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Queder N, Tien VB, Abraham SA, Urchs SGW, Helmer KG, Chaplin D, van Erp TGM, Kennedy DN, Poline JB, Grethe JS, Ghosh SS, Keator DB. NIDM-Terms: community-based terminology management for improved neuroimaging dataset descriptions and query. Front Neuroinform 2023; 17:1174156. [PMID: 37533796 PMCID: PMC10392125 DOI: 10.3389/fninf.2023.1174156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 06/27/2023] [Indexed: 08/04/2023] Open
Abstract
The biomedical research community is motivated to share and reuse data from studies and projects by funding agencies and publishers. Effectively combining and reusing neuroimaging data from publicly available datasets, requires the capability to query across datasets in order to identify cohorts that match both neuroimaging and clinical/behavioral data criteria. Critical barriers to operationalizing such queries include, in part, the broad use of undefined study variables with limited or no annotations that make it difficult to understand the data available without significant interaction with the original authors. Using the Brain Imaging Data Structure (BIDS) to organize neuroimaging data has made querying across studies for specific image types possible at scale. However, in BIDS, beyond file naming and tightly controlled imaging directory structures, there are very few constraints on ancillary variable naming/meaning or experiment-specific metadata. In this work, we present NIDM-Terms, a set of user-friendly terminology management tools and associated software to better manage individual lab terminologies and help with annotating BIDS datasets. Using these tools to annotate BIDS data with a Neuroimaging Data Model (NIDM) semantic web representation, enables queries across datasets to identify cohorts with specific neuroimaging and clinical/behavioral measurements. This manuscript describes the overall informatics structures and demonstrates the use of tools to annotate BIDS datasets to perform integrated cross-cohort queries.
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Affiliation(s)
- Nazek Queder
- Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, Irvine, CA, United States
- Department of Neurobiology and Behavior and Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, United States
| | - Vivian B. Tien
- Fairmont Preparatory Academy, Anaheim, CA, United States
| | - Sanu Ann Abraham
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Sebastian Georg Wenzel Urchs
- NeuroDataScience–ORIGAMI Laboratory, McConnell Brain Imaging Centre, The Neuro (Montreal Neurological Institute-Hospital), Faculty of Medicine, McGill University, Montreal, QC, Canada
| | - Karl G. Helmer
- Massachusetts General Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Derek Chaplin
- Massachusetts General Hospital, Boston, MA, United States
| | - Theo G. M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, Irvine, CA, United States
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, United States
| | - David N. Kennedy
- Departments of Psychiatry and Radiology, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Jean-Baptiste Poline
- NeuroDataScience–ORIGAMI Laboratory, McConnell Brain Imaging Centre, The Neuro (Montreal Neurological Institute-Hospital), Faculty of Medicine, McGill University, Montreal, QC, Canada
| | - Jeffrey S. Grethe
- Department of Neurosciences, School of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Satrajit S. Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - David B. Keator
- Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, Irvine, CA, United States
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Schmithorst V, Ceschin R, Lee V, Wallace J, Sahel A, Chenevert TL, Parmar H, Berman JI, Vossough A, Qiu D, Kadom N, Grant PE, Gagoski B, LaViolette PS, Maheshwari M, Sleeper LA, Bellinger DC, Ilardi D, O’Neil S, Miller TA, Detterich J, Hill KD, Atz AM, Richmond ME, Cnota J, Mahle WT, Ghanayem NS, Gaynor JW, Goldberg CS, Newburger JW, Panigrahy A. Single Ventricle Reconstruction III: Brain Connectome and Neurodevelopmental Outcomes: Design, Recruitment, and Technical Challenges of a Multicenter, Observational Neuroimaging Study. Diagnostics (Basel) 2023; 13:1604. [PMID: 37174995 PMCID: PMC10178603 DOI: 10.3390/diagnostics13091604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 04/25/2023] [Accepted: 04/27/2023] [Indexed: 05/15/2023] Open
Abstract
Patients with hypoplastic left heart syndrome who have been palliated with the Fontan procedure are at risk for adverse neurodevelopmental outcomes, lower quality of life, and reduced employability. We describe the methods (including quality assurance and quality control protocols) and challenges of a multi-center observational ancillary study, SVRIII (Single Ventricle Reconstruction Trial) Brain Connectome. Our original goal was to obtain advanced neuroimaging (Diffusion Tensor Imaging and Resting-BOLD) in 140 SVR III participants and 100 healthy controls for brain connectome analyses. Linear regression and mediation statistical methods will be used to analyze associations of brain connectome measures with neurocognitive measures and clinical risk factors. Initial recruitment challenges occurred that were related to difficulties with: (1) coordinating brain MRI for participants already undergoing extensive testing in the parent study, and (2) recruiting healthy control subjects. The COVID-19 pandemic negatively affected enrollment late in the study. Enrollment challenges were addressed by: (1) adding additional study sites, (2) increasing the frequency of meetings with site coordinators, and (3) developing additional healthy control recruitment strategies, including using research registries and advertising the study to community-based groups. Technical challenges that emerged early in the study were related to the acquisition, harmonization, and transfer of neuroimages. These hurdles were successfully overcome with protocol modifications and frequent site visits that involved human and synthetic phantoms.
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Affiliation(s)
- Vanessa Schmithorst
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Avenue, Floor 2, Pittsburgh, PA 15224, USA
| | - Rafael Ceschin
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Avenue, Floor 2, Pittsburgh, PA 15224, USA
- Department of Biomedical Informatics, University of Pittsburgh School, 5607 Baum Blvd., Pittsburgh, PA 15206, USA
| | - Vincent Lee
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Avenue, Floor 2, Pittsburgh, PA 15224, USA
| | - Julia Wallace
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Avenue, Floor 2, Pittsburgh, PA 15224, USA
| | - Aurelia Sahel
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Avenue, Floor 2, Pittsburgh, PA 15224, USA
| | - Thomas L. Chenevert
- Michigan Medicine Department of Radiology, University of Michigan, 1500 E Medical Center Dr., Ann Arbor, MI 48109, USA
| | - Hemant Parmar
- Michigan Medicine Department of Radiology, University of Michigan, 1500 E Medical Center Dr., Ann Arbor, MI 48109, USA
| | - Jeffrey I. Berman
- Department of Radiology, Children’s Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA 19104, USA
| | - Arastoo Vossough
- Department of Radiology, Children’s Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA 19104, USA
| | - Deqiang Qiu
- Department of Radiology and Imaging Sciences, Children’s Healthcare of Atlanta, Emory University, 1364 Clifton Rd, Atlanta, GA 30322, USA
| | - Nadja Kadom
- Department of Radiology and Imaging Sciences, Children’s Healthcare of Atlanta, Emory University, 1364 Clifton Rd, Atlanta, GA 30322, USA
| | - Patricia Ellen Grant
- Children’s Hospital Boston, Fetal-Neonatal Neuroimaging and Developmental Science Center (FNNDSC), 300 Longwood Avenue, Boston, MA 02115, USA
| | - Borjan Gagoski
- Department of Radiology, Children’s Hospital Boston, 300 Longwood Avenue, Boston, MA 02115, USA
| | - Peter S. LaViolette
- Department of Radiology, Medical College of Wisconsin, 9200 W Wisconsin Avenue, Milwaukee, WI 53226, USA
| | - Mohit Maheshwari
- Department of Radiology, Medical College of Wisconsin, 9200 W Wisconsin Avenue, Milwaukee, WI 53226, USA
| | - Lynn A. Sleeper
- Department of Cardiology, Boston Children’s Hospital, 300 Longwood Avenue, Boston, MA 02115, USA
- Department of Pediatrics, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - David C. Bellinger
- Cardiac Neurodevelopmental Program, Department of Neurology, Boston Children’s Hospital, 300 Longwood Avenue, Boston, MA 02115, USA
| | - Dawn Ilardi
- Department of Neuropsychology, Children’s Healthcare of Atlanta, 1400 Tullie Road NE, Atlanta, GA 30329, USA
| | - Sharon O’Neil
- Children’s Hospital Los Angeles, Neuropsychology Core of the Saban Research Institute, 4661 Sunset Blvd., Los Angeles, CA 90027, USA
| | - Thomas A. Miller
- Division of Pediatric Cardiology, Department of Pediatrics, University of Utah School of Medicine, 30 N 1900 E, Salt Lake City, UT 84132, USA
| | - Jon Detterich
- Division of Pediatric Cardiology, Children’s Hospital Los Angeles, 4650 Sunset Blvd., Los Angeles, CA 90027, USA
| | - Kevin D. Hill
- Division of Pediatric Cardiology, Department of Pediatrics, Duke University School of Medicine, 7506 Hospital North, DUMC Box 3090, Durham, NC 27710, USA
| | - Andrew M. Atz
- Division of Pediatric Cardiology, Medical University of South Carolina, 96 Jonathan Lucas St. Ste. 601, MSC 617, Charleston, SC 29425, USA
| | - Marc E. Richmond
- Program for Pediatric Cardiomyopathy, Heart Failure, and Transplantation, New York-Presbyterian Morgan Stanley Children’s Hospital, 3959 Broadway MSCH North, 2nd Floor, New York, NY 10032, USA
| | - James Cnota
- Fetal Heart Program, Cincinnati Children’s, 3333 Burnet Avenue, Cincinnati, OH 45229, USA
| | - William T. Mahle
- Division of Pediatric Cardiology, Children’s Healthcare of Atlanta, 1400 Tullie Rd NE Suite 630, Atlanta, GA 30329, USA
| | - Nancy S. Ghanayem
- Section of Pediatric Critical Care, Department of Pediatrics, Comer Children’s Hospital, University of Chicago Medicine, 5721 S. Maryland Avenue, Chicago, IL 60637, USA
- Department of Pediatrics, Medical College of Wisconsin Section of Pediatric Critical Care, 9000 W. Wisconsin Avenue MS 681, Milwaukee, WI 53226, USA
| | - J. William Gaynor
- Heart Failure and Transplant Program, Children’s Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA 19104, USA
| | - Caren S. Goldberg
- Department of Pediatrics, Division of Cardiology, C.S. Mott Children’s Hospital, 1540 E Hospital Dr #4204, Ann Arbor, MI 48109, USA
| | - Jane W. Newburger
- Department of Cardiology, Boston Children’s Hospital, 300 Longwood Avenue, Boston, MA 02115, USA
| | - Ashok Panigrahy
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Avenue, Floor 2, Pittsburgh, PA 15224, USA
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Schmithorst V, Ceschin R, Lee V, Wallace J, Sahel A, Chenevert T, Parmar H, Berman JI, Vossough A, Qiu D, Kadom N, Grant PE, Gagoski B, LaViolette P, Maheshwari M, Sleeper LA, Bellinger D, Ilardi D, O’Neil S, Miller TA, Detterich J, Hill KD, Atz AM, Richmond M, Cnota J, Mahle WT, Ghanayem N, Gaynor W, Goldberg CS, Newburger JW, Panigrahy A. Single Ventricle Reconstruction III: Brain Connectome and Neurodevelopmental Outcomes: Design, Recruitment, and Technical Challenges of a Multicenter, Observational Neuroimaging Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.12.23288433. [PMID: 37131744 PMCID: PMC10153324 DOI: 10.1101/2023.04.12.23288433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Patients with hypoplastic left heart syndrome who have been palliated with the Fontan procedure are at risk for adverse neurodevelopmental outcomes, lower quality of life, and reduced employability. We describe the methods (including quality assurance and quality control protocols) and challenges of a multi-center observational ancillary study, SVRIII (Single Ventricle Reconstruction Trial) Brain Connectome. Our original goal was to obtain advanced neuroimaging (Diffusion Tensor Imaging and Resting-BOLD) in 140 SVR III participants and 100 healthy controls for brain connectome analyses. Linear regression and mediation statistical methods will be used to analyze associations of brain connectome measures with neurocognitive measures and clinical risk factors. Initial recruitment challenges occurred related to difficulties with: 1) coordinating brain MRI for participants already undergoing extensive testing in the parent study, and 2) recruiting healthy control subjects. The COVID-19 pandemic negatively affected enrollment late in the study. Enrollment challenges were addressed by 1) adding additional study sites, 2) increasing the frequency of meetings with site coordinators and 3) developing additional healthy control recruitment strategies, including using research registries and advertising the study to community-based groups. Technical challenges that emerged early in the study were related to the acquisition, harmonization, and transfer of neuroimages. These hurdles were successfully overcome with protocol modifications and frequent site visits that involved human and synthetic phantoms. Trial registration number ClinicalTrials.gov Registration Number: NCT02692443.
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Affiliation(s)
- Vanessa Schmithorst
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave, Floor 2, Pittsburgh, PA 15224 USA
| | - Rafael Ceschin
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave, Floor 2, Pittsburgh, PA 15224 USA
- Department of Biomedical Informatics, University of Pittsburgh School, 5607 Baum Blvd, Pittsburgh, PA 15206-3701 USA
| | - Vince Lee
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave, Floor 2, Pittsburgh, PA 15224 USA
| | - Julia Wallace
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave, Floor 2, Pittsburgh, PA 15224 USA
| | - Aurelia Sahel
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave, Floor 2, Pittsburgh, PA 15224 USA
| | - Thomas Chenevert
- Department of Radiology, Michigan Medicine, University of Michigan, University of Michigan, 1500 E Medical Center Dr, Ann Arbor, MI 48109 USA
| | - Hemant Parmar
- Department of Radiology, Michigan Medicine, University of Michigan, University of Michigan, 1500 E Medical Center Dr, Ann Arbor, MI 48109 USA
| | - Jeffrey I. Berman
- Department of Radiology, Children’s Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, USA
| | - Arastoo Vossough
- Department of Radiology, Children’s Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, USA
| | - Deqiang Qiu
- Department of Radiology and Imaging Sciences, Children’s Healthcare of Atlanta, Emory University, 1364 Clifton Rd, Atlanta, GA 30322 USA
| | - Nadja Kadom
- Department of Radiology and Imaging Sciences, Children’s Healthcare of Atlanta, Emory University, 1364 Clifton Rd, Atlanta, GA 30322 USA
| | - Patricia Ellen Grant
- Fetal-Neonatal Neuroimaging and Developmental Science Center (FNNDSC), Children’s Hospital Boston, 300 Longwood Avenue, Boston, MA 02115 USA
| | - Borjan Gagoski
- Department of Radiology, Children’s Hospital Boston, 300 Longwood Ave, Boston, MA 02115 USA
| | - Peter LaViolette
- Department of Radiology, Medical College of Wisconsin, 9200 W Wisconsin Ave, Milwaukee, WI 53226 USA
| | - Mohit Maheshwari
- Department of Radiology, Medical College of Wisconsin, 9200 W Wisconsin Ave, Milwaukee, WI 53226 USA
| | - Lynn A. Sleeper
- Department of Cardiology, Boston Children’s Hospital, 300 Longwood Avenue, Boston, MA 02115
- Department of Pediatrics, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115 USA
| | - David Bellinger
- Cardiac Neurodevelopmental Program, Department of Neurology, Boston, Children’s Hospital, 300 Longwood Avenue, Boston, MA 02115 USA
| | - Dawn Ilardi
- Department of Neuropsychology, Children’s Healthcare of Atlanta, 1400 Tullie Road NE, Atlanta, GA 30329
| | - Sharon O’Neil
- Neuropsychology Core of the Saban Research Institute, Children’s Hospital Los Angeles, 4661 Sunset Blvd., Los Angeles, CA 90027 USA
| | - Thomas A. Miller
- Division of Pediatric Cardiology, Department of Pediatrics, University of Utah, School of Medicine, 30 N 1900 E, Salt Lake City, UT 84132 USA
| | - Jon Detterich
- Division of Pediatric Cardiology, Children’s Hospital Los Angeles, 4650 Sunset Blvd, Los Angeles, CA 90027 USA
| | - Kevin D. Hill
- Division of Pediatric Cardiology, Department of Pediatrics, Duke University, School of Medicine, 7506 Hospital North, DUMC Box 3090, Durham, NC 27710 USA
| | - Andrew M. Atz
- Division of Pediatric Cardiology, Medical University of South Carolina, 96 Jonathan Lucas St. Ste. 601, MSC 617, Charleston, SC 29425 USA
| | - Marc Richmond
- Program for Pediatric Cardiomyopathy, Heart Failure, and Transplantation, New York-Presbyterian Morgan Stanley Children’s Hospital, 3959 Broadway MSCH North, 2 Floor, New York, NY 10032 USA
| | - James Cnota
- Fetal Heart Program, Cincinnati Children’s, 3333 Burnet Avenue, Cincinnati, Ohio 45229-3026 USA
| | - William T. Mahle
- Division of Pediatric Cardiology, Children’s Healthcare of Atlanta, 1400 Tullie Rd NE Suite 630, Atlanta, GA 30329
| | - Nancy Ghanayem
- Section of Pediatric Critical Care, Department of Pediatrics, University of Chicago Medicine, Comer Children’s Hospital, 5721 S. Maryland Ave., Chicago, IL 60637 USA
- Section of Pediatric Critical Care, Department of Pediatrics, Medical College of Wisconsin, 9000 W. Wisconsin Ave. MS 681, Milwaukee, WI 53226 USA
| | - William Gaynor
- Heart Failure and Transplant Program, Children’s Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA 19104 USA
| | - Caren S. Goldberg
- Department of Pediatrics, Division of Cardiology, C.S. Mott Children’s Hospital, 1540 E Hospital Dr #4204, Ann Arbor, MI 48109 USA
| | - Jane W. Newburger
- Department of Cardiology, Boston Children’s Hospital, 300 Longwood Avenue, Boston, MA 02115
| | - Ashok Panigrahy
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave, Floor 2, Pittsburgh, PA 15224 USA
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Cooper DM, Bar-Yoseph R, Liem RI, Keens TG, McColley SA, Radom-Aizik S. Pediatric Cardiopulmonary Exercise Testing: Interoperability Through Domain Analysis Modeling and a National Survey. Med Sci Sports Exerc 2022; 54:741-750. [PMID: 35148537 DOI: 10.1249/mss.0000000000002894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
PURPOSE The electronic health record, data science advances, and dynamic environmental and infectious threats to child health highlight the need for harmonized and interoperable approaches to pediatric cardiopulmonary exercise testing (CPET). Accordingly, we developed a terminology harmonization in exercise medicine and exercise science domain analysis model (THEMES DAM) to structure CPET data elements. METHODS THEMES DAM identified 114 data elements, including participant information, calibration, equipment, protocols, laboratory personnel, encouragement strategies, and analysis procedures. We used the THEMES DAM, vetted by the international data standards organization HL7, to construct a current-state survey of pediatric CPET centers in the United States. Forty-eight of 101 centers responded to a questionnaire covering seven major topic areas (38 items). RESULTS Centers predominantly performed between 100 and 500 tests annually. Cardiac disease represented 55% of referrals. Almost all centers calibrated gas concentrations and flow daily, but 42% never calibrated their treadmill or cycle ergometers. All centers measured V̇O2peakbut calculated differently. Centers used a variety of protocols (e.g., for treadmill: 61%, Bruce; 43%, modified Bruce; 59%, other); 44% calculated CPET slopes from submaximal portions of CPET (e.g., V̇O2-HR). All centers verbally encouraged participants, but only 40% used a standardized approach. The interpretation of CPET was done by physicians (60%), exercise physiologists (25%), exercise technicians (10%), nurses (1%), or others (4%). Ninety-one percent would agree to collaborate in multicenter research, 89% to establish dynamic reference values, and 83% to better interpret CPET. CONCLUSIONS The survey data and the implementation of THEMES DAM could accelerate interoperability across multiple centers. This would facilitate a nimble approach to create pediatric reference values responsive to the constantly changing health environment and stimulate novel approaches to CPET research and clinical application.
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Affiliation(s)
- Dan M Cooper
- Institute of Clinical Translational Science and Pediatric Exercise and Genomics Research Center, Department of Pediatrics, School of Medicine, University of California at Irvine, Irvine, CA
| | - Ronen Bar-Yoseph
- Pediatric Exercise and Genomics Research Center, Department of Pediatrics, School of Medicine, University of California at Irvine, Irvine, CA
| | - Robert I Liem
- Division of Hematology, Oncology and Stem Cell Transplant, Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL
| | - Thomas G Keens
- Division of Pediatric Pulmonology, Department of Pediatrics, Children's Hospital Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Susanna A McColley
- Division of Pulmonary and Sleep Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL
| | - Shlomit Radom-Aizik
- Pediatric Exercise and Genomics Research Center, Department of Pediatrics, School of Medicine, University of California at Irvine, Irvine, CA
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Hoffmann M, Billot B, Greve DN, Iglesias JE, Fischl B, Dalca AV. SynthMorph: Learning Contrast-Invariant Registration Without Acquired Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:543-558. [PMID: 34587005 PMCID: PMC8891043 DOI: 10.1109/tmi.2021.3116879] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
We introduce a strategy for learning image registration without acquired imaging data, producing powerful networks agnostic to contrast introduced by magnetic resonance imaging (MRI). While classical registration methods accurately estimate the spatial correspondence between images, they solve an optimization problem for every new image pair. Learning-based techniques are fast at test time but limited to registering images with contrasts and geometric content similar to those seen during training. We propose to remove this dependency on training data by leveraging a generative strategy for diverse synthetic label maps and images that exposes networks to a wide range of variability, forcing them to learn more invariant features. This approach results in powerful networks that accurately generalize to a broad array of MRI contrasts. We present extensive experiments with a focus on 3D neuroimaging, showing that this strategy enables robust and accurate registration of arbitrary MRI contrasts even if the target contrast is not seen by the networks during training. We demonstrate registration accuracy surpassing the state of the art both within and across contrasts, using a single model. Critically, training on arbitrary shapes synthesized from noise distributions results in competitive performance, removing the dependency on acquired data of any kind. Additionally, since anatomical label maps are often available for the anatomy of interest, we show that synthesizing images from these dramatically boosts performance, while still avoiding the need for real intensity images. Our code is available at doic https://w3id.org/synthmorph.
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Harrell W, Gipson DS, Belger A, Matsuda-Abedini M, Bjornson B, Hooper SR. Functional Magnetic Resonance Imaging Findings in Children and Adolescents With Chronic Kidney Disease: Preliminary Findings. Semin Nephrol 2021; 41:462-475. [PMID: 34916008 DOI: 10.1016/j.semnephrol.2021.09.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
This cross-sectional study provides preliminary findings from one of the first functional brain imaging studies in children with chronic kidney disease (CKD). The sample included 21 children with CKD (ages, 14.4 ± 3.0 y) and 11 healthy controls (ages, 14.5 ± 3.4 y). Using functional magnetic resonance imaging during a visual-spatial working memory task, findings showed that the CKD group and healthy controls invoked similar brain regions for encoding and retrieval phases of the task, but significant group differences were noted in the activation patterns for both components of the task. For the encoding phase, the CKD group showed lower activation in the posterior cingulate, anterior cingulate, precuneus, and middle occipital gyrus than the control group, but more activation in the superior temporal gyrus, middle frontal gyrus, middle temporal gyrus, and the insula. For the retrieval phase, the CKD group showed underactivation for brain systems involving the posterior cingulate, medial frontal gyrus, occipital lobe, and middle temporal gyrus, and greater activation than the healthy controls in the postcentral gyrus. Few group differences were noted with respect to disease severity. These preliminary findings support evidence showing a neurologic basis to the cognitive difficulties evident in pediatric CKD, and lay the foundation for future studies to explore the neural underpinnings for neurocognitive (dys)function in this population.
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Affiliation(s)
- Waverly Harrell
- School of Education, University of North Carolina-Chapel Hill, Chapel Hill, NC
| | - Debbie S Gipson
- Division of Nephrology, Department of Pediatrics, University of Michigan, Ann Arbor, MI
| | - Aysenil Belger
- Department of Psychiatry, School of Medicine, University of North Carolina-Chapel Hill, Chapel Hill, NC
| | - Mina Matsuda-Abedini
- Division of Nephrology, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada
| | - Bruce Bjornson
- Division of Neurology, B.C. Children's' Hospital, Vancouver, British Columbia, Canada
| | - Stephen R Hooper
- Department of Allied Health Sciences, School of Medicine, University of North Carolina-Chapel Hill, Chapel Hill, NC.
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7
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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.
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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
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8
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Hoffmann M, Billot B, Iglesias JE, Fischl B, Dalca AV. LEARNING MRI CONTRAST-AGNOSTIC REGISTRATION. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2021; 2023:899-903. [PMID: 38213549 PMCID: PMC10782386 DOI: 10.1109/isbi48211.2021.9434113] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2024]
Abstract
We introduce a strategy for learning image registration without acquired imaging data, producing powerful networks agnostic to magnetic resonance imaging (MRI) contrast. While classical methods accurately estimate the spatial correspondence between images, they solve an optimization problem for every new image pair. Learning methods are fast at test time but limited to images with contrasts and geometric content similar to those seen during training. We propose to remove this dependency using a generative strategy that exposes networks to a wide range of images synthesized from segmentations during training, forcing them to generalize across contrasts. We show that networks trained within this framework generalize to a broad array of unseen MRI contrasts and surpass classical state-of-the-art brain registration accuracy by up to 12.4 Dice points for a variety of tested contrast combinations. Critically, training on arbitrary shapes synthesized from noise distributions results in competitive performance, removing the dependency on acquired data of any kind. Additionally, since anatomical label maps are often available for the anatomy of interest, we show that synthesizing images from these dramatically boosts performance, while still avoiding the need for real intensity images during training.
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Affiliation(s)
- Malte Hoffmann
- Athinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
| | - Benjamin Billot
- Centre for Medical Image Computing, University College London, WC1E 6BT, UK
| | - Juan E Iglesias
- Athinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
- Centre for Medical Image Computing, University College London, WC1E 6BT, UK
- Computer Science and Artificial Intelligence Lab, MIT, Cambridge, MA 02139, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
- Computer Science and Artificial Intelligence Lab, MIT, Cambridge, MA 02139, USA
| | - Adrian V Dalca
- Athinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
- Computer Science and Artificial Intelligence Lab, MIT, Cambridge, MA 02139, USA
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Mandal PK, Sandal K, Shukla D, Tripathi M, Singh K, Roy S. ANSH: Multimodal Neuroimaging Database Including MR Spectroscopic Data From Each Continent to Advance Alzheimer's Disease Research. Front Neuroinform 2020; 14:571039. [PMID: 33214792 PMCID: PMC7641007 DOI: 10.3389/fninf.2020.571039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 08/31/2020] [Indexed: 12/22/2022] Open
Abstract
Alzheimer's disease (AD) is a devastating neurodegenerative disorder affecting millions of people worldwide. The etiology of AD is not known, and intense research involving multimodal neuroimaging data (e.g., MRI, functional MRI, PET etc.) is extensively used to identify the causal molecular process for AD. In this context, various imaging-based databases accessible to researchers globally, are useful for an independent analysis. Apart from MRI-based brain imaging data, the neurochemical data using magnetic resonance spectroscopy (MRS) provide early molecular processes before the structural or functional changes are manifested. The existing imaging-based databases in AD lack the integration of MRS modality and, thus, limits the availability of neurochemical information to the AD research community. This perspective is an initiative to bring attention to the development of the neuroimaging database, "ANSH," that includes brain glutathione (GSH), gamma aminobutyric acid (GABA) levels, and other neurochemicals along with MRI-based information for AD, mild cognitive impairment (MCI), and healthy subjects. ANSH is supported by a JAVA-based workflow environment and python providing a simple, dynamic, and distributed platform with data security. The platform consists of two-tiered architecture for data collection and management further supporting quality control, report generation for analyzed data, and data backup with a dedicated storage system. The ANSH database aims to present a single neuroimaging data platform incorporating diverse data types from healthy control and patient groups to provide better insights pertaining to disease progression. This data management platform provides flexible data sharing across users with continuous project monitoring. The development of ANSH platform will facilitate collaborative research and multi-site data sharing across the globe.
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Affiliation(s)
- Pravat K Mandal
- NeuroImaging and NeuroSpectroscopy (NINS) Laboratory, National Brain Research Centre, Manesar, India.,Florey Institute of Neuroscience and Mental Health, Parkville, VIC, Australia
| | - Kanika Sandal
- NeuroImaging and NeuroSpectroscopy (NINS) Laboratory, National Brain Research Centre, Manesar, India
| | - Deepika Shukla
- NeuroImaging and NeuroSpectroscopy (NINS) Laboratory, National Brain Research Centre, Manesar, India
| | - Manjari Tripathi
- Department of Neurology, All Indian Institute of Medical Sciences, New Delhi, India
| | - Kuldeep Singh
- NeuroImaging and NeuroSpectroscopy (NINS) Laboratory, National Brain Research Centre, Manesar, India
| | - Saurav Roy
- NeuroImaging and NeuroSpectroscopy (NINS) Laboratory, National Brain Research Centre, Manesar, India
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DeRamus T, Silva R, Iraji A, Damaraju E, Belger A, Ford J, McEwen S, Mathalon D, Mueller B, Pearlson G, Potkin S, Preda A, Turner J, Vaidya J, van Erp T, Calhoun V. Covarying structural alterations in laterality of the temporal lobe in schizophrenia: A case for source-based laterality. NMR IN BIOMEDICINE 2020; 33:e4294. [PMID: 32207187 PMCID: PMC8311554 DOI: 10.1002/nbm.4294] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 02/21/2020] [Accepted: 02/24/2020] [Indexed: 06/10/2023]
Abstract
The human brain is asymmetrically lateralized for certain functions (such as language processing) to regions in one hemisphere relative to the other. Asymmetries are measured with a laterality index (LI). However, traditional LI measures are limited by a lack of consensus on metrics used for its calculation. To address this limitation, source-based laterality (SBL) leverages an independent component analysis for the identification of laterality-specific alterations, identifying covarying components between hemispheres across subjects. SBL is successfully implemented with simulated data with inherent differences in laterality. SBL is then compared with a voxel-wise analysis utilizing structural data from a sample of patients with schizophrenia and controls without schizophrenia. SBL group comparisons identified three distinct temporal regions and one cerebellar region with significantly altered laterality in patients with schizophrenia relative to controls. Previous work highlights reductions in laterality (ie, reduced left gray matter volume) in patients with schizophrenia compared with controls without schizophrenia. Results from this pilot SBL project are the first, to our knowledge, to identify covarying laterality differences within discrete temporal brain regions. The authors argue SBL provides a unique focus to detect covarying laterality differences in patients with schizophrenia, facilitating the discovery of laterality aspects undetected in previous work.
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Affiliation(s)
- T.P. DeRamus
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - R.F. Silva
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - A. Iraji
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - E. Damaraju
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - A. Belger
- Department of Psychiatry, University of North Carolina Chapel Hill, North Carolina, USA
| | - J.M. Ford
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA
| | - S. McEwen
- Pacific Neuroscience Institute Foundation, Santa Monica, CA, USA
| | - D.H. Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA
| | - B.A. Mueller
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA
| | - G.D. Pearlson
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Institute of Living, Olin Neuropsychiatry Research Center, Hartford, CT, USA
| | - S.G. Potkin
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - A. Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - J.A. Turner
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Department of Psychology, Georgia State University, GA, USA
| | - J.G. Vaidya
- Department of Psychiatry, University of Iowa, IA, USA
| | - T.G.M. van Erp
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - V.D. Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychology, Georgia State University, GA, USA
- Department of Electrical and Computer Engineering, Georgia Tech, GA, USA
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11
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12
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Lou Y, Zhao L, Yu S, Sun B, Hou Z, Zhang Z, Tang Y, Liu S. Brain asymmetry differences between Chinese and Caucasian populations: a surface-based morphometric comparison study. Brain Imaging Behav 2019; 14:2323-2332. [PMID: 31435899 DOI: 10.1007/s11682-019-00184-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Asymmetry has been proved to exist in the human brain structure, function and behavior. Most of the existing brain asymmetry findings are originated from the western populations, while studies about the brain structural and functional asymmetries in East Asians are limited. Extensive evidence suggested that cultural differences, e.g. education and language, may lead to differences in brain structure and function between races. Therefore, we hypothesized that differences in brain structural asymmetries exist between East Asians and Westerners. In this study, we performed a comprehensive surface-based morphometric (SBM) analysis of brain asymmetries in cortical thickness, volume and surface area in two well-matched groups of right-handed, Chinese (n = 45) and Caucasian (n = 45) young male adults (age = 22-29 years). Our results showed consistent inter-hemispheric asymmetries in the three brain morphological measures in multiple brain regions in the Chinese young adults, including the temporal, frontal, parietal, occipital, insular cortices and the cingulate gyrus. Comparing with the Caucasians, the Chinese group showed greater structural asymmetry in the frontal, temporal, occipital and insular cortices, and smaller asymmetry in the parietal cortex and cingulate gyrus. These findings could provide a new neuroanatomical basis for understanding the distinctions between East Asian and Caucasian in brain functional lateralization.
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Affiliation(s)
- Yunxia Lou
- Research Center for Sectional and Imaging Anatomy, Shandong University Cheeloo College of Medicine, Jinan, China.,School of Basic Medical Sciences, Shandong University, Jinan, China
| | - Lu Zhao
- Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Shui Yu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Jinan, China
| | - Bo Sun
- Research Center for Sectional and Imaging Anatomy, Shandong University Cheeloo College of Medicine, Jinan, China.,Shandong Medical Imaging Research Institute, Jinan, China
| | - Zhongyu Hou
- Research Center for Sectional and Imaging Anatomy, Shandong University Cheeloo College of Medicine, Jinan, China.,Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China
| | - Zhonghe Zhang
- Research Center for Sectional and Imaging Anatomy, Shandong University Cheeloo College of Medicine, Jinan, China.,Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China
| | - Yuchun Tang
- Research Center for Sectional and Imaging Anatomy, Shandong University Cheeloo College of Medicine, Jinan, China. .,School of Basic Medical Sciences, Shandong University, Jinan, China.
| | - Shuwei Liu
- Research Center for Sectional and Imaging Anatomy, Shandong University Cheeloo College of Medicine, Jinan, China.,School of Basic Medical Sciences, Shandong University, Jinan, China
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13
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Keshavan A, Poline JB. From the Wet Lab to the Web Lab: A Paradigm Shift in Brain Imaging Research. Front Neuroinform 2019; 13:3. [PMID: 30881299 PMCID: PMC6405692 DOI: 10.3389/fninf.2019.00003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Accepted: 01/22/2019] [Indexed: 01/08/2023] Open
Abstract
Web technology has transformed our lives, and has led to a paradigm shift in the computational sciences. As the neuroimaging informatics research community amasses large datasets to answer complex neuroscience questions, we find that the web is the best medium to facilitate novel insights by way of improved collaboration and communication. Here, we review the landscape of web technologies used in neuroimaging research, and discuss future applications, areas for improvement, and the limitations of using web technology in research. Fully incorporating web technology in our research lifecycle requires not only technical skill, but a widespread culture change; a shift from the small, focused "wet lab" to a multidisciplinary and largely collaborative "web lab."
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Affiliation(s)
- Anisha Keshavan
- Department of Speech and Hearing, Institute for Neuroengineering, eScience Institute, University of Washington, Seattle, WA, United States
| | - Jean-Baptiste Poline
- Faculty of Medicine, McConnell Brain Imaging Centre, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
- Henry H. Wheeler Jr. Brain Imaging Center, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
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14
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Altered white matter connectivity in patients with schizophrenia: An investigation using public neuroimaging data from SchizConnect. PLoS One 2018; 13:e0205369. [PMID: 30300425 PMCID: PMC6177186 DOI: 10.1371/journal.pone.0205369] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2018] [Accepted: 09/23/2018] [Indexed: 01/01/2023] Open
Abstract
Several studies have produced extensive evidence on white matter abnormalities in schizophrenia (SZ). However, optimum consistency and reproducibility have not been achieved, and reported low white matter tract integrity in patients with SZ varies between studies. A whole-brain imaging study with a large sample size is needed. This study aimed to investigate white matter integrity in the corpus callosum and connections between regions of interests (ROIs) in the same hemisphere in 122 patients with SZ and 129 healthy controls with public neuroimaging data from SchizConnect. For each diffusion-weighted image (DWI), two-tensor full-brain tractography was performed; DWIs were parcellated by processing and registering T1 images with FreeSurfer and Advanced Normalization Tools. White matter query language was used to extract white matter fiber tracts. We evaluated group differences in means of diffusion measures between the patients and controls, and correlations of diffusion measures with the severity of clinical symptoms and cognitive impairment in the patients using the Positive and Negative Syndrome Scale (PANSS), a letter-number sequencing (LNS) test, vocabulary test, letter fluency test, category fluency test, and trail-making test, part A. To correct for multiple comparisons, a false discovery rate of q < 0.05 was applied. In patients with SZ, we observed significant radial diffusivity (RD) and trace (TR) increases in left thalamo-occipital tracts and the right uncinate fascicle, and a significant RD increase in the right middle longitudinal fascicle (MDLF) and the right superior longitudinal fascicle ii. Correlations were present between TR of left thalamo-occipital tracts, and the letter fluency test and the LNS test, and RD in the right MDLF and PANSS positive subscale score. However, these correlations were not significant after correction for multiple comparisons. These results indicated widespread white matter fiber tract abnormalities in patients with SZ, contributing to SZ pathophysiology.
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15
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Lavigne KM, Woodward TS. Hallucination- and speech-specific hypercoupling in frontotemporal auditory and language networks in schizophrenia using combined task-based fMRI data: An fBIRN study. Hum Brain Mapp 2017; 39:1582-1595. [PMID: 29271110 DOI: 10.1002/hbm.23934] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2017] [Revised: 09/26/2017] [Accepted: 11/08/2017] [Indexed: 01/23/2023] Open
Abstract
Hypercoupling of activity in speech-perception-specific brain networks has been proposed to play a role in the generation of auditory-verbal hallucinations (AVHs) in schizophrenia; however, it is unclear whether this hypercoupling extends to nonverbal auditory perception. We investigated this by comparing schizophrenia patients with and without AVHs, and healthy controls, on task-based functional magnetic resonance imaging (fMRI) data combining verbal speech perception (SP), inner verbal thought generation (VTG), and nonverbal auditory oddball detection (AO). Data from two previously published fMRI studies were simultaneously analyzed using group constrained principal component analysis for fMRI (group fMRI-CPCA), which allowed for comparison of task-related functional brain networks across groups and tasks while holding the brain networks under study constant, leading to determination of the degree to which networks are common to verbal and nonverbal perception conditions, and which show coordinated hyperactivity in hallucinations. Three functional brain networks emerged: (a) auditory-motor, (b) language processing, and (c) default-mode (DMN) networks. Combining the AO and sentence tasks allowed the auditory-motor and language networks to separately emerge, whereas they were aggregated when individual tasks were analyzed. AVH patients showed greater coordinated activity (deactivity for DMN regions) than non-AVH patients during SP in all networks, but this did not extend to VTG or AO. This suggests that the hypercoupling in AVH patients in speech-perception-related brain networks is specific to perceived speech, and does not extend to perceived nonspeech or inner verbal thought generation.
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Affiliation(s)
- Katie M Lavigne
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada.,BC Mental Health and Addictions Research Institute, Vancouver, British Columbia, Canada
| | - Todd S Woodward
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada.,BC Mental Health and Addictions Research Institute, Vancouver, British Columbia, Canada
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16
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Retico A, Arezzini S, Bosco P, Calderoni S, Ciampa A, Coscetti S, Cuomo S, De Santis L, Fabiani D, Fantacci ME, Giuliano A, Mazzoni E, Mercatali P, Miscali G, Pardini M, Prosperi M, Romano F, Tamburini E, Tosetti M, Muratori F. ARIANNA: A research environment for neuroimaging studies in autism spectrum disorders. Comput Biol Med 2017; 87:1-7. [PMID: 28544911 DOI: 10.1016/j.compbiomed.2017.05.017] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Revised: 05/15/2017] [Accepted: 05/15/2017] [Indexed: 01/07/2023]
Abstract
The complexity and heterogeneity of Autism Spectrum Disorders (ASD) require the implementation of dedicated analysis techniques to obtain the maximum from the interrelationship among many variables that describe affected individuals, spanning from clinical phenotypic characterization and genetic profile to structural and functional brain images. The ARIANNA project has developed a collaborative interdisciplinary research environment that is easily accessible to the community of researchers working on ASD (https://arianna.pi.infn.it). The main goals of the project are: to analyze neuroimaging data acquired in multiple sites with multivariate approaches based on machine learning; to detect structural and functional brain characteristics that allow the distinguishing of individuals with ASD from control subjects; to identify neuroimaging-based criteria to stratify the population with ASD to support the future development of personalized treatments. Secure data handling and storage are guaranteed within the project, as well as the access to fast grid/cloud-based computational resources. This paper outlines the web-based architecture, the computing infrastructure and the collaborative analysis workflows at the basis of the ARIANNA interdisciplinary working environment. It also demonstrates the full functionality of the research platform. The availability of this innovative working environment for analyzing clinical and neuroimaging information of individuals with ASD is expected to support researchers in disentangling complex data thus facilitating their interpretation.
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Affiliation(s)
- Alessandra Retico
- National Institute for Nuclear Physics (INFN), Largo Bruno Pontecorvo 3, 56127 Pisa, Italy.
| | - Silvia Arezzini
- National Institute for Nuclear Physics (INFN), Largo Bruno Pontecorvo 3, 56127 Pisa, Italy
| | - Paolo Bosco
- National Institute for Nuclear Physics (INFN), Largo Bruno Pontecorvo 3, 56127 Pisa, Italy
| | - Sara Calderoni
- IRCCS Stella Maris Foundation, Viale del Tirreno 331, 56128 Pisa, Italy; Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Alberto Ciampa
- National Institute for Nuclear Physics (INFN), Largo Bruno Pontecorvo 3, 56127 Pisa, Italy
| | - Simone Coscetti
- National Institute for Nuclear Physics (INFN), Largo Bruno Pontecorvo 3, 56127 Pisa, Italy
| | - Stefano Cuomo
- Institute of Legal Information Theory and Techniques (ITTIG) of the National Research Council, Via de' Barucci 20, 50127 Florence, Italy
| | | | - Dario Fabiani
- National Institute for Nuclear Physics (INFN), Largo Bruno Pontecorvo 3, 56127 Pisa, Italy
| | - Maria Evelina Fantacci
- National Institute for Nuclear Physics (INFN), Largo Bruno Pontecorvo 3, 56127 Pisa, Italy; University of Pisa, Physics Department, Largo Bruno Pontecorvo 3, 56127 Pisa, Italy
| | - Alessia Giuliano
- National Institute for Nuclear Physics (INFN), Largo Bruno Pontecorvo 3, 56127 Pisa, Italy
| | - Enrico Mazzoni
- National Institute for Nuclear Physics (INFN), Largo Bruno Pontecorvo 3, 56127 Pisa, Italy
| | - Pietro Mercatali
- Institute of Legal Information Theory and Techniques (ITTIG) of the National Research Council, Via de' Barucci 20, 50127 Florence, Italy
| | | | | | | | - Francesco Romano
- Institute of Legal Information Theory and Techniques (ITTIG) of the National Research Council, Via de' Barucci 20, 50127 Florence, Italy
| | | | - Michela Tosetti
- IRCCS Stella Maris Foundation, Viale del Tirreno 331, 56128 Pisa, Italy
| | - Filippo Muratori
- IRCCS Stella Maris Foundation, Viale del Tirreno 331, 56128 Pisa, Italy; Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
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17
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Arbabshirani MR, Plis S, Sui J, Calhoun VD. Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. Neuroimage 2017; 145:137-165. [PMID: 27012503 PMCID: PMC5031516 DOI: 10.1016/j.neuroimage.2016.02.079] [Citation(s) in RCA: 504] [Impact Index Per Article: 72.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2015] [Revised: 02/03/2016] [Accepted: 02/25/2016] [Indexed: 01/18/2023] Open
Abstract
Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hundreds of studies have been carried out for accurate classification of patients with heterogeneous mental and neurodegenerative disorders such as schizophrenia and Alzheimer's disease. More than 500 studies have been published during the past quarter century on single subject prediction focused on a multiple brain disorders. In the first part of this study, we provide a survey of more than 200 reports in this field with a focus on schizophrenia, mild cognitive impairment (MCI), Alzheimer's disease (AD), depressive disorders, autism spectrum disease (ASD) and attention-deficit hyperactivity disorder (ADHD). Detailed information about those studies such as sample size, type and number of extracted features and reported accuracy are summarized and discussed. To our knowledge, this is by far the most comprehensive review of neuroimaging-based single subject prediction of brain disorders. In the second part, we present our opinion on major pitfalls of those studies from a machine learning point of view. Common biases are discussed and suggestions are provided. Moreover, emerging trends such as decentralized data sharing, multimodal brain imaging, differential diagnosis, disease subtype classification and deep learning are also discussed. Based on this survey, there is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders. However, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper. Emerging big data technologies and advanced data-intensive machine learning methodologies such as deep learning have coincided with an increasing need for accurate, robust and generalizable single subject prediction of brain disorders during an exciting time. In this report, we survey the past and offer some opinions regarding the road ahead.
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Affiliation(s)
- Mohammad R Arbabshirani
- The Mind Research Network, Albuquerque, NM 87106, USA; Geisinger Health System, Danville, PA 17822, USA
| | - Sergey Plis
- The Mind Research Network, Albuquerque, NM 87106, USA
| | - Jing Sui
- The Mind Research Network, Albuquerque, NM 87106, USA; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM 87106, USA; Department of ECE, University of New Mexico, Albuquerque, NM, USA
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18
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Noble S, Scheinost D, Finn ES, Shen X, Papademetris X, McEwen SC, Bearden CE, Addington J, Goodyear B, Cadenhead KS, Mirzakhanian H, Cornblatt BA, Olvet DM, Mathalon DH, McGlashan TH, Perkins DO, Belger A, Seidman LJ, Thermenos H, Tsuang MT, van Erp TGM, Walker EF, Hamann S, Woods SW, Cannon TD, Constable RT. Multisite reliability of MR-based functional connectivity. Neuroimage 2016; 146:959-970. [PMID: 27746386 DOI: 10.1016/j.neuroimage.2016.10.020] [Citation(s) in RCA: 103] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2016] [Revised: 10/10/2016] [Accepted: 10/12/2016] [Indexed: 11/26/2022] Open
Abstract
Recent years have witnessed an increasing number of multisite MRI functional connectivity (fcMRI) studies. While multisite studies provide an efficient way to accelerate data collection and increase sample sizes, especially for rare clinical populations, any effects of site or MRI scanner could ultimately limit power and weaken results. Little data exists on the stability of functional connectivity measurements across sites and sessions. In this study, we assess the influence of site and session on resting state functional connectivity measurements in a healthy cohort of traveling subjects (8 subjects scanned twice at each of 8 sites) scanned as part of the North American Prodrome Longitudinal Study (NAPLS). Reliability was investigated in three types of connectivity analyses: (1) seed-based connectivity with posterior cingulate cortex (PCC), right motor cortex (RMC), and left thalamus (LT) as seeds; (2) the intrinsic connectivity distribution (ICD), a voxel-wise connectivity measure; and (3) matrix connectivity, a whole-brain, atlas-based approach to assessing connectivity between nodes. Contributions to variability in connectivity due to subject, site, and day-of-scan were quantified and used to assess between-session (test-retest) reliability in accordance with Generalizability Theory. Overall, no major site, scanner manufacturer, or day-of-scan effects were found for the univariate connectivity analyses; instead, subject effects dominated relative to the other measured factors. However, summaries of voxel-wise connectivity were found to be sensitive to site and scanner manufacturer effects. For all connectivity measures, although subject variance was three times the site variance, the residual represented 60-80% of the variance, indicating that connectivity differed greatly from scan to scan independent of any of the measured factors (i.e., subject, site, and day-of-scan). Thus, for a single 5min scan, reliability across connectivity measures was poor (ICC=0.07-0.17), but increased with increasing scan duration (ICC=0.21-0.36 at 25min). The limited effects of site and scanner manufacturer support the use of multisite studies, such as NAPLS, as a viable means of collecting data on rare populations and increasing power in univariate functional connectivity studies. However, the results indicate that aggregation of fcMRI data across longer scan durations is necessary to increase the reliability of connectivity estimates at the single-subject level.
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Affiliation(s)
- Stephanie Noble
- Yale University, Interdepartmental Neuroscience Program, New Haven, CT, USA.
| | - Dustin Scheinost
- Yale University, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
| | - Emily S Finn
- Yale University, Interdepartmental Neuroscience Program, New Haven, CT, USA
| | - Xilin Shen
- Yale University, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
| | - Xenophon Papademetris
- Yale University, Department of Radiology and Biomedical Imaging, New Haven, CT, USA; Yale University, Department of Biomedical Engineering, New Haven, CT, USA
| | - Sarah C McEwen
- University of California, Los Angeles, Departments of Psychology and Psychiatry, Los Angeles, CA, USA
| | - Carrie E Bearden
- University of California, Los Angeles, Departments of Psychology and Psychiatry, Los Angeles, CA, USA
| | - Jean Addington
- University of Calgary, Department of Psychiatry, Calgary, Alberta, Canada
| | - Bradley Goodyear
- University of Calgary, Departments of Radiology, Clinical Neurosciences and Psychiatry, Calgary, Alberta, Canada
| | - Kristin S Cadenhead
- University of California, San Diego, Department of Psychiatry, La Jolla, CA, USA
| | - Heline Mirzakhanian
- University of California, San Diego, Department of Psychiatry, La Jolla, CA, USA
| | - Barbara A Cornblatt
- Zucker Hillside Hospital, Department of Psychiatry Research, Glen Oaks, NY, USA
| | - Doreen M Olvet
- Zucker Hillside Hospital, Department of Psychiatry Research, Glen Oaks, NY, USA
| | - Daniel H Mathalon
- University of California, San Francisco, Department of Psychiatry, San Francisco, CA, USA
| | | | - Diana O Perkins
- Yale University, Department of Psychiatry, New Haven, CT, USA
| | - Aysenil Belger
- University of North Carolina, Chapel Hill, Department of Psychiatry, Chapel Hill, NC, USA
| | - Larry J Seidman
- Beth Israel Deaconess Medical Center, Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Heidi Thermenos
- Beth Israel Deaconess Medical Center, Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Ming T Tsuang
- University of California, San Diego, Department of Psychiatry, La Jolla, CA, USA
| | - Theo G M van Erp
- University of California, Irvine, Department of Psychiatry and Human Behavior, Irvine, CA, USA
| | - Elaine F Walker
- Emory University, Department of Psychology, Atlanta, GA, USA
| | - Stephan Hamann
- Emory University, Department of Psychology, Atlanta, GA, USA
| | - Scott W Woods
- Yale University, Department of Psychiatry, New Haven, CT, USA
| | - Tyrone D Cannon
- Yale University, Departments of Psychology and Psychiatry, New Haven, CT, USA
| | - R Todd Constable
- Yale University, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
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19
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Data-driven forward model inference for EEG brain imaging. Neuroimage 2016; 139:249-258. [DOI: 10.1016/j.neuroimage.2016.06.017] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Revised: 06/08/2016] [Accepted: 06/10/2016] [Indexed: 11/23/2022] Open
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20
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Lavigne KM, Menon M, Woodward TS. Impairment in subcortical suppression in schizophrenia: Evidence from the fBIRN Oddball Task. Hum Brain Mapp 2016; 37:4640-4653. [PMID: 27477494 DOI: 10.1002/hbm.23334] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Revised: 07/20/2016] [Accepted: 07/22/2016] [Indexed: 12/28/2022] Open
Abstract
Schizophrenia patients show widespread impairments in brain activity during oddball tasks, which involve responding to infrequent target stimuli while refraining from responding during continuous non-target stimuli. In a network-based investigation comparing schizophrenia or schizoaffective patients to healthy controls, we sought to clarify which networks were specifically associated with target detection using a multivariate analysis technique that identifies task-specific functional brain networks. We acquired data from the publicly available function biomedical informatics research network collaboration, including 58 patients and 50 controls. Two task-based functional brain networks were identified: (1) a response modulation network including bilateral temporal pole, supramarginal gyrus, striatum, and thalamus, on which patients showed decreased activity relative to controls; and (2) an auditory-motor response activation network, on which patients showed a slower return to baseline than controls, but no difference in peak activation. For both groups, baseline to peak activation of the response modulation network correlated negatively with peak to baseline activity in the response activation network, suggesting a role in suppressing the motor response following targets. Patients' impaired activity in the response modulation network, and subsequent longer return to baseline in the response activation network, correspond with their later and less accurate behavioral performance, suggesting that impairment in suppression of the auditory-motor response activation network could underlie oddball task deficits in schizophrenia. In addition, the magnitude of the activity in the response modulation network was correlated with intensity of delusions of reference, supporting the notion that increased referential ideation is associated with hyperactivity within the subcortical striatal-limbic network. Hum Brain Mapp 37:4640-4653, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Katie M Lavigne
- Department of Psychiatry, University of British Columbia, Vancouver, British Colombia, Canada.,BC Mental Health and Addictions Research Institute, Provincial Health Services Authority, Vancouver, British Colombia, Canada
| | - Mahesh Menon
- Department of Psychiatry, University of British Columbia, Vancouver, British Colombia, Canada
| | - Todd S Woodward
- Department of Psychiatry, University of British Columbia, Vancouver, British Colombia, Canada.,BC Mental Health and Addictions Research Institute, Provincial Health Services Authority, Vancouver, British Colombia, Canada
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21
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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: 723] [Impact Index Per Article: 90.4] [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.
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22
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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: 34] [Impact Index Per Article: 4.3] [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.
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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
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23
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HAFNI-enabled largescale platform for neuroimaging informatics (HELPNI). Brain Inform 2015; 2:225-238. [PMID: 27747565 PMCID: PMC4737667 DOI: 10.1007/s40708-015-0024-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2015] [Accepted: 11/12/2015] [Indexed: 10/26/2022] Open
Abstract
Tremendous efforts have thus been devoted on the establishment of functional MRI informatics systems that recruit a comprehensive collection of statistical/computational approaches for fMRI data analysis. However, the state-of-the-art fMRI informatics systems are especially designed for specific fMRI sessions or studies of which the data size is not really big, and thus has difficulty in handling fMRI 'big data.' Given the size of fMRI data are growing explosively recently due to the advancement of neuroimaging technologies, an effective and efficient fMRI informatics system which can process and analyze fMRI big data is much needed. To address this challenge, in this work, we introduce our newly developed informatics platform, namely, 'HAFNI-enabled largescale platform for neuroimaging informatics (HELPNI).' HELPNI implements our recently developed computational framework of sparse representation of whole-brain fMRI signals which is called holistic atlases of functional networks and interactions (HAFNI) for fMRI data analysis. HELPNI provides integrated solutions to archive and process large-scale fMRI data automatically and structurally, to extract and visualize meaningful results information from raw fMRI data, and to share open-access processed and raw data with other collaborators through web. We tested the proposed HELPNI platform using publicly available 1000 Functional Connectomes dataset including over 1200 subjects. We identified consistent and meaningful functional brain networks across individuals and populations based on resting state fMRI (rsfMRI) big data. Using efficient sampling module, the experimental results demonstrate that our HELPNI system has superior performance than other systems for large-scale fMRI data in terms of processing and storing the data and associated results much faster.
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24
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Shaffer JJ, Peterson MJ, McMahon MA, Bizzell J, Calhoun V, van Erp TGM, Ford JM, Lauriello J, Lim KO, Manoach DS, McEwen SC, Mathalon DH, O'Leary D, Potkin SG, Preda A, Turner J, Voyvodic J, Wible CG, Belger A. Neural Correlates of Schizophrenia Negative Symptoms: Distinct Subtypes Impact Dissociable Brain Circuits. MOLECULAR NEUROPSYCHIATRY 2015; 1:191-200. [PMID: 27606313 DOI: 10.1159/000440979] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2015] [Accepted: 09/09/2015] [Indexed: 11/19/2022]
Abstract
BACKGROUND The negative symptoms of schizophrenia include deficits in emotional expression and motivation. These deficits are stable over the course of illness and respond poorly to current medications. Previous studies have focused on negative symptoms as a single category; however, individual symptoms might be related to separate neurological disturbances. We analyzed data from the Functional Biomedical Informatics Research Network dataset to explore the relationship between individual negative symptoms and functional brain activity during an auditory oddball task. METHODS Functional magnetic resonance imaging was conducted on 89 schizophrenia patients and 106 healthy controls during a two-tone auditory oddball task. Blood oxygenation level-dependent (BOLD) signal during the target tone was correlated with severity of five negative symptom domains from the Scale for the Assessment of Negative Symptoms. RESULTS The severity of alogia, avolition/apathy and anhedonia/asociality was negatively correlated with BOLD activity in distinct sets of brain regions associated with processing of the target tone, including basal ganglia, thalamus, insular cortex, prefrontal cortex, posterior cingulate and parietal cortex. CONCLUSIONS Individual symptoms were related to different patterns of functional activation during the oddball task, suggesting that individual symptoms might arise from distinct neural mechanisms. This work has potential to inform interventions that target these symptom-related neural disruptions.
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Affiliation(s)
- Joseph J Shaffer
- Department of Psychiatry, University of North Carolina, Chapel Hill, N.C., USA
| | - Michael J Peterson
- Department of Psychiatry, University of North Carolina, Chapel Hill, N.C., USA
| | - Mary Agnes McMahon
- Colorado Clinical and Translational Sciences Institute, University of Colorado, Denver, Colo., USA
| | - Joshua Bizzell
- Department of Psychiatry, University of North Carolina, Chapel Hill, N.C., USA; Duke/University of North Carolina Brain Imaging and Analysis Center, Durham, N.C., USA
| | - Vince Calhoun
- The Mind Research Network, University of New Mexico, Albuquerque, N. Mex., USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, N. Mex., USA
| | - Theo G M van Erp
- Departments of Psychiatry and Human Behavior, University of California Irvine, Irvine, Calif., USA
| | - Judith M Ford
- Department of Psychiatry, University of California San Francisco, San Francisco, Calif., USA
| | - John Lauriello
- Department of Psychiatry, University of Missouri, Columbia, Mo., USA
| | - Kelvin O Lim
- Department of Psychiatry, University of Minnesota, Minneapolis, Minn., USA
| | - Dara S Manoach
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, Mass., USA
| | - Sarah C McEwen
- Department of Psychology, University of California Los Angeles, Los Angeles, Calif., USA
| | - Daniel H Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, Calif., USA
| | - Daniel O'Leary
- Department of Neuroscience, University of Iowa, Iowa City, Iowa, USA
| | - Steven G Potkin
- Departments of Psychiatry, University of California Irvine, Irvine, Calif., USA; Department of Psychiatry, University of California San Francisco, San Francisco, Calif., USA
| | - Adrian Preda
- Departments of Psychiatry, University of California Irvine, Irvine, Calif., USA
| | - Jessica Turner
- Department of Psychology, Georgia State University, Atlanta, Ga., USA
| | - Jim Voyvodic
- Duke/University of North Carolina Brain Imaging and Analysis Center, Durham, N.C., USA
| | - Cynthia G Wible
- Department of Psychiatry, Harvard Medical School, Boston, Mass., USA; Department of Psychiatry, VA Medical Center Brockton, Brockton, Mass., USA
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina, Chapel Hill, N.C., USA; Duke/University of North Carolina Brain Imaging and Analysis Center, Durham, N.C., USA
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25
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Ambite JL, Tallis M, Alpert K, Keator DB, King M, Landis D, Konstantinidis G, Calhoun VD, Potkin SG, Turner JA, Wang L. SchizConnect: Virtual Data Integration in Neuroimaging. DATA INTEGRATION IN THE LIFE SCIENCES : ... INTERNATIONAL WORKSHOP, DILS ... : PROCEEDINGS. DILS (CONFERENCE) 2015; 9162:37-51. [PMID: 26688837 DOI: 10.1007/978-3-319-21843-4_4] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
In many scientific domains, including neuroimaging studies, there is a need to obtain increasingly larger cohorts to achieve the desired statistical power for discovery. However, the economics of imaging studies make it unlikely that any single study or consortia can achieve the desired sample sizes. What is needed is an architecture that can easily incorporate additional studies as they become available. We present such architecture based on a virtual data integration approach, where data remains at the original sources, and is retrieved and harmonized in response to user queries. This is in contrast to approaches that move the data to a central warehouse. We implemented our approach in the SchizConnect system that integrates data from three neuroimaging consortia on Schizophrenia: FBIRN's Human Imaging Database (HID), MRN's Collaborative Imaging and Neuroinformatics System (COINS), and the NUSDAST project at XNAT Central. A portal providing harmonized access to these sources is publicly deployed at schizconnect.org.
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Affiliation(s)
- Jose Luis Ambite
- University of Southern California, Los Angeles, California, USA { , , }
| | | | | | - David B Keator
- Mind Research Network, Albuquerque, New Mexico, USA { , , }
| | - Margaret King
- University of New Mexico, Albuquerque, New Mexico, USA
| | - Drew Landis
- Georgia State University, Atlanta, Georgia, USA
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26
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Lee HJ, Preda A, Ford JM, Mathalon DH, Keator DB, van Erp TG, Turner JA, Potkin SG. Functional magnetic resonance imaging of motor cortex activation in schizophrenia. J Korean Med Sci 2015; 30:625-31. [PMID: 25931795 PMCID: PMC4414648 DOI: 10.3346/jkms.2015.30.5.625] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2014] [Accepted: 01/20/2015] [Indexed: 11/22/2022] Open
Abstract
Previous fMRI studies of sensorimotor activation in schizophrenia have found in some cases hypoactivity, no difference, or hyperactivity when comparing patients with controls; similar disagreement exists in studies of motor laterality. In this multi-site fMRI study of a sensorimotor task in individuals with chronic schizophrenia and matched healthy controls, subjects responded with a right-handed finger press to an irregularly flashing visual checker board. The analysis includes eighty-five subjects with schizophrenia diagnosed according to the DSM-IV criteria and eighty-six healthy volunteer subjects. Voxel-wise statistical parametric maps were generated for each subject and analyzed for group differences; the percent Blood Oxygenation Level Dependent (BOLD) signal changes were also calculated over predefined anatomical regions of the primary sensory, motor, and visual cortex. Both healthy controls and subjects with schizophrenia showed strongly lateralized activation in the precentral gyrus, inferior frontal gyrus, and inferior parietal lobule, and strong activations in the visual cortex. There were no significant differences between subjects with schizophrenia and controls in this multi-site fMRI study. Furthermore, there was no significant difference in laterality found between healthy controls and schizophrenic subjects. This study can serve as a baseline measurement of schizophrenic dysfunction in other cognitive processes.
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Affiliation(s)
- Hyo Jong Lee
- Division of Computer Science and Engineering, CAIIT, Chonbuk National University, Jeonju, Korea
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA
| | - Judith M. Ford
- Department of Psychiatry, San Francisco VAMC and University of California, San Francisco, CA, USA
| | - Daniel H. Mathalon
- Department of Psychiatry, San Francisco VAMC and University of California, San Francisco, CA, USA
| | - 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
- Department of Psychology and Neuroscience, Georgia State University, Atlanta, GA, USA
| | - Steven G. Potkin
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA
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27
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Jernigan TL, Brown TT, Hagler DJ, Akshoomoff N, Bartsch H, Newman E, Thompson WK, Bloss CS, Murray SS, Schork N, Kennedy DN, Kuperman JM, McCabe C, Chung Y, Libiger O, Maddox M, Casey BJ, Chang L, Ernst TM, Frazier JA, Gruen JR, Sowell ER, Kenet T, Kaufmann WE, Mostofsky S, Amaral DG, Dale AM. The Pediatric Imaging, Neurocognition, and Genetics (PING) Data Repository. Neuroimage 2015; 124:1149-1154. [PMID: 25937488 DOI: 10.1016/j.neuroimage.2015.04.057] [Citation(s) in RCA: 182] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2015] [Revised: 04/26/2015] [Accepted: 04/27/2015] [Indexed: 11/16/2022] Open
Abstract
The main objective of the multi-site Pediatric Imaging, Neurocognition, and Genetics (PING) study was to create a large repository of standardized measurements of behavioral and imaging phenotypes accompanied by whole genome genotyping acquired from typically-developing children varying widely in age (3 to 20 years). This cross-sectional study produced sharable data from 1493 children, and these data have been described in several publications focusing on brain and cognitive development. Researchers may gain access to these data by applying for an account on the PING portal and filing a data use agreement. Here we describe the recruiting and screening of the children and give a brief overview of the assessments performed, the imaging methods applied, the genetic data produced, and the numbers of cases for whom different data types are available. We also cite sources of more detailed information about the methods and data. Finally we describe the procedures for accessing the data and for using the PING data exploration portal.
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Affiliation(s)
- Terry L Jernigan
- Center for Human Development, University of California, San Diego, La Jolla, CA, USA; Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA; Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA.
| | - Timothy T Brown
- Multimodal Imaging Laboratory, University of California, San Diego, La Jolla, CA, USA; Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Donald J Hagler
- Multimodal Imaging Laboratory, University of California, San Diego, La Jolla, CA, USA; Department of Radiology, University of California, San Diego, La Jolla, CA, USA
| | - Natacha Akshoomoff
- Center for Human Development, University of California, San Diego, La Jolla, CA, USA; Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Hauke Bartsch
- Multimodal Imaging Laboratory, University of California, San Diego, La Jolla, CA, USA
| | - Erik Newman
- Center for Human Development, University of California, San Diego, La Jolla, CA, USA; Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Wesley K Thompson
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA; Stein Institute for Research on Aging, University of California, San Diego, La Jolla, CA, USA
| | - Cinnamon S Bloss
- The Qualcomm Institute, University of California, San Diego, La Jolla, CA, USA
| | - Sarah S Murray
- Department of Pathology, University of California, San Diego, La Jolla, CA, USA
| | | | - David N Kennedy
- Department of Psychiatry, University of Massachusetts Medical School, Boston, MA, USA
| | - Joshua M Kuperman
- Multimodal Imaging Laboratory, University of California, San Diego, La Jolla, CA, USA; Department of Radiology, University of California, San Diego, La Jolla, CA, USA
| | - Connor McCabe
- Department of Psychology, University of Washington, Seattle, WA, USA
| | - Yoonho Chung
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Ondrej Libiger
- The Qualcomm Institute, University of California, San Diego, La Jolla, CA, USA
| | - Melanie Maddox
- Center for Human Development, University of California, San Diego, La Jolla, CA, USA
| | - B J Casey
- Sackler Institute for Developmental Psychobiology, Weil Cornell Medical College, New York, NY, USA
| | - Linda Chang
- Department of Medicine, University of Hawaii, Queen's Medical Center, Honolulu, HI, USA
| | - Thomas M Ernst
- Department of Medicine, University of Hawaii, Queen's Medical Center, Honolulu, HI, USA
| | - Jean A Frazier
- Department of Psychiatry, University of Massachusetts Medical School, Boston, MA, USA
| | - Jeffrey R Gruen
- Departments of Pediatrics and Genetics, Yale University, School of Medicine, New Haven, CT, USA
| | - Elizabeth R Sowell
- Department of Pediatrics, University of Southern California, Children's Hospital Los Angeles, Los Angeles, CA, USA
| | - Tal Kenet
- Department of Neurology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | | | - Stewart Mostofsky
- Kennedy Krieger Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - David G Amaral
- Department of Psychiatry and Behavioral Sciences, University of California-Davis, Davis, CA, USA
| | - Anders M Dale
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA; Multimodal Imaging Laboratory, University of California, San Diego, La Jolla, CA, USA; Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA; Department of Radiology, University of California, San Diego, La Jolla, CA, USA
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28
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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]
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Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 2014; 26:1045-57. [PMID: 23884657 DOI: 10.1007/s10278-013-9622-7] [Citation(s) in RCA: 1734] [Impact Index Per Article: 173.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
The National Institutes of Health have placed significant emphasis on sharing of research data to support secondary research. Investigators have been encouraged to publish their clinical and imaging data as part of fulfilling their grant obligations. Realizing it was not sufficient to merely ask investigators to publish their collection of imaging and clinical data, the National Cancer Institute (NCI) created the open source National Biomedical Image Archive software package as a mechanism for centralized hosting of cancer related imaging. NCI has contracted with Washington University in Saint Louis to create The Cancer Imaging Archive (TCIA)-an open-source, open-access information resource to support research, development, and educational initiatives utilizing advanced medical imaging of cancer. In its first year of operation, TCIA accumulated 23 collections (3.3 million images). Operating and maintaining a high-availability image archive is a complex challenge involving varied archive-specific resources and driven by the needs of both image submitters and image consumers. Quality archives of any type (traditional library, PubMed, refereed journals) require management and customer service. This paper describes the management tasks and user support model for TCIA.
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Affiliation(s)
- Kenneth Clark
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, ERL 510 South Kingshighway Boulevard, St. Louis, MO, 63110, USA,
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Dinov ID, Petrosyan P, Liu Z, Eggert P, Hobel S, Vespa P, Woo Moon S, Van Horn JD, Franco J, Toga AW. High-throughput neuroimaging-genetics computational infrastructure. Front Neuroinform 2014; 8:41. [PMID: 24795619 PMCID: PMC4005931 DOI: 10.3389/fninf.2014.00041] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2014] [Accepted: 03/27/2014] [Indexed: 01/01/2023] Open
Abstract
Many contemporary neuroscientific investigations face significant challenges in terms of data management, computational processing, data mining, and results interpretation. These four pillars define the core infrastructure necessary to plan, organize, orchestrate, validate, and disseminate novel scientific methods, computational resources, and translational healthcare findings. Data management includes protocols for data acquisition, archival, query, transfer, retrieval, and aggregation. Computational processing involves the necessary software, hardware, and networking infrastructure required to handle large amounts of heterogeneous neuroimaging, genetics, clinical, and phenotypic data and meta-data. Data mining refers to the process of automatically extracting data features, characteristics and associations, which are not readily visible by human exploration of the raw dataset. Result interpretation includes scientific visualization, community validation of findings and reproducible findings. In this manuscript we describe the novel high-throughput neuroimaging-genetics computational infrastructure available at the Institute for Neuroimaging and Informatics (INI) and the Laboratory of Neuro Imaging (LONI) at University of Southern California (USC). INI and LONI include ultra-high-field and standard-field MRI brain scanners along with an imaging-genetics database for storing the complete provenance of the raw and derived data and meta-data. In addition, the institute provides a large number of software tools for image and shape analysis, mathematical modeling, genomic sequence processing, and scientific visualization. A unique feature of this architecture is the Pipeline environment, which integrates the data management, processing, transfer, and visualization. Through its client-server architecture, the Pipeline environment provides a graphical user interface for designing, executing, monitoring validating, and disseminating of complex protocols that utilize diverse suites of software tools and web-services. These pipeline workflows are represented as portable XML objects which transfer the execution instructions and user specifications from the client user machine to remote pipeline servers for distributed computing. Using Alzheimer's and Parkinson's data, we provide several examples of translational applications using this infrastructure.
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Affiliation(s)
- Ivo D. Dinov
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern CaliforniaLos Angeles, CA, USA
- Biomedical Informatics Research Network, Information Sciences Institute, University of Southern CaliforniaLos Angeles, CA, USA
- Statistics Online Computational Resource, University of Michigan, UMSNAnn Arbor, MI, USA
| | - Petros Petrosyan
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern CaliforniaLos Angeles, CA, USA
| | - Zhizhong Liu
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern CaliforniaLos Angeles, CA, USA
| | - Paul Eggert
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern CaliforniaLos Angeles, CA, USA
- Department of Computer Science, University of CaliforniaLos Angeles, Los Angeles, CA, USA
| | - Sam Hobel
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern CaliforniaLos Angeles, CA, USA
| | - Paul Vespa
- Brain Injury Research Center, Department of Neurosurgery, David Geffen School of Medicine, University of CaliforniaLos Angeles, Los Angeles, CA, USA
| | - Seok Woo Moon
- Department of Neuropsychiatry, Konkuk University School of MedicineSeoul, Korea
| | - John D. Van Horn
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern CaliforniaLos Angeles, CA, USA
| | - Joseph Franco
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern CaliforniaLos Angeles, CA, USA
| | - Arthur W. Toga
- Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern CaliforniaLos Angeles, CA, USA
- Biomedical Informatics Research Network, Information Sciences Institute, University of Southern CaliforniaLos Angeles, CA, USA
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Neuroinformatics Database (NiDB)--a modular, portable database for the storage, analysis, and sharing of neuroimaging data. Neuroinformatics 2014; 11:495-505. [PMID: 23912507 DOI: 10.1007/s12021-013-9194-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
We present a modular, high performance, open-source database system that incorporates popular neuroimaging database features with novel peer-to-peer sharing, and a simple installation. An increasing number of imaging centers have created a massive amount of neuroimaging data since fMRI became popular more than 20 years ago, with much of that data unshared. The Neuroinformatics Database (NiDB) provides a stable platform to store and manipulate neuroimaging data and addresses several of the impediments to data sharing presented by the INCF Task Force on Neuroimaging Datasharing, including 1) motivation to share data, 2) technical issues, and 3) standards development. NiDB solves these problems by 1) minimizing PHI use, providing a cost effective simple locally stored platform, 2) storing and associating all data (including genome) with a subject and creating a peer-to-peer sharing model, and 3) defining a sample, normalized definition of a data storage structure that is used in NiDB. NiDB not only simplifies the local storage and analysis of neuroimaging data, but also enables simple sharing of raw data and analysis methods, which may encourage further sharing.
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Veeraraghavan H, Miller JV. Faceted visualization of three dimensional neuroanatomy by combining ontology with faceted search. Neuroinformatics 2014; 12:245-59. [PMID: 24006207 PMCID: PMC3943828 DOI: 10.1007/s12021-013-9202-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
In this work, we present a faceted-search based approach for visualization of anatomy by combining a three dimensional digital atlas with an anatomy ontology. Specifically, our approach provides a drill-down search interface that exposes the relevant pieces of information (obtained by searching the ontology) for a user query. Hence, the user can produce visualizations starting with minimally specified queries. Furthermore, by automatically translating the user queries into the controlled terminology our approach eliminates the need for the user to use controlled terminology. We demonstrate the scalability of our approach using an abdominal atlas and the same ontology. We implemented our visualization tool on the opensource 3D Slicer software. We present results of our visualization approach by combining a modified Foundational Model of Anatomy (FMA) ontology with the Surgical Planning Laboratory (SPL) Brain 3D digital atlas, and geometric models specific to patients computed using the SPL brain tumor dataset.
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Viangteeravat T, Nagisetty NSVR. Giving raw data a chance to talk: a demonstration of exploratory visual analytics with a pediatric research database using Microsoft Live Labs Pivot to promote cohort discovery, research, and quality assessment. PERSPECTIVES IN HEALTH INFORMATION MANAGEMENT 2014; 11:1d. [PMID: 24808811 PMCID: PMC3995483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Secondary use of large and open data sets provides researchers with an opportunity to address high-impact questions that would otherwise be prohibitively expensive and time consuming to study. Despite the availability of data, generating hypotheses from huge data sets is often challenging, and the lack of complex analysis of data might lead to weak hypotheses. To overcome these issues and to assist researchers in building hypotheses from raw data, we are working on a visual and analytical platform called PRD Pivot. PRD Pivot is a de-identified pediatric research database designed to make secondary use of rich data sources, such as the electronic health record (EHR). The development of visual analytics using Microsoft Live Labs Pivot makes the process of data elaboration, information gathering, knowledge generation, and complex information exploration transparent to tool users and provides researchers with the ability to sort and filter by various criteria, which can lead to strong, novel hypotheses.
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Affiliation(s)
- Teeradache Viangteeravat
- The Biomedical Informatics Core at the Children's Foundation Research Institute and assistant professor of biomedical informatics in the Department of Pediatrics at the University of Tennessee Health Science Center in Memphis, TN
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Jahangirian M, Taylor SJ. Profiling e-health projects in Africa: trends and funding patterns. INFORMATION DEVELOPMENT 2013. [DOI: 10.1177/0266666913511478] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
There is a severe shortage of healthcare provision in Africa. e-Health, the use of Information and Communication Technologies (ICT) to support healthcare, may help to ease this problem. e-Health projects support a wide range of applications ranging from telemedicine to global research collaborations made possible via e-Infrastructures, worldwide systems of integrated advanced high performance networking and computing ICT. To try to understand the state of e-Health in Africa, this paper aims to create a picture and to present an analytical review of some of these initiatives in Africa. A review framework composed of multiple search methods is developed and applied to yield a broad coverage of e-Health projects over the African continent. Seven quantitative analyses on the projects are presented. Major observations include that there is a tendency for e-Health projects to grow in number in some African countries over time; that African countries with larger Gross National Incomes tend to attract more e-Health projects; that e-Health projects in Africa focus on telemedicine, health education and health-related research; that there is a wide range of funding bodies, some of which have a geographical focus, and that the number of m-Health projects has been rising sharply.
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Wang L, Kogan A, Cobia D, Alpert K, Kolasny A, Miller MI, Marcus D. Northwestern University Schizophrenia Data and Software Tool (NUSDAST). Front Neuroinform 2013; 7:25. [PMID: 24223551 PMCID: PMC3819522 DOI: 10.3389/fninf.2013.00025] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2013] [Accepted: 10/12/2013] [Indexed: 11/13/2022] Open
Abstract
The schizophrenia research community has invested substantial resources on collecting, managing and sharing large neuroimaging datasets. As part of this effort, our group has collected high resolution magnetic resonance (MR) datasets from individuals with schizophrenia, their non-psychotic siblings, healthy controls and their siblings. This effort has resulted in a growing resource, the Northwestern University Schizophrenia Data and Software Tool (NUSDAST), an NIH-funded data sharing project to stimulate new research. This resource resides on XNAT Central, and it contains neuroimaging (MR scans, landmarks and surface maps for deep subcortical structures, and FreeSurfer cortical parcellation and measurement data), cognitive (cognitive domain scores for crystallized intelligence, working memory, episodic memory, and executive function), clinical (demographic, sibling relationship, SAPS and SANS psychopathology), and genetic (20 polymorphisms) data, collected from more than 450 subjects, most with 2-year longitudinal follow-up. A neuroimaging mapping, analysis and visualization software tool, CAWorks, is also part of this resource. Moreover, in making our existing neuroimaging data along with the associated meta-data and computational tools publically accessible, we have established a web-based information retrieval portal that allows the user to efficiently search the collection. This research-ready dataset meaningfully combines neuroimaging data with other relevant information, and it can be used to help facilitate advancing neuroimaging research. It is our hope that this effort will help to overcome some of the commonly recognized technical barriers in advancing neuroimaging research such as lack of local organization and standard descriptions.
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Affiliation(s)
- Lei Wang
- Department of Radiology, Northwestern University Feinberg School of MedicineChicago, IL, USA
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of MedicineChicago, IL, USA
| | - Alex Kogan
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of MedicineChicago, IL, USA
| | - Derin Cobia
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of MedicineChicago, IL, USA
| | - Kathryn Alpert
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of MedicineChicago, IL, USA
| | - Anthony Kolasny
- Department of Biomedical Engineering, Center for Imaging Science, Johns Hopkins UniversityBaltimore, MD, USA
| | - Michael I. Miller
- Department of Biomedical Engineering, Center for Imaging Science, Johns Hopkins UniversityBaltimore, MD, USA
| | - Daniel Marcus
- Department of Radiology, Washington University School of MedicineSt. Louis, MO, USA
- Department of Psychology, Washington University School of MedicineSt. Louis, MO, USA
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Lin MK, Nicolini O, Waxenegger H, Galloway GJ, Ullmann JFP, Janke AL. Interpretation of medical imaging data with a mobile application: a mobile digital imaging processing environment. Front Neurol 2013; 4:85. [PMID: 23847587 PMCID: PMC3701154 DOI: 10.3389/fneur.2013.00085] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2013] [Accepted: 06/19/2013] [Indexed: 11/28/2022] Open
Abstract
Digital Imaging Processing (DIP) requires data extraction and output from a visualization tool to be consistent. Data handling and transmission between the server and a user is a systematic process in service interpretation. The use of integrated medical services for management and viewing of imaging data in combination with a mobile visualization tool can be greatly facilitated by data analysis and interpretation. This paper presents an integrated mobile application and DIP service, called M-DIP. The objective of the system is to (1) automate the direct data tiling, conversion, pre-tiling of brain images from Medical Imaging NetCDF (MINC), Neuroimaging Informatics Technology Initiative (NIFTI) to RAW formats; (2) speed up querying of imaging measurement; and (3) display high-level of images with three dimensions in real world coordinates. In addition, M-DIP provides the ability to work on a mobile or tablet device without any software installation using web-based protocols. M-DIP implements three levels of architecture with a relational middle-layer database, a stand-alone DIP server, and a mobile application logic middle level realizing user interpretation for direct querying and communication. This imaging software has the ability to display biological imaging data at multiple zoom levels and to increase its quality to meet users’ expectations. Interpretation of bioimaging data is facilitated by an interface analogous to online mapping services using real world coordinate browsing. This allows mobile devices to display multiple datasets simultaneously from a remote site. M-DIP can be used as a measurement repository that can be accessed by any network environment, such as a portable mobile or tablet device. In addition, this system and combination with mobile applications are establishing a virtualization tool in the neuroinformatics field to speed interpretation services.
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Affiliation(s)
- Meng Kuan Lin
- Centre for Advanced Imaging, The University of Queensland , Brisbane, QLD , Australia
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Chen Y, Ren X, Zhang GQ, Xu R. Ontology-guided organ detection to retrieve web images of disease manifestation: towards the construction of a consumer-based health image library. J Am Med Inform Assoc 2013; 20:1076-81. [PMID: 23792805 DOI: 10.1136/amiajnl-2012-001380] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
BACKGROUND Visual information is a crucial aspect of medical knowledge. Building a comprehensive medical image base, in the spirit of the Unified Medical Language System (UMLS), would greatly benefit patient education and self-care. However, collection and annotation of such a large-scale image base is challenging. OBJECTIVE To combine visual object detection techniques with medical ontology to automatically mine web photos and retrieve a large number of disease manifestation images with minimal manual labeling effort. METHODS As a proof of concept, we first learnt five organ detectors on three detection scales for eyes, ears, lips, hands, and feet. Given a disease, we used information from the UMLS to select affected body parts, ran the pretrained organ detectors on web images, and combined the detection outputs to retrieve disease images. RESULTS Compared with a supervised image retrieval approach that requires training images for every disease, our ontology-guided approach exploits shared visual information of body parts across diseases. In retrieving 2220 web images of 32 diseases, we reduced manual labeling effort to 15.6% while improving the average precision by 3.9% from 77.7% to 81.6%. For 40.6% of the diseases, we improved the precision by 10%. CONCLUSIONS The results confirm the concept that the web is a feasible source for automatic disease image retrieval for health image database construction. Our approach requires a small amount of manual effort to collect complex disease images, and to annotate them by standard medical ontology terms.
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Affiliation(s)
- Yang Chen
- Department of Electrical Engineering and Computer Science, Case Western Reserve University School of Engineering, Cleveland, Ohio, USA
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38
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Networks of task co-activations. Neuroimage 2013; 80:505-14. [PMID: 23631994 DOI: 10.1016/j.neuroimage.2013.04.073] [Citation(s) in RCA: 138] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2013] [Revised: 04/15/2013] [Accepted: 04/16/2013] [Indexed: 01/13/2023] Open
Abstract
Recent progress in neuroimaging informatics and meta-analytic techniques has enabled a novel domain of human brain connectomics research that focuses on task-dependent co-activation patterns across behavioral tasks and cognitive domains. Here, we review studies utilizing the BrainMap database to investigate data trends in the activation literature using methods such as meta-analytic connectivity modeling (MACM), connectivity-based parcellation (CPB), and independent component analysis (ICA). We give examples of how these methods are being applied to learn more about the functional connectivity of areas such as the amygdala, the default mode network, and visual area V5. Methods for analyzing the behavioral metadata corresponding to regions of interest and to their intrinsically connected networks are described as a tool for local functional decoding. We finally discuss the relation of observed co-activation connectivity results to resting state connectivity patterns, and provide implications for future work in this domain.
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Weber GM. Federated queries of clinical data repositories: the sum of the parts does not equal the whole. J Am Med Inform Assoc 2013; 20:e155-61. [PMID: 23349080 DOI: 10.1136/amiajnl-2012-001299] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
BACKGROUND AND OBJECTIVE In 2008 we developed a shared health research information network (SHRINE), which for the first time enabled research queries across the full patient populations of four Boston hospitals. It uses a federated architecture, where each hospital returns only the aggregate count of the number of patients who match a query. This allows hospitals to retain control over their local databases and comply with federal and state privacy laws. However, because patients may receive care from multiple hospitals, the result of a federated query might differ from what the result would be if the query were run against a single central repository. This paper describes the situations when this happens and presents a technique for correcting these errors. METHODS We use a one-time process of identifying which patients have data in multiple repositories by comparing one-way hash values of patient demographics. This enables us to partition the local databases such that all patients within a given partition have data at the same subset of hospitals. Federated queries are then run separately on each partition independently, and the combined results are presented to the user. RESULTS Using theoretical bounds and simulated hospital networks, we demonstrate that once the partitions are made, SHRINE can produce more precise estimates of the number of patients matching a query. CONCLUSIONS Uncertainty in the overlap of patient populations across hospitals limits the effectiveness of SHRINE and other federated query tools. Our technique reduces this uncertainty while retaining an aggregate federated architecture.
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Affiliation(s)
- Griffin M Weber
- Information Technology, Harvard Medical School, Boston, Massachusetts 02115, USA.
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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.
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Affiliation(s)
- Luca Corradi
- University of Genoa, Dept. of Computer Science, Bioengineering, Robotics and Systems Engineering, Genoa, Italy
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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.
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van Haren NEM, Rijsdijk F, Schnack HG, Picchioni MM, Toulopoulou T, Weisbrod M, Sauer H, van Erp TG, Cannon TD, Huttunen MO, Boomsma DI, Hulshoff Pol HE, Murray RM, Kahn RS. The genetic and environmental determinants of the association between brain abnormalities and schizophrenia: the schizophrenia twins and relatives consortium. Biol Psychiatry 2012; 71:915-21. [PMID: 22341827 PMCID: PMC3343260 DOI: 10.1016/j.biopsych.2012.01.010] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2011] [Revised: 12/16/2011] [Accepted: 01/03/2012] [Indexed: 12/24/2022]
Abstract
BACKGROUND Structural brain abnormalities are consistently found in schizophrenia (Sz) and have been associated with the familial risk for the disorder. We aim to define the relative contributions of genetic and nongenetic factors to the association between structural brain abnormalities and Sz in a uniquely powered cohort (Schizophrenia Twins and Relatives consortium). METHODS An international multicenter magnetic resonance imaging collaboration was set up to pool magnetic resonance imaging scans from twin pairs in Utrecht (The Netherlands), Helsinki (Finland), London (United Kingdom), and Jena (Germany). A sample of 684 subjects took part, consisting of monozygotic twins (n = 410, with 51 patients from concordant and 52 from discordant pairs) and dizygotic twins (n = 274, with 39 patients from discordant pairs). The additive genetic, common, and unique environmental contributions to the association between brain volumes and risk for Sz were estimated by structural equation modeling. RESULTS The heritabilities of most brain volumes were significant and ranged between 52% (temporal cortical gray matter) and 76% (cerebrum). Heritability of cerebral gray matter did not reach significance (34%). Significant phenotypic correlations were found between Sz and reduced volumes of the cerebrum (-.22 [-.30/-.14]) and white matter (-.17 [-.25/-.09]) and increased volume of the third ventricle (.18 [.08/.28]). These were predominantly due to overlapping genetic effects (77%, 94%, and 83%, respectively). CONCLUSIONS Some of the genes that transmit the risk for Sz also influence cerebral (white matter) volume.
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Affiliation(s)
- Neeltje E M van Haren
- University Medical Center Utrecht, Department of Psychiatry, Division of Neuroscience, Rudolf Magnus Institute, Utrecht, The Netherlands.
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Turner JA, Van Horn JD. Electronic data capture, representation, and applications for neuroimaging. Front Neuroinform 2012; 6:16. [PMID: 22586393 PMCID: PMC3345526 DOI: 10.3389/fninf.2012.00016] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2012] [Accepted: 04/11/2012] [Indexed: 11/24/2022] Open
Affiliation(s)
- Jessica A Turner
- Mind Research Network, University of New Mexico Albuquerque, NM, USA
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Abstract
Clinical research informatics is the rapidly evolving sub-discipline within biomedical informatics that focuses on developing new informatics theories, tools, and solutions to accelerate the full translational continuum: basic research to clinical trials (T1), clinical trials to academic health center practice (T2), diffusion and implementation to community practice (T3), and ‘real world’ outcomes (T4). We present a conceptual model based on an informatics-enabled clinical research workflow, integration across heterogeneous data sources, and core informatics tools and platforms. We use this conceptual model to highlight 18 new articles in the JAMIA special issue on clinical research informatics.
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Affiliation(s)
- Michael G Kahn
- Department of Pediatrics, University of Colorado, Aurora, Colorado 80045, USA.
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Abstract
We present the basic structure of the Cognitive Paradigm Ontology (CogPO) for human behavioral experiments. While the experimental psychology and cognitive neuroscience literature may refer to certain behavioral tasks by name (e.g., the Stroop paradigm or the Sternberg paradigm) or by function (a working memory task, a visual attention task), these paradigms can vary tremendously in the stimuli that are presented to the subject, the response expected from the subject, and the instructions given to the subject. Drawing from the taxonomy developed and used by the BrainMap project ( www.brainmap.org ) for almost two decades to describe key components of published functional imaging results, we have developed an ontology capable of representing certain characteristics of the cognitive paradigms used in the fMRI and PET literature. The Cognitive Paradigm Ontology is being developed to be compliant with the Basic Formal Ontology (BFO), and to harmonize where possible with larger ontologies such as RadLex, NeuroLex, or the Ontology of Biomedical Investigations (OBI). The key components of CogPO include the representation of experimental conditions focused on the stimuli presented, the instructions given, and the responses requested. The use of alternate and even competitive terminologies can often impede scientific discoveries. Categorization of paradigms according to stimulus, response, and instruction has been shown to allow advanced data retrieval techniques by searching for similarities and contrasts across multiple paradigm levels. The goal of CogPO is to develop, evaluate, and distribute a domain ontology of cognitive paradigms for application and use in the functional neuroimaging community.
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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: 174] [Impact Index Per Article: 14.5] [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.
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Affiliation(s)
- Jean-Baptiste Poline
- Neurospin, Commissariat à l'Energie Atomique et aux Energies Alternatives Gif-sur-Yvette, France
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47
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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: 178] [Impact Index Per Article: 14.8] [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.
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Affiliation(s)
- Gary H Glover
- Department of Radiology, Stanford University, Stanford, California, USA.
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48
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Schuler R, Smith DE, Kumaraguruparan G, Chervenak A, Lewis AD, Hyde DM, Kesselman C. A flexible, open, decentralized system for digital pathology networks. Stud Health Technol Inform 2012; 175:29-38. [PMID: 22941985 PMCID: PMC3966426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
High-resolution digital imaging is enabling digital archiving and sharing of digitized microscopy slides and new methods for digital pathology. Collaborative research centers, outsourced medical services, and multi-site organizations stand to benefit from sharing pathology data in a digital pathology network. Yet significant technological challenges remain due to the large size and volume of digitized whole slide images. While information systems do exist for managing local pathology laboratories, they tend to be oriented toward narrow clinical use cases or offer closed ecosystems around proprietary formats. Few solutions exist for networking digital pathology operations. Here we present a system architecture and implementation of a digital pathology network and share results from a production system that federates major research centers.
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Affiliation(s)
- Robert Schuler
- Information Sciences Institute, University of Southern California, CA, USA
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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/ .
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Affiliation(s)
- Syam Gadde
- Brain Imaging and Analysis Center, Duke University, Durham, NC, USA.
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50
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Turner JA, Lane SR, Bockholt HJ, Calhoun VD. The clinical assessment and remote administration tablet. Front Neuroinform 2011; 5:31. [PMID: 22207845 PMCID: PMC3246293 DOI: 10.3389/fninf.2011.00031] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2011] [Accepted: 11/21/2011] [Indexed: 11/18/2022] Open
Abstract
Electronic data capture of case report forms, demographic, neuropsychiatric, or clinical assessments, can vary from scanning hand-written forms into databases to fully electronic systems. Web-based forms can be extremely useful for self-assessment; however, in the case of neuropsychiatric assessments, self-assessment is often not an option. The clinician often must be the person either summarizing or making their best judgment about the subject’s response in order to complete an assessment, and having the clinician turn away to type into a web browser may be disruptive to the flow of the interview. The Mind Research Network has developed a prototype for a software tool for the real-time acquisition and validation of clinical assessments in remote environments. We have developed the clinical assessment and remote administration tablet on a Microsoft Windows PC tablet system, which has been adapted to interact with various data models already in use in several large-scale databases of neuroimaging studies in clinical populations. The tablet has been used successfully to collect and administer clinical assessments in several large-scale studies, so that the correct clinical measures are integrated with the correct imaging and other data. It has proven to be incredibly valuable in confirming that data collection across multiple research groups is performed similarly, quickly, and with accountability for incomplete datasets. We present the overall architecture and an evaluation of its use.
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