBV (p = 0.003) and WMLV (p ≤ 0.001). GBV was lower after QuickShear and Defacing (both p < 0.001). Conclusions All three outcome measures were affected differently by FFR, including failure of analysis methods and both “random” variation and systematic differences. Further study is warranted to ensure high-quality neuroimaging research while protecting participants’ privacy. Key Points • Protecting participants’ privacy when sharing MRI data is important. • Impact of three facial features removal methods on subsequent analysis was assessed in three clinical groups. • Removing facial features degrades performance of image analysis methods. Electronic supplementary material The online version of this article (10.1007/s00330-019-06459-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- A de Sitter
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience Amsterdam UMC, location VUmc, Amsterdam, the Netherlands.
| | - M Visser
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience Amsterdam UMC, location VUmc, Amsterdam, the Netherlands
| | - I Brouwer
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience Amsterdam UMC, location VUmc, Amsterdam, the Netherlands
| | - K S Cover
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience Amsterdam UMC, location VUmc, Amsterdam, the Netherlands
| | - R A van Schijndel
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience Amsterdam UMC, location VUmc, Amsterdam, the Netherlands
| | - R S Eijgelaar
- Department of Radiotherapy, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - D M J Müller
- Department of Neurosurgery, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands
| | - S Ropele
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - L Kappos
- Department of Neurology, University Hospital, Kantonsspital, Basel, Switzerland
| | - Á Rovira
- Unitat de Ressonància Magnètica (Servei de Radiologia), Hospital universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - M Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, UniSR, Milan, Italy
| | - C Enzinger
- Division of Neuroradiology, Vascular and Interventional Radiology, Department of Radiology, Medical University of Graz, Graz, Austria
| | - J Frederiksen
- Department of Neurology, Glostrup University Hospital, Copenhagen, Denmark
| | - O Ciccarelli
- UK/NIHR UCL-UCLH Biomedical Research Centre, Institute of Neurology, UCL, London, UK
| | - C R G Guttmann
- Center for Neurological Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - M P Wattjes
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience Amsterdam UMC, location VUmc, Amsterdam, the Netherlands.,Department of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Hannover, Germany
| | - M G Witte
- Department of Radiotherapy, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - P C de Witt Hamer
- Department of Neurosurgery, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands
| | - F Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience Amsterdam UMC, location VUmc, Amsterdam, the Netherlands.,Institutes of Neurology & Healthcare Engineering, UCL, London, UK
| | - H Vrenken
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience Amsterdam UMC, location VUmc, Amsterdam, the Netherlands
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Eckert MA, Vaden KI, Maxwell AB, Cute SL, Gebregziabher M, Berninger VW. Common Brain Structure Findings Across Children with Varied Reading Disability Profiles. Sci Rep 2017; 7:6009. [PMID: 28729533 PMCID: PMC5519686 DOI: 10.1038/s41598-017-05691-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Accepted: 05/31/2017] [Indexed: 11/12/2022] Open
Abstract
Dyslexia is a developmental disorder in reading that exhibits varied patterns of expression across children. Here we examined the degree to which different kinds of reading disabilities (defined as profiles or patterns of reading problems) contribute to brain morphology results in Jacobian determinant images that represent local brain shape and volume. A matched-pair brain morphometry approach was used to control for confounding from brain size and research site effects in this retrospective multi-site study of 134 children from eight different research sites. Parietal operculum, corona radiata, and internal capsule differences between cases and controls were consistently observed across children with evidence of classic dyslexia, specific comprehension deficit, and language learning disability. Thus, there can be common brain morphology findings across children with quite varied reading disability profiles that we hypothesize compound the developmental difficulties of children with unique reading disability profiles and reasons for their reading disability.
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Affiliation(s)
- Mark A Eckert
- Department of Otolaryngology - Head and Neck Surgery, Medical University of South Carolina, Charleston, SC, 29425, USA.
| | - Kenneth I Vaden
- Department of Otolaryngology - Head and Neck Surgery, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Amanda B Maxwell
- Department of Otolaryngology - Head and Neck Surgery, Medical University of South Carolina, Charleston, SC, 29425, USA
| | | | - Mulugeta Gebregziabher
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Virginia W Berninger
- Department of Educational Psychology, University of Washington, Seattle, WA, 98105, USA
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