MASiVar: Multisite, multiscanner, and multisubject acquisitions for studying variability in diffusion weighted MRI
Purpose Diffusion‐weighted imaging allows investigators to identify structural, microstructural, and connectivity‐based differences between subjects, but variability due to session and scanner biases is a challenge. Methods To investigate DWI variability, we present MASiVar, a multisite data set con...
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| Published in | Magnetic resonance in medicine Vol. 86; no. 6; pp. 3304 - 3320 |
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| Main Authors | , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Wiley Subscription Services, Inc
01.12.2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0740-3194 1522-2594 1522-2594 |
| DOI | 10.1002/mrm.28926 |
Cover
| Abstract | Purpose
Diffusion‐weighted imaging allows investigators to identify structural, microstructural, and connectivity‐based differences between subjects, but variability due to session and scanner biases is a challenge.
Methods
To investigate DWI variability, we present MASiVar, a multisite data set consisting of 319 diffusion scans acquired at 3 T from b = 1000 to 3000 s/mm2 across 14 healthy adults, 83 healthy children (5 to 8 years), three sites, and four scanners as a publicly available, preprocessed, and de‐identified data set. With the adult data, we demonstrate the capacity of MASiVar to simultaneously quantify the intrasession, intersession, interscanner, and intersubject variability of four common DWI processing approaches: (1) a tensor signal representation, (2) a multi‐compartment neurite orientation dispersion and density model, (3) white‐matter bundle segmentation, and (4) structural connectomics. Respectively, we evaluate region‐wise fractional anisotropy, mean diffusivity, and principal eigenvector; region‐wise CSF volume fraction, intracellular volume fraction, and orientation dispersion index; bundle‐wise shape, volume, fractional anisotropy, and length; and whole connectome correlation and maximized modularity, global efficiency, and characteristic path length.
Results
We plot the variability in these measures at each level and find that it consistently increases with intrasession to intersession to interscanner to intersubject effects across all processing approaches and that sometimes interscanner variability can approach intersubject variability.
Conclusions
This study demonstrates the potential of MASiVar to more globally investigate DWI variability across multiple levels and processing approaches simultaneously and suggests harmonization between scanners for multisite analyses should be considered before inference of group differences on subjects. |
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| AbstractList | PurposeDiffusion‐weighted imaging allows investigators to identify structural, microstructural, and connectivity‐based differences between subjects, but variability due to session and scanner biases is a challenge.MethodsTo investigate DWI variability, we present MASiVar, a multisite data set consisting of 319 diffusion scans acquired at 3 T from b = 1000 to 3000 s/mm2 across 14 healthy adults, 83 healthy children (5 to 8 years), three sites, and four scanners as a publicly available, preprocessed, and de‐identified data set. With the adult data, we demonstrate the capacity of MASiVar to simultaneously quantify the intrasession, intersession, interscanner, and intersubject variability of four common DWI processing approaches: (1) a tensor signal representation, (2) a multi‐compartment neurite orientation dispersion and density model, (3) white‐matter bundle segmentation, and (4) structural connectomics. Respectively, we evaluate region‐wise fractional anisotropy, mean diffusivity, and principal eigenvector; region‐wise CSF volume fraction, intracellular volume fraction, and orientation dispersion index; bundle‐wise shape, volume, fractional anisotropy, and length; and whole connectome correlation and maximized modularity, global efficiency, and characteristic path length.ResultsWe plot the variability in these measures at each level and find that it consistently increases with intrasession to intersession to interscanner to intersubject effects across all processing approaches and that sometimes interscanner variability can approach intersubject variability.ConclusionsThis study demonstrates the potential of MASiVar to more globally investigate DWI variability across multiple levels and processing approaches simultaneously and suggests harmonization between scanners for multisite analyses should be considered before inference of group differences on subjects. Diffusion-weighted imaging allows investigators to identify structural, microstructural, and connectivity-based differences between subjects, but variability due to session and scanner biases is a challenge. To investigate DWI variability, we present MASiVar, a multisite data set consisting of 319 diffusion scans acquired at 3 T from b = 1000 to 3000 s/mm across 14 healthy adults, 83 healthy children (5 to 8 years), three sites, and four scanners as a publicly available, preprocessed, and de-identified data set. With the adult data, we demonstrate the capacity of MASiVar to simultaneously quantify the intrasession, intersession, interscanner, and intersubject variability of four common DWI processing approaches: (1) a tensor signal representation, (2) a multi-compartment neurite orientation dispersion and density model, (3) white-matter bundle segmentation, and (4) structural connectomics. Respectively, we evaluate region-wise fractional anisotropy, mean diffusivity, and principal eigenvector; region-wise CSF volume fraction, intracellular volume fraction, and orientation dispersion index; bundle-wise shape, volume, fractional anisotropy, and length; and whole connectome correlation and maximized modularity, global efficiency, and characteristic path length. We plot the variability in these measures at each level and find that it consistently increases with intrasession to intersession to interscanner to intersubject effects across all processing approaches and that sometimes interscanner variability can approach intersubject variability. This study demonstrates the potential of MASiVar to more globally investigate DWI variability across multiple levels and processing approaches simultaneously and suggests harmonization between scanners for multisite analyses should be considered before inference of group differences on subjects. Diffusion-weighted imaging allows investigators to identify structural, microstructural, and connectivity-based differences between subjects, but variability due to session and scanner biases is a challenge.PURPOSEDiffusion-weighted imaging allows investigators to identify structural, microstructural, and connectivity-based differences between subjects, but variability due to session and scanner biases is a challenge.To investigate DWI variability, we present MASiVar, a multisite data set consisting of 319 diffusion scans acquired at 3 T from b = 1000 to 3000 s/mm2 across 14 healthy adults, 83 healthy children (5 to 8 years), three sites, and four scanners as a publicly available, preprocessed, and de-identified data set. With the adult data, we demonstrate the capacity of MASiVar to simultaneously quantify the intrasession, intersession, interscanner, and intersubject variability of four common DWI processing approaches: (1) a tensor signal representation, (2) a multi-compartment neurite orientation dispersion and density model, (3) white-matter bundle segmentation, and (4) structural connectomics. Respectively, we evaluate region-wise fractional anisotropy, mean diffusivity, and principal eigenvector; region-wise CSF volume fraction, intracellular volume fraction, and orientation dispersion index; bundle-wise shape, volume, fractional anisotropy, and length; and whole connectome correlation and maximized modularity, global efficiency, and characteristic path length.METHODSTo investigate DWI variability, we present MASiVar, a multisite data set consisting of 319 diffusion scans acquired at 3 T from b = 1000 to 3000 s/mm2 across 14 healthy adults, 83 healthy children (5 to 8 years), three sites, and four scanners as a publicly available, preprocessed, and de-identified data set. With the adult data, we demonstrate the capacity of MASiVar to simultaneously quantify the intrasession, intersession, interscanner, and intersubject variability of four common DWI processing approaches: (1) a tensor signal representation, (2) a multi-compartment neurite orientation dispersion and density model, (3) white-matter bundle segmentation, and (4) structural connectomics. Respectively, we evaluate region-wise fractional anisotropy, mean diffusivity, and principal eigenvector; region-wise CSF volume fraction, intracellular volume fraction, and orientation dispersion index; bundle-wise shape, volume, fractional anisotropy, and length; and whole connectome correlation and maximized modularity, global efficiency, and characteristic path length.We plot the variability in these measures at each level and find that it consistently increases with intrasession to intersession to interscanner to intersubject effects across all processing approaches and that sometimes interscanner variability can approach intersubject variability.RESULTSWe plot the variability in these measures at each level and find that it consistently increases with intrasession to intersession to interscanner to intersubject effects across all processing approaches and that sometimes interscanner variability can approach intersubject variability.This study demonstrates the potential of MASiVar to more globally investigate DWI variability across multiple levels and processing approaches simultaneously and suggests harmonization between scanners for multisite analyses should be considered before inference of group differences on subjects.CONCLUSIONSThis study demonstrates the potential of MASiVar to more globally investigate DWI variability across multiple levels and processing approaches simultaneously and suggests harmonization between scanners for multisite analyses should be considered before inference of group differences on subjects. Purpose Diffusion‐weighted imaging allows investigators to identify structural, microstructural, and connectivity‐based differences between subjects, but variability due to session and scanner biases is a challenge. Methods To investigate DWI variability, we present MASiVar, a multisite data set consisting of 319 diffusion scans acquired at 3 T from b = 1000 to 3000 s/mm2 across 14 healthy adults, 83 healthy children (5 to 8 years), three sites, and four scanners as a publicly available, preprocessed, and de‐identified data set. With the adult data, we demonstrate the capacity of MASiVar to simultaneously quantify the intrasession, intersession, interscanner, and intersubject variability of four common DWI processing approaches: (1) a tensor signal representation, (2) a multi‐compartment neurite orientation dispersion and density model, (3) white‐matter bundle segmentation, and (4) structural connectomics. Respectively, we evaluate region‐wise fractional anisotropy, mean diffusivity, and principal eigenvector; region‐wise CSF volume fraction, intracellular volume fraction, and orientation dispersion index; bundle‐wise shape, volume, fractional anisotropy, and length; and whole connectome correlation and maximized modularity, global efficiency, and characteristic path length. Results We plot the variability in these measures at each level and find that it consistently increases with intrasession to intersession to interscanner to intersubject effects across all processing approaches and that sometimes interscanner variability can approach intersubject variability. Conclusions This study demonstrates the potential of MASiVar to more globally investigate DWI variability across multiple levels and processing approaches simultaneously and suggests harmonization between scanners for multisite analyses should be considered before inference of group differences on subjects. |
| Author | Hansen, Colin B. Edmonson, Heidi A. Kanakaraj, Praitayini Ramadass, Karthik Descoteaux, Maxime Yeh, Fang‐Cheng Kang, Hakmook Nath, Vishwesh Landman, Bennett A. Luci, Jeffrey Price, Gavin R. Kerley, Cailey I. Rheault, Francois Garyfallidis, Eleftherios Cai, Leon Y. Schilling, Kurt G. Yang, Qi Newton, Allen T. Conrad, Benjamin N. |
| AuthorAffiliation | 3 Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA 10 Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA 11 Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA 4 Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA 5 Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA 7 Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, New Jersey, USA 12 Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, USA 8 Neuroscience Graduate Program, Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA 9 Department of Psychology and Human Development, Peabody College, Vanderbilt University, Nashville, Tennessee, USA 2 Department of Electrical Engineering and Computer Science, |
| AuthorAffiliation_xml | – name: 11 Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA – name: 8 Neuroscience Graduate Program, Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA – name: 12 Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, USA – name: 9 Department of Psychology and Human Development, Peabody College, Vanderbilt University, Nashville, Tennessee, USA – name: 6 Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, USA – name: 7 Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, New Jersey, USA – name: 5 Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA – name: 10 Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA – name: 13 Department of Computer Science, Université de Sherbrooke, Sherbrooke, Québec, Canada – name: 2 Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA – name: 4 Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA – name: 1 Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA – name: 3 Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA |
| Author_xml | – sequence: 1 givenname: Leon Y. orcidid: 0000-0002-5812-5397 surname: Cai fullname: Cai, Leon Y. email: leon.y.cai@vanderbilt.edu organization: Vanderbilt University – sequence: 2 givenname: Qi surname: Yang fullname: Yang, Qi organization: Vanderbilt University – sequence: 3 givenname: Praitayini surname: Kanakaraj fullname: Kanakaraj, Praitayini organization: Vanderbilt University – sequence: 4 givenname: Vishwesh surname: Nath fullname: Nath, Vishwesh organization: Vanderbilt University – sequence: 5 givenname: Allen T. surname: Newton fullname: Newton, Allen T. organization: Vanderbilt University – sequence: 6 givenname: Heidi A. surname: Edmonson fullname: Edmonson, Heidi A. organization: Mayo Clinic – sequence: 7 givenname: Jeffrey surname: Luci fullname: Luci, Jeffrey organization: Robert Wood Johnson Medical School, Rutgers University – sequence: 8 givenname: Benjamin N. orcidid: 0000-0003-3755-6988 surname: Conrad fullname: Conrad, Benjamin N. organization: Vanderbilt University – sequence: 9 givenname: Gavin R. surname: Price fullname: Price, Gavin R. organization: Vanderbilt University – sequence: 10 givenname: Colin B. surname: Hansen fullname: Hansen, Colin B. organization: Vanderbilt University – sequence: 11 givenname: Cailey I. surname: Kerley fullname: Kerley, Cailey I. organization: Vanderbilt University – sequence: 12 givenname: Karthik surname: Ramadass fullname: Ramadass, Karthik organization: Vanderbilt University – sequence: 13 givenname: Fang‐Cheng surname: Yeh fullname: Yeh, Fang‐Cheng organization: University of Pittsburgh School of Medicine – sequence: 14 givenname: Hakmook surname: Kang fullname: Kang, Hakmook organization: Vanderbilt University Medical Center – sequence: 15 givenname: Eleftherios surname: Garyfallidis fullname: Garyfallidis, Eleftherios organization: Indiana University – sequence: 16 givenname: Maxime surname: Descoteaux fullname: Descoteaux, Maxime organization: Université de Sherbrooke – sequence: 17 givenname: Francois surname: Rheault fullname: Rheault, Francois organization: Université de Sherbrooke – sequence: 18 givenname: Kurt G. surname: Schilling fullname: Schilling, Kurt G. organization: Vanderbilt University – sequence: 19 givenname: Bennett A. orcidid: 0000-0001-5733-2127 surname: Landman fullname: Landman, Bennett A. organization: Vanderbilt University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34270123$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.neuroimage.2007.02.049 10.1016/j.neuroimage.2012.11.049 10.1007/s00234-019-02350-6 10.1007/s00429-020-02129-z 10.1038/sdata.2016.44 10.1016/j.neuroimage.2015.10.019 10.1016/j.neuroscience.2018.03.048 10.1016/j.neuroimage.2016.08.016 10.1002/mrm.20426 10.1016/j.neuroimage.2007.07.053 10.1016/j.neuroimage.2010.03.046 10.1002/aur.1243 10.3174/ajnr.A5025 10.1007/s00429-017-1508-x 10.1371/journal.pone.0236418 10.1371/journal.pone.0034125 10.1016/B978-044451741-8/50003-2 10.1016/j.neuroimage.2020.117329 10.1002/nbm.3269 10.1002/jmri.26197 10.1002/jmri.26794 10.1016/j.neuroimage.2018.12.015 10.1002/mrm.26059 10.1371/journal.pone.0080713 10.1016/j.neuroimage.2014.05.044 10.1002/mrm.28678 10.3389/fnins.2018.01055 10.1016/j.neuroimage.2020.116923 10.1016/S1053-8119(03)00336-7 10.1016/j.neuroimage.2013.05.028 10.1038/s41597-020-0493-8 10.1002/nbm.3752 10.1101/2020.06.12.148999 10.1016/S0006-3495(94)80775-1 10.1093/cercor/bhp280 10.1016/j.dcn.2018.03.001 10.1176/appi.ajp.162.7.1256 10.1002/mrm.1910370115 10.1016/j.neuroimage.2010.11.029 10.1002/hbm.24917 10.1016/j.neuroimage.2012.03.072 10.1016/j.neuroimage.2016.06.058 10.1016/j.neuroimage.2019.116137 10.1016/j.neuroimage.2020.117406 10.1371/journal.pbio.0060159 10.3389/fninf.2014.00008 10.1016/j.neuroimage.2009.10.003 10.1016/j.neuroimage.2019.06.039 10.1111/jon.12298 10.1002/mrm.26259 10.2307/1932409 10.1016/j.biopsych.2006.06.027 10.1016/j.nicl.2017.07.020 10.2337/db08-0724 10.1038/s41380-019-0509-y 10.1016/j.neuroimage.2017.07.015 10.1016/j.neuroimage.2007.02.016 10.1016/j.schres.2005.11.020 10.1016/j.media.2018.10.009 10.1016/j.media.2007.06.004 10.1016/j.neuroimage.2018.08.050 10.1007/s12031-007-0029-0 10.3389/fninf.2011.00023 10.3389/fnhum.2017.00306 10.1038/s41380-019-0504-3 10.1016/j.neuroimage.2013.05.041 10.1089/brain.2012.0112 10.1016/j.nec.2010.12.004 10.1371/journal.pcbi.0010042 10.1016/j.neuroimage.2006.01.021 10.1038/sdata.2017.10 10.1016/j.neuroimage.2011.09.081 |
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| References | 2012; 61 2021; 26 2006; 31 2017; 4 2019; 52 2020; 62 2013; 67 2008; 39 2016; 76 1994; 66 2008; 34 2020; 15 2011; 54 2019; 202 2008; 6 2019; 403 2012; 59 2013; 8 2016; 142 2007; 35 2019; 200 2016; 141 2007; 36 2020; 7 2010; 20 2018; 170 2010; 26 2020; 51 2017; 38 2017; 77 2011; 22 2020; 217 2007; 61 2014; 8 2018; 32 2014; 99 2021; 86 2018; 183 2020; 41 2019; 32 2018; 223 2020; 225 1997 2008; 12 2008; 57 2006 2005 2016; 125 2020; 223 2011; 5 2019; 188 2012; 2 2015; 28 2005; 162 2006; 83 2016; 3 2020 2017; 16 1997; 37 1945; 26 2017; 11 2013; 80 2019; 49 2005; 53 2020; 25 2013; 81 2005; 1 2015 2013 2018; 12 2012; 7 2012; 5 2016; 26 2003; 20 2010; 52 2010; 51 e_1_2_7_5_1 e_1_2_7_3_1 e_1_2_7_9_1 e_1_2_7_7_1 e_1_2_7_19_1 e_1_2_7_60_1 e_1_2_7_17_1 e_1_2_7_62_1 e_1_2_7_15_1 e_1_2_7_41_1 e_1_2_7_64_1 e_1_2_7_13_1 e_1_2_7_43_1 e_1_2_7_66_1 Hollander M (e_1_2_7_33_1) 2013 e_1_2_7_11_1 e_1_2_7_45_1 e_1_2_7_68_1 e_1_2_7_47_1 e_1_2_7_26_1 e_1_2_7_49_1 e_1_2_7_28_1 e_1_2_7_73_1 e_1_2_7_50_1 e_1_2_7_71_1 e_1_2_7_25_1 e_1_2_7_31_1 e_1_2_7_52_1 e_1_2_7_77_1 e_1_2_7_23_1 e_1_2_7_54_1 e_1_2_7_75_1 e_1_2_7_21_1 e_1_2_7_35_1 e_1_2_7_56_1 e_1_2_7_37_1 e_1_2_7_58_1 e_1_2_7_79_1 e_1_2_7_39_1 e_1_2_7_6_1 e_1_2_7_4_1 e_1_2_7_80_1 e_1_2_7_8_1 e_1_2_7_18_1 e_1_2_7_16_1 e_1_2_7_40_1 e_1_2_7_61_1 e_1_2_7_2_1 e_1_2_7_14_1 e_1_2_7_42_1 e_1_2_7_63_1 e_1_2_7_12_1 e_1_2_7_44_1 e_1_2_7_65_1 e_1_2_7_10_1 e_1_2_7_46_1 e_1_2_7_67_1 e_1_2_7_48_1 e_1_2_7_69_1 e_1_2_7_27_1 e_1_2_7_29_1 e_1_2_7_72_1 e_1_2_7_51_1 e_1_2_7_70_1 e_1_2_7_30_1 e_1_2_7_53_1 e_1_2_7_76_1 e_1_2_7_24_1 e_1_2_7_32_1 e_1_2_7_55_1 e_1_2_7_74_1 e_1_2_7_22_1 e_1_2_7_57_1 e_1_2_7_20_1 e_1_2_7_36_1 e_1_2_7_59_1 e_1_2_7_78_1 e_1_2_7_38_1 Farrell JAD (e_1_2_7_34_1) 2010; 26 |
| References_xml | – volume: 61 start-page: 1000 year: 2012 end-page: 1016 article-title: NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain publication-title: Neuroimage – volume: 4 start-page: 170010 year: 2017 article-title: Enhancing studies of the connectome in autism using the autism brain imaging data exchange II publication-title: Sci Data – volume: 32 start-page: 43 year: 2018 end-page: 54 article-title: The adolescent brain cognitive development (ABCD) study: imaging acquisition across 21 sites publication-title: Dev Cogn Neurosci – volume: 200 start-page: 391 year: 2019 end-page: 404 article-title: Complex diffusion‐weighted image estimation via matrix recovery under general noise models publication-title: Neuroimage – volume: 52 start-page: 1059 year: 2010 end-page: 1069 article-title: Complex network measures of brain connectivity: uses and interpretations publication-title: Neuroimage – volume: 183 start-page: 456 year: 2018 end-page: 468 article-title: The lifespan human connectome project in development: a large‐scale study of brain connectivity development in 5–21 year olds publication-title: Neuroimage – volume: 2 start-page: 345 year: 2012 end-page: 355 article-title: Multicenter reliability of diffusion tensor imaging publication-title: Brain Connect – volume: 162 start-page: 1256 year: 2005 end-page: 1265 article-title: Structural brain magnetic resonance imaging of limbic and thalamic volumes in pediatric bipolar disorder publication-title: Am J Psychiatry – volume: 53 start-page: 1088 year: 2005 end-page: 1095 article-title: RESTORE: robust estimation of tensors by outlier rejection publication-title: Magn Reson Med – year: 2005 – volume: 8 start-page: 1 year: 2014 end-page: 17 article-title: Dipy, a library for the analysis of diffusion MRI data publication-title: Front Neuroinform – volume: 11 start-page: 1 year: 2017 end-page: 12 article-title: Probabilistic white matter atlases of human auditory, basal ganglia, language, precuneus, sensorimotor, visual and visuospatial networks publication-title: Front Hum Neurosci – volume: 25 start-page: 3208 year: 2020 end-page: 3219 article-title: White matter abnormalities across the lifespan of schizophrenia: a harmonized multi‐site diffusion MRI study publication-title: Mol Psychiatry – volume: 223 start-page: 635 year: 2018 end-page: 651 article-title: A probabilistic atlas of fiber crossings for variability reduction of anisotropy measures publication-title: Brain Struct Funct – volume: 80 start-page: 62 year: 2013 end-page: 79 article-title: The WU‐Minn human connectome project: an overview publication-title: Neuroimage – volume: 38 start-page: 537 year: 2017 end-page: 545 article-title: Towards precision and reproducibility of DTI publication-title: AJNR Am J Neuroradiol – volume: 26 start-page: 297 year: 1945 end-page: 302 article-title: Measures of the amount of ecologic association between species publication-title: Ecology – volume: 62 start-page: 483 year: 2020 end-page: 494 article-title: Scan–rescan and inter‐vendor reproducibility of neurite orientation dispersion and density imaging metrics publication-title: Neuroradiology – volume: 86 start-page: 456 year: 2021 end-page: 470 article-title: PreQual: an automated pipeline for integrated preprocessing and quality assurance of diffusion weighted MRI images publication-title: Magn Reson Med – volume: 83 start-page: 155 year: 2006 end-page: 171 article-title: Decreased volume of left and total anterior insular lobule in schizophrenia publication-title: Schizophr Res – volume: 66 start-page: 259 year: 1994 end-page: 267 article-title: MR diffusion tensor spectroscopy and imaging publication-title: Biophys J – volume: 28 start-page: 468 year: 2015 end-page: 485 article-title: Multi‐centre reproducibility of diffusion MRI parameters for clinical sequences in the brain publication-title: NMR Biomed – volume: 15 year: 2020 article-title: Distortion correction of diffusion weighted MRI without reverse phase‐encoding scans or field‐maps publication-title: PLoS One – volume: 5 start-page: 1 year: 2011 end-page: 12 article-title: Automated probabilistic reconstruction of white‐matter pathways in health and disease using an atlas of the underlying anatomy publication-title: Front Neuroinform – volume: 26 start-page: 46 year: 2016 end-page: 57 article-title: Reproducibility of the structural connectome reconstruction across diffusion methods publication-title: J Neuroimaging – volume: 41 start-page: 1859 year: 2020 end-page: 1874 article-title: Tractostorm: the what, why, and how of tractography dissection reproducibility publication-title: Hum Brain Mapp – volume: 39 start-page: 336 year: 2008 end-page: 347 article-title: Tract probability maps in stereotaxic spaces: analyses of white matter anatomy and tract‐specific quantification publication-title: Neuroimage – volume: 6 start-page: 1479 year: 2008 end-page: 1493 article-title: Mapping the structural core of human cerebral cortex publication-title: PLoS Biol – volume: 8 start-page: 1 year: 2013 end-page: 16 article-title: Deterministic diffusion fiber tracking improved by quantitative anisotropy publication-title: PLoS One – volume: 20 start-page: 2055 year: 2010 end-page: 2068 article-title: Life‐span changes of the human brain white matter: diffusion tensor imaging (DTI) and volumetry publication-title: Cereb Cortex – volume: 403 start-page: 17 year: 2019 end-page: 26 article-title: Characterizing microstructural tissue properties in multiple sclerosis with diffusion MRI at 7 T and 3 T: the impact of the experimental design publication-title: Neuroscience – volume: 49 start-page: 456 year: 2019 end-page: 465 article-title: The Canadian dementia imaging protocol: harmonizing national cohorts publication-title: J Magn Reson Imaging – volume: 99 start-page: 166 year: 2014 end-page: 179 article-title: Large‐scale evaluation of ANTs and FreeSurfer cortical thickness measurements publication-title: Neuroimage – volume: 12 start-page: 1055 year: 2018 article-title: Test‐retest reliability of diffusion measures extracted along white matter language fiber bundles using Hardi‐based tractography publication-title: Front Neurosci – volume: 54 start-page: 2557 year: 2011 end-page: 2562 article-title: Men and women are different: diffusion tensor imaging reveals sexual dimorphism in the microstructure of the thalamus, corpus callosum and cingulum publication-title: Neuroimage – year: 1997 – volume: 52 start-page: 56 year: 2019 end-page: 67 article-title: Reproducibility and intercorrelation of graph theoretical measures in structural brain connectivity networks publication-title: Med Image Anal – volume: 81 start-page: 335 year: 2013 end-page: 346 article-title: Weighted linear least squares estimation of diffusion MRI parameters: strengths, limitations, and pitfalls publication-title: Neuroimage – volume: 5 start-page: 289 year: 2012 end-page: 313 article-title: Diffusion tensor imaging in autism spectrum disorder: a review publication-title: Autism Res – volume: 76 start-page: 1582 year: 2016 end-page: 1593 article-title: Diffusion MRI noise mapping using random matrix theory publication-title: Magn Reson Med – volume: 202 start-page: 116137 year: 2019 article-title: MRtrix3: a fast, flexible and open software framework for medical image processing and visualisation publication-title: Neuroimage – volume: 35 start-page: 1459 year: 2007 end-page: 1472 article-title: Robust determination of the fibre orientation distribution in diffusion MRI: non‐negativity constrained super‐resolved spherical deconvolution publication-title: Neuroimage – volume: 77 start-page: 1797 year: 2017 end-page: 1809 article-title: “MASSIVE” brain dataset: multiple acquisitions for standardization of structural imaging validation and evaluation publication-title: Magn Reson Med – year: 2015 – volume: 61 start-page: 935 year: 2007 end-page: 945 article-title: Hypothalamic abnormalities in schizophrenia: sex effects and genetic vulnerability publication-title: Biol Psychiatry – volume: 225 start-page: 117406 year: 2020 article-title: MICRA: microstructural image compilation with repeated acquisitions publication-title: Neuroimage – volume: 142 start-page: 394 year: 2016 end-page: 406 article-title: Denoising of diffusion MRI using random matrix theory publication-title: Neuroimage – volume: 7 year: 2012 article-title: Tract specific reproducibility of tractography based morphology and diffusion metrics publication-title: PLoS One – volume: 225 start-page: 2387 year: 2020 end-page: 2402 article-title: Brain connections derived from diffusion MRI tractography can be highly anatomically accurate—if we know where white matter pathways start, where they end, and where they do not go publication-title: Brain Struct Funct – volume: 32 start-page: 1 year: 2019 end-page: 23 article-title: Building connectomes using diffusion MRI: why, how and but publication-title: NMR Biomed – volume: 125 start-page: 1063 year: 2016 end-page: 1078 article-title: An integrated approach to correction for off‐resonance effects and subject movement in diffusion MR imaging publication-title: Neuroimage – volume: 51 start-page: 1384 year: 2010 end-page: 1394 article-title: Identical, but not the same: intra‐site and inter‐site reproducibility of fractional anisotropy measures on two 3.0T scanners publication-title: Neuroimage – volume: 22 start-page: 185 year: 2011 end-page: 196 article-title: An introduction to diffusion tensor image analysis publication-title: Neurosurg Clin N Am – volume: 51 start-page: 234 year: 2020 end-page: 249 article-title: Tractography reproducibility challenge with empirical data (TraCED): the 2017 ISMRM diffusion study group challenge publication-title: J Magn Reson Imaging – volume: 7 start-page: 157 year: 2020 article-title: Multicenter dataset of multi‐shell diffusion MRI in healthy traveling adults with identical settings publication-title: Sci Data – volume: 57 start-page: 3083 year: 2008 end-page: 3089 article-title: Diffusion tensor imaging identifies deficits in white matter microstructure in subjects with type 1 diabetes that correlate with reduced neurocognitive function publication-title: Diabetes – volume: 170 start-page: 283 year: 2018 end-page: 295 article-title: Recognition of white matter bundles using local and global streamline‐based registration and clustering publication-title: Neuroimage – volume: 59 start-page: 2241 year: 2012 end-page: 2254 article-title: Compartment models of the diffusion MR signal in brain white matter: a taxonomy and comparison publication-title: Neuroimage – volume: 3 start-page: 160044 year: 2016 article-title: The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments publication-title: Sci Data – volume: 26 start-page: 1399 year: 2021 end-page: 1408 article-title: Variation of HbA1c affects cognition and white matter microstructure in healthy, young adults publication-title: Mol Psychiatry – volume: 1 start-page: 245 year: 2005 end-page: 251 article-title: The human connectome: a structural description of the human brain publication-title: PLoS Comput Biol – volume: 16 start-page: 222 year: 2017 end-page: 233 article-title: A test‐retest study on Parkinson’s PPMI dataset yields statistically significant white matter fascicles publication-title: NeuroImage Clin – year: 2006 – year: 2020 – volume: 36 start-page: 630 year: 2007 end-page: 644 article-title: Reproducibility of quantitative tractography methods applied to cerebral white matter publication-title: Neuroimage – volume: 20 start-page: 870 year: 2003 end-page: 888 article-title: How to correct susceptibility distortions in spin‐echo echo‐planar images: application to diffusion tensor imaging publication-title: Neuroimage – volume: 26 start-page: 756 year: 2010 end-page: 767 article-title: Effects of SNR on the accuracy and reproducibility of DTI‐derived fractional anisotropy, mean diffusivity, and principal Eigenvector measurements at 1.5T publication-title: J Magn Reson – volume: 217 start-page: 1 year: 2020 end-page: 15 article-title: XTRACT—standardised protocols for automated tractography in the human and macaque brain publication-title: Neuroimage – volume: 31 start-page: 968 year: 2006 end-page: 980 article-title: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest publication-title: Neuroimage – volume: 34 start-page: 51 year: 2008 end-page: 61 article-title: Diffusion tensor imaging (DTI)‐based white matter mapping in brain research: a review publication-title: J Mol Neurosci – volume: 37 start-page: 103 year: 1997 end-page: 111 article-title: An analytical model of restricted diffusion in bovine optic nerve publication-title: Magn Reson Med – volume: 141 start-page: 556 year: 2016 end-page: 572 article-title: Incorporating outlier detection and replacement into a non‐parametric framework for movement and distortion correction of diffusion MR images publication-title: Neuroimage – volume: 67 start-page: 298 year: 2013 end-page: 312 article-title: Spherical‐deconvolution informed filtering of tractograms publication-title: Neuroimage – volume: 223 start-page: 117329 year: 2020 article-title: Shape analysis of the human association pathways publication-title: Neuroimage – volume: 188 start-page: 598 year: 2019 end-page: 615 article-title: Using GPUs to accelerate computational diffusion MRI: from microstructure estimation to tractography and connectomes publication-title: Neuroimage – year: 2013 – volume: 12 start-page: 26 year: 2008 end-page: 41 article-title: Symmetric diffeomorphic image registration with cross‐correlation: evaluating automated labeling of elderly and neurodegenerative brain publication-title: Med Image Anal – ident: e_1_2_7_79_1 – ident: e_1_2_7_41_1 doi: 10.1016/j.neuroimage.2007.02.049 – ident: e_1_2_7_46_1 doi: 10.1016/j.neuroimage.2012.11.049 – ident: e_1_2_7_54_1 doi: 10.1007/s00234-019-02350-6 – ident: e_1_2_7_8_1 doi: 10.1007/s00429-020-02129-z – ident: e_1_2_7_75_1 doi: 10.1038/sdata.2016.44 – ident: e_1_2_7_29_1 doi: 10.1016/j.neuroimage.2015.10.019 – ident: e_1_2_7_14_1 doi: 10.1016/j.neuroscience.2018.03.048 – ident: e_1_2_7_25_1 doi: 10.1016/j.neuroimage.2016.08.016 – ident: e_1_2_7_61_1 doi: 10.1002/mrm.20426 – ident: e_1_2_7_42_1 doi: 10.1016/j.neuroimage.2007.07.053 – ident: e_1_2_7_52_1 doi: 10.1016/j.neuroimage.2010.03.046 – ident: e_1_2_7_11_1 doi: 10.1002/aur.1243 – ident: e_1_2_7_53_1 doi: 10.3174/ajnr.A5025 – ident: e_1_2_7_64_1 doi: 10.1007/s00429-017-1508-x – ident: e_1_2_7_23_1 doi: 10.1371/journal.pone.0236418 – ident: e_1_2_7_56_1 doi: 10.1371/journal.pone.0034125 – ident: e_1_2_7_40_1 doi: 10.1016/B978-044451741-8/50003-2 – ident: e_1_2_7_67_1 doi: 10.1016/j.neuroimage.2020.117329 – ident: e_1_2_7_76_1 doi: 10.1002/nbm.3269 – ident: e_1_2_7_21_1 doi: 10.1002/jmri.26197 – ident: e_1_2_7_57_1 doi: 10.1002/jmri.26794 – ident: e_1_2_7_60_1 doi: 10.1016/j.neuroimage.2018.12.015 – ident: e_1_2_7_26_1 doi: 10.1002/mrm.26059 – ident: e_1_2_7_68_1 doi: 10.1371/journal.pone.0080713 – ident: e_1_2_7_38_1 doi: 10.1016/j.neuroimage.2014.05.044 – ident: e_1_2_7_24_1 doi: 10.1002/mrm.28678 – ident: e_1_2_7_78_1 doi: 10.3389/fnins.2018.01055 – ident: e_1_2_7_66_1 doi: 10.1016/j.neuroimage.2020.116923 – ident: e_1_2_7_28_1 doi: 10.1016/S1053-8119(03)00336-7 – ident: e_1_2_7_36_1 doi: 10.1016/j.neuroimage.2013.05.028 – ident: e_1_2_7_22_1 doi: 10.1038/s41597-020-0493-8 – ident: e_1_2_7_9_1 doi: 10.1002/nbm.3752 – ident: e_1_2_7_63_1 doi: 10.1101/2020.06.12.148999 – ident: e_1_2_7_4_1 doi: 10.1016/S0006-3495(94)80775-1 – ident: e_1_2_7_37_1 – ident: e_1_2_7_16_1 doi: 10.1093/cercor/bhp280 – ident: e_1_2_7_73_1 doi: 10.1016/j.dcn.2018.03.001 – ident: e_1_2_7_48_1 doi: 10.1176/appi.ajp.162.7.1256 – ident: e_1_2_7_62_1 – ident: e_1_2_7_6_1 doi: 10.1002/mrm.1910370115 – ident: e_1_2_7_17_1 doi: 10.1016/j.neuroimage.2010.11.029 – ident: e_1_2_7_70_1 doi: 10.1002/hbm.24917 – ident: e_1_2_7_5_1 doi: 10.1016/j.neuroimage.2012.03.072 – ident: e_1_2_7_30_1 doi: 10.1016/j.neuroimage.2016.06.058 – ident: e_1_2_7_45_1 doi: 10.1016/j.neuroimage.2019.116137 – ident: e_1_2_7_20_1 doi: 10.1016/j.neuroimage.2020.117406 – ident: e_1_2_7_71_1 doi: 10.1371/journal.pbio.0060159 – ident: e_1_2_7_43_1 doi: 10.3389/fninf.2014.00008 – ident: e_1_2_7_32_1 doi: 10.1016/j.neuroimage.2009.10.003 – ident: e_1_2_7_27_1 doi: 10.1016/j.neuroimage.2019.06.039 – ident: e_1_2_7_58_1 doi: 10.1111/jon.12298 – ident: e_1_2_7_18_1 doi: 10.1002/mrm.26259 – volume: 26 start-page: 756 year: 2010 ident: e_1_2_7_34_1 article-title: Effects of SNR on the accuracy and reproducibility of DTI‐derived fractional anisotropy, mean diffusivity, and principal Eigenvector measurements at 1.5T publication-title: J Magn Reson – ident: e_1_2_7_35_1 doi: 10.2307/1932409 – ident: e_1_2_7_50_1 doi: 10.1016/j.biopsych.2006.06.027 – ident: e_1_2_7_55_1 – ident: e_1_2_7_77_1 doi: 10.1016/j.nicl.2017.07.020 – ident: e_1_2_7_13_1 doi: 10.2337/db08-0724 – ident: e_1_2_7_15_1 doi: 10.1038/s41380-019-0509-y – ident: e_1_2_7_31_1 doi: 10.1016/j.neuroimage.2017.07.015 – volume-title: Nonparametric Statistical Methods year: 2013 ident: e_1_2_7_33_1 – ident: e_1_2_7_44_1 doi: 10.1016/j.neuroimage.2007.02.016 – ident: e_1_2_7_47_1 doi: 10.1016/j.schres.2005.11.020 – ident: e_1_2_7_59_1 doi: 10.1016/j.media.2018.10.009 – ident: e_1_2_7_80_1 – ident: e_1_2_7_39_1 doi: 10.1016/j.media.2007.06.004 – ident: e_1_2_7_74_1 doi: 10.1016/j.neuroimage.2018.08.050 – ident: e_1_2_7_3_1 doi: 10.1007/s12031-007-0029-0 – ident: e_1_2_7_69_1 doi: 10.3389/fninf.2011.00023 – ident: e_1_2_7_65_1 doi: 10.3389/fnhum.2017.00306 – ident: e_1_2_7_12_1 doi: 10.1038/s41380-019-0504-3 – ident: e_1_2_7_19_1 doi: 10.1016/j.neuroimage.2013.05.041 – ident: e_1_2_7_51_1 doi: 10.1089/brain.2012.0112 – ident: e_1_2_7_2_1 doi: 10.1016/j.nec.2010.12.004 – ident: e_1_2_7_72_1 doi: 10.1371/journal.pcbi.0010042 – ident: e_1_2_7_49_1 doi: 10.1016/j.neuroimage.2006.01.021 – ident: e_1_2_7_10_1 doi: 10.1038/sdata.2017.10 – ident: e_1_2_7_7_1 doi: 10.1016/j.neuroimage.2011.09.081 |
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Diffusion‐weighted imaging allows investigators to identify structural, microstructural, and connectivity‐based differences between subjects, but... Diffusion-weighted imaging allows investigators to identify structural, microstructural, and connectivity-based differences between subjects, but variability... PurposeDiffusion‐weighted imaging allows investigators to identify structural, microstructural, and connectivity‐based differences between subjects, but... |
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| Title | MASiVar: Multisite, multiscanner, and multisubject acquisitions for studying variability in diffusion weighted MRI |
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