1
|
Weng T, Zheng Y, Xie Y, Qin W, Guo L. Diagnosing schizophrenia using deep learning: Novel interpretation approaches and multi-site validation. Brain Res 2024; 1833:148876. [PMID: 38513996 DOI: 10.1016/j.brainres.2024.148876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 02/28/2024] [Accepted: 03/18/2024] [Indexed: 03/23/2024]
Abstract
Schizophrenia is a profound and enduring mental disorder that imposes significant negative impacts on individuals, their families, and society at large. The development of more accurate and objective diagnostic tools for schizophrenia can be expedited through the employment of deep learning (DL), that excels at deciphering complex hierarchical non-linear patterns. However, the limited interpretability of deep learning has eroded confidence in the model and restricted its clinical utility. At the same time, if the data source is only derived from a single center, the model's generalizability is difficult to test. To enhance the model's reliability and applicability, leave-one-center-out validation with a large and diverse sample from multiple centers is crucial. In this study, we utilized Nine different global centers to train and test the 3D Resnet model's generalizability, resulting in an 82% classification performance (area under the curve) on all datasets sourced from different countries, employing a leave-one-center-out-validation approach. Per our approximation of the feature significance of each region on the atlas, we identified marked differences in the thalamus, pallidum, and inferior frontal gyrus between individuals with schizophrenia and healthy controls, lending credence to prior research findings. At the same time, in order to translate the model's output into clinically applicable insights, the SHapley Additive exPlanations (SHAP) permutation explainer method with an anatomical atlas have been refined, thereby offering precise neuroanatomical and functional interpretations of different brain regions.
Collapse
Affiliation(s)
- Tingting Weng
- School of Medical Imaging, Tianjin Medical University, Tianjin 300203, China
| | - Yuemei Zheng
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Shandong 100038, China
| | - Yingying Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Li Guo
- School of Medical Imaging, Tianjin Medical University, Tianjin 300203, China.
| |
Collapse
|
2
|
Xie Y, Ding H, Du X, Chai C, Wei X, Sun J, Zhuo C, Wang L, Li J, Tian H, Liang M, Zhang S, Yu C, Qin W. Morphometric Integrated Classification Index: A Multisite Model-Based, Interpretable, Shareable and Evolvable Biomarker for Schizophrenia. Schizophr Bull 2022; 48:1217-1227. [PMID: 35925032 PMCID: PMC9673259 DOI: 10.1093/schbul/sbac096] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND AND HYPOTHESIS Multisite massive schizophrenia neuroimaging data sharing is becoming critical in understanding the pathophysiological mechanism and making an objective diagnosis of schizophrenia; it remains challenging to obtain a generalizable and interpretable, shareable, and evolvable neuroimaging biomarker for schizophrenia diagnosis. STUDY DESIGN A Morphometric Integrated Classification Index (MICI) was proposed as a potential biomarker for schizophrenia diagnosis based on structural magnetic resonance imaging data of 1270 subjects from 10 sites (588 schizophrenia patients and 682 normal controls). An optimal XGBoost classifier plus sample-weighted SHapley Additive explanation algorithms were used to construct the MICI measure. STUDY RESULTS The MICI measure achieved comparable performance with the sample-weighted ensembling model and merged model based on raw data (Delong test, P > 0.82) while outperformed the single-site models (Delong test, P < 0.05) in either the independent-sample testing datasets from the 9 sites or the independent-site dataset (generalizable). Besides, when new sites were embedded in, the performance of this measure was gradually increasing (evolvable). Finally, MICI was strongly associated with the severity of schizophrenia brain structural abnormality, with the patients' positive and negative symptoms, and with the brain expression profiles of schizophrenia risk genes (interpretable). CONCLUSIONS In summary, the proposed MICI biomarker may provide a simple and explainable way to support clinicians for objectively diagnosing schizophrenia. Finally, we developed an online model share platform to promote biomarker generalization and provide free individual prediction services (http://micc.tmu.edu.cn/mici/index.html).
Collapse
Affiliation(s)
- Yingying Xie
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Hao Ding
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China,School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Xiaotong Du
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Chao Chai
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Xiaotong Wei
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Jie Sun
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Chuanjun Zhuo
- Department of Psychiatry Functional Neuroimaging Laboratory, Tianjin Mental Health Center, Tianjin Anding Hospital, Tianjin, China
| | - Lina Wang
- Department of Psychiatry Functional Neuroimaging Laboratory, Tianjin Mental Health Center, Tianjin Anding Hospital, Tianjin, China
| | - Jie Li
- Department of Psychiatry Functional Neuroimaging Laboratory, Tianjin Mental Health Center, Tianjin Anding Hospital, Tianjin, China
| | | | - Meng Liang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China,School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | | | | | - Wen Qin
- To whom correspondence should be addressed; Department of Radiology, and Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital. Anshan Road No 154, Heping District, Tianjin 300052, China.
| |
Collapse
|
3
|
Pemmaraju R, Minahan R, Wang E, Schadl K, Daldrup-Link H, Habte F. Web-Based Application for Biomedical Image Registry, Analysis, and Translation (BiRAT). Tomography 2022; 8:1453-1462. [PMID: 35736865 PMCID: PMC9228304 DOI: 10.3390/tomography8030117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 05/23/2022] [Accepted: 05/27/2022] [Indexed: 11/18/2022] Open
Abstract
Imaging has become an invaluable tool in preclinical research for its capability to non-invasively detect and monitor disease and assess treatment response. With the increased use of preclinical imaging, large volumes of image data are being generated requiring critical data management tools. Due to proprietary issues and continuous technology development, preclinical images, unlike DICOM-based images, are often stored in an unstructured data file in company-specific proprietary formats. This limits the available DICOM-based image management database to be effectively used for preclinical applications. A centralized image registry and management tool is essential for advances in preclinical imaging research. Specifically, such tools may have a high impact in generating large image datasets for the evolving artificial intelligence applications and performing retrospective analyses of previously acquired images. In this study, a web-based server application is developed to address some of these issues. The application is designed to reflect the actual experimentation workflow maintaining detailed records of both individual images and experimental data relevant to specific studies and/or projects. The application also includes a web-based 3D/4D image viewer to easily and quickly view and evaluate images. This paper briefly describes the initial implementation of the web-based application.
Collapse
Affiliation(s)
- Rahul Pemmaraju
- School of Bioengineering and Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA;
| | - Robert Minahan
- Computational and Systems Biology, University of California-Los Angeles, Los Angeles, CA 90095, USA;
| | - Elise Wang
- School of Medicine, University of Rochester, Rochester, NY 14642, USA;
| | - Kornel Schadl
- Department of Orthopedic Surgery, Stanford School of Medicine, Stanford, CA 94305, USA;
| | - Heike Daldrup-Link
- Department of Radiology, Stanford School of Medicine, Stanford, CA 94305, USA;
| | - Frezghi Habte
- Department of Radiology, Stanford School of Medicine, Stanford, CA 94305, USA;
- Correspondence:
| |
Collapse
|
4
|
Zullino S, Paglialonga A, Dastrù W, Longo DL, Aime S. XNAT-PIC: Extending XNAT to Preclinical Imaging Centers. J Digit Imaging 2022; 35:860-875. [PMID: 35304674 PMCID: PMC9485318 DOI: 10.1007/s10278-022-00612-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 02/07/2022] [Accepted: 02/15/2022] [Indexed: 11/22/2022] Open
Abstract
Molecular imaging generates large volumes of heterogeneous biomedical imagery with an impelling need of guidelines for handling image data. Although several successful solutions have been implemented for human epidemiologic studies, few and limited approaches have been proposed for animal population studies. Preclinical imaging research deals with a variety of machinery yielding tons of raw data but the current practices to store and distribute image data are inadequate. Therefore, standard tools for the analysis of large image datasets need to be established. In this paper, we present an extension of XNAT for Preclinical Imaging Centers (XNAT-PIC). XNAT is a worldwide used, open-source platform for securely hosting, sharing, and processing of clinical imaging studies. Despite its success, neither tools for importing large, multimodal preclinical image datasets nor pipelines for processing whole imaging studies are yet available in XNAT. In order to overcome these limitations, we have developed several tools to expand the XNAT core functionalities for supporting preclinical imaging facilities. Our aim is to streamline the management and exchange of image data within the preclinical imaging community, thereby enhancing the reproducibility of the results of image processing and promoting open science practices.
Collapse
Affiliation(s)
- Sara Zullino
- Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy.,Euro-BioImaging ERIC, Torino, Italy
| | - Alessandro Paglialonga
- Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy
| | - Walter Dastrù
- Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy.,Euro-BioImaging ERIC, Torino, Italy
| | - Dario Livio Longo
- Institute of Biostructures and Bioimaging (IBB), Italian National Research Council (CNR), Via Nizza 52, 10126, Torino, Italy.
| | - Silvio Aime
- Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy.,Euro-BioImaging ERIC, Torino, Italy
| |
Collapse
|
5
|
Turner JA, Calhoun VD, Thompson PM, Jahanshad N, Ching CRK, Thomopoulos SI, Verner E, Strauss GP, Ahmed AO, Turner MD, Basodi S, Ford JM, Mathalon DH, Preda A, Belger A, Mueller BA, Lim KO, van Erp TGM. ENIGMA + COINSTAC: Improving Findability, Accessibility, Interoperability, and Re-usability. Neuroinformatics 2022; 20:261-275. [PMID: 34846691 PMCID: PMC9149142 DOI: 10.1007/s12021-021-09559-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/19/2021] [Indexed: 01/07/2023]
Abstract
The FAIR principles, as applied to clinical and neuroimaging data, reflect the goal of making research products Findable, Accessible, Interoperable, and Reusable. The use of the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymized Computation (COINSTAC) platform in the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium combines the technological approach of decentralized analyses with the sociological approach of sharing data. In addition, ENIGMA + COINSTAC provides a platform to facilitate the use of machine-actionable data objects. We first present how ENIGMA and COINSTAC support the FAIR principles, and then showcase their integration with a decentralized meta-analysis of sex differences in negative symptom severity in schizophrenia, and finally present ongoing activities and plans to advance FAIR principles in ENIGMA + COINSTAC. ENIGMA and COINSTAC currently represent efforts toward improved Access, Interoperability, and Reusability. We highlight additional improvements needed in these areas, as well as future connections to other resources for expanded Findability.
Collapse
Affiliation(s)
- Jessica A Turner
- Psychology Department, Georgia State University, Atlanta, GA, USA.
| | - Vince D Calhoun
- Psychology Department, Georgia State University, Atlanta, GA, USA
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, 30303, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Christopher R K Ching
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Eric Verner
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, 30303, USA
| | - Gregory P Strauss
- Departments of Psychology and Neuroscience, University of Georgia, Athens, GA, USA
| | - Anthony O Ahmed
- Weill Cornell Medicine, Department of Psychiatry, White Plains, NY, 10605, USA
| | - Matthew D Turner
- Psychology Department, Georgia State University, Atlanta, GA, USA
| | - Sunitha Basodi
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, 30303, USA
| | - Judith M Ford
- Veterans Affairs San Francisco Healthcare System, San Francisco, CA, 94121, USA
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, 94121, USA
| | - Daniel H Mathalon
- Veterans Affairs San Francisco Healthcare System, San Francisco, CA, 94121, USA
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, 94121, USA
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, University of California Irvine Medical Center, 101 The City Drive S, Orange, CA, 92868, USA
| | - Aysenil Belger
- Department of Psychiatry and Frank Porter Graham Child Development Institute, University of North Carolina at Chapel Hill, 105 Smith Level Road, Chapel Hill, NC, 27599-8180, USA
| | - Bryon A Mueller
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, 55414, USA
| | - Kelvin O Lim
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, 55414, USA
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, 5251 California Ave, Irvine, CA, 92617, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, 309 Qureshey Research Lab, Irvine, CA, 92697, USA
| |
Collapse
|
6
|
Spahr A, Rosli Z, Legault M, Tran LT, Fournier S, Toutounchi H, Darbelli L, Madjar C, Lucia C, St-Jean ML, Das S, Evans AC, Bernard G. The LORIS MyeliNeuroGene rare disease database for natural history studies and clinical trial readiness. Orphanet J Rare Dis 2021; 16:328. [PMID: 34301277 PMCID: PMC8299589 DOI: 10.1186/s13023-021-01953-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 07/11/2021] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Rare diseases are estimated to affect 150-350 million people worldwide. With advances in next generation sequencing, the number of known disease-causing genes has increased significantly, opening the door for therapy development. Rare disease research has therefore pivoted from gene discovery to the exploration of potential therapies. With impending clinical trials on the horizon, researchers are in urgent need of natural history studies to help them identify surrogate markers, validate outcome measures, define historical control patients, and design therapeutic trials. RESULTS We customized a browser-accessible multi-modal (e.g. genetics, imaging, behavioral, patient-determined outcomes) database to increase cohort sizes, identify surrogate markers, and foster international collaborations. Ninety data entry forms were developed including family, perinatal, developmental history, clinical examinations, diagnostic investigations, neurological evaluations (i.e. spasticity, dystonia, ataxia, etc.), disability measures, parental stress, and quality of life. A customizable clinical letter generator was created to assist in continuity of patient care. CONCLUSIONS Small cohorts and underpowered studies are a major challenge for rare disease research. This online, rare disease database will be accessible from all over the world, making it easier to share and disseminate data. We have outlined the methodology to become Title 21 Code of Federal Regulations Part 11 Compliant, which is a requirement to use electronic records as historical controls in clinical trials in the United States. Food and Drug Administration compliant databases will be life-changing for patients and families when historical control data is used for emerging clinical trials. Future work will leverage these tools to delineate the natural history of several rare diseases and we are confident that this database will be used on a larger scale to improve care for patients affected with rare diseases.
Collapse
Affiliation(s)
- Aaron Spahr
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Department of Pediatrics, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada
| | - Zaliqa Rosli
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, Québec, Canada
| | - Mélanie Legault
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, Québec, Canada
| | - Luan T Tran
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Department of Pediatrics, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada
| | - Simon Fournier
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Department of Pediatrics, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada
| | - Helia Toutounchi
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Department of Pediatrics, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada
| | - Lama Darbelli
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Department of Pediatrics, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada
| | - Cécile Madjar
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, Québec, Canada
| | - Cassandra Lucia
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Department of Pediatrics, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada
| | - Marie-Lou St-Jean
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Department of Pediatrics, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada
| | - Samir Das
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, Québec, Canada
| | - Alan C Evans
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, Québec, Canada
| | - Geneviève Bernard
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada.
- Department of Pediatrics, McGill University, Montréal, Québec, Canada.
- Department of Human Genetics, McGill University, Montréal, Québec, Canada.
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada.
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada.
| |
Collapse
|
7
|
Kuplicki R, Touthang J, Al Zoubi O, Mayeli A, Misaki M, Aupperle RL, Teague TK, McKinney BA, Paulus MP, Bodurka J. Common Data Elements, Scalable Data Management Infrastructure, and Analytics Workflows for Large-Scale Neuroimaging Studies. Front Psychiatry 2021; 12:682495. [PMID: 34220587 PMCID: PMC8247461 DOI: 10.3389/fpsyt.2021.682495] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 05/19/2021] [Indexed: 01/16/2023] Open
Abstract
Neuroscience studies require considerable bioinformatic support and expertise. Numerous high-dimensional and multimodal datasets must be preprocessed and integrated to create robust and reproducible analysis pipelines. We describe a common data elements and scalable data management infrastructure that allows multiple analytics workflows to facilitate preprocessing, analysis and sharing of large-scale multi-level data. The process uses the Brain Imaging Data Structure (BIDS) format and supports MRI, fMRI, EEG, clinical, and laboratory data. The infrastructure provides support for other datasets such as Fitbit and flexibility for developers to customize the integration of new types of data. Exemplar results from 200+ participants and 11 different pipelines demonstrate the utility of the infrastructure.
Collapse
Affiliation(s)
- Rayus Kuplicki
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - James Touthang
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - Obada Al Zoubi
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - Ahmad Mayeli
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - Masaya Misaki
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - NeuroMAP-Investigators
- Laureate Institute for Brain Research, Tulsa, OK, United States
- Department of Community Medicine, Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, United States
| | - Robin L. Aupperle
- Laureate Institute for Brain Research, Tulsa, OK, United States
- Department of Community Medicine, Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, United States
| | - T. Kent Teague
- Department of Surgery, University of Oklahoma School of Community Medicine, Tulsa, OK, United States
- Department of Psychiatry, University of Oklahoma School of Community Medicine, Tulsa, OK, United States
- Department of Biochemistry and Microbiology, Oklahoma State University Center for Health Sciences, Tulsa, OK, United States
| | - Brett A. McKinney
- Department of Mathematics, University of Tulsa, Tulsa, OK, United States
- Tandy School of Computer Science, University of Tulsa, Tulsa, OK, United States
| | | | - Jerzy Bodurka
- Laureate Institute for Brain Research, Tulsa, OK, United States
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, United States
| |
Collapse
|
8
|
Buimer EEL, Schnack HG, Caspi Y, van Haren NEM, Milchenko M, Pas P, Hulshoff Pol HE, Brouwer RM. De-identification procedures for magnetic resonance images and the impact on structural brain measures at different ages. Hum Brain Mapp 2021; 42:3643-3655. [PMID: 33973694 PMCID: PMC8249889 DOI: 10.1002/hbm.25459] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 02/26/2021] [Accepted: 04/05/2021] [Indexed: 11/12/2022] Open
Abstract
Surface rendering of MRI brain scans may lead to identification of the participant through facial characteristics. In this study, we evaluate three methods that overwrite voxels containing privacy‐sensitive information: Face Masking, FreeSurfer defacing, and FSL defacing. We included structural T1‐weighted MRI scans of children, young adults and older adults. For the young adults, test–retest data were included with a 1‐week interval. The effects of the de‐identification methods were quantified using different statistics to capture random variation and systematic noise in measures obtained through the FreeSurfer processing pipeline. Face Masking and FSL defacing impacted brain voxels in some scans especially in younger participants. FreeSurfer defacing left brain tissue intact in all cases. FSL defacing and FreeSurfer defacing preserved identifiable characteristics around the eyes or mouth in some scans. For all de‐identification methods regional brain measures of subcortical volume, cortical volume, cortical surface area, and cortical thickness were on average highly replicable when derived from original versus de‐identified scans with average regional correlations >.90 for children, young adults, and older adults. Small systematic biases were found that incidentally resulted in significantly different brain measures after de‐identification, depending on the studied subsample, de‐identification method, and brain metric. In young adults, test–retest intraclass correlation coefficients (ICCs) were comparable for original scans and de‐identified scans with average regional ICCs >.90 for (sub)cortical volume and cortical surface area and ICCs >.80 for cortical thickness. We conclude that apparent visual differences between de‐identification methods minimally impact reliability of brain measures, although small systematic biases can occur.
Collapse
Affiliation(s)
- Elizabeth E L Buimer
- UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Hugo G Schnack
- UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Yaron Caspi
- UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Neeltje E M van Haren
- UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.,Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, Rotterdam, Netherlands
| | - Mikhail Milchenko
- Department of Radiology, Washington University School of Medicine, Mallinckrodt Institute of Radiology, Saint Louis, Missouri, USA
| | - Pascal Pas
- UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | | | - Hilleke E Hulshoff Pol
- UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Rachel M Brouwer
- UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| |
Collapse
|
9
|
Les big data , généralités et intégration en radiothérapie. Cancer Radiother 2018; 22:73-84. [DOI: 10.1016/j.canrad.2017.04.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2017] [Revised: 04/11/2017] [Accepted: 04/19/2017] [Indexed: 12/25/2022]
|
10
|
Abstract
Many investigators recognize the importance of data sharing; however, they lack the capability to share data. Research efforts could be vastly expanded if Alzheimer disease data from around the world was linked by a global infrastructure that would enable scientists to access and utilize a secure network of data with thousands of study participants at risk for or already suffering from the disease. We discuss the benefits of data sharing, impediments today, and solutions to achieving this on a global scale. We introduce the Global Alzheimer's Association Interactive Network (GAAIN), a novel approach to create a global network of Alzheimer disease data, researchers, analytical tools, and computational resources to better our understanding of this debilitating condition. GAAIN has addressed the key impediments to Alzheimer disease data sharing with its model and approach. It presents practical, promising, yet, data owner-sensitive data-sharing solutions.
Collapse
|
11
|
|
12
|
Doel T, Shakir DI, Pratt R, Aertsen M, Moggridge J, Bellon E, David AL, Deprest J, Vercauteren T, Ourselin S. GIFT-Cloud: A data sharing and collaboration platform for medical imaging research. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 139:181-190. [PMID: 28187889 PMCID: PMC5312116 DOI: 10.1016/j.cmpb.2016.11.004] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2016] [Revised: 10/03/2016] [Accepted: 11/03/2016] [Indexed: 05/06/2023]
Abstract
OBJECTIVES Clinical imaging data are essential for developing research software for computer-aided diagnosis, treatment planning and image-guided surgery, yet existing systems are poorly suited for data sharing between healthcare and academia: research systems rarely provide an integrated approach for data exchange with clinicians; hospital systems are focused towards clinical patient care with limited access for external researchers; and safe haven environments are not well suited to algorithm development. We have established GIFT-Cloud, a data and medical image sharing platform, to meet the needs of GIFT-Surg, an international research collaboration that is developing novel imaging methods for fetal surgery. GIFT-Cloud also has general applicability to other areas of imaging research. METHODS GIFT-Cloud builds upon well-established cross-platform technologies. The Server provides secure anonymised data storage, direct web-based data access and a REST API for integrating external software. The Uploader provides automated on-site anonymisation, encryption and data upload. Gateways provide a seamless process for uploading medical data from clinical systems to the research server. RESULTS GIFT-Cloud has been implemented in a multi-centre study for fetal medicine research. We present a case study of placental segmentation for pre-operative surgical planning, showing how GIFT-Cloud underpins the research and integrates with the clinical workflow. CONCLUSIONS GIFT-Cloud simplifies the transfer of imaging data from clinical to research institutions, facilitating the development and validation of medical research software and the sharing of results back to the clinical partners. GIFT-Cloud supports collaboration between multiple healthcare and research institutions while satisfying the demands of patient confidentiality, data security and data ownership.
Collapse
Affiliation(s)
- Tom Doel
- Translational Imaging Group, Centre for Medical Imaging Computing, University College London, London, UK.
| | - Dzhoshkun I Shakir
- Translational Imaging Group, Centre for Medical Imaging Computing, University College London, London, UK
| | - Rosalind Pratt
- Translational Imaging Group, Centre for Medical Imaging Computing, University College London, London, UK; Institute for Women's Health, University College London, London, UK
| | - Michael Aertsen
- Department of Imaging & Pathology, UZ Leuven, Leuven, Belgium
| | | | - Erwin Bellon
- Department of Imaging & Pathology, UZ Leuven, Leuven, Belgium; Department of Information Technology, UZ Leuven, Leuven, Belgium
| | - Anna L David
- Institute for Women's Health, University College London, London, UK
| | - Jan Deprest
- Institute for Women's Health, University College London, London, UK; Department of Obstetrics, UZ Leuven, Leuven, Belgium
| | - Tom Vercauteren
- Translational Imaging Group, Centre for Medical Imaging Computing, University College London, London, UK
| | - Sébastien Ourselin
- Translational Imaging Group, Centre for Medical Imaging Computing, University College London, London, UK
| |
Collapse
|
13
|
The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data 2016; 3:160044. [PMID: 27326542 PMCID: PMC4978148 DOI: 10.1038/sdata.2016.44] [Citation(s) in RCA: 793] [Impact Index Per Article: 99.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Accepted: 05/19/2016] [Indexed: 11/15/2022] Open
Abstract
The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain. Despite the fact that MRI is routinely used to obtain data for neuroscience research, there has been no widely adopted standard for organizing and describing the data collected in an imaging experiment. This renders sharing and reusing data (within or between labs) difficult if not impossible and unnecessarily complicates the application of automatic pipelines and quality assurance protocols. To solve this problem, we have developed the Brain Imaging Data Structure (BIDS), a standard for organizing and describing MRI datasets. The BIDS standard uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations.
Collapse
|
14
|
Kessel KA, Combs SE. Review of Developments in Electronic, Clinical Data Collection, and Documentation Systems over the Last Decade - Are We Ready for Big Data in Routine Health Care? Front Oncol 2016; 6:75. [PMID: 27066456 PMCID: PMC4812063 DOI: 10.3389/fonc.2016.00075] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2016] [Accepted: 03/18/2016] [Indexed: 11/24/2022] Open
Abstract
Recently, information availability has become more elaborate and widespread, and treatment decisions are based on a multitude of factors, including imaging, molecular or pathological markers, surgical results, and patient’s preference. In this context, the term “Big Data” evolved also in health care. The “hype” is heavily discussed in literature. In interdisciplinary medical specialties, such as radiation oncology, not only heterogeneous and voluminous amount of data must be evaluated but also spread in different styles across various information systems. Exactly this problem is also referred to in many ongoing discussions about Big Data – the “three V’s”: volume, velocity, and variety. We reviewed 895 articles extracted from the NCBI databases about current developments in electronic clinical data management systems and their further analysis or postprocessing procedures. Few articles show first ideas and ways to immediately make use of collected data, particularly imaging data. Many developments can be noticed in the field of clinical trial or analysis documentation, mobile devices for documentation, and genomics research. Using Big Data to advance medical research is definitely on the rise. Health care is perhaps the most comprehensive, important, and economically viable field of application.
Collapse
Affiliation(s)
- Kerstin A Kessel
- Department of Radiation Oncology, Technische Universität München, Munich, Germany; Institute of Innovative Radiotherapy (iRT), Helmholtz Zentrum München, Neuherberg, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Technische Universität München, Munich, Germany; Institute of Innovative Radiotherapy (iRT), Helmholtz Zentrum München, Neuherberg, Germany
| |
Collapse
|
15
|
Keator DB, van Erp TGM, Turner JA, Glover GH, Mueller BA, Liu TT, Voyvodic JT, Rasmussen J, Calhoun VD, Lee HJ, Toga AW, McEwen S, Ford JM, Mathalon DH, Diaz M, O'Leary DS, Jeremy Bockholt H, Gadde S, Preda A, Wible CG, Stern HS, Belger A, McCarthy G, Ozyurt B, Potkin SG. The Function Biomedical Informatics Research Network Data Repository. Neuroimage 2016; 124:1074-1079. [PMID: 26364863 PMCID: PMC4651841 DOI: 10.1016/j.neuroimage.2015.09.003] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2015] [Revised: 08/14/2015] [Accepted: 09/02/2015] [Indexed: 11/21/2022] Open
Abstract
The Function Biomedical Informatics Research Network (FBIRN) developed methods and tools for conducting multi-scanner functional magnetic resonance imaging (fMRI) studies. Method and tool development were based on two major goals: 1) to assess the major sources of variation in fMRI studies conducted across scanners, including instrumentation, acquisition protocols, challenge tasks, and analysis methods, and 2) to provide a distributed network infrastructure and an associated federated database to host and query large, multi-site, fMRI and clinical data sets. In the process of achieving these goals the FBIRN test bed generated several multi-scanner brain imaging data sets to be shared with the wider scientific community via the BIRN Data Repository (BDR). The FBIRN Phase 1 data set consists of a traveling subject study of 5 healthy subjects, each scanned on 10 different 1.5 to 4 T scanners. The FBIRN Phase 2 and Phase 3 data sets consist of subjects with schizophrenia or schizoaffective disorder along with healthy comparison subjects scanned at multiple sites. In this paper, we provide concise descriptions of FBIRN's multi-scanner brain imaging data sets and details about the BIRN Data Repository instance of the Human Imaging Database (HID) used to publicly share the data.
Collapse
Affiliation(s)
- David B Keator
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA.
| | - Theo G M van Erp
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA
| | - Jessica A Turner
- Mind Research Network, Albuquerque, NM, USA; Department of Psychiatry and Neuroscience Institute, Georgia State University, Atlanta, GA, USA
| | - Gary H Glover
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Bryon A Mueller
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA
| | - Thomas T Liu
- Center for Functional MRI, University of California, San Diego, CA, USA
| | - James T Voyvodic
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA
| | - Jerod Rasmussen
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA
| | - Vince D Calhoun
- Mind Research Network, Albuquerque, NM, USA; Department of ECE, University of New Mexico, Albuquerque, NM, USA; Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Hyo Jong Lee
- Department of Computer Science and Engineering, Chonbuk National University, Republic of Korea
| | - Arthur W Toga
- Laboratory of Neuro Imaging, University of Southern California, Los Angeles, USA; Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, USA; Keck School of Medicine of USC, University of Southern California, Los Angeles, USA
| | - Sarah McEwen
- Department of Psychology, University of California, Los Angeles, CA, USA
| | - Judith M Ford
- Department of Psychiatry, University of California, San Francisco, CA, USA; Brain Imaging and EEG Laboratory, University of California, San Francisco, CA, USA; San Francisco VA Medical Center, San Francisco, CA, USA
| | - Daniel H Mathalon
- Department of Psychiatry, University of California, San Francisco, CA, USA; Brain Imaging and EEG Laboratory, University of California, San Francisco, CA, USA; San Francisco VA Medical Center, San Francisco, CA, USA
| | - Michele Diaz
- Department of Psychology, Penn State University, University Park, PA, USA
| | - Daniel S O'Leary
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA
| | - H Jeremy Bockholt
- Department of ECE, University of New Mexico, Albuquerque, NM, USA; Department of Psychiatry, University of Iowa, Iowa City, IA, USA
| | - Syam Gadde
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA
| | - Cynthia G Wible
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA; Brockton VAMC, Boston, MA, USA
| | - Hal S Stern
- Department of Statistics, University of California, Irvine, CA, USA
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina at Chapel Hill, NC, USA; Department of Psychology, University of North Carolina at Chapel Hill, NC, USA
| | | | - Burak Ozyurt
- Department of Psychiatry, University of California, San Diego, CA, USA
| | - Steven G Potkin
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA
| |
Collapse
|
16
|
Wang L, Alpert KI, Calhoun VD, Cobia DJ, Keator DB, King MD, Kogan A, Landis D, Tallis M, Turner MD, Potkin SG, Turner JA, Ambite JL. SchizConnect: Mediating neuroimaging databases on schizophrenia and related disorders for large-scale integration. Neuroimage 2016; 124:1155-1167. [PMID: 26142271 PMCID: PMC4651768 DOI: 10.1016/j.neuroimage.2015.06.065] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2015] [Revised: 05/19/2015] [Accepted: 06/23/2015] [Indexed: 02/02/2023] Open
Abstract
SchizConnect (www.schizconnect.org) is built to address the issues of multiple data repositories in schizophrenia neuroimaging studies. It includes a level of mediation--translating across data sources--so that the user can place one query, e.g. for diffusion images from male individuals with schizophrenia, and find out from across participating data sources how many datasets there are, as well as downloading the imaging and related data. The current version handles the Data Usage Agreements across different studies, as well as interpreting database-specific terminologies into a common framework. New data repositories can also be mediated to bring immediate access to existing datasets. Compared with centralized, upload data sharing models, SchizConnect is a unique, virtual database with a focus on schizophrenia and related disorders that can mediate live data as information is being updated at each data source. It is our hope that SchizConnect can facilitate testing new hypotheses through aggregated datasets, promoting discovery related to the mechanisms underlying schizophrenic dysfunction.
Collapse
Affiliation(s)
- Lei Wang
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA; Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
| | - Kathryn I Alpert
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM, USA; University of New Mexico Health Sciences Center, Albuquerque, NM, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA; Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA; Department of Psychiatry, School of Medicine, Yale University, New Haven, CT, USA
| | - Derin J Cobia
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - David B Keator
- Brain Imaging Center, University of California, Irvine, CA, USA
| | | | - Alexandr Kogan
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Drew Landis
- The Mind Research Network, Albuquerque, NM, USA
| | - Marcelo Tallis
- Information Sciences Institute, University of Southern California, Marina del Rey, CA, USA
| | - Matthew D Turner
- Department of Computer Science, Georgia State University, Atlanta, GA, USA; Neuroscience Institute, Georgia State University, Atlanta, GA, USA
| | - Steven G Potkin
- Brain Imaging Center, University of California, Irvine, CA, USA; Department of Psychiatry & Human Behavior, University of California, Irvine, School of Medicine, Irvine, CA, USA
| | - Jessica A Turner
- The Mind Research Network, Albuquerque, NM, USA; Department of Psychology, Georgia State University, Atlanta, GA, USA; Neuroscience Institute, Georgia State University, Atlanta, GA, USA
| | - Jose Luis Ambite
- Information Sciences Institute, University of Southern California, Marina del Rey, CA, USA; Digital Government Research Center, University of Southern California, Los Angeles, CA, USA; Department of Computer Science, University of Southern California, Los Angeles, CA, USA
| |
Collapse
|
17
|
Development of a large-scale neuroimages and clinical variables data atlas in the neuGRID4You (N4U) project. J Biomed Inform 2015; 57:245-62. [DOI: 10.1016/j.jbi.2015.08.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2015] [Revised: 07/13/2015] [Accepted: 08/03/2015] [Indexed: 11/24/2022]
|
18
|
Korfiatis PD, Kline TL, Blezek DJ, Langer SG, Ryan WJ, Erickson BJ. MIRMAID: A Content Management System for Medical Image Analysis Research. Radiographics 2015; 35:1461-8. [PMID: 26284301 DOI: 10.1148/rg.2015140031] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Today, a typical clinical study can involve thousands of participants, with imaging data acquired over several time points across multiple institutions. The additional associated information (metadata) accompanying these data can cause data management to be a study-hindering bottleneck. Consistent data management is crucial for large-scale modern clinical imaging research studies. If the study is to be used for regulatory submissions, such systems must be able to meet regulatory compliance requirements for systems that manage clinical image trials, including protecting patient privacy. Our aim was to develop a system to address these needs by leveraging the capabilities of an open-source content management system (CMS) that has a highly configurable workflow; has a single interface that can store, manage, and retrieve imaging-based studies; and can handle the requirement for data auditing and project management. We developed a Web-accessible CMS for medical images called Medical Imaging Research Management and Associated Information Database (MIRMAID). From its inception, MIRMAID was developed to be highly flexible and to meet the needs of diverse studies. It fulfills the need for a complete system for medical imaging research management.
Collapse
Affiliation(s)
- Panagiotis D Korfiatis
- From the Departments of Radiology (P.D.K., T.L.K., S.G.L., B.J.E.) and Information Services (D.J.B., W.J.R.), Mayo Clinic, 200 1st St SW, Rochester, MN 55905
| | - Timothy L Kline
- From the Departments of Radiology (P.D.K., T.L.K., S.G.L., B.J.E.) and Information Services (D.J.B., W.J.R.), Mayo Clinic, 200 1st St SW, Rochester, MN 55905
| | - Daniel J Blezek
- From the Departments of Radiology (P.D.K., T.L.K., S.G.L., B.J.E.) and Information Services (D.J.B., W.J.R.), Mayo Clinic, 200 1st St SW, Rochester, MN 55905
| | - Steve G Langer
- From the Departments of Radiology (P.D.K., T.L.K., S.G.L., B.J.E.) and Information Services (D.J.B., W.J.R.), Mayo Clinic, 200 1st St SW, Rochester, MN 55905
| | - William J Ryan
- From the Departments of Radiology (P.D.K., T.L.K., S.G.L., B.J.E.) and Information Services (D.J.B., W.J.R.), Mayo Clinic, 200 1st St SW, Rochester, MN 55905
| | - Bradley J Erickson
- From the Departments of Radiology (P.D.K., T.L.K., S.G.L., B.J.E.) and Information Services (D.J.B., W.J.R.), Mayo Clinic, 200 1st St SW, Rochester, MN 55905
| |
Collapse
|
19
|
Turner JA, Pasquerello D, Turner MD, Keator DB, Alpert K, King M, Landis D, Calhoun VD, Potkin SG, Tallis M, Ambite JL, Wang L. Terminology development towards harmonizing multiple clinical neuroimaging research repositories. DATA INTEGRATION IN THE LIFE SCIENCES : ... INTERNATIONAL WORKSHOP, DILS ... : PROCEEDINGS. DILS (CONFERENCE) 2015; 9162:104-117. [PMID: 26688838 PMCID: PMC4682911 DOI: 10.1007/978-3-319-21843-4_8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Data sharing and mediation across disparate neuroimaging repositories requires extensive effort to ensure that the different domains of data types are referred to by commonly agreed upon terms. Within the SchizConnect project, which enables querying across decentralized databases of neuroimaging, clinical, and cognitive data from various studies of schizophrenia, we developed a model for each data domain, identified common usable terms that could be agreed upon across the repositories, and linked them to standard ontological terms where possible. We had the goal of facilitating both the current user experience in querying and future automated computations and reasoning regarding the data. We found that existing terminologies are incomplete for these purposes, even with the history of neuroimaging data sharing in the field; and we provide a model for efforts focused on querying multiple clinical neuroimaging repositories.
Collapse
Affiliation(s)
- Jessica A. Turner
- Georgia State University, Atlanta, Georgia, USA
- Mind Research Network, Albuquerque, New Mexico, USA
| | | | | | | | | | | | - Drew Landis
- Mind Research Network, Albuquerque, New Mexico, USA
| | - Vince D. Calhoun
- Mind Research Network, Albuquerque, New Mexico, USA
- University of New Mexico, Albuquerque, New Mexico, USA
| | | | - Marcelo Tallis
- University of Southern California, Los Angeles, California, USA
| | | | - Lei Wang
- Northwestern University, Chicago, Illinois, USA
| |
Collapse
|
20
|
Neu SC, Crawford KL, Toga AW. Sharing data in the global alzheimer's association interactive network. Neuroimage 2015; 124:1168-1174. [PMID: 26049147 DOI: 10.1016/j.neuroimage.2015.05.082] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2015] [Revised: 05/26/2015] [Accepted: 05/27/2015] [Indexed: 12/17/2022] Open
Abstract
The Global Alzheimer's Association Interactive Network (GAAIN) aims to be a shared network of research data, analysis tools, and computational resources for studying the causes of Alzheimer's disease. Central to its design are policies that honor data ownership, prevent unauthorized data distribution, and respect the boundaries of contributing institutions. The results of data queries are displayed in graphs and summary tables, which protects data ownership while providing sufficient information to view trends in aggregated data and discover new data sets. In this article we report on our progress in sharing data through the integration of geographically-separated and independently-operated Alzheimer's disease research studies around the world.
Collapse
Affiliation(s)
- Scott C Neu
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90095, USA
| | - Karen L Crawford
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90095, USA
| | - Arthur W Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90095, USA.
| |
Collapse
|
21
|
Shin DD, Ozyurt IB, Brown GG, Fennema-Notestine C, Liu TT. The Cerebral Blood Flow Biomedical Informatics Research Network (CBFBIRN) data repository. Neuroimage 2015; 124:1202-1207. [PMID: 26032887 DOI: 10.1016/j.neuroimage.2015.05.059] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2015] [Revised: 05/19/2015] [Accepted: 05/21/2015] [Indexed: 12/20/2022] Open
Abstract
Arterial spin labeling (ASL) MRI provides an accurate and reliable measure of cerebral blood flow (CBF). A rapidly growing number of CBF measures are being collected both in clinical and research settings around the world, resulting in a large volume of data across a wide spectrum of study populations and health conditions. Here, we describe a central CBF data repository with integrated processing workflows, referred to as the Cerebral Blood Flow Biomedical Informatics Research Network (CBFBIRN). The CBFBIRN provides an integrated framework for the analysis and comparison of CBF measures across studies and sites. In this work, we introduce the main capabilities of the CBFBIRN (data storage, processing, and sharing), describe what types of data are available, explain how users can contribute to the data repository and access existing data from it, and discuss our long-term plans for the CBFBIRN.
Collapse
Affiliation(s)
- David D Shin
- Center for Functional Magnetic Resonance Imaging, University of California San Diego, La Jolla, CA, USA.
| | - I Burak Ozyurt
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Gregory G Brown
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | | | - Thomas T Liu
- Center for Functional Magnetic Resonance Imaging, University of California San Diego, La Jolla, CA, USA.
| |
Collapse
|
22
|
Liu TT, Glover GH, Mueller BA, Greve DN, Rasmussen J, Voyvodic JT, Turner JA, van Erp TGM, Mathalon DH, Andersen K, Lu K, Brown GG, Keator DB, Calhoun VD, Lee HJ, Ford JM, Diaz M, O’Leary DS, Gadde S, Preda A, Lim KO, Wible CG, Stern HS, Belger A, McCarthy G, Ozyurt B, Potkin SG. Quality Assurance in Functional MRI. FMRI: FROM NUCLEAR SPINS TO BRAIN FUNCTIONS 2015. [DOI: 10.1007/978-1-4899-7591-1_10] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
|
23
|
Munir K, Liaquat Kiani S, Hasham K, McClatchey R, Branson A, Shamdasani J. Provision of an integrated data analysis platform for computational neuroscience experiments. ACTA ACUST UNITED AC 2014. [DOI: 10.1108/jsit-01-2014-0004] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Purpose
– The purpose of this paper is to provide an integrated analysis base to facilitate computational neuroscience experiments, following a user-led approach to provide access to the integrated neuroscience data and to enable the analyses demanded by the biomedical research community.
Design/methodology/approach
– The design and development of the N4U analysis base and related information services addresses the existing research and practical challenges by offering an integrated medical data analysis environment with the necessary building blocks for neuroscientists to optimally exploit neuroscience workflows, large image data sets and algorithms to conduct analyses.
Findings
– The provision of an integrated e-science environment of computational neuroimaging can enhance the prospects, speed and utility of the data analysis process for neurodegenerative diseases.
Originality/value
– The N4U analysis base enables conducting biomedical data analyses by indexing and interlinking the neuroimaging and clinical study data sets stored on the grid infrastructure, algorithms and scientific workflow definitions along with their associated provenance information.
Collapse
|
24
|
Haselgrove C, Poline JB, Kennedy DN. A simple tool for neuroimaging data sharing. Front Neuroinform 2014; 8:52. [PMID: 24904398 PMCID: PMC4033259 DOI: 10.3389/fninf.2014.00052] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2013] [Accepted: 04/28/2014] [Indexed: 11/13/2022] Open
Abstract
Data sharing is becoming increasingly common, but despite encouragement and facilitation by funding agencies, journals, and some research efforts, most neuroimaging data acquired today is still not shared due to political, financial, social, and technical barriers to sharing data that remain. In particular, technical solutions are few for researchers that are not a part of larger efforts with dedicated sharing infrastructures, and social barriers such as the time commitment required to share can keep data from becoming publicly available. We present a system for sharing neuroimaging data, designed to be simple to use and to provide benefit to the data provider. The system consists of a server at the International Neuroinformatics Coordinating Facility (INCF) and user tools for uploading data to the server. The primary design principle for the user tools is ease of use: the user identifies a directory containing Digital Imaging and Communications in Medicine (DICOM) data, provides their INCF Portal authentication, and provides identifiers for the subject and imaging session. The user tool anonymizes the data and sends it to the server. The server then runs quality control routines on the data, and the data and the quality control reports are made public. The user retains control of the data and may change the sharing policy as they need. The result is that in a few minutes of the user's time, DICOM data can be anonymized and made publicly available, and an initial quality control assessment can be performed on the data. The system is currently functional, and user tools and access to the public image database are available at http://xnat.incf.org/.
Collapse
Affiliation(s)
| | | | - David N Kennedy
- University of Massachusetts Medical School Worcester, MA, USA
| |
Collapse
|
25
|
Ruggeri B, Sarkans U, Schumann G, Persico AM. Biomarkers in autism spectrum disorder: the old and the new. Psychopharmacology (Berl) 2014; 231:1201-16. [PMID: 24096533 DOI: 10.1007/s00213-013-3290-7] [Citation(s) in RCA: 118] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2013] [Accepted: 09/07/2013] [Indexed: 12/21/2022]
Abstract
RATIONALE Autism spectrum disorder (ASD) is a complex heterogeneous neurodevelopmental disorder with onset during early childhood and typically a life-long course. The majority of ASD cases stems from complex, 'multiple-hit', oligogenic/polygenic underpinnings involving several loci and possibly gene-environment interactions. These multiple layers of complexity spur interest into the identification of biomarkers able to define biologically homogeneous subgroups, predict autism risk prior to the onset of behavioural abnormalities, aid early diagnoses, predict the developmental trajectory of ASD children, predict response to treatment and identify children at risk for severe adverse reactions to psychoactive drugs. OBJECTIVES The present paper reviews (a) similarities and differences between the concepts of 'biomarker' and 'endophenotype', (b) established biomarkers and endophenotypes in autism research (biochemical, morphological, hormonal, immunological, neurophysiological and neuroanatomical, neuropsychological, behavioural), (c) -omics approaches towards the discovery of novel biomarker panels for ASD, (d) bioresource infrastructures and (e) data management for biomarker research in autism. RESULTS Known biomarkers, such as abnormal blood levels of serotonin, oxytocin, melatonin, immune cytokines and lymphocyte subtypes, multiple neuropsychological, electrophysiological and brain imaging parameters, will eventually merge with novel biomarkers identified using unbiased genomic, epigenomic, transcriptomic, proteomic and metabolomic methods, to generate multimarker panels. Bioresource infrastructures, data management and data analysis using artificial intelligence networks will be instrumental in supporting efforts to identify these biomarker panels. CONCLUSIONS Biomarker research has great heuristic potential in targeting autism diagnosis and treatment.
Collapse
Affiliation(s)
- Barbara Ruggeri
- MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King's College London, De Crespigny Park, London, SE5 8AF, UK
| | | | | | | |
Collapse
|
26
|
Nichols BN, Mejino JL, Detwiler LT, Nilsen TT, Martone ME, Turner JA, Rubin DL, Brinkley JF. Neuroanatomical domain of the foundational model of anatomy ontology. J Biomed Semantics 2014; 5:1. [PMID: 24398054 PMCID: PMC3944952 DOI: 10.1186/2041-1480-5-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2013] [Accepted: 12/24/2013] [Indexed: 11/10/2022] Open
Abstract
Background The diverse set of human brain structure and function analysis methods represents a difficult challenge for reconciling multiple views of neuroanatomical organization. While different views of organization are expected and valid, no widely adopted approach exists to harmonize different brain labeling protocols and terminologies. Our approach uses the natural organizing framework provided by anatomical structure to correlate terminologies commonly used in neuroimaging. Description The Foundational Model of Anatomy (FMA) Ontology provides a semantic framework for representing the anatomical entities and relationships that constitute the phenotypic organization of the human body. In this paper we describe recent enhancements to the neuroanatomical content of the FMA that models cytoarchitectural and morphological regions of the cerebral cortex, as well as white matter structure and connectivity. This modeling effort is driven by the need to correlate and reconcile the terms used in neuroanatomical labeling protocols. By providing an ontological framework that harmonizes multiple views of neuroanatomical organization, the FMA provides developers with reusable and computable knowledge for a range of biomedical applications. Conclusions A requirement for facilitating the integration of basic and clinical neuroscience data from diverse sources is a well-structured ontology that can incorporate, organize, and associate neuroanatomical data. We applied the ontological framework of the FMA to align the vocabularies used by several human brain atlases, and to encode emerging knowledge about structural connectivity in the brain. We highlighted several use cases of these extensions, including ontology reuse, neuroimaging data annotation, and organizing 3D brain models.
Collapse
|
27
|
Keator DB, Helmer K, Steffener J, Turner JA, Van Erp TGM, Gadde S, Ashish N, Burns GA, Nichols BN. Towards structured sharing of raw and derived neuroimaging data across existing resources. Neuroimage 2013; 82:647-61. [PMID: 23727024 PMCID: PMC4028152 DOI: 10.1016/j.neuroimage.2013.05.094] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2012] [Revised: 05/11/2013] [Accepted: 05/18/2013] [Indexed: 10/26/2022] Open
Abstract
Data sharing efforts increasingly contribute to the acceleration of scientific discovery. Neuroimaging data is accumulating in distributed domain-specific databases and there is currently no integrated access mechanism nor an accepted format for the critically important meta-data that is necessary for making use of the combined, available neuroimaging data. In this manuscript, we present work from the Derived Data Working Group, an open-access group sponsored by the Biomedical Informatics Research Network (BIRN) and the International Neuroimaging Coordinating Facility (INCF) focused on practical tools for distributed access to neuroimaging data. The working group develops models and tools facilitating the structured interchange of neuroimaging meta-data and is making progress towards a unified set of tools for such data and meta-data exchange. We report on the key components required for integrated access to raw and derived neuroimaging data as well as associated meta-data and provenance across neuroimaging resources. The components include (1) a structured terminology that provides semantic context to data, (2) a formal data model for neuroimaging with robust tracking of data provenance, (3) a web service-based application programming interface (API) that provides a consistent mechanism to access and query the data model, and (4) a provenance library that can be used for the extraction of provenance data by image analysts and imaging software developers. We believe that the framework and set of tools outlined in this manuscript have great potential for solving many of the issues the neuroimaging community faces when sharing raw and derived neuroimaging data across the various existing database systems for the purpose of accelerating scientific discovery.
Collapse
Affiliation(s)
- D B Keator
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA 92617, USA.
| | | | | | | | | | | | | | | | | |
Collapse
|
28
|
Shin DD, Ozyurt IB, Liu TT. The Cerebral Blood Flow Biomedical Informatics Research Network (CBFBIRN) database and analysis pipeline for arterial spin labeling MRI data. Front Neuroinform 2013; 7:21. [PMID: 24151465 PMCID: PMC3798866 DOI: 10.3389/fninf.2013.00021] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2013] [Accepted: 09/26/2013] [Indexed: 11/13/2022] Open
Abstract
Arterial spin labeling (ASL) is a magnetic resonance imaging technique that provides a non-invasive and quantitative measure of cerebral blood flow (CBF). After more than a decade of active research, ASL is now emerging as a robust and reliable CBF measurement technique with increased availability and ease of use. There is a growing number of research and clinical sites using ASL for neuroscience research and clinical care. In this paper, we present an online CBF Database and Analysis Pipeline, collectively called the Cerebral Blood Flow Biomedical Informatics Research Network (CBFBIRN) that allows researchers to upload and share ASL and clinical data. In addition to serving the role as a central data repository, the CBFBIRN provides a streamlined data processing infrastructure for CBF quantification and group analysis, which has the potential to accelerate the discovery of new scientific and clinical knowledge. All capabilities and features built into the CBFBIRN are accessed online using a web browser through a secure login. In this work, we begin with a general description of the CBFBIRN system data model and its architecture, then devote the remainder of the paper to the CBFBIRN capabilities. The latter part of our work is divided into two processing modules: (1) Data Upload and CBF Quantification Module; (2) Group Analysis Module that supports three types of analysis commonly used in neuroscience research. To date, the CBFBIRN hosts CBF maps and associated clinical data from more than 1,300 individual subjects. The data have been contributed by more than 20 different research studies, investigating the effect of various conditions on CBF including Alzheimer’s, schizophrenia, bipolar disorder, depression, traumatic brain injury, HIV, caffeine usage, and methamphetamine abuse. Several example results, generated by the CBFBIRN processing modules, are presented. We conclude with the lessons learned during implementation and deployment of the CBFBIRN and our experience in promoting data sharing.
Collapse
Affiliation(s)
- David D Shin
- Center for Functional Magnetic Resonance Imaging, University of California at San Diego La Jolla, CA, USA
| | | | | |
Collapse
|
29
|
Gorgolewski KJ, Margulies DS, Milham MP. Making data sharing count: a publication-based solution. Front Neurosci 2013; 7:9. [PMID: 23390412 PMCID: PMC3565154 DOI: 10.3389/fnins.2013.00009] [Citation(s) in RCA: 72] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2012] [Accepted: 01/11/2013] [Indexed: 11/22/2022] Open
Abstract
The neuroimaging community has been increasingly called up to openly share data. Although data sharing has been a cornerstone of large-scale data consortia, the incentive for the individual researcher remains unclear. Other fields have benefited from embracing a data publication form – the data paper – that allows researchers to publish their datasets as a citable scientific publication. Such publishing mechanisms both give credit that is recognizable within the scientific ecosystem, and also ensure the quality of the published data and metadata through the peer review process. We discuss the specific challenges of adapting data papers to the needs of the neuroimaging community, and we propose guidelines for the structure as well as review process.
Collapse
|
30
|
Corradi L, Porro I, Schenone A, Momeni P, Ferrari R, Nobili F, Ferrara M, Arnulfo G, Fato MM. A repository based on a dynamically extensible data model supporting multidisciplinary research in neuroscience. BMC Med Inform Decis Mak 2012; 12:115. [PMID: 23043673 PMCID: PMC3560115 DOI: 10.1186/1472-6947-12-115] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2011] [Accepted: 09/22/2012] [Indexed: 12/03/2022] Open
Abstract
Background Robust, extensible and distributed databases integrating clinical, imaging and molecular data represent a substantial challenge for modern neuroscience. It is even more difficult to provide extensible software environments able to effectively target the rapidly changing data requirements and structures of research experiments. There is an increasing request from the neuroscience community for software tools addressing technical challenges about: (i) supporting researchers in the medical field to carry out data analysis using integrated bioinformatics services and tools; (ii) handling multimodal/multiscale data and metadata, enabling the injection of several different data types according to structured schemas; (iii) providing high extensibility, in order to address different requirements deriving from a large variety of applications simply through a user runtime configuration. Methods A dynamically extensible data structure supporting collaborative multidisciplinary research projects in neuroscience has been defined and implemented. We have considered extensibility issues from two different points of view. First, the improvement of data flexibility has been taken into account. This has been done through the development of a methodology for the dynamic creation and use of data types and related metadata, based on the definition of “meta” data model. This way, users are not constrainted to a set of predefined data and the model can be easily extensible and applicable to different contexts. Second, users have been enabled to easily customize and extend the experimental procedures in order to track each step of acquisition or analysis. This has been achieved through a process-event data structure, a multipurpose taxonomic schema composed by two generic main objects: events and processes. Then, a repository has been built based on such data model and structure, and deployed on distributed resources thanks to a Grid-based approach. Finally, data integration aspects have been addressed by providing the repository application with an efficient dynamic interface designed to enable the user to both easily query the data depending on defined datatypes and view all the data of every patient in an integrated and simple way. Results The results of our work have been twofold. First, a dynamically extensible data model has been implemented and tested based on a “meta” data-model enabling users to define their own data types independently from the application context. This data model has allowed users to dynamically include additional data types without the need of rebuilding the underlying database. Then a complex process-event data structure has been built, based on this data model, describing patient-centered diagnostic processes and merging information from data and metadata. Second, a repository implementing such a data structure has been deployed on a distributed Data Grid in order to provide scalability both in terms of data input and data storage and to exploit distributed data and computational approaches in order to share resources more efficiently. Moreover, data managing has been made possible through a friendly web interface. The driving principle of not being forced to preconfigured data types has been satisfied. It is up to users to dynamically configure the data model for the given experiment or data acquisition program, thus making it potentially suitable for customized applications. Conclusions Based on such repository, data managing has been made possible through a friendly web interface. The driving principle of not being forced to preconfigured data types has been satisfied. It is up to users to dynamically configure the data model for the given experiment or data acquisition program, thus making it potentially suitable for customized applications.
Collapse
Affiliation(s)
- Luca Corradi
- University of Genoa, Dept. of Computer Science, Bioengineering, Robotics and Systems Engineering, Genoa, Italy
| | | | | | | | | | | | | | | | | |
Collapse
|
31
|
Schwartz Y, Barbot A, Thyreau B, Frouin V, Varoquaux G, Siram A, Marcus DS, Poline JB. PyXNAT: XNAT in Python. Front Neuroinform 2012; 6:12. [PMID: 22654752 PMCID: PMC3354345 DOI: 10.3389/fninf.2012.00012] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2011] [Accepted: 03/28/2012] [Indexed: 11/13/2022] Open
Abstract
As neuroimaging databases grow in size and complexity, the time researchers spend investigating and managing the data increases to the expense of data analysis. As a result, investigators rely more and more heavily on scripting using high-level languages to automate data management and processing tasks. For this, a structured and programmatic access to the data store is necessary. Web services are a first step toward this goal. They however lack in functionality and ease of use because they provide only low-level interfaces to databases. We introduce here PyXNAT, a Python module that interacts with The Extensible Neuroimaging Archive Toolkit (XNAT) through native Python calls across multiple operating systems. The choice of Python enables PyXNAT to expose the XNAT Web Services and unify their features with a higher level and more expressive language. PyXNAT provides XNAT users direct access to all the scientific packages in Python. Finally PyXNAT aims to be efficient and easy to use, both as a back-end library to build XNAT clients and as an alternative front-end from the command line.
Collapse
|
32
|
Poline JB, Breeze JL, Ghosh S, Gorgolewski K, Halchenko YO, Hanke M, Haselgrove C, Helmer KG, Keator DB, Marcus DS, Poldrack RA, Schwartz Y, Ashburner J, Kennedy DN. Data sharing in neuroimaging research. Front Neuroinform 2012; 6:9. [PMID: 22493576 PMCID: PMC3319918 DOI: 10.3389/fninf.2012.00009] [Citation(s) in RCA: 148] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2011] [Accepted: 03/09/2012] [Indexed: 11/13/2022] Open
Abstract
Significant resources around the world have been invested in neuroimaging studies of brain function and disease. Easier access to this large body of work should have profound impact on research in cognitive neuroscience and psychiatry, leading to advances in the diagnosis and treatment of psychiatric and neurological disease. A trend toward increased sharing of neuroimaging data has emerged in recent years. Nevertheless, a number of barriers continue to impede momentum. Many researchers and institutions remain uncertain about how to share data or lack the tools and expertise to participate in data sharing. The use of electronic data capture (EDC) methods for neuroimaging greatly simplifies the task of data collection and has the potential to help standardize many aspects of data sharing. We review here the motivations for sharing neuroimaging data, the current data sharing landscape, and the sociological or technical barriers that still need to be addressed. The INCF Task Force on Neuroimaging Datasharing, in conjunction with several collaborative groups around the world, has started work on several tools to ease and eventually automate the practice of data sharing. It is hoped that such tools will allow researchers to easily share raw, processed, and derived neuroimaging data, with appropriate metadata and provenance records, and will improve the reproducibility of neuroimaging studies. By providing seamless integration of data sharing and analysis tools within a commodity research environment, the Task Force seeks to identify and minimize barriers to data sharing in the field of neuroimaging.
Collapse
Affiliation(s)
- Jean-Baptiste Poline
- Neurospin, Commissariat à l'Energie Atomique et aux Energies Alternatives Gif-sur-Yvette, France
| | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
33
|
Glover GH, Mueller BA, Turner JA, van Erp TGM, Liu TT, Greve DN, Voyvodic JT, Rasmussen J, Brown GG, Keator DB, Calhoun VD, Lee HJ, Ford JM, Mathalon DH, Diaz M, O'Leary DS, Gadde S, Preda A, Lim KO, Wible CG, Stern HS, Belger A, McCarthy G, Ozyurt B, Potkin SG. Function biomedical informatics research network recommendations for prospective multicenter functional MRI studies. J Magn Reson Imaging 2012; 36:39-54. [PMID: 22314879 DOI: 10.1002/jmri.23572] [Citation(s) in RCA: 183] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2011] [Accepted: 12/06/2012] [Indexed: 11/08/2022] Open
Abstract
This report provides practical recommendations for the design and execution of multicenter functional MRI (MC-fMRI) studies based on the collective experience of the Function Biomedical Informatics Research Network (FBIRN). The study was inspired by many requests from the fMRI community to FBIRN group members for advice on how to conduct MC-fMRI studies. The introduction briefly discusses the advantages and complexities of MC-fMRI studies. Prerequisites for MC-fMRI studies are addressed before delving into the practical aspects of carefully and efficiently setting up a MC-fMRI study. Practical multisite aspects include: (i) establishing and verifying scan parameters including scanner types and magnetic fields, (ii) establishing and monitoring of a scanner quality program, (iii) developing task paradigms and scan session documentation, (iv) establishing clinical and scanner training to ensure consistency over time, (v) developing means for uploading, storing, and monitoring of imaging and other data, (vi) the use of a traveling fMRI expert, and (vii) collectively analyzing imaging data and disseminating results. We conclude that when MC-fMRI studies are organized well with careful attention to unification of hardware, software and procedural aspects, the process can be a highly effective means for accessing a desired participant demographics while accelerating scientific discovery.
Collapse
Affiliation(s)
- Gary H Glover
- Department of Radiology, Stanford University, Stanford, California, USA.
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
34
|
Das S, Zijdenbos AP, Harlap J, Vins D, Evans AC. LORIS: a web-based data management system for multi-center studies. Front Neuroinform 2012; 5:37. [PMID: 22319489 PMCID: PMC3262165 DOI: 10.3389/fninf.2011.00037] [Citation(s) in RCA: 78] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2011] [Accepted: 12/21/2011] [Indexed: 12/04/2022] Open
Abstract
Longitudinal Online Research and Imaging System (LORIS) is a modular and extensible web-based data management system that integrates all aspects of a multi-center study: from heterogeneous data acquisition (imaging, clinical, behavior, and genetics) to storage, processing, and ultimately dissemination. It provides a secure, user-friendly, and streamlined platform to automate the flow of clinical trials and complex multi-center studies. A subject-centric internal organization allows researchers to capture and subsequently extract all information, longitudinal or cross-sectional, from any subset of the study cohort. Extensive error-checking and quality control procedures, security, data management, data querying, and administrative functions provide LORIS with a triple capability (1) continuous project coordination and monitoring of data acquisition (2) data storage/cleaning/querying, (3) interface with arbitrary external data processing “pipelines.” LORIS is a complete solution that has been thoroughly tested through a full 10 year life cycle of a multi-center longitudinal project1 and is now supporting numerous international neurodevelopment and neurodegeneration research projects.
Collapse
Affiliation(s)
- Samir Das
- Montreal Neurological Institute, McGill University Montreal, Canada
| | | | | | | | | |
Collapse
|
35
|
Gadde S, Aucoin N, Grethe JS, Keator DB, Marcus DS, Pieper S. XCEDE: an extensible schema for biomedical data. Neuroinformatics 2012; 10:19-32. [PMID: 21479735 PMCID: PMC3836560 DOI: 10.1007/s12021-011-9119-9] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
The XCEDE (XML-based Clinical and Experimental Data Exchange) XML schema, developed by members of the BIRN (Biomedical Informatics Research Network), provides an extensive metadata hierarchy for storing, describing and documenting the data generated by scientific studies. Currently at version 2.0, the XCEDE schema serves as a specification for the exchange of scientific data between databases, analysis tools, and web services. It provides a structured metadata hierarchy, storing information relevant to various aspects of an experiment (project, subject, protocol, etc.). Each hierarchy level also provides for the storage of data provenance information allowing for a traceable record of processing and/or changes to the underlying data. The schema is extensible to support the needs of various data modalities and to express types of data not originally envisioned by the developers. The latest version of the XCEDE schema and manual are available from http://www.xcede.org/ .
Collapse
Affiliation(s)
- Syam Gadde
- Brain Imaging and Analysis Center, Duke University, Durham, NC, USA.
| | | | | | | | | | | |
Collapse
|
36
|
|
37
|
Keator DB, Wei D, Gadde S, Bockholt J, Grethe JS, Marcus D, Aucoin N, Ozyurt IB. Derived Data Storage and Exchange Workflow for Large-Scale Neuroimaging Analyses on the BIRN Grid. Front Neuroinform 2009; 3:30. [PMID: 19826494 PMCID: PMC2759340 DOI: 10.3389/neuro.11.030.2009] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2009] [Accepted: 08/16/2009] [Indexed: 11/13/2022] Open
Abstract
Organizing and annotating biomedical data in structured ways has gained much interest and focus in the last 30 years. Driven by decreases in digital storage costs and advances in genetics sequencing, imaging, electronic data collection, and microarray technologies, data is being collected at an ever increasing rate. The need to store and exchange data in meaningful ways in support of data analysis, hypothesis testing and future collaborative use is pervasive. Because trans-disciplinary projects rely on effective use of data from many domains, there is a genuine interest in informatics community on how best to store and combine this data while maintaining a high level of data quality and documentation. The difficulties in sharing and combining raw data become amplified after post-processing and/or data analysis in which the new dataset of interest is a function of the original data and may have been collected by multiple collaborating sites. Simple meta-data, documenting which subject and version of data were used for a particular analysis, becomes complicated by the heterogeneity of the collecting sites yet is critically important to the interpretation and reuse of derived results. This manuscript will present a case study of using the XML-Based Clinical Experiment Data Exchange (XCEDE) schema and the Human Imaging Database (HID) in the Biomedical Informatics Research Network's (BIRN) distributed environment to document and exchange derived data. The discussion includes an overview of the data structures used in both the XML and the database representations, insight into the design considerations, and the extensibility of the design to support additional analysis streams.
Collapse
Affiliation(s)
- David B. Keator
- Psychiatry and Human Behavior, College of Medicine, University of CaliforniaIrvine, CA, USA
| | - Dingying Wei
- Psychiatry and Human Behavior, College of Medicine, University of CaliforniaIrvine, CA, USA
| | - Syam Gadde
- Brain Imaging and Analysis Center, Duke UniversityDurham, NC, USA
| | | | - Jeffrey S. Grethe
- Center for Research on Biological Systems, University of California San DiegoSan Diego, CA, USA
| | - Daniel Marcus
- Neuroinformatics Research Group, Washington UniversitySaint Louis, MO, USA
| | - Nicole Aucoin
- Brigham and Women's Hospital, Harvard UniversityBoston, MA, USA
| | | |
Collapse
|