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Lee BK, Schendel DE, Shea LL. Big data in autism research: Methodological challenges and solutions. Autism Res 2023; 16:1852-1858. [PMID: 37596816 PMCID: PMC11168478 DOI: 10.1002/aur.3007] [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] [Received: 12/05/2022] [Accepted: 07/22/2023] [Indexed: 08/20/2023]
Abstract
While the concept of big data has emerged over the past decade as a hot topic in nearly all areas of scientific inquiry, it has rarely been discussed in the context of autism research. In this commentary we describe aspects of big data that are relevant to autism research and methodological issues such as confounding and data error that can hamper scientific investigation. Although big data studies can have transformative impact, bigger is not always better, and big data require the same methodological considerations and interdisciplinary collaboration as "small data" to extract useful scientific insight.
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Affiliation(s)
- Brian K. Lee
- Department of Epidemiology and Biostatistics, Drexel University School of Public Health, 3215 Market St, Philadelphia, PA, USA, 19104
- A.J. Drexel Autism Institute, 3020 Market St, Philadelphia, PA, USA, 19104
| | - Diana E. Schendel
- Department of Epidemiology and Biostatistics, Drexel University School of Public Health, 3215 Market St, Philadelphia, PA, USA, 19104
- A.J. Drexel Autism Institute, 3020 Market St, Philadelphia, PA, USA, 19104
| | - Lindsay L. Shea
- A.J. Drexel Autism Institute, 3020 Market St, Philadelphia, PA, USA, 19104
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2
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Fortier I, Wey TW, Bergeron J, Pinot de Moira A, Nybo-Andersen AM, Bishop T, Murtagh MJ, Miočević M, Swertz MA, van Enckevort E, Marcon Y, Mayrhofer MT, Ornelas JP, Sebert S, Santos AC, Rocha A, Wilson RC, Griffith LE, Burton P. Life course of retrospective harmonization initiatives: key elements to consider. J Dev Orig Health Dis 2023; 14:190-198. [PMID: 35957574 DOI: 10.1017/s2040174422000460] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Optimizing research on the developmental origins of health and disease (DOHaD) involves implementing initiatives maximizing the use of the available cohort study data; achieving sufficient statistical power to support subgroup analysis; and using participant data presenting adequate follow-up and exposure heterogeneity. It also involves being able to undertake comparison, cross-validation, or replication across data sets. To answer these requirements, cohort study data need to be findable, accessible, interoperable, and reusable (FAIR), and more particularly, it often needs to be harmonized. Harmonization is required to achieve or improve comparability of the putatively equivalent measures collected by different studies on different individuals. Although the characteristics of the research initiatives generating and using harmonized data vary extensively, all are confronted by similar issues. Having to collate, understand, process, host, and co-analyze data from individual cohort studies is particularly challenging. The scientific success and timely management of projects can be facilitated by an ensemble of factors. The current document provides an overview of the 'life course' of research projects requiring harmonization of existing data and highlights key elements to be considered from the inception to the end of the project.
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Affiliation(s)
- Isabel Fortier
- Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - Tina W Wey
- Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - Julie Bergeron
- Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | | | | | - Tom Bishop
- Epidemiology Unit, University of Cambridge, England, UK
| | - Madeleine J Murtagh
- School of Social and Political Sciences, University of Glasgow, Scotland, UK
| | - Milica Miočević
- Department of Psychology, McGill University, Montreal, QC, Canada
| | - Morris A Swertz
- University Medical Center Groningen, University of Groningen, Netherlands
| | - Esther van Enckevort
- Department of Genetics, University Medical Center Groningen, University of Groningen, Netherlands
| | | | | | - Jos Pedro Ornelas
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
| | | | - Ana Cristina Santos
- Department of Epidemiology, Institute of Public Health of the University of Porto, Portugal
| | - Artur Rocha
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
| | - Rebecca C Wilson
- Department of Public Health, Policy and Systems, University of Liverpool, Liverpool, England, UK
| | - Lauren E Griffith
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Paul Burton
- Population Health Sciences Institute, Newcastle University, Newcastle-upon-Tyne, England, UK
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3
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Dwyer D, Koutsouleris N. Annual Research Review: Translational machine learning for child and adolescent psychiatry. J Child Psychol Psychiatry 2022; 63:421-443. [PMID: 35040130 DOI: 10.1111/jcpp.13545] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/06/2021] [Indexed: 12/14/2022]
Abstract
Children and adolescents could benefit from the use of predictive tools that facilitate personalized diagnoses, prognoses, and treatment selection. Such tools have not yet been deployed using traditional statistical methods, potentially due to the limitations of the paradigm and the need to leverage large amounts of digital data. This review will suggest that a machine learning approach could address these challenges and is designed to introduce new readers to the background, methods, and results in the field. A rationale is first introduced followed by an outline of fundamental elements of machine learning approaches. To provide an overview of the use of the techniques in child and adolescent literature, a scoping review of broad trends is then presented. Selected studies are also highlighted in order to draw attention to research areas that are closest to translation and studies that exhibit a high degree of experimental innovation. Limitations to the research, and machine learning approaches generally, are outlined in the penultimate section highlighting issues related to sample sizes, validation, clinical utility, and ethical challenges. Finally, future directions are discussed that could enhance the possibility of clinical implementation and address specific questions relevant to the child and adolescent psychiatry. The review gives a broad overview of the machine learning paradigm in order to highlight the benefits of a shift in perspective towards practically oriented statistical solutions that aim to improve clinical care of children and adolescents.
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Affiliation(s)
- Dominic Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia.,Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Max-Planck Institute of Psychiatry, Munich, Germany.,Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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4
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Saha DK, Calhoun VD, Du Y, Fu Z, Kwon SM, Sarwate AD, Panta SR, Plis SM. Privacy-preserving quality control of neuroimaging datasets in federated environments. Hum Brain Mapp 2022; 43:2289-2310. [PMID: 35243723 PMCID: PMC8996357 DOI: 10.1002/hbm.25788] [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: 05/05/2021] [Revised: 12/10/2021] [Accepted: 12/13/2021] [Indexed: 11/18/2022] Open
Abstract
Privacy concerns for rare disease data, institutional or IRB policies, access to local computational or storage resources or download capabilities are among the reasons that may preclude analyses that pool data to a single site. A growing number of multisite projects and consortia were formed to function in the federated environment to conduct productive research under constraints of this kind. In this scenario, a quality control tool that visualizes decentralized data in its entirety via global aggregation of local computations is especially important, as it would allow the screening of samples that cannot be jointly evaluated otherwise. To solve this issue, we present two algorithms: decentralized data stochastic neighbor embedding, dSNE, and its differentially private counterpart, DP‐dSNE. We leverage publicly available datasets to simultaneously map data samples located at different sites according to their similarities. Even though the data never leaves the individual sites, dSNE does not provide any formal privacy guarantees. To overcome that, we rely on differential privacy: a formal mathematical guarantee that protects individuals from being identified as contributors to a dataset. We implement DP‐dSNE with AdaCliP, a method recently proposed to add less noise to the gradients per iteration. We introduce metrics for measuring the embedding quality and validate our algorithms on these metrics against their centralized counterpart on two toy datasets. Our validation on six multisite neuroimaging datasets shows promising results for the quality control tasks of visualization and outlier detection, highlighting the potential of our private, decentralized visualization approach.
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Affiliation(s)
- Debbrata K Saha
- Georgia Institute of Technology, Atlanta, Georgia, USA.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Vince D Calhoun
- Georgia Institute of Technology, Atlanta, Georgia, USA.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.,Georgia State University, Atlanta, Georgia, USA
| | - Yuhui Du
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Soo Min Kwon
- Rutgers, The State University of New Jersey, New Brunswick, New Jersey, USA
| | - Anand D Sarwate
- Rutgers, The State University of New Jersey, New Brunswick, New Jersey, USA
| | - Sandeep R Panta
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Sergey M Plis
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.,Georgia State University, Atlanta, Georgia, USA
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5
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Avraam D, Jones E, Burton P. A deterministic approach for protecting privacy in sensitive personal data. BMC Med Inform Decis Mak 2022; 22:24. [PMID: 35090447 PMCID: PMC8796499 DOI: 10.1186/s12911-022-01754-4] [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] [Received: 03/17/2021] [Accepted: 01/09/2022] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Data privacy is one of the biggest challenges for any organisation which processes personal data, especially in the area of medical research where data include sensitive information about patients and study participants. Sharing of data is therefore problematic, which is at odds with the principle of open data that is so important to the advancement of society and science. Several statistical methods and computational tools have been developed to help data custodians and analysts overcome this challenge. METHODS In this paper, we propose a new deterministic approach for anonymising personal data. The method stratifies the underlying data by the categorical variables and re-distributes the continuous variables through a k nearest neighbours based algorithm. RESULTS We demonstrate the use of the deterministic anonymisation on real data, including data from a sample of Titanic passengers, and data from participants in the 1958 Birth Cohort. CONCLUSIONS The proposed procedure makes data re-identification difficult while minimising the loss of utility (by preserving the spatial properties of the underlying data); the latter means that informative statistical analysis can still be conducted.
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Affiliation(s)
- Demetris Avraam
- Population Health Sciences Institute, Newcastle University, Newcastle, UK
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Elinor Jones
- Department of Statistical Science, University College London, London, UK
| | - Paul Burton
- Population Health Sciences Institute, Newcastle University, Newcastle, UK
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6
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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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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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Imtiaz H, Mohammadi J, Silva R, Baker B, Plis SM, Sarwate AD, Calhoun VD. A Correlated Noise-assisted Decentralized Differentially Private Estimation Protocol, and its application to fMRI Source Separation. IEEE TRANSACTIONS ON SIGNAL PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2021; 69:6355-6370. [PMID: 35755147 PMCID: PMC9232162 DOI: 10.1109/tsp.2021.3126546] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Blind source separation algorithms such as independent component analysis (ICA) are widely used in the analysis of neuroimaging data. To leverage larger sample sizes, different data holders/sites may wish to collaboratively learn feature representations. However, such datasets are often privacy-sensitive, precluding centralized analyses that pool the data at one site. In this work, we propose a differentially private algorithm for performing ICA in a decentralized data setting. Due to the high dimension and small sample size, conventional approaches to decentralized differentially private algorithms suffer in terms of utility. When centralizing the data is not possible, we investigate the benefit of enabling limited collaboration in the form of generating jointly distributed random noise. We show that such (anti) correlated noise improves the privacy-utility trade-off, and can reach the same level of utility as the corresponding non-private algorithm for certain parameter choices. We validate this benefit using synthetic and real neuroimaging datasets. We conclude that it is possible to achieve meaningful utility while preserving privacy, even in complex signal processing systems.
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Affiliation(s)
- Hafiz Imtiaz
- Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
| | | | - Rogers Silva
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, 55 Park Place NE, Atlanta, GA 30303
| | - Bradley Baker
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, 55 Park Place NE, Atlanta, GA 30303
| | - Sergey M Plis
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, 55 Park Place NE, Atlanta, GA 30303
| | - Anand D Sarwate
- Department of Electrical and Computer Engineering, Rutgers University, 94 Brett Road, Piscataway, NJ 08854
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, 55 Park Place NE, Atlanta, GA 30303
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9
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Koutsouleris N, Worthington M, Dwyer DB, Kambeitz-Ilankovic L, Sanfelici R, Fusar-Poli P, Rosen M, Ruhrmann S, Anticevic A, Addington J, Perkins DO, Bearden CE, Cornblatt BA, Cadenhead KS, Mathalon DH, McGlashan T, Seidman L, Tsuang M, Walker EF, Woods SW, Falkai P, Lencer R, Bertolino A, Kambeitz J, Schultze-Lutter F, Meisenzahl E, Salokangas RKR, Hietala J, Brambilla P, Upthegrove R, Borgwardt S, Wood S, Gur RE, McGuire P, Cannon TD. Toward Generalizable and Transdiagnostic Tools for Psychosis Prediction: An Independent Validation and Improvement of the NAPLS-2 Risk Calculator in the Multisite PRONIA Cohort. Biol Psychiatry 2021; 90:632-642. [PMID: 34482951 PMCID: PMC8500930 DOI: 10.1016/j.biopsych.2021.06.023] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 06/03/2021] [Accepted: 06/27/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND Transition to psychosis is among the most adverse outcomes of clinical high-risk (CHR) syndromes encompassing ultra-high risk (UHR) and basic symptom states. Clinical risk calculators may facilitate an early and individualized interception of psychosis, but their real-world implementation requires thorough validation across diverse risk populations, including young patients with depressive syndromes. METHODS We validated the previously described NAPLS-2 (North American Prodrome Longitudinal Study 2) calculator in 334 patients (26 with transition to psychosis) with CHR or recent-onset depression (ROD) drawn from the multisite European PRONIA (Personalised Prognostic Tools for Early Psychosis Management) study. Patients were categorized into three risk enrichment levels, ranging from UHR, over CHR, to a broad-risk population comprising patients with CHR or ROD (CHR|ROD). We assessed how risk enrichment and different predictive algorithms influenced prognostic performance using reciprocal external validation. RESULTS After calibration, the NAPLS-2 model predicted psychosis with a balanced accuracy (BAC) (sensitivity, specificity) of 68% (73%, 63%) in the PRONIA-UHR cohort, 67% (74%, 60%) in the CHR cohort, and 70% (73%, 66%) in patients with CHR|ROD. Multiple model derivation in PRONIA-CHR|ROD and validation in NAPLS-2-UHR patients confirmed that broader risk definitions produced more accurate risk calculators (CHR|ROD-based vs. UHR-based performance: 67% [68%, 66%] vs. 58% [61%, 56%]). Support vector machines were superior in CHR|ROD (BAC = 71%), while ridge logistic regression and support vector machines performed similarly in CHR (BAC = 67%) and UHR cohorts (BAC = 65%). Attenuated psychotic symptoms predicted psychosis across risk levels, while younger age and reduced processing speed became increasingly relevant for broader risk cohorts. CONCLUSIONS Clinical-neurocognitive machine learning models operating in young patients with affective and CHR syndromes facilitate a more precise and generalizable prediction of psychosis. Future studies should investigate their therapeutic utility in large-scale clinical trials.
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Affiliation(s)
- Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany; Max-Planck Institute of Psychiatry, Munich, Germany; Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, United Kingdom.
| | | | - Dominic B Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Lana Kambeitz-Ilankovic
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany; Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Rachele Sanfelici
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Paolo Fusar-Poli
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy; Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, United Kingdom
| | - Marlene Rosen
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Stephan Ruhrmann
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Alan Anticevic
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Jean Addington
- Hotchkiss Brain Institute, Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada
| | - Diana O Perkins
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina
| | - Carrie E Bearden
- Departments of Psychiatry and Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, California
| | | | | | - Daniel H Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, California; San Francisco VA Medical Center, San Francisco, California
| | - Thomas McGlashan
- Department of Psychiatry, Yale University, New Haven, Connecticut
| | - Larry Seidman
- Department of Psychiatry, Harvard Medical School at Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Ming Tsuang
- University of California San Diego, San Diego, California
| | - Elaine F Walker
- Department of Psychology and Psychiatry, Emory University, Atlanta, Georgia
| | - Scott W Woods
- Department of Psychiatry, Yale University, New Haven, Connecticut
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Rebekka Lencer
- Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany; Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Alessandro Bertolino
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Joseph Kambeitz
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Frauke Schultze-Lutter
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine-Universität Düsseldorf, Germany
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine-Universität Düsseldorf, Germany
| | | | - Jarmo Hietala
- Department of Psychiatry, University of Turku, Turku, Finland
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Rachel Upthegrove
- Institute of Mental Health, University of Birmingham, Birmingham, United Kingdom; School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Stefan Borgwardt
- Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany; Department of Psychiatry (Psychiatric University Hospital, UPK), University of Basel, Basel, Switzerland
| | - Stephen Wood
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia; Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Victoria, Australia
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Philip McGuire
- Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, United Kingdom
| | - Tyrone D Cannon
- Department of Psychology, Yale University, New Haven, Connecticut; Department of Psychiatry, Yale University, New Haven, Connecticut
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10
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Hall GL, Filipow N, Ruppel G, Okitika T, Thompson B, Kirkby J, Steenbruggen I, Cooper BG, Stanojevic S. Official ERS technical standard: Global Lung Function Initiative reference values for static lung volumes in individuals of European ancestry. Eur Respir J 2021; 57:57/3/2000289. [PMID: 33707167 DOI: 10.1183/13993003.00289-2020] [Citation(s) in RCA: 153] [Impact Index Per Article: 51.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Accepted: 07/27/2020] [Indexed: 11/05/2022]
Abstract
BACKGROUND Measurement of lung volumes across the life course is critical to the diagnosis and management of lung disease. The aim of the study was to use the Global Lung Function Initiative methodology to develop all-age multi-ethnic reference equations for lung volume indices determined using body plethysmography and gas dilution techniques. METHODS Static lung volume data from body plethysmography and gas dilution techniques from individual, healthy participants were collated. Reference equations were derived using the LMS (lambda-mu-sigma) method and the generalised additive models of location shape and scale programme in R. The impact of measurement technique, equipment type and being overweight or obese on the derived lung volume reference ranges was assessed. RESULTS Data from 17 centres were submitted and reference equations were derived from 7190 observations from participants of European ancestry between the ages of 5 and 80 years. Data from non-European ancestry populations were insufficient to develop multi-ethnic equations. Measurements of functional residual capacity (FRC) collected using plethysmography and dilution techniques showed physiologically insignificant differences and were combined. Sex-specific reference equations including height and age were developed for total lung capacity (TLC), FRC, residual volume (RV), inspiratory capacity, vital capacity, expiratory reserve volume and RV/TLC. The derived equations were similar to previously published equations for FRC and TLC, with closer agreement during childhood and adolescence than in adulthood. CONCLUSIONS Global Lung Function Initiative reference equations for lung volumes provide a generalisable standard for reporting and interpretation of lung volumes measurements in individuals of European ancestry.
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Affiliation(s)
- Graham L Hall
- Children's Lung Health, Wal-yan Respiratory Research Centre, Telethon Kids Institute, Perth, Australia .,School of Physiotherapy and Exercise Science, Curtin University, Perth, Australia
| | - Nicole Filipow
- Translational Medicine, Hospital for Sick Children, Toronto, ON, Canada
| | - Gregg Ruppel
- Pulmonary, Critical Care and Sleep Medicine, Saint Louis University School of Medicine, St Louis, MO, USA
| | - Tolu Okitika
- Children's Lung Health, Wal-yan Respiratory Research Centre, Telethon Kids Institute, Perth, Australia
| | - Bruce Thompson
- School of Health Sciences, Swinburne University of Technology, Melbourne, Australia
| | - Jane Kirkby
- Respiratory Medicine, Sheffield Children's Hospital NHS Foundation Trust, Sheffield, UK
| | | | - Brendan G Cooper
- Lung Function and Sleep, University Hospital Birmingham and Institute of Clinical Sciences, University of Birmingham, Birmingham, UK
| | - Sanja Stanojevic
- Translational Medicine, Hospital for Sick Children, Toronto, ON, Canada
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Khomtchouk BB, Tran DT, Vand KA, Might M, Gozani O, Assimes TL. Cardioinformatics: the nexus of bioinformatics and precision cardiology. Brief Bioinform 2020; 21:2031-2051. [PMID: 31802103 PMCID: PMC7947182 DOI: 10.1093/bib/bbz119] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 08/08/2019] [Accepted: 08/13/2019] [Indexed: 12/12/2022] Open
Abstract
Cardiovascular disease (CVD) is the leading cause of death worldwide, causing over 17 million deaths per year, which outpaces global cancer mortality rates. Despite these sobering statistics, most bioinformatics and computational biology research and funding to date has been concentrated predominantly on cancer research, with a relatively modest footprint in CVD. In this paper, we review the existing literary landscape and critically assess the unmet need to further develop an emerging field at the multidisciplinary interface of bioinformatics and precision cardiovascular medicine, which we refer to as 'cardioinformatics'.
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Affiliation(s)
- Bohdan B Khomtchouk
- Department of Biology, Stanford University, Stanford, CA, USA
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Stanford, CA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Department of Medicine, Section of Computational Biomedicine and Biomedical Data Science, University of Chicago, Chicago, IL, USA
| | - Diem-Trang Tran
- School of Computing, University of Utah, Salt Lake City, UT, USA
| | | | - Matthew Might
- Hugh Kaul Personalized Medicine Institute, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Or Gozani
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Themistocles L Assimes
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Stanford, CA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
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12
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van Wanrooij LL, Hoevenaar-Blom MP, Coley N, Ngandu T, Meiller Y, Guillemont J, Rosenberg A, Beishuizen CRL, Moll van Charante EP, Soininen H, Brayne C, Andrieu S, Kivipelto M, Richard E. Pooling individual participant data from randomized controlled trials: Exploring potential loss of information. PLoS One 2020; 15:e0232970. [PMID: 32396543 PMCID: PMC7217432 DOI: 10.1371/journal.pone.0232970] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Accepted: 04/24/2020] [Indexed: 11/30/2022] Open
Abstract
Background Pooling individual participant data to enable pooled analyses is often complicated by diversity in variables across available datasets. Therefore, recoding original variables is often necessary to build a pooled dataset. We aimed to quantify how much information is lost in this process and to what extent this jeopardizes validity of analyses results. Methods Data were derived from a platform that was developed to pool data from three randomized controlled trials on the effect of treatment of cardiovascular risk factors on cognitive decline or dementia. We quantified loss of information using the R-squared of linear regression models with pooled variables as a function of their original variable(s). In case the R-squared was below 0.8, we additionally explored the potential impact of loss of information for future analyses. We did this second step by comparing whether the Beta coefficient of the predictor differed more than 10% when adding original or recoded variables as a confounder in a linear regression model. In a simulation we randomly sampled numbers, recoded those < = 1000 to 0 and those >1000 to 1 and varied the range of the continuous variable, the ratio of recoded zeroes to recoded ones, or both, and again extracted the R-squared from linear models to quantify information loss. Results The R-squared was below 0.8 for 8 out of 91 recoded variables. In 4 cases this had a substantial impact on the regression models, particularly when a continuous variable was recoded into a discrete variable. Our simulation showed that the least information is lost when the ratio of recoded zeroes to ones is 1:1. Conclusions Large, pooled datasets provide great opportunities, justifying the efforts for data harmonization. Still, caution is warranted when using recoded variables which variance is explained limitedly by their original variables as this may jeopardize the validity of study results.
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Affiliation(s)
- Lennard L. van Wanrooij
- Department of Neurology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- * E-mail:
| | - Marieke P. Hoevenaar-Blom
- Department of Neurology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Nicola Coley
- Department of Epidemiology and Public Health, Toulouse University Hospital, Toulouse, France
- INSERM, University of Toulouse UMR1027, Toulouse, France
| | - Tiia Ngandu
- Chronic Disease Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland
| | - Yannick Meiller
- Department of Information and Operations Management, ESCP Europe, Paris, France
| | | | - Anna Rosenberg
- Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | | | | | - Hilkka Soininen
- Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
- Neurocenter, Neurology, Kuopio University Hospital, Kuopio, Finland
| | - Carol Brayne
- Department of Public Health and Primary Care, Cambridge Institute of Public Health, University of Cambridge, Cambridge, United Kingdom
| | - Sandrine Andrieu
- Department of Epidemiology and Public Health, Toulouse University Hospital, Toulouse, France
- INSERM, University of Toulouse UMR1027, Toulouse, France
| | - Miia Kivipelto
- Chronic Disease Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland
- Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
- Aging Research Center, Karolinska Institutet, Stockholm University, Stockholm, Sweden
- Karolinska Institutet Center for Alzheimer Research, Stockholm, Sweden
| | - Edo Richard
- Department of Neurology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
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13
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Hazo JB, Brunn M, Wykes T, McDaid D, Dorsey M, Demotes-Mainard J, van der Feltz-Cornelis CM, Wahlbeck K, Knappe S, Meyer-Lindenberg A, Obradors-Tarragó C, Haro JM, Leboyer M, Chevreul K. European mental health research resources: Picture and recommendations of the ROAMER project. Eur Neuropsychopharmacol 2019; 29:179-194. [PMID: 30579654 DOI: 10.1016/j.euroneuro.2018.11.1111] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Revised: 11/13/2018] [Accepted: 11/15/2018] [Indexed: 11/29/2022]
Abstract
As part of the Roamer project, we sought to have a picture of the available mental health research (MHR) funding, capacity-building and infrastructures resources and to establish consensus-based recommendations that would allow an increase of European MHR resources and enable better use and accessibility to them. The methods fell into three sections (i) a review of the literature, (ii) a mental health-related keywords search within the Cordis®, On-Course® and Meril® databases which contain information on European research funding, training and infrastructures. These reviews provided an overview that was presented to (iii) two experts workshops with 28 participants drawn from academic which identified gaps and produced recommendations. The literature review illustrates the debates in the scientific community on funding, training and infrastructures. The database searches estimated the fraction of health research resources available for mental health. Eight overarching goals for MHR resources were identified by the workshops; each of them was carried out with several practical recommendations. Resources for MHR are scarce considering the burden of mental disorders, the high rate of return of MHR and the under-investment of the field. The recommendations are urgently warranted to increase resources and their optimal access and use.
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Affiliation(s)
- Jean-Baptiste Hazo
- ECEVE, UMRS 1123, Université Paris Diderot, Sorbonne Paris Cité, INSERM, Paris, France; AP-HP, URC-Eco, DHU PePSY, F-75 004 Paris, France; Fondation FondaMental, French Scientific Foundation, Créteil, France; World Health Organization Collaborating Centre for Research and Training in Mental Health, CCOMS, Lille 59260, Hellemmes, France.
| | - Matthias Brunn
- ECEVE, UMRS 1123, Université Paris Diderot, Sorbonne Paris Cité, INSERM, Paris, France; AP-HP, URC-Eco, DHU PePSY, F-75 004 Paris, France; Fondation FondaMental, French Scientific Foundation, Créteil, France
| | - Til Wykes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, South London and Maudsley NHS Foundation Trust, UK
| | - David McDaid
- PSSRU, London School of Economics and Political Science, London, UK
| | - Maya Dorsey
- ECEVE, UMRS 1123, Université Paris Diderot, Sorbonne Paris Cité, INSERM, Paris, France; AP-HP, URC-Eco, DHU PePSY, F-75 004 Paris, France; Fondation FondaMental, French Scientific Foundation, Créteil, France
| | | | - Christina M van der Feltz-Cornelis
- Department of Health Sciences, MHARG, University of York, York, United Kingdom; Tranzo Department, Tilburg University, Tilburg, The Netherlands
| | | | - Susanne Knappe
- Institut für Klinische Psychologie und Psychotherapie, Behaviorale Epidemiologie & Center for Clinical Epidemiology and Longitudinal Studies, Technische Universität Dresden, Chemnitzer Str. 46, 01187 Dresden, Germany
| | - Andreas Meyer-Lindenberg
- Medical Faculty Mannheim, Central Institute of Mental Health, University of Heidelberg, 68159 Mannheim, Germany
| | | | - Josep Maria Haro
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain; Institut de Recerca Sant Joan de Déu, Sant Boi de llobregat, Universitat de Barcelona, Barcelona, Spain
| | - Marion Leboyer
- Fondation FondaMental, French Scientific Foundation, Créteil, France; AP-HP, Department of Psychiatry of Mondor Hospital, DHU PePSY, Paris-Est-Créteil University (UPEC), Créteil, France; INSERM, U955, Translational Psychiatry, Créteil, France
| | - Karine Chevreul
- ECEVE, UMRS 1123, Université Paris Diderot, Sorbonne Paris Cité, INSERM, Paris, France; AP-HP, URC-Eco, DHU PePSY, F-75 004 Paris, France
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14
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Lewis N, Gazula H, Plis SM, Calhoun VD. Decentralized distribution-sampled classification models with application to brain imaging. J Neurosci Methods 2019; 329:108418. [PMID: 31630085 DOI: 10.1016/j.jneumeth.2019.108418] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Revised: 08/27/2019] [Accepted: 08/27/2019] [Indexed: 11/29/2022]
Abstract
BACKGROUND In this age of big data, certain models require very large data stores in order to be informative and accurate. In many cases however, the data are stored in separate locations requiring data transfer between local sites which can cause various practical hurdles, such as privacy concerns or heavy network load. This is especially true for medical imaging data, which can be constrained due to the health insurance portability and accountability act (HIPAA) which provides security protocols for medical data. Medical imaging datasets can also contain many thousands or millions of features, requiring heavy network load. NEW METHOD Our research expands upon current decentralized classification research by implementing a new singleshot method for both neural networks and support vector machines. Our approach is to estimate the statistical distribution of the data at each local site and pass this information to the other local sites where each site resamples from the individual distributions and trains a model on both locally available data and the resampled data. The model for each local site produces its own accuracy value which are then averaged together to produce the global average accuracy. RESULTS We show applications of our approach to handwritten digit classification as well as to multi-subject classification of brain imaging data collected from patients with schizophrenia and healthy controls. Overall, the results showed comparable classification accuracy to the centralized model with lower network load than multishot methods. COMPARISON WITH EXISTING METHODS Many decentralized classifiers are multishot, requiring heavy network traffic. Our model attempts to alleviate this load while preserving prediction accuracy. CONCLUSIONS We show that our proposed approach performs comparably to a centralized approach while minimizing network traffic compared to multishot methods.
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Affiliation(s)
- Noah Lewis
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States; Department of Computer Science, The University of New Mexico, Albuquerque, NM, United States.
| | - Harshvardhan Gazula
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - 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, United States
| | - 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, United States; Department of Computer Science, The University of New Mexico, Albuquerque, NM, United States; Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, NM, United States
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15
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Bai D, Yip BHK, Windham GC, Sourander A, Francis R, Yoffe R, Glasson E, Mahjani B, Suominen A, Leonard H, Gissler M, Buxbaum JD, Wong K, Schendel D, Kodesh A, Breshnahan M, Levine SZ, Parner ET, Hansen SN, Hultman C, Reichenberg A, Sandin S. Association of Genetic and Environmental Factors With Autism in a 5-Country Cohort. JAMA Psychiatry 2019; 76:1035-1043. [PMID: 31314057 PMCID: PMC6646998 DOI: 10.1001/jamapsychiatry.2019.1411] [Citation(s) in RCA: 299] [Impact Index Per Article: 59.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
IMPORTANCE The origins and development of autism spectrum disorder (ASD) remain unresolved. No individual-level study has provided estimates of additive genetic, maternal, and environmental effects in ASD across several countries. OBJECTIVE To estimate the additive genetic, maternal, and environmental effects in ASD. DESIGN, SETTING, AND PARTICIPANTS Population-based, multinational cohort study including full birth cohorts of children from Denmark, Finland, Sweden, Israel, and Western Australia born between January 1, 1998, and December 31, 2011, and followed up to age 16 years. Data were analyzed from September 23, 2016 through February 4, 2018. MAIN OUTCOMES AND MEASURES Across 5 countries, models were fitted to estimate variance components describing the total variance in risk for ASD occurrence owing to additive genetics, maternal, and shared and nonshared environmental effects. RESULTS The analytic sample included 2 001 631 individuals, of whom 1 027 546 (51.3%) were male. Among the entire sample, 22 156 were diagnosed with ASD. The median (95% CI) ASD heritability was 80.8% (73.2%-85.5%) for country-specific point estimates, ranging from 50.9% (25.1%-75.6%) (Finland) to 86.8% (69.8%-100.0%) (Israel). For the Nordic countries combined, heritability estimates ranged from 81.2% (73.9%-85.3%) to 82.7% (79.1%-86.0%). Maternal effect was estimated to range from 0.4% to 1.6%. Estimates of genetic, maternal, and environmental effects for autistic disorder were similar with ASD. CONCLUSIONS AND RELEVANCE Based on population data from 5 countries, the heritability of ASD was estimated to be approximately 80%, indicating that the variation in ASD occurrence in the population is mostly owing to inherited genetic influences, with no support for contribution from maternal effects. The results suggest possible modest differences in the sources of ASD risk between countries.
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Affiliation(s)
- Dan Bai
- Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR
| | - Benjamin Hon Kei Yip
- Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Gayle C. Windham
- Center for Health Communities, Environmental Health Investigations Branch, California Department of Public Health, Richmond
| | - Andre Sourander
- Department of Child Psychiatry, Turku University, Turku University Hospital, Turku, Finland
| | - Richard Francis
- Telethon Kids Institute, Centre for Child Health Research, The University of Western Australia, Perth, Australia
| | | | - Emma Glasson
- Telethon Kids Institute, Centre for Child Health Research, The University of Western Australia, Perth, Australia
| | - Behrang Mahjani
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York,Seaver Autism Center for Research and Treatment at Mount Sinai, New York, New York
| | - Auli Suominen
- Department of Child Psychiatry, Turku University, Turku University Hospital, Turku, Finland
| | - Helen Leonard
- Telethon Kids Institute, Centre for Child Health Research, The University of Western Australia, Perth, Australia
| | - Mika Gissler
- Department of Child Psychiatry, Turku University, Turku University Hospital, Turku, Finland,Information Services Department, National Institute for Health and Welfare, Helsinki, Finland,Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Joseph D. Buxbaum
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York,Seaver Autism Center for Research and Treatment at Mount Sinai, New York, New York,Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York,Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York,The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Kingsley Wong
- Telethon Kids Institute, Centre for Child Health Research, The University of Western Australia, Perth, Australia
| | - Diana Schendel
- Department of Public Health, Aarhus University, Aarhus, Denmark,Department of Economics and Business, National Centre for Register-based Research, Aarhus University, Aarhus, Denmark,iPSYCH, Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus University, Aarhus, Denmark
| | - Arad Kodesh
- Department of Community Mental Health, University of Haifa, Haifa, Israel,Meuhedet Health Services, Israel
| | - Michaeline Breshnahan
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York,New York State Psychiatric Institute, New York
| | - Stephen Z. Levine
- Department of Community Mental Health, University of Haifa, Haifa, Israel
| | - Erik T. Parner
- Section for Biostatistics, Department of Public Health, Aarhus University, Aarhus, Denmark
| | - Stefan N. Hansen
- Section for Biostatistics, Department of Public Health, Aarhus University, Aarhus, Denmark
| | - Christina Hultman
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Abraham Reichenberg
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York,Seaver Autism Center for Research and Treatment at Mount Sinai, New York, New York,Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York,The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Sven Sandin
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York,Seaver Autism Center for Research and Treatment at Mount Sinai, New York, New York
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16
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Hansen SN, Schendel DE, Francis RW, Windham GC, Bresnahan M, Levine SZ, Reichenberg A, Gissler M, Kodesh A, Bai D, Yip BHK, Leonard H, Sandin S, Buxbaum JD, Hultman C, Sourander A, Glasson EJ, Wong K, Öberg R, Parner ET. Recurrence Risk of Autism in Siblings and Cousins: A Multinational, Population-Based Study. J Am Acad Child Adolesc Psychiatry 2019; 58:866-875. [PMID: 30851399 PMCID: PMC6708733 DOI: 10.1016/j.jaac.2018.11.017] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Revised: 11/05/2018] [Accepted: 12/12/2018] [Indexed: 10/27/2022]
Abstract
OBJECTIVE Familial recurrence risk is an important population-level measure of the combined genetic and shared familial liability of autism spectrum disorder (ASD). Objectives were to estimate ASD recurrence risk among siblings and cousins by varying degree of relatedness and by sex. METHOD This is a population-based cohort study of livebirths from 1998 to 2007 in California, Denmark, Finland, Israel, Sweden and Western Australia followed through 2011 to 2015. Subjects were monitored for an ASD diagnosis in their older siblings or cousins (exposure) and for their ASD diagnosis (outcome). The relative recurrence risk was estimated for different sibling and cousin pairs, for each site separately and combined, and by sex. RESULTS During follow-up, 29,998 cases of ASD were observed among the 2,551,918 births used to estimate recurrence in ASD and 33,769 cases of childhood autism (CA) were observed among the 6,110,942 births used to estimate CA recurrence. Compared with the risk in unaffected families, there was an 8.4-fold increase in the risk of ASD following an older sibling with ASD and a 17.4-fold increase in the risk of CA following an older sibling with CA. A 2-fold increase in the risk for cousin recurrence was observed for the 2 disorders. There also was a significant difference in sibling ASD recurrence risk by sex. CONCLUSION The present estimates of relative recurrence risks for ASD and CA will assist clinicians and families in understanding autism risk in the context of other families in their population. The observed variation by sex underlines the need to deepen the understanding of factors influencing ASD familial risk.
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Affiliation(s)
| | - Diana E Schendel
- Aarhus University, Aarhus, Denmark; Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, National Centre for Register-based Research, Aarhus University
| | - Richard W Francis
- Telethon Kids Institute, The University of Western Australia, Perth, Australia
| | - Gayle C Windham
- Environmental Health Investigations Branch, California Department of Public Health, Richmond, CA
| | - Michaeline Bresnahan
- Columbia University, Mailman School of Public Health, New York, NY; New York State Psychiatric Institute, New York, NY
| | | | - Abraham Reichenberg
- Icahn School of Medicine at Mount Sinai, New York, NY; Seaver Autism Center for Research and Treatment at Mount Sinai, New York, NY
| | - Mika Gissler
- Division of Family Medicine, Karolinska Institutet; the University of Turku, Research Centre for Child Psychiatry, Turku, Finland; THL National Institute for Health and Welfare, Information Services Department, Helsinki, Finland
| | - Arad Kodesh
- University of Haifa, Haifa, Israel; Meuhedet Health Services, Tel Aviv, Israel
| | - Dan Bai
- The Chinese University of Hong Kong, the Jockey Club of School of Public Health and Primary Care, Division of Family Medicine, Hong Kong
| | - Benjamin Hon Kei Yip
- The Chinese University of Hong Kong, the Jockey Club of School of Public Health and Primary Care, Division of Family Medicine, Hong Kong
| | - Helen Leonard
- Telethon Kids Institute, The University of Western Australia, Perth, Australia
| | - Sven Sandin
- Icahn School of Medicine at Mount Sinai, New York, NY; Seaver Autism Center for Research and Treatment at Mount Sinai, New York, NY; Karolinska Institutet, Stockholm, Sweden
| | - Joseph D Buxbaum
- Icahn School of Medicine at Mount Sinai, New York, NY; Seaver Autism Center for Research and Treatment at Mount Sinai, New York, NY
| | | | | | - Emma J Glasson
- Telethon Kids Institute, The University of Western Australia, Perth, Australia
| | - Kingsley Wong
- Telethon Kids Institute, The University of Western Australia, Perth, Australia
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17
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Kalter J, Sweegers MG, Verdonck-de Leeuw IM, Brug J, Buffart LM. Development and use of a flexible data harmonization platform to facilitate the harmonization of individual patient data for meta-analyses. BMC Res Notes 2019; 12:164. [PMID: 30902064 PMCID: PMC6431032 DOI: 10.1186/s13104-019-4210-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Accepted: 03/18/2019] [Indexed: 12/13/2022] Open
Abstract
Objective Harmonizing individual patient data (IPD) for meta-analysis has clinical and statistical advantages. Harmonizing IPD from multiple studies may benefit from a flexible data harmonization platform (DHP) that allows harmonization of IPD already during data collection. This paper describes the development and use of a flexible DHP that was initially developed for the Predicting OptimaL cAncer RehabIlitation and Supportive care (POLARIS) study. Results The DHP that we developed (I) allows IPD harmonization with a flexible approach, (II) has the ability to store data in a centralized and secured database server with large capacity, (III) is transparent and easy in use, and (IV) has the ability to export harmonized IPD and corresponding data dictionary to a statistical program. The DHP uses Microsoft Access as front-end application and requires a relational database management system such as Microsoft Structured Query Language (SQL) Server or MySQL as back-end application. The DHP consists of five user friendly interfaces which support the user to import original study data, to harmonize the data with a master data dictionary, and to export the harmonized data into a statistical software program of choice for further analyses. The DHP is now also adopted in two other studies. Electronic supplementary material The online version of this article (10.1186/s13104-019-4210-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Joeri Kalter
- Department of Epidemiology and Biostatistics, Amsterdam University Medical Centres, Vrije Universiteit Amsterdam, De Boelelaan 1089a, 1081 HV, Amsterdam, The Netherlands.,Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.,Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Maike G Sweegers
- Department of Epidemiology and Biostatistics, Amsterdam University Medical Centres, Vrije Universiteit Amsterdam, De Boelelaan 1089a, 1081 HV, Amsterdam, The Netherlands.,Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.,Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Irma M Verdonck-de Leeuw
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.,Cancer Center Amsterdam, Amsterdam, The Netherlands.,Department of Otolaryngology-Head and Neck Surgery, Amsterdam University Medical Centres, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Clinical- Developmental- and Neuro Psychology, Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Johannes Brug
- Department of Epidemiology and Biostatistics, Amsterdam University Medical Centres, Vrije Universiteit Amsterdam, De Boelelaan 1089a, 1081 HV, Amsterdam, The Netherlands.,Amsterdam School of Communication Research (ASCoR), University of Amsterdam, Amsterdam, The Netherlands
| | - Laurien M Buffart
- Department of Epidemiology and Biostatistics, Amsterdam University Medical Centres, Vrije Universiteit Amsterdam, De Boelelaan 1089a, 1081 HV, Amsterdam, The Netherlands. .,Amsterdam Public Health Research Institute, Amsterdam, The Netherlands. .,Cancer Center Amsterdam, Amsterdam, The Netherlands. .,Department of Medical Oncology, Amsterdam University Medical Centres, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
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18
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van Veen EB. Observational health research in Europe: understanding the General Data Protection Regulation and underlying debate. Eur J Cancer 2018; 104:70-80. [PMID: 30336359 DOI: 10.1016/j.ejca.2018.09.032] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Accepted: 09/27/2018] [Indexed: 01/26/2023]
Abstract
Insights into the incidence and survival of cancer, the influence of lifestyle and environmental factors and the interaction of treatment regimens with outcomes are hugely dependent on observational research, patient data derived from the healthcare system and from volunteers participating in cohort studies, often non-selective. Since 25th May 2018, the European General Data Protection Regulation (GDPR) applies to such data. The GDPR focusses on more individual control for data subjects of 'their' data. Yet, the GDPR was preceded by a long debate. The research community participated actively in that debate, and as a result, the GDPR has research exemptions as well. Some of those apply directly; other exemptions need to be implemented into national law. Those exemptions will be discussed together with a general outline of the GDPR. I propose a substantive definition of research-absent in the GDPR-which can warrant its special status in the GDPR. The debate is not over yet. Most legal texts exhibit ambiguity and are interpreted against a background of values. In this case, those could be subsumed under informational self-determination versus solidarity and the deeper meaning of autonomy. Values will also guide national implementation and their interpretation. The value of individual control or informational self-determination should be balanced by nuanced visions about our mutual dependency in healthcare, as an ever-learning system, especially in the European solidarity-based healthcare systems. Good research governance might be a way forward to escape the consent or anonymise dichotomy.
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Affiliation(s)
- Evert-Ben van Veen
- MLC Foundation, Dagelijkse Groenmarkt 2, 2513 AL Den Haag, the Netherlands.
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19
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Yoshida K, Gruber S, Fireman BH, Toh S. Comparison of privacy-protecting analytic and data-sharing methods: A simulation study. Pharmacoepidemiol Drug Saf 2018; 27:1034-1041. [PMID: 30022561 PMCID: PMC6135666 DOI: 10.1002/pds.4615] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2017] [Revised: 04/09/2018] [Accepted: 06/11/2018] [Indexed: 11/06/2022]
Abstract
PURPOSE Privacy-protecting analytic and data-sharing methods that minimize the disclosure risk of sensitive information are increasingly important due to the growing interest in utilizing data across multiple sources. We conducted a simulation study to examine how avoiding sharing individual-level data in a distributed data network can affect analytic results. METHODS The base scenario had four sites of varying sizes with 5% outcome incidence, 50% treatment prevalence, and seven confounders. We varied treatment prevalence, outcome incidence, treatment effect, site size, number of sites, and covariate distribution. Confounding adjustment was conducted using propensity score or disease risk score. We compared analyses of three types of aggregate-level data requested from sites: risk-set, summary-table, or effect-estimate data (meta-analysis) with benchmark results of analysis of pooled individual-level data. We assessed bias and precision of hazard ratio estimates as well as the accuracy of standard error estimates. RESULTS All the aggregate-level data-sharing approaches, regardless of confounding adjustment methods, successfully approximated pooled individual-level data analysis in most simulation scenarios. Meta-analysis showed minor bias when using inverse probability of treatment weights (IPTW) in infrequent exposure (5%), rare outcome (0.01%), and small site (5,000 patients) settings. SE estimates became less accurate for IPTW risk-set approach with less frequent exposure and for propensity score-matching meta-analysis approach with rare outcomes. CONCLUSIONS Overall, we found that we can avoid sharing individual-level data and obtain valid results in many settings, although care must be taken with meta-analysis approach in infrequent exposure and rare outcome scenarios, particularly when confounding adjustment is performed with IPTW.
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Affiliation(s)
- Kazuki Yoshida
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Susan Gruber
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Bruce H Fireman
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Sengwee Toh
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
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20
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Gazula H, Baker BT, Damaraju E, Plis SM, Panta SR, Silva RF, Calhoun VD. Decentralized Analysis of Brain Imaging Data: Voxel-Based Morphometry and Dynamic Functional Network Connectivity. Front Neuroinform 2018; 12:55. [PMID: 30210327 PMCID: PMC6119966 DOI: 10.3389/fninf.2018.00055] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Accepted: 08/06/2018] [Indexed: 12/30/2022] Open
Abstract
In the field of neuroimaging, there is a growing interest in developing collaborative frameworks that enable researchers to address challenging questions about the human brain by leveraging data across multiple sites all over the world. Additionally, efforts are also being directed at developing algorithms that enable collaborative analysis and feature learning from multiple sites without requiring the often large data to be centrally located. In this paper, we propose two new decentralized algorithms: (1) A decentralized regression algorithm for performing a voxel-based morphometry analysis on structural magnetic resonance imaging (MRI) data and, (2) A decentralized dynamic functional network connectivity algorithm which includes decentralized group ICA and sliding-window analysis of functional MRI data. We compare results against those obtained from their pooled (or centralized) counterparts on the same data i.e., as if they are at one site. Results produced by the decentralized algorithms are similar to the pooled-case and showcase the potential of performing multi-voxel and multivariate analyses of data located at multiple sites. Such approaches enable many more collaborative and comparative analysis in the context of large-scale neuroimaging studies.
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Affiliation(s)
| | - Bradley T Baker
- The Mind Research Network, Albuquerque, NM, United States.,Department of Computer Science, The University of New Mexico, Albuquerque, NM, United States
| | - Eswar Damaraju
- The Mind Research Network, Albuquerque, NM, United States.,Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, NM, United States
| | - Sergey M Plis
- The Mind Research Network, Albuquerque, NM, United States
| | | | - Rogers F Silva
- The Mind Research Network, Albuquerque, NM, United States
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM, United States.,Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, NM, United States
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21
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Yip BHK, Leonard H, Stock S, Stoltenberg C, Francis RW, Gissler M, Gross R, Schendel D, Sandin S. Caesarean section and risk of autism across gestational age: a multi-national cohort study of 5 million births. Int J Epidemiol 2018; 46:429-439. [PMID: 28017932 DOI: 10.1093/ije/dyw336] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/31/2016] [Indexed: 12/16/2022] Open
Abstract
Background The positive association between caesarean section (CS) and autism spectrum disorder (ASD) may be attributed to preterm delivery. However, due to lack of statistical power, no previous study thoroughly examined this association across gestational age. Moreover, most studies did not differentiate between emergency and planned CS. Methods Using population-based registries of four Nordic countries and Western Australia, our study population included 4 987 390 singletons surviving their first year of life, which included 671 646 CS deliveries and 31 073 ASD children. We used logistic regression to estimate odds ratios (OR) and their 95% confidence intervals (CI) for CS, adjusted for gestational age, site, maternal age and birth year. Stratified analyses were conducted by both gestational age subgroups and by week of gestation. We compared emergency versus planned CS to investigate their potential difference in the risk of ASD. Results Compared with vaginal delivery, the overall adjusted OR for ASD in CS delivery was 1.26 (95% CI 1.22-1.30). Stratified ORs were 1.25 (1.15-1.37), 1.16 (1.09-1.23), 1.34 (1.28-1.40) and 1.17 (1.04-1.30) for subgroups of gestational weeks 26-36, 37-38, 39-41 and 42-44, respectively. CS was significantly associated with risk of ASD for each week of gestation, from week 36 to 42, consistently across study sites (OR ranged 1.16-1.38). There was no statistically significant difference between emergency and planned CS in the risk of ASD. Conclusion Across the five countries, emergency or planned CS is consistently associated with a modest increased risk of ASD from gestational weeks 36 to 42 when compared with vaginal delivery.
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Affiliation(s)
- Benjamin Hon Kei Yip
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Helen Leonard
- Telethon Kids Institute.,Centre for Child Health Research, University of Western Australia, Crawley, WA, Australia
| | - Sarah Stock
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,MRC Centre for Reproductive Health, University of Edinburgh Queen's Medical Research Institute, Edinburgh, UK.,Norwegian Institute of Public Health, Oslo, Norway
| | - Camilla Stoltenberg
- Norwegian Institute of Public Health, Oslo, Norway.,Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
| | - Richard W Francis
- Telethon Kids Institute.,Centre for Child Health Research, University of Western Australia, Crawley, WA, Australia
| | - Mika Gissler
- National Institute for Health and Welfare, Helsinki, Finland.,Department of Child Psychiatry, Turku University and Turku University Hospital, Turku, Finland
| | - Raz Gross
- Department of Epidemiology and Preventive Medicine, Tel Aviv University, Tel Aviv, Israel.,Division of Psychiatry, Chaim Sheba Medical Center, Tel Hashomer, Israel
| | - Diana Schendel
- Department of Public Health, Institute of Epidemiology and Social Medicine, Aarhus University, Aarhus, Denmark.,Department of Economics and Business, National Centre for Register-based Research, Aarhus, Denmark.,Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus University, Aarhus, Denmark
| | - Sven Sandin
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA and
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22
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Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, Way GP, Ferrero E, Agapow PM, Zietz M, Hoffman MM, Xie W, Rosen GL, Lengerich BJ, Israeli J, Lanchantin J, Woloszynek S, Carpenter AE, Shrikumar A, Xu J, Cofer EM, Lavender CA, Turaga SC, Alexandari AM, Lu Z, Harris DJ, DeCaprio D, Qi Y, Kundaje A, Peng Y, Wiley LK, Segler MHS, Boca SM, Swamidass SJ, Huang A, Gitter A, Greene CS. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface 2018; 15:20170387. [PMID: 29618526 PMCID: PMC5938574 DOI: 10.1098/rsif.2017.0387] [Citation(s) in RCA: 826] [Impact Index Per Article: 137.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Accepted: 03/07/2018] [Indexed: 11/12/2022] Open
Abstract
Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.
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Affiliation(s)
- Travers Ching
- Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Daniel S Himmelstein
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Brett K Beaulieu-Jones
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Alexandr A Kalinin
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | | | - Gregory P Way
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Enrico Ferrero
- Computational Biology and Stats, Target Sciences, GlaxoSmithKline, Stevenage, UK
| | | | - Michael Zietz
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael M Hoffman
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Wei Xie
- Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Gail L Rosen
- Ecological and Evolutionary Signal-processing and Informatics Laboratory, Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA
| | - Benjamin J Lengerich
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Johnny Israeli
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - Jack Lanchantin
- Department of Computer Science, University of Virginia, Charlottesville, VA, USA
| | - Stephen Woloszynek
- Ecological and Evolutionary Signal-processing and Informatics Laboratory, Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA
| | - Anne E Carpenter
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Avanti Shrikumar
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Jinbo Xu
- Toyota Technological Institute at Chicago, Chicago, IL, USA
| | - Evan M Cofer
- Department of Computer Science, Trinity University, San Antonio, TX, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Christopher A Lavender
- Integrative Bioinformatics, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Srinivas C Turaga
- Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA, USA
| | - Amr M Alexandari
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information and National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - David J Harris
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL, USA
| | | | - Yanjun Qi
- Department of Computer Science, University of Virginia, Charlottesville, VA, USA
| | - Anshul Kundaje
- Department of Computer Science, Stanford University, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Yifan Peng
- National Center for Biotechnology Information and National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Laura K Wiley
- Division of Biomedical Informatics and Personalized Medicine, University of Colorado School of Medicine, Aurora, CO, USA
| | - Marwin H S Segler
- Institute of Organic Chemistry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Simina M Boca
- Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA
| | - S Joshua Swamidass
- Department of Pathology and Immunology, Washington University in Saint Louis, St Louis, MO, USA
| | - Austin Huang
- Department of Medicine, Brown University, Providence, RI, USA
| | - Anthony Gitter
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
- Morgridge Institute for Research, Madison, WI, USA
| | - Casey S Greene
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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23
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Dwyer DB, Falkai P, Koutsouleris N. Machine Learning Approaches for Clinical Psychology and Psychiatry. Annu Rev Clin Psychol 2018; 14:91-118. [PMID: 29401044 DOI: 10.1146/annurev-clinpsy-032816-045037] [Citation(s) in RCA: 406] [Impact Index Per Article: 67.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Machine learning approaches for clinical psychology and psychiatry explicitly focus on learning statistical functions from multidimensional data sets to make generalizable predictions about individuals. The goal of this review is to provide an accessible understanding of why this approach is important for future practice given its potential to augment decisions associated with the diagnosis, prognosis, and treatment of people suffering from mental illness using clinical and biological data. To this end, the limitations of current statistical paradigms in mental health research are critiqued, and an introduction is provided to critical machine learning methods used in clinical studies. A selective literature review is then presented aiming to reinforce the usefulness of machine learning methods and provide evidence of their potential. In the context of promising initial results, the current limitations of machine learning approaches are addressed, and considerations for future clinical translation are outlined.
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Affiliation(s)
- Dominic B Dwyer
- Department of Psychiatry and Psychotherapy, Section for Neurodiagnostic Applications, Ludwig-Maximilian University, Munich 80638, Germany; , ,
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, Section for Neurodiagnostic Applications, Ludwig-Maximilian University, Munich 80638, Germany; , ,
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Section for Neurodiagnostic Applications, Ludwig-Maximilian University, Munich 80638, Germany; , ,
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24
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Cooper BG, Stocks J, Hall GL, Culver B, Steenbruggen I, Carter KW, Thompson BR, Graham BL, Miller MR, Ruppel G, Henderson J, Vaz Fragoso CA, Stanojevic S. The Global Lung Function Initiative (GLI) Network: bringing the world's respiratory reference values together. Breathe (Sheff) 2017; 13:e56-e64. [PMID: 28955406 PMCID: PMC5607614 DOI: 10.1183/20734735.012717] [Citation(s) in RCA: 132] [Impact Index Per Article: 18.9] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The Global Lung Function Initiative (GLI) Network has become the largest resource for reference values for routine lung function testing ever assembled. This article addresses how the GLI Network came about, why it is important, and its current challenges and future directions. It is an extension of an article published in Breathe in 2013 [1], and summarises recent developments and the future of the GLI Network. Learn about the GLI Network, the largest resource reference for routine lung function testinghttp://ow.ly/ZZor30epWgi
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Affiliation(s)
- Brendan G Cooper
- Lung Function and Sleep, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Janet Stocks
- Respiratory, Critical Care and Anaesthesia section, UCL Great Ormond Street Institute of Child Health, London, UK
| | - Graham L Hall
- Telethon Kids Institute, Perth, Australia.,School of Physiotherapy and Exercise Science, Curtin University, Perth, Australia.,Centre for Child Health Research, University of Western Australia, Perth, Australia
| | - Bruce Culver
- Pulmonary, Critical Care and Sleep Medicine, University of Washington, Seattle, WA, USA
| | | | | | - Bruce Robert Thompson
- Allergy Immunology and Respiratory Medicine, The Alfred Hospital and Monash University, Melbourne, Australia
| | - Brian L Graham
- Division of Respirology, Critical Care and Sleep Medicine, University of Saskatchewan, Saskatoon, Canada
| | - Martin R Miller
- Institute of Occupational and Environmental Medicine, University of Birmingham, Birmingham, UK
| | - Gregg Ruppel
- Pulmonary, Critical Care and Sleep Medicine, Saint Louis University School of Medicine, Saint Louis, MO USA
| | - John Henderson
- School of Social and Community Medicine, Faculty of Health Sciences, University of Bristol, Bristol, UK
| | - Carlos A Vaz Fragoso
- Dept of Internal Medicine, Veterans Affairs Clinical Epidemiology Research Center, West Haven, CT, USA
| | - Sanja Stanojevic
- Respiratory Medicine, Hospital for Sick Children, Toronto, Canada.,Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Canada
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25
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26
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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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27
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Abstract
Prospects have never seemed better for a truly global approach to science to improve human health, with leaders of national initiatives laying out their vision of a worldwide network of related projects. An extensive literature addresses obstacles to global genomic data sharing, yet a series of public polls suggests that the scientific community may be overlooking a significant barrier: potential public resistance to data sharing across national borders. In several large United States surveys, university researchers in other countries were deemed the least acceptable group of data users, and a just-completed US survey found a marked increase in privacy and security concerns related to data access by non-US researchers. Furthermore, diminished support for sharing beyond national borders is not unique to the US, although the limited data from outside the US suggest variation across countries as well as demographic groups. Possible sources of resistance include apprehension about privacy and security protections. Strategies for building public support include making the affirmative case for global data sharing, addressing privacy, security, and other legitimate concerns, and investigating public concerns in greater depth.
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Affiliation(s)
- Mary A. Majumder
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, Texas, United States of America
- * E-mail:
| | - Robert Cook-Deegan
- School for the Future of Innovation in Society, Arizona State University Washington Center, Washington, D.C., United States of America
- FasterCures, a Center of the Milken Institute, Washington, D.C., United States of America
| | - Amy L. McGuire
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, Texas, United States of America
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28
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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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Selmer R, Haglund B, Furu K, Andersen M, Nørgaard M, Zoëga H, Kieler H. Individual-based versus aggregate meta-analysis in multi-database studies of pregnancy outcomes: the Nordic example of selective serotonin reuptake inhibitors and venlafaxine in pregnancy. Pharmacoepidemiol Drug Saf 2016; 25:1160-1169. [PMID: 27193296 DOI: 10.1002/pds.4033] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Revised: 03/16/2016] [Accepted: 04/23/2016] [Indexed: 11/10/2022]
Abstract
PURPOSE Compare analyses of a pooled data set on the individual level with aggregate meta-analysis in a multi-database study. METHODS We reanalysed data on 2.3 million births in a Nordic register based cohort study. We compared estimated odds ratios (OR) for the effect of selective serotonin reuptake inhibitors (SSRI) and venlafaxine use in pregnancy on any cardiovascular birth defect and the rare outcome right ventricular outflow tract obstructions (RVOTO). Common covariates included maternal age, calendar year, birth order, maternal diabetes, and co-medication. Additional covariates were added in analyses with country-optimized adjustment. RESULTS Country adjusted OR (95%CI) for any cardiovascular birth defect in the individual-based pooled analysis was 1.27 (1.17-1.39), 1.17 (1.07-1.27) adjusted for common covariates and 1.15 (1.05-1.26) adjusted for all covariates. In fixed effects meta-analyses pooled OR was 1.29 (1.19-1.41) based on crude country specific ORs, 1.19 (1.09-1.29) adjusted for common covariates, and 1.16 (1.06-1.27) for country-optimized adjustment. In a random effects model the adjusted OR was 1.07 (0.87-1.32). For RVOTO, OR was 1.48 (1.15-1.89) adjusted for all covariates in the pooled data set, and 1.53 (1.19-1.96) after country-optimized adjustment. Country-specific adjusted analyses at the substance level were not possible for RVOTO. CONCLUSION Results of fixed effects meta-analysis and individual-based analyses of a pooled dataset were similar in this study on the association of SSRI/venlafaxine and cardiovascular birth defects. Country-optimized adjustment attenuated the estimates more than adjustment for common covariates only. When data are sparse pooled data on the individual level are needed for adjusted analyses. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Randi Selmer
- Department of Pharmacoepidemiology, Norwegian Institute of Public Health, Oslo, Norway.
| | - Bengt Haglund
- Centre for Pharmacoepidemiology, Karolinska Institutet, Stockholm, Sweden
| | - Kari Furu
- Department of Pharmacoepidemiology, Norwegian Institute of Public Health, Oslo, Norway
| | - Morten Andersen
- Centre for Pharmacoepidemiology, Karolinska Institutet, Stockholm, Sweden.,Research Unit for General Practice, University of Southern Denmark, Odense, Denmark
| | - Mette Nørgaard
- Department of Clinical Epidemiology, Institute of Clinical Medicine, Aarhus University Hospital, Aarhus, Denmark
| | - Helga Zoëga
- Centre of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Helle Kieler
- Centre for Pharmacoepidemiology, Karolinska Institutet, Stockholm, Sweden
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Forgetta V, Richards JB. Software Application Profiles: useful and novel software for epidemiological data analysis. Int J Epidemiol 2016; 45:309-10. [DOI: 10.1093/ije/dyw064] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
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Ferrie JE. Accomplishments, malfeasances, misfeasances and nonfeasances. Int J Epidemiol 2016. [DOI: 10.1093/ije/dyw068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
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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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