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Cheng C, Messerschmidt L, Bravo I, Waldbauer M, Bhavikatti R, Schenk C, Grujic V, Model T, Kubinec R, Barceló J. A General Primer for Data Harmonization. Sci Data 2024; 11:152. [PMID: 38297013 PMCID: PMC10831085 DOI: 10.1038/s41597-024-02956-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 01/11/2024] [Indexed: 02/02/2024] Open
Affiliation(s)
- Cindy Cheng
- Hochschule für Politik, Technical University of Munich, Richard-Wagner Str. 1, Munich, 80333, Bavaria, Germany.
| | - Luca Messerschmidt
- Hochschule für Politik, Technical University of Munich, Richard-Wagner Str. 1, Munich, 80333, Bavaria, Germany
| | - Isaac Bravo
- Hochschule für Politik, Technical University of Munich, Richard-Wagner Str. 1, Munich, 80333, Bavaria, Germany
| | - Marco Waldbauer
- Hochschule für Politik, Technical University of Munich, Richard-Wagner Str. 1, Munich, 80333, Bavaria, Germany
| | | | - Caress Schenk
- School of Humanities and Social Sciences, Nazarbayev University, Kabanbay Batry Ave., 53, Astana, 010000, Kazakhstan
| | - Vanja Grujic
- Faculty of Law, University of Brasilia, Campus Universitário Darcy Ribeiro Asa Norte, Brasília, 10587, Brazil
| | - Tim Model
- Delve, 2225 3rd St, San Francisco, 94107, California, USA
| | - Robert Kubinec
- Division of Social Science, New York University Abu Dhabi, Social Science Building (A5), Abu Dhabi, 129188, United Arab Emirates
| | - Joan Barceló
- Division of Social Science, New York University Abu Dhabi, Social Science Building (A5), Abu Dhabi, 129188, United Arab Emirates
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Aja-Fernández S, Martín-Martín C, Planchuelo-Gómez Á, Faiyaz A, Uddin MN, Schifitto G, Tiwari A, Shigwan SJ, Kumar Singh R, Zheng T, Cao Z, Wu D, Blumberg SB, Sen S, Goodwin-Allcock T, Slator PJ, Yigit Avci M, Li Z, Bilgic B, Tian Q, Wang X, Tang Z, Cabezas M, Rauland A, Merhof D, Manzano Maria R, Campos VP, Santini T, da Costa Vieira MA, HashemizadehKolowri S, DiBella E, Peng C, Shen Z, Chen Z, Ullah I, Mani M, Abdolmotalleby H, Eckstrom S, Baete SH, Filipiak P, Dong T, Fan Q, de Luis-García R, Tristán-Vega A, Pieciak T. Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies. Neuroimage Clin 2023; 39:103483. [PMID: 37572514 PMCID: PMC10440596 DOI: 10.1016/j.nicl.2023.103483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 07/24/2023] [Accepted: 07/25/2023] [Indexed: 08/14/2023]
Abstract
The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise.
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Affiliation(s)
- Santiago Aja-Fernández
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Spain.
| | - Carmen Martín-Martín
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Spain
| | - Álvaro Planchuelo-Gómez
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Spain; Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | | | | | | | | | | | | | | | | | - Dan Wu
- Zhejiang University, China
| | | | | | | | | | | | - Zihan Li
- Athinoula A. Martinos Center for Biomedical Imaging, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | - Zan Chen
- Zhejiang University of Technology, China
| | | | | | | | | | | | | | | | | | - Rodrigo de Luis-García
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Spain
| | - Antonio Tristán-Vega
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Spain
| | - Tomasz Pieciak
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Spain
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Chicco D, Shiradkar R. Ten quick tips for computational analysis of medical images. PLoS Comput Biol 2023; 19:e1010778. [PMID: 36602952 PMCID: PMC9815662 DOI: 10.1371/journal.pcbi.1010778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Medical imaging is a great asset for modern medicine, since it allows physicians to spatially interrogate a disease site, resulting in precise intervention for diagnosis and treatment, and to observe particular aspect of patients' conditions that otherwise would not be noticeable. Computational analysis of medical images, moreover, can allow the discovery of disease patterns and correlations among cohorts of patients with the same disease, thus suggesting common causes or providing useful information for better therapies and cures. Machine learning and deep learning applied to medical images, in particular, have produced new, unprecedented results that can pave the way to advanced frontiers of medical discoveries. While computational analysis of medical images has become easier, however, the possibility to make mistakes or generate inflated or misleading results has become easier, too, hindering reproducibility and deployment. In this article, we provide ten quick tips to perform computational analysis of medical images avoiding common mistakes and pitfalls that we noticed in multiple studies in the past. We believe our ten guidelines, if taken into practice, can help the computational-medical imaging community to perform better scientific research that eventually can have a positive impact on the lives of patients worldwide.
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Affiliation(s)
- Davide Chicco
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
| | - Rakesh Shiradkar
- Department of Biomedical Engineering, Emory University, Atlanta, Georgia, United States of America
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4
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Meyers B, Lee VK, Dennis L, Wallace J, Schmithorst V, Votava-Smith JK, Rajagopalan V, Herrup E, Baust T, Tran NN, Hunter J, Licht DJ, Gaynor JW, Andropoulos DB, Panigrahy A, Ceschin R. Harmonization of Multi-Center Diffusion Tensor Tractography in Neonates with Congenital Heart Disease: Optimizing Post-Processing and Application of ComBat. NEUROIMAGE. REPORTS 2022; 2:100114. [PMID: 36258783 PMCID: PMC9575513 DOI: 10.1016/j.ynirp.2022.100114] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Advanced brain imaging of neonatal macrostructure and microstructure, which has prognosticating importance, is more frequently being incorporated into multi-center trials of neonatal neuroprotection. Multicenter neuroimaging studies, designed to overcome small sample sized clinical cohorts, are essential but lead to increased technical variability. Few harmonization techniques have been developed for neonatal brain microstructural (diffusion tensor) analysis. The work presented here aims to remedy two common problems that exist with the current state of the art approaches: 1) variance in scanner and protocol in data collection can limit the researcher's ability to harmonize data acquired under different conditions or using different clinical populations. 2) The general lack of objective guidelines for dealing with anatomically abnormal anatomy and pathology. Often, subjects are excluded due to subjective criteria, or due to pathology that could be informative to the final analysis, leading to the loss of reproducibility and statistical power. This proves to be a barrier in the analysis of large multi-center studies and is a particularly salient problem given the relative scarcity of neonatal imaging data. We provide an objective, data-driven, and semi-automated neonatal processing pipeline designed to harmonize compartmentalized variant data acquired under different parameters. This is done by first implementing a search space reduction step of extracting the along-tract diffusivity values along each tract of interest, rather than performing whole-brain harmonization. This is followed by a data-driven outlier detection step, with the purpose of removing unwanted noise and outliers from the final harmonization. We then use an empirical Bayes harmonization algorithm performed at the along-tract level, with the output being a lower dimensional space but still spatially informative. After applying our pipeline to this large multi-site dataset of neonates and infants with congenital heart disease (n= 398 subjects recruited across 4 centers, with a total of n=763 MRI pre-operative/post-operative time points), we show that infants with single ventricle cardiac physiology demonstrate greater white matter microstructural alterations compared to infants with bi-ventricular heart disease, supporting what has previously been shown in literature. Our method is an open-source pipeline for delineating white matter tracts in subject space but provides the necessary modular components for performing atlas space analysis. As such, we validate and introduce Diffusion Imaging of Neonates by Group Organization (DINGO), a high-level, semi-automated framework that can facilitate harmonization of subject-space tractography generated from diffusion tensor imaging acquired across varying scanners, institutions, and clinical populations. Datasets acquired using varying protocols or cohorts are compartmentalized into subsets, where a cohort-specific template is generated, allowing for the propagation of the tractography mask set with higher spatial specificity. Taken together, this pipeline can reduce multi-scanner technical variability which can confound important biological variability in relation to neonatal brain microstructure.
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Affiliation(s)
- Benjamin Meyers
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Vincent K. Lee
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Lauren Dennis
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Julia Wallace
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Vanessa Schmithorst
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Jodie K. Votava-Smith
- Division of Cardiology, Department of Pediatrics, Children’s Hospital of Los Angeles and Keck School of Medicine University of Southern California, Los Angeles, CA
| | - Vidya Rajagopalan
- Department of Radiology, Children’s Hospital of Los Angeles and Keck School of Medicine University of Southern California Los Angeles, CA
| | - Elizabeth Herrup
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA
| | - Tracy Baust
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA
| | - Nhu N. Tran
- Division of Cardiology, Department of Pediatrics, Children’s Hospital of Los Angeles and Keck School of Medicine University of Southern California, Los Angeles, CA
| | - Jill Hunter
- Department of Radiology, Texas Children’s Hospital, Houston, TX
| | - Daniel J. Licht
- Department of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA
| | - J. William Gaynor
- Department of Surgery, Children’s Hospital of Philadelphia, Philadelphia, PA
| | | | - Ashok Panigrahy
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Rafael Ceschin
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA
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5
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Liew S, Zavaliangos‐Petropulu A, Jahanshad N, Lang CE, Hayward KS, Lohse KR, Juliano JM, Assogna F, Baugh LA, Bhattacharya AK, Bigjahan B, Borich MR, Boyd LA, Brodtmann A, Buetefisch CM, Byblow WD, Cassidy JM, Conforto AB, Craddock RC, Dimyan MA, Dula AN, Ermer E, Etherton MR, Fercho KA, Gregory CM, Hadidchi S, Holguin JA, Hwang DH, Jung S, Kautz SA, Khlif MS, Khoshab N, Kim B, Kim H, Kuceyeski A, Lotze M, MacIntosh BJ, Margetis JL, Mohamed FB, Piras F, Ramos‐Murguialday A, Richard G, Roberts P, Robertson AD, Rondina JM, Rost NS, Sanossian N, Schweighofer N, Seo NJ, Shiroishi MS, Soekadar SR, Spalletta G, Stinear CM, Suri A, Tang WKW, Thielman GT, Vecchio D, Villringer A, Ward NS, Werden E, Westlye LT, Winstein C, Wittenberg GF, Wong KA, Yu C, Cramer SC, Thompson PM. The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke. Hum Brain Mapp 2022; 43:129-148. [PMID: 32310331 PMCID: PMC8675421 DOI: 10.1002/hbm.25015] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 04/03/2020] [Accepted: 04/08/2020] [Indexed: 01/28/2023] Open
Abstract
The goal of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well-powered meta- and mega-analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large-scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided.
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Affiliation(s)
- Sook‐Lei Liew
- Chan Division of Occupational Science and Occupational TherapyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
- Department of NeurologyUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern CaliforniaLos AngelesCaliforniaUSA
- Department of Biomedical Engineering, University of Southern CaliforniaLos AngelesCaliforniaUSA
- Neuroscience Graduate ProgramUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Artemis Zavaliangos‐Petropulu
- Department of NeurologyUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern CaliforniaLos AngelesCaliforniaUSA
- Neuroscience Graduate ProgramUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
- Imaging Genetics CenterUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Neda Jahanshad
- Department of NeurologyUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern CaliforniaLos AngelesCaliforniaUSA
- Imaging Genetics CenterUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Catherine E. Lang
- Program in Physical TherapyWashington University School of MedicineSt. LouisMissouriUSA
| | - Kathryn S. Hayward
- Department of Physiotherapyand Florey Institute of Neuroscience and Mental Health, University of MelbourneParkvilleVictoriaAustralia
- NHMRC Centre of Research Excellence in Stroke Rehabilitation and Brain Recovery, University of MelbourneParkvilleVictoriaAustralia
| | - Keith R. Lohse
- Department of Health, Kinesiology, and RecreationUniversity of UtahSalt Lake CityUtahUSA
- Department of Physical Therapy and Athletic TrainingUniversity of UtahSalt Lake CityUtahUSA
| | - Julia M. Juliano
- Neuroscience Graduate ProgramUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Francesca Assogna
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral NeurologyIRCCS Santa Lucia FoundationRomeItaly
| | - Lee A. Baugh
- Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South DakotaVermillionSouth DakotaUSA
- Sioux Falls VA Health Care SystemSioux FallsSouth DakotaUSA
| | - Anup K. Bhattacharya
- Mallinckrodt Institute of Radiology, Washington University School of MedicineSt. LouisMissouriUSA
| | - Bavrina Bigjahan
- Department of NeurologyUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern CaliforniaLos AngelesCaliforniaUSA
- Department of Radiology, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Michael R. Borich
- Department of Rehabilitation MedicineEmory UniversityAtlantaGeorgiaUSA
| | - Lara A. Boyd
- Department of Physical Therapy, Faculty of MedicineUniversity of British ColumbiaVancouverBritish ColumbiaCanada
- Djavad Mowafaghian Centre for Brain HealthVancouverBritish ColumbiaCanada
| | - Amy Brodtmann
- Florey Institute for Neuroscience and Mental Health, University of MelbourneParkvilleVictoriaAustralia
| | - Cathrin M. Buetefisch
- Department of Rehabilitation MedicineEmory UniversityAtlantaGeorgiaUSA
- Department of NeurologyEmory UniversityAtlantaGeorgiaUSA
| | - Winston D. Byblow
- Department of Exercise Sciences, Centre for Brain ResearchUniversity of AucklandAucklandNew Zealand
| | - Jessica M. Cassidy
- Division of Physical Therapy, Department Allied Health SciencesUniversity of North Carolina, Chapel HillChapel HillNorth CarolinaUSA
| | - Adriana B. Conforto
- Neurology Clinical Division, Hospital das Clínicas/São Paulo UniversitySão PauloBrazil
- Hospital Israelita Albert EinsteinSão PauloBrazil
| | - R. Cameron Craddock
- Department of Diagnostic MedicineThe University of Texas at Austin Dell Medical SchoolAustinTexasUSA
| | - Michael A. Dimyan
- Department of Neurology and Neurorehabilitation, School of MedicineUniversity of Maryland, BaltimoreBaltimoreMarylandUSA
- VA Maryland Health Care SystemBaltimoreMarylandUSA
| | - Adrienne N. Dula
- Department of Diagnostic MedicineThe University of Texas at Austin Dell Medical SchoolAustinTexasUSA
- Department of NeurologyDell Medical School at University of Texas at AustinAustinTexasUSA
| | - Elsa Ermer
- Department of Neurology and Neurorehabilitation, School of MedicineUniversity of Maryland, BaltimoreBaltimoreMarylandUSA
| | - Mark R. Etherton
- Department of NeurologyMassachusetts General HospitalBostonMassachusettsUSA
- J. Philip Kistler Stroke Research CenterHarvard Medical SchoolBostonMassachusettsUSA
| | - Kelene A. Fercho
- Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South DakotaVermillionSouth DakotaUSA
- Federal Aviation Administration, Civil Aerospace Medical InstituteOklahoma CityOklahomaUSA
| | - Chris M. Gregory
- Department of Health Sciences and ResearchMedical University of South CarolinaCharlestonSouth CarolinaUSA
| | - Shahram Hadidchi
- Department of RadiologyWayne State University/Detroit Medical CenterDetroitMichiganUSA
- Department of Internal MedicineWayne State University/Detroit Medical CenterDetroitMichiganUSA
| | - Jess A. Holguin
- Chan Division of Occupational Science and Occupational TherapyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Darryl H. Hwang
- Department of Radiology, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Simon Jung
- Department of Neurology, University of BernBernSwitzerland
| | - Steven A. Kautz
- Department of Health Sciences and ResearchMedical University of South CarolinaCharlestonSouth CarolinaUSA
- Ralph H Johnson VA Medical CenterCharlestonSouth CarolinaUSA
| | - Mohamed Salah Khlif
- Florey Institute for Neuroscience and Mental Health, University of MelbourneParkvilleVictoriaAustralia
| | - Nima Khoshab
- Department of Anatomy and NeurobiologyUniversity of CaliforniaIrvineCaliforniaUSA
| | - Bokkyu Kim
- Department of Physical Therapy EducationState University of New York Upstate Medical UniversitySyracuseNew YorkUSA
- Division of Biokinesiology and Physical TherapyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Hosung Kim
- Department of NeurologyUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Amy Kuceyeski
- Department of RadiologyWeill Cornell MedicineNew YorkNew YorkUSA
- Brain and Mind Research Institute, Weill Cornell MedicineNew YorkNew YorkUSA
| | - Martin Lotze
- Functional Imaging Unit, Center for Diagnostic RadiologySchool of Medicine, University of GreifswaldGreifswaldGermany
| | - Bradley J. MacIntosh
- Department of Medical BiophysicsUniversity of TorontoTorontoOntarioCanada
- Physical Sciences Platform, Brain Sciences ProgramSunnybrook Research InstituteTorontoOntarioCanada
| | - John L. Margetis
- Chan Division of Occupational Science and Occupational TherapyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Feroze B. Mohamed
- Department of RadiologyThomas Jefferson UniversityPhiladelphiaPennsylvaniaUSA
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral NeurologyIRCCS Santa Lucia FoundationRomeItaly
| | - Ander Ramos‐Murguialday
- TECNALIA, Basque Research and Technology Alliance (BRTA), Neurotechnology LaboratoryDerioSpain
- Institute of Medical Psychology and Behavioural Neurobiology, University of TubingenTübingenGermany
| | - Geneviève Richard
- Department of PsychologyUniversity of OsloOsloNorway
- NORMENT, Division of Mental Health and AddictionOslo University HospitalOsloNorway
- Institute of Clinical Medicine, University of OsloOsloNorway
| | - Pamela Roberts
- Department of Physical Medicine and RehabilitationCedars‐SinaiLos AngelesCaliforniaUSA
| | - Andrew D. Robertson
- Department of KinesiologyUniversity of WaterlooWaterlooOntarioCanada
- Schlegel‐UW Research Institute for Aging, University of WaterlooWaterlooOntarioCanada
| | - Jane M. Rondina
- Department of Clinical and Movement NeurosciencesUCL Queen Square Institute of Neurology, University College LondonLondonUK
| | - Natalia S. Rost
- Stroke Division, Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Nerses Sanossian
- Division of Neurocritical Care and Stroke, Department of Neurology, Keck School of Medicine, University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Nicolas Schweighofer
- Division of Biokinesiology and Physical Therapy, University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Na Jin Seo
- Department of Health Sciences and ResearchMedical University of South CarolinaCharlestonSouth CarolinaUSA
- Ralph H Johnson VA Medical CenterCharlestonSouth CarolinaUSA
- Division of Occupational Therapy, Department of Health Professions, Medical University of South CarolinaCharlestonSouth CarolinaUSA
| | - Mark S. Shiroishi
- Division of Neuroradiology, Department of RadiologyKeck School of Medicine, University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Surjo R. Soekadar
- Department of Psychiatry and Psychotherapy, Clinical Neurotechnology LaboratoryCharité ‐ University Medicine BerlinBerlinGermany
- Applied Neurotechnology Laboratory, Department of Psychiatry and PsychotherapyUniversity of TübingenTübingenGermany
| | - Gianfranco Spalletta
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral NeurologyIRCCS Santa Lucia FoundationRomeItaly
- Division of Neuropsychiatry, Menninger Department of Psychiatry and Behavioral SciencesBaylor College of MedicineHoustonTexasUSA
| | | | - Anisha Suri
- Department of Electrical and Computer EngineeringUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Wai Kwong W. Tang
- Department of PsychiatryThe Chinese University of Hong KongHong KongPeople's Republic of China
| | - Gregory T. Thielman
- Physical Therapy and Neuroscience, University of the SciencesPhiladelphiaPennsylvaniaUSA
- Samson CollegeQuezon CityPhilippines
| | - Daniela Vecchio
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral NeurologyIRCCS Santa Lucia FoundationRomeItaly
| | - Arno Villringer
- Department of NeurologyMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Department of Cognitive NeurologyUniversity Hospital LeipzigLeipzigGermany
- Center for Stroke Research, Charité‐Universitätsmedizin BerlinBerlinGermany
| | - Nick S. Ward
- UCL Queen Square Institute of Neurology, University College LondonLondonUK
| | - Emilio Werden
- Florey Institute for Neuroscience and Mental Health, University of MelbourneParkvilleVictoriaAustralia
| | - Lars T. Westlye
- Department of PsychologyUniversity of OsloOsloNorway
- NORMENT, Division of Mental Health and AddictionOslo University HospitalOsloNorway
| | - Carolee Winstein
- Division of Biokinesiology and Physical Therapy, University of Southern CaliforniaLos AngelesCaliforniaUSA
- Department of NeurologyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - George F. Wittenberg
- Department of NeurologyUniversity of PittsburghPittsburghPennsylvaniaUSA
- Department of Veterans AffairsUniversity Drive CampusPittsburghPennsylvaniaUSA
| | - Kristin A. Wong
- Department of Physical Medicine and RehabilitationDell Medical School, University of Texas AustinAustinTexasUSA
| | - Chunshui Yu
- Department of RadiologyTianjin Medical University General HospitalTianjinChina
- Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Steven C. Cramer
- Department of NeurologyUCLA and California Rehabilitation InstituteLos AngelesCaliforniaUSA
| | - Paul M. Thompson
- Department of NeurologyUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern CaliforniaLos AngelesCaliforniaUSA
- Imaging Genetics CenterUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern CaliforniaLos AngelesCaliforniaUSA
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6
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Chen D, Jutkowitz E, Iosepovici SL, Lin JC, Gross AL. Pre-statistical harmonization of behavrioal instruments across eight surveys and trials. BMC Med Res Methodol 2021; 21:227. [PMID: 34689753 PMCID: PMC8543796 DOI: 10.1186/s12874-021-01431-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Accepted: 10/08/2021] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Data harmonization is a powerful method to equilibrate items in measures that evaluate the same underlying construct. There are multiple measures to evaluate dementia related behavioral symptoms. Pre-statistical harmonization of behavioral instruments in dementia research is the first step to develop a statistical crosswalk between measures. Studies that conduct pre-statistical harmonization of behavioral instruments rarely document their methods in a structured, reproducible manner. This is a crucial step which entails careful review, documentation and scrutiny of source data to ensure sufficient comparability between items prior to data pooling. Here, we document the pre-statistical harmonization of items measuring behavioral and psychological symptoms among people with dementia. We provide a box of recommended procedure for future studies. METHODS We identified behavioral instruments that are used in clinical practice, a national survey, and randomized trials of dementia care interventions. We rigorously reviewed question content and scoring procedures to establish sufficient comparability across items as well as item quality prior to data pooling. Additionally, we standardized coding to Stata-readable format, which allowed us to automate approaches to identify potential cross-study differences in items and low-quality items. To ensure reasonable model fit for statistical co-calibration, we estimated two-parameter logistic Item Response Theory models within each of the eight studies. RESULTS We identified 59 items from 11 behavioral instruments across the eight datasets. We found considerable cross-study heterogeneity in administration and coding procedures for items that measure the same attribute. Discrepancies existed in terms of directionality and quantification of behavioral symptoms for even seemingly comparable items. We resolved item response heterogeneity, missingness and skewness, conditional dependency prior to estimation of item response theory models for statistical co-calibration. We used several rigorous data transformation procedures to address these issues, including re-coding and truncation. CONCLUSIONS This study highlights the importance of each aspect involved in the pre-statistical harmonization process of behavioral instruments. We provide guidelines and recommendations for how future research may detect and account for similar issues in pooling behavioral and related instruments.
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Affiliation(s)
- Diefei Chen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States, 2024 E. Monument Street, Baltimore, MD 21205 USA
| | - Eric Jutkowitz
- Health Services, Policy & Practice, Brown School of Public Health, Providence, RI USA
- Center of Innovation in Long Term Services and Supports, Providence VA Medical Center, Providence, RI USA
| | - Skylar L. Iosepovici
- Health Services, Policy & Practice, Brown School of Public Health, Providence, RI USA
| | - John C. Lin
- Health Services, Policy & Practice, Brown School of Public Health, Providence, RI USA
| | - Alden L. Gross
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States, 2024 E. Monument Street, Baltimore, MD 21205 USA
- Johns Hopkins University Center on Aging and Health, Baltimore, MD USA
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7
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Oathes DJ, Balderston NL, Kording KP, DeLuisi JA, Perez GM, Medaglia JD, Fan Y, Duprat RJ, Satterthwaite TD, Sheline YI, Linn KA. Combining transcranial magnetic stimulation with functional magnetic resonance imaging for probing and modulating neural circuits relevant to affective disorders. WILEY INTERDISCIPLINARY REVIEWS. COGNITIVE SCIENCE 2021; 12:e1553. [PMID: 33470055 DOI: 10.1002/wcs.1553] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 12/02/2020] [Accepted: 12/23/2020] [Indexed: 12/14/2022]
Abstract
Combining transcranial magnetic stimulation (TMS) with functional magnetic resonance imaging offers an unprecedented tool for studying how brain networks interact in vivo and how repetitive trains of TMS modulate those networks among patients diagnosed with affective disorders. TMS compliments neuroimaging by allowing the interrogation of causal control among brain circuits. Together with TMS, neuroimaging can provide valuable insight into the mechanisms underlying treatment effects and downstream circuit communication. Here we provide a background of the method, review relevant study designs, consider methodological and equipment options, and provide statistical recommendations. We conclude by describing emerging approaches that will extend these tools into exciting new applications. This article is categorized under: Psychology > Emotion and Motivation Psychology > Theory and Methods Neuroscience > Clinical Neuroscience.
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Affiliation(s)
- Desmond J Oathes
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Nicholas L Balderston
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Konrad P Kording
- Department of Neuroscience, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Joseph A DeLuisi
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Gianna M Perez
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - John D Medaglia
- Department of Psychology, Drexel University, Philadelphia, Pennsylvania, USA.,Department of Neurology, Drexel University, Philadelphia, Pennsylvania, USA.,Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics (CBICA), Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Romain J Duprat
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Yvette I Sheline
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Kristin A Linn
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.,Center for Biomedical Image Computing and Analytics (CBICA), Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.,Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
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8
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Dennis EL, Caeyenberghs K, Asarnow RF, Babikian T, Bartnik-Olson B, Bigler ED, Figaji A, Giza CC, Goodrich-Hunsaker NJ, Hodges CB, Hoskinson KR, Königs M, Levin HS, Lindsey HM, Livny A, Max JE, Merkley TL, Newsome MR, Olsen A, Ryan NP, Spruiell MS, Suskauer SJ, Thomopoulos SI, Ware AL, Watson CG, Wheeler AL, Yeates KO, Zielinski BA, Thompson PM, Tate DF, Wilde EA. Challenges and opportunities for neuroimaging in young patients with traumatic brain injury: a coordinated effort towards advancing discovery from the ENIGMA pediatric moderate/severe TBI group. Brain Imaging Behav 2021; 15:555-575. [PMID: 32734437 PMCID: PMC7855317 DOI: 10.1007/s11682-020-00363-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Traumatic brain injury (TBI) is a major cause of death and disability in children in both developed and developing nations. Children and adolescents suffer from TBI at a higher rate than the general population, and specific developmental issues require a unique context since findings from adult research do not necessarily directly translate to children. Findings in pediatric cohorts tend to lag behind those in adult samples. This may be due, in part, both to the smaller number of investigators engaged in research with this population and may also be related to changes in safety laws and clinical practice that have altered length of hospital stays, treatment, and access to this population. The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Pediatric Moderate/Severe TBI (msTBI) group aims to advance research in this area through global collaborative meta-analysis of neuroimaging data. In this paper, we discuss important challenges in pediatric TBI research and opportunities that we believe the ENIGMA Pediatric msTBI group can provide to address them. With the paucity of research studies examining neuroimaging biomarkers in pediatric patients with TBI and the challenges of recruiting large numbers of participants, collaborating to improve statistical power and to address technical challenges like lesions will significantly advance the field. We conclude with recommendations for future research in this field of study.
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Affiliation(s)
- Emily L Dennis
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA.
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, Los Angeles, CA, USA.
- Psychiatry Neuroimaging Laboratory, Brigham & Women's Hospital, Boston, MA, USA.
| | - Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia
| | - Robert F Asarnow
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA, USA
- Brain Research Institute, UCLA, Los Angeles, CA, USA
- Department of Psychology, UCLA, Los Angeles, CA, USA
| | - Talin Babikian
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA, USA
- UCLA Steve Tisch BrainSPORT Program, Los Angeles, CA, USA
| | - Brenda Bartnik-Olson
- Department of Radiology, Loma Linda University Medical Center, Loma Linda, CA, USA
| | - Erin D Bigler
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- Neuroscience Center, Brigham Young University, Provo, UT, USA
| | - Anthony Figaji
- Division of Neurosurgery, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Christopher C Giza
- UCLA Steve Tisch BrainSPORT Program, Los Angeles, CA, USA
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Naomi J Goodrich-Hunsaker
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- George E. Wahlen Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, USA
| | - Cooper B Hodges
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- George E. Wahlen Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, USA
| | - Kristen R Hoskinson
- Center for Biobehavioral Health, The Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Marsh Königs
- Emma Children's Hospital, Amsterdam UMC, University of Amsterdam, Emma Neuroscience Group, Amsterdam, The Netherlands
| | - Harvey S Levin
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
- Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA
| | - Hannah M Lindsey
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- George E. Wahlen Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, USA
| | - Abigail Livny
- Department of Diagnostic Imaging, Sheba Medical Center, Ramat Gan, Tel-Hashomer, Israel
- Joseph Sagol Neuroscience Center, Sheba Medical Center, Ramat Gan, Tel-Hashomer, Israel
| | - Jeffrey E Max
- Department of Psychiatry, University of California, La Jolla, San Diego, CA, USA
- Department of Psychiatry, Rady Children's Hospital, San Diego, CA, USA
| | - Tricia L Merkley
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- Neuroscience Center, Brigham Young University, Provo, UT, USA
| | - Mary R Newsome
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
- Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA
| | - Alexander Olsen
- Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Physical Medicine and Rehabilitation, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Nicholas P Ryan
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia
- Department of Paediatrics, The University of Melbourne, Melbourne, Australia
- Department of Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia
| | - Matthew S Spruiell
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
| | - Stacy J Suskauer
- Kennedy Krieger Institute, Baltimore, MD, USA
- Departments of Physical Medicine & Rehabilitation and Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, Los Angeles, CA, USA
| | - Ashley L Ware
- Department of Psychology, University of Calgary, Calgary, Alberta, Canada
| | - Christopher G Watson
- Department of Pediatrics, Children's Learning Institute, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Anne L Wheeler
- Hospital for Sick Children, Neuroscience and Mental Health Program, Toronto, Canada
- Physiology Department, University of Toronto, Toronto, Canada
| | - Keith Owen Yeates
- Department of Psychology, University of Calgary, Calgary, Alberta, Canada
- Alberta Children's Hospital Research Institute and Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Departments of Pediatrics and Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
| | - Brandon A Zielinski
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, Los Angeles, CA, USA
- Departments of Neurology, Pediatrics, Psychiatry, Radiology, Engineering, and Ophthalmology, USC, Los Angeles, CA, USA
| | - David F Tate
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- George E. Wahlen Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, USA
- Missouri Institute of Mental Health and University of Missouri, St Louis, MO, USA
| | - Elisabeth A Wilde
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- George E. Wahlen Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, USA
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
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9
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St‐Jean S, Viergever MA, Leemans A. Harmonization of diffusion MRI data sets with adaptive dictionary learning. Hum Brain Mapp 2020; 41:4478-4499. [PMID: 32851729 PMCID: PMC7555079 DOI: 10.1002/hbm.25117] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 06/11/2020] [Accepted: 06/16/2020] [Indexed: 01/05/2023] Open
Abstract
Diffusion magnetic resonance imaging can indirectly infer the microstructure of tissues and provide metrics subject to normal variability in a population. Potentially abnormal values may yield essential information to support analysis of controls and patients cohorts, but subtle confounds could be mistaken for purely biologically driven variations amongst subjects. In this work, we propose a new harmonization algorithm based on adaptive dictionary learning to mitigate the unwanted variability caused by different scanner hardware while preserving the natural biological variability of the data. Our harmonization algorithm does not require paired training data sets, nor spatial registration or matching spatial resolution. Overcomplete dictionaries are learned iteratively from all data sets at the same time with an adaptive regularization criterion, removing variability attributable to the scanners in the process. The obtained mapping is applied directly in the native space of each subject toward a scanner-space. The method is evaluated with a public database which consists of two different protocols acquired on three different scanners. Results show that the effect size of the four studied diffusion metrics is preserved while removing variability attributable to the scanner. Experiments with alterations using a free water compartment, which is not simulated in the training data, shows that the modifications applied to the diffusion weighted images are preserved in the diffusion metrics after harmonization, while still reducing global variability at the same time. The algorithm could help multicenter studies pooling their data by removing scanner specific confounds, and increase statistical power in the process.
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Affiliation(s)
- Samuel St‐Jean
- Image Sciences InstituteUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Max A. Viergever
- Image Sciences InstituteUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Alexander Leemans
- Image Sciences InstituteUniversity Medical Center UtrechtUtrechtThe Netherlands
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10
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Pinto MS, Paolella R, Billiet T, Van Dyck P, Guns PJ, Jeurissen B, Ribbens A, den Dekker AJ, Sijbers J. Harmonization of Brain Diffusion MRI: Concepts and Methods. Front Neurosci 2020; 14:396. [PMID: 32435181 PMCID: PMC7218137 DOI: 10.3389/fnins.2020.00396] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Accepted: 03/30/2020] [Indexed: 11/13/2022] Open
Abstract
MRI diffusion data suffers from significant inter- and intra-site variability, which hinders multi-site and/or longitudinal diffusion studies. This variability may arise from a range of factors, such as hardware, reconstruction algorithms and acquisition settings. To allow a reliable comparison and joint analysis of diffusion data across sites and over time, there is a clear need for robust data harmonization methods. This review article provides a comprehensive overview of diffusion data harmonization concepts and methods, and their limitations. Overall, the methods for the harmonization of multi-site diffusion images can be categorized in two main groups: diffusion parametric map harmonization (DPMH) and diffusion weighted image harmonization (DWIH). Whereas DPMH harmonizes the diffusion parametric maps (e.g., FA, MD, and MK), DWIH harmonizes the diffusion-weighted images. Defining a gold standard harmonization technique for dMRI data is still an ongoing challenge. Nevertheless, in this paper we provide two classification tools, namely a feature table and a flowchart, which aim to guide the readers in selecting an appropriate harmonization method for their study.
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Affiliation(s)
- Maíra Siqueira Pinto
- Department of Radiology, Antwerp University Hospital, University of Antwerp, Antwerp, Belgium.,imec-Vision Lab, University of Antwerp, Antwerp, Belgium
| | - Roberto Paolella
- imec-Vision Lab, University of Antwerp, Antwerp, Belgium.,Icometrix, Leuven, Belgium
| | | | - Pieter Van Dyck
- Department of Radiology, Antwerp University Hospital, University of Antwerp, Antwerp, Belgium
| | | | - Ben Jeurissen
- imec-Vision Lab, University of Antwerp, Antwerp, Belgium
| | | | | | - Jan Sijbers
- imec-Vision Lab, University of Antwerp, Antwerp, Belgium
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