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      Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias

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          Abstract

          When collecting large amounts of neuroimaging data associated with psychiatric disorders, images must be acquired from multiple sites because of the limited capacity of a single site. However, site differences represent a barrier when acquiring multisite neuroimaging data. We utilized a traveling-subject dataset in conjunction with a multisite, multidisorder dataset to demonstrate that site differences are composed of biological sampling bias and engineering measurement bias. The effects on resting-state functional MRI connectivity based on pairwise correlations because of both bias types were greater than or equal to psychiatric disorder differences. Furthermore, our findings indicated that each site can sample only from a subpopulation of participants. This result suggests that it is essential to collect large amounts of neuroimaging data from as many sites as possible to appropriately estimate the distribution of the grand population. Finally, we developed a novel harmonization method that removed only the measurement bias by using a traveling-subject dataset and achieved the reduction of the measurement bias by 29% and improvement of the signal-to-noise ratios by 40%. Our results provide fundamental knowledge regarding site effects, which is important for future research using multisite, multidisorder resting-state functional MRI data.

          Abstract

          Acquiring and sharing large amounts of resting-state fMRI data from multiple imaging sites has recently become critical for bridging the gap between basic neuroscience research and clinical applications. A harmonization method using traveling-subject data achieved a reduction of between-site differences from a multisite dataset.

          Author summary

          Recently, the importance of acquiring and sharing large amounts of resting-state functional magnetic resonance imaging (rs-fMRI) data from multiple geographical locations or sites has increased. However, differences in the data acquired from multiple sites create heterogeneities that present a barrier to the analysis. To properly manage these heterogeneous multisite data, it is important to have a deeper understanding of the origin of these between-site differences and to harmonize rs-fMRI data among sites. In this study, we demonstrate that site differences are composed of biological sampling bias (differences between the participant groups) and engineering measurement bias (differences in the properties of the MRI scanners used). We found that the effects of both types of bias on rs-fMRI functional connectivity were greater than or equal to those driven by psychiatric disorders. Furthermore, our results identified the specific properties of MRI scanners that affect the rs-fMRI connectivity. To overcome the limitations associated with site differences, we used a traveling-subject dataset, wherein multiple participants travel to multiple sites to assess measurement bias by controlling for participant effects between sites. Our results indicated that the traveling-subject dataset can help the proper harmonization of rs-fMRI data between sites.

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          Most cited references30

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          Harmonization of cortical thickness measurements across scanners and sites

          With the proliferation of multi-site neuroimaging studies, there is a greater need for handling non-biological variance introduced by differences in MRI scanners and acquisition protocols. Such unwanted sources of variation, which we refer to as “scanner effects”, can hinder the detection of imaging features associated with clinical covariates of interest and cause spurious findings. In this paper, we investigate scanner effects in two large multi-site studies on cortical thickness measurements across a total of 11 scanners. We propose a set of tools for visualizing and identifying scanner effects that are generalizable to other modalities. We then propose to use ComBat, a technique adopted from the genomics literature and recently applied to diffusion tensor imaging data, to combine and harmonize cortical thickness values across scanners. We show that ComBat removes unwanted sources of scan variability while simultaneously increasing the power and reproducibility of subsequent statistical analyses. We also show that ComBat is useful for combining imaging data with the goal of studying life-span trajectories in the brain.
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            Harmonization of multi-site diffusion tensor imaging data

            Diffusion tensor imaging (DTI) is a well-established magnetic resonance imaging (MRI) technique used for studying microstructural changes in the white matter. As with many other imaging modalities, DTI images suffer from technical between-scanner variation that hinders comparisons of images across imaging sites, scanners and over time. Using fractional anisotropy (FA) and mean diffusivity (MD) maps of 205 healthy participants acquired on two different scanners, we show that the DTI measurements are highly site-specific, highlighting the need of correcting for site effects before performing downstream statistical analyses. We first show evidence that combining DTI data from multiple sites, without harmonization, may be counter-productive and negatively impacts the inference. Then, we propose and compare several harmonization approaches for DTI data, and show that ComBat, a popular batch-effect correction tool used in genomics, performs best at modeling and removing the unwanted inter-site variability in FA and MD maps. Using age as a biological phenotype of interest, we show that ComBat both preserves biological variability and removes the unwanted variation introduced by site. Finally, we assess the different harmonization methods in the presence of different levels of confounding between site and age, in addition to test robustness to small sample size studies.
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              Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity.

              Since initial reports regarding the impact of motion artifact on measures of functional connectivity, there has been a proliferation of participant-level confound regression methods to limit its impact. However, many of the most commonly used techniques have not been systematically evaluated using a broad range of outcome measures. Here, we provide a systematic evaluation of 14 participant-level confound regression methods in 393 youths. Specifically, we compare methods according to four benchmarks, including the residual relationship between motion and connectivity, distance-dependent effects of motion on connectivity, network identifiability, and additional degrees of freedom lost in confound regression. Our results delineate two clear trade-offs among methods. First, methods that include global signal regression minimize the relationship between connectivity and motion, but result in distance-dependent artifact. In contrast, censoring methods mitigate both motion artifact and distance-dependence, but use additional degrees of freedom. Importantly, less effective de-noising methods are also unable to identify modular network structure in the connectome. Taken together, these results emphasize the heterogeneous efficacy of existing methods, and suggest that different confound regression strategies may be appropriate in the context of specific scientific goals.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: MethodologyRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: MethodologyRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: MethodologyRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: Data curation
                Role: Data curationRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: SupervisionRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Biol
                PLoS Biol
                plos
                plosbiol
                PLoS Biology
                Public Library of Science (San Francisco, CA USA )
                1544-9173
                1545-7885
                18 April 2019
                April 2019
                18 April 2019
                : 17
                : 4
                : e3000042
                Affiliations
                [1 ] Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
                [2 ] Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
                [3 ] Department of Molecular Imaging and Theranostics, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan
                [4 ] Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
                [5 ] Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
                [6 ] Department of Psychiatry, Kyoto University Graduate School of Medicine, Kyoto, Japan
                [7 ] Department of Radiology, IMSUT Hospital, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
                [8 ] Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
                [9 ] The International Research Center for Neurointelligence (WPI-IRCN) at the University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, Japan
                [10 ] Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Yamaguchi, Japan
                [11 ] Department of Psychiatry, Faculty of Medicine, Saitama Medical University, Saitama, Japan
                [12 ] Department of Language Sciences, Tokyo Metropolitan University, Tokyo, Japan
                [13 ] Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
                [14 ] Brain Activity Imaging Center, ATR-Promotions Inc., Kyoto, Japan
                [15 ] Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
                [16 ] Department of Psychology, Graduate School of Humanities and Sociology, The University of Tokyo, Tokyo, Japan
                University of Edinburgh, UNITED KINGDOM
                Author notes

                I have read the journal's policy and the authors of this manuscript have the following competing interests: M.K., J.M., N.Y., R.H., H.I., N.K., and K.K. are inventors of a patent owned by Advanced Telecommunications Research (ATR) Institute International related to the present work (PCT/JP2014/061543 [WO2014178322]). J.M., M.K., N.Y., R.H., N.K., and K.K. are inventors of a patent owned by ATR Institute International related to the present work (PCT/JP2014/061544 [WO2014178323]). G.L., J.M., M.K., and N.Y. are inventors of a patent application submitted by ATR Institute International related to the present work (JP2015-228970). A.Y., O.Y., and M.K. are inventors of a patent application submitted by ATR Institute International related to the present work (JP2018-192842).

                Author information
                http://orcid.org/0000-0003-3825-2919
                http://orcid.org/0000-0001-7706-7850
                http://orcid.org/0000-0002-7894-148X
                http://orcid.org/0000-0003-3664-947X
                http://orcid.org/0000-0001-9742-152X
                http://orcid.org/0000-0002-7141-2585
                http://orcid.org/0000-0002-8338-2758
                http://orcid.org/0000-0002-5783-7237
                http://orcid.org/0000-0002-2002-5526
                http://orcid.org/0000-0002-9661-3412
                http://orcid.org/0000-0003-2475-8548
                http://orcid.org/0000-0001-9281-0200
                http://orcid.org/0000-0002-4443-4535
                http://orcid.org/0000-0002-7001-5051
                http://orcid.org/0000-0002-6039-3657
                http://orcid.org/0000-0003-1024-0051
                Article
                PBIOLOGY-D-18-00647
                10.1371/journal.pbio.3000042
                6472734
                30998673
                163ebebe-85b0-41c4-b807-b1936e6da687
                © 2019 Yamashita et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 8 September 2018
                : 14 March 2019
                Page count
                Figures: 7, Tables: 2, Pages: 34
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100009619, Japan Agency for Medical Research and Development;
                Award ID: JP18dm0307008
                Funded by: funder-id http://dx.doi.org/10.13039/501100009025, Accelerated Innovation Research Initiative Turning Top Science and Ideas into High-Impact Values;
                Funded by: funder-id http://dx.doi.org/10.13039/501100001691, Japan Society for the Promotion of Science;
                Award ID: 26120002
                Award Recipient :
                This study was conducted under the contract research Grant Number JP18dm0307008, JP17dm0107044 (“Development of BMI Technologies for Clinical Application” of the Strategic Research Program for Brain Sciences), JP18dm0307002, JP18dm0307004, and JP18dm0307009 supported by the Japan Agency for Medical Research and Development (AMED). This study was also partially supported by the ImPACT Program of the Council for Science, Technology and Innovation (Cabinet Office, Government of Japan). H.I. was partially supported by JSPS KAKENHI 26120002. A.Y. was partially supported by JSPS KAKENHI 15J06788. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Meta-Research Article
                Medicine and Health Sciences
                Mental Health and Psychiatry
                Mood Disorders
                Depression
                Biology and Life Sciences
                Neuroscience
                Brain Mapping
                Functional Magnetic Resonance Imaging
                Medicine and Health Sciences
                Diagnostic Medicine
                Diagnostic Radiology
                Magnetic Resonance Imaging
                Functional Magnetic Resonance Imaging
                Research and Analysis Methods
                Imaging Techniques
                Diagnostic Radiology
                Magnetic Resonance Imaging
                Functional Magnetic Resonance Imaging
                Medicine and Health Sciences
                Radiology and Imaging
                Diagnostic Radiology
                Magnetic Resonance Imaging
                Functional Magnetic Resonance Imaging
                Research and Analysis Methods
                Imaging Techniques
                Neuroimaging
                Functional Magnetic Resonance Imaging
                Biology and Life Sciences
                Neuroscience
                Neuroimaging
                Functional Magnetic Resonance Imaging
                Computer and Information Sciences
                Data Acquisition
                Research and Analysis Methods
                Imaging Techniques
                Neuroimaging
                Biology and Life Sciences
                Neuroscience
                Neuroimaging
                Medicine and Health Sciences
                Diagnostic Medicine
                Diagnostic Radiology
                Magnetic Resonance Imaging
                Research and Analysis Methods
                Imaging Techniques
                Diagnostic Radiology
                Magnetic Resonance Imaging
                Medicine and Health Sciences
                Radiology and Imaging
                Diagnostic Radiology
                Magnetic Resonance Imaging
                Biology and Life Sciences
                Psychology
                Developmental Psychology
                Pervasive Developmental Disorders
                Autism Spectrum Disorder
                Social Sciences
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                Pervasive Developmental Disorders
                Autism Spectrum Disorder
                Engineering and Technology
                Telecommunications
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                Custom metadata
                The authors confirm that all the individual numerical values that underlie the summary data displayed in the figures are fully available without restriction. All relevant data are within the Supporting Information files. Connectivity-matrix data and MRI images of individuals can be downloaded from our institutional site ( https://bicr.atr.jp/dcn/en/download/harmonization/) without sending a request to the authors (user registration in the database is necessary). However, there are limitations on the data availability and commercial reuse depending on the informed consent approved by the Institutional Review Board at each site. We provided detailed information on the limitations as supporting information ( S2 Data).

                Life sciences
                Life sciences

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