Effect of in-painting on cortical thickness measurements in multiple sclerosis: A large cohort study
A comprehensive analysis of the effect of lesion in‐painting on the estimation of cortical thickness using magnetic resonance imaging was performed on a large cohort of 918 relapsing‐remitting multiple sclerosis patients who participated in a phase III multicenter clinical trial. An automatic lesion...
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| Published in | Human brain mapping Vol. 36; no. 10; pp. 3749 - 3760 |
|---|---|
| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Blackwell Publishing Ltd
01.10.2015
John Wiley & Sons, Inc John Wiley and Sons Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1065-9471 1097-0193 1097-0193 |
| DOI | 10.1002/hbm.22875 |
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| Abstract | A comprehensive analysis of the effect of lesion in‐painting on the estimation of cortical thickness using magnetic resonance imaging was performed on a large cohort of 918 relapsing‐remitting multiple sclerosis patients who participated in a phase III multicenter clinical trial. An automatic lesion in‐painting algorithm was developed and implemented. Cortical thickness was measured using the FreeSurfer pipeline with and without in‐painting. The effect of in‐painting was evaluated using FreeSurfer's paired analysis pipeline. Multivariate regression analysis was also performed with field strength and lesion load as additional factors. Overall, the estimated cortical thickness was different with in‐painting than without. The effect of in‐painting was observed to be region dependent, more significant in the left hemisphere compared to the right, was more prominent at 1.5 T relative to 3 T, and was greater at higher lesion volumes. Our results show that even for data acquired at 1.5 T in patients with high lesion load, the mean cortical thickness difference with and without in‐painting is ∼2%. Based on these results, it appears that in‐painting has only a small effect on the estimated regional and global cortical thickness. Hum Brain Mapp 36:3749–3760, 2015. © 2015 Wiley Periodicals, Inc. |
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| AbstractList | A comprehensive analysis of the effect of lesion in‐painting on the estimation of cortical thickness using magnetic resonance imaging was performed on a large cohort of 918 relapsing‐remitting multiple sclerosis patients who participated in a phase III multicenter clinical trial. An automatic lesion in‐painting algorithm was developed and implemented. Cortical thickness was measured using the FreeSurfer pipeline with and without in‐painting. The effect of in‐painting was evaluated using FreeSurfer's paired analysis pipeline. Multivariate regression analysis was also performed with field strength and lesion load as additional factors. Overall, the estimated cortical thickness was different with in‐painting than without. The effect of in‐painting was observed to be region dependent, more significant in the left hemisphere compared to the right, was more prominent at 1.5 T relative to 3 T, and was greater at higher lesion volumes. Our results show that even for data acquired at 1.5 T in patients with high lesion load, the mean cortical thickness difference with and without in‐painting is ∼2%. Based on these results, it appears that in‐painting has only a small effect on the estimated regional and global cortical thickness.
Hum Brain Mapp 36:3749–3760, 2015
. ©
2015 Wiley Periodicals, Inc. A comprehensive analysis of the effect of lesion in‐painting on the estimation of cortical thickness using magnetic resonance imaging was performed on a large cohort of 918 relapsing‐remitting multiple sclerosis patients who participated in a phase III multicenter clinical trial. An automatic lesion in‐painting algorithm was developed and implemented. Cortical thickness was measured using the FreeSurfer pipeline with and without in‐painting. The effect of in‐painting was evaluated using FreeSurfer's paired analysis pipeline. Multivariate regression analysis was also performed with field strength and lesion load as additional factors. Overall, the estimated cortical thickness was different with in‐painting than without. The effect of in‐painting was observed to be region dependent, more significant in the left hemisphere compared to the right, was more prominent at 1.5 T relative to 3 T, and was greater at higher lesion volumes. Our results show that even for data acquired at 1.5 T in patients with high lesion load, the mean cortical thickness difference with and without in‐painting is ∼2%. Based on these results, it appears that in‐painting has only a small effect on the estimated regional and global cortical thickness. Hum Brain Mapp 36:3749–3760, 2015. © 2015 Wiley Periodicals, Inc. A comprehensive analysis of the effect of lesion in-painting on the estimation of cortical thickness using magnetic resonance imaging was performed on a large cohort of 918 relapsing-remitting multiple sclerosis patients who participated in a phase III multicenter clinical trial. An automatic lesion in-painting algorithm was developed and implemented. Cortical thickness was measured using the FreeSurfer pipeline with and without in-painting. The effect of in-painting was evaluated using FreeSurfer's paired analysis pipeline. Multivariate regression analysis was also performed with field strength and lesion load as additional factors. Overall, the estimated cortical thickness was different with in-painting than without. The effect of in-painting was observed to be region dependent, more significant in the left hemisphere compared to the right, was more prominent at 1.5 T relative to 3 T, and was greater at higher lesion volumes. Our results show that even for data acquired at 1.5 T in patients with high lesion load, the mean cortical thickness difference with and without in-painting is ∼2%. Based on these results, it appears that in-painting has only a small effect on the estimated regional and global cortical thickness. Hum Brain Mapp 36:3749-3760, 2015. © 2015 Wiley Periodicals, Inc.A comprehensive analysis of the effect of lesion in-painting on the estimation of cortical thickness using magnetic resonance imaging was performed on a large cohort of 918 relapsing-remitting multiple sclerosis patients who participated in a phase III multicenter clinical trial. An automatic lesion in-painting algorithm was developed and implemented. Cortical thickness was measured using the FreeSurfer pipeline with and without in-painting. The effect of in-painting was evaluated using FreeSurfer's paired analysis pipeline. Multivariate regression analysis was also performed with field strength and lesion load as additional factors. Overall, the estimated cortical thickness was different with in-painting than without. The effect of in-painting was observed to be region dependent, more significant in the left hemisphere compared to the right, was more prominent at 1.5 T relative to 3 T, and was greater at higher lesion volumes. Our results show that even for data acquired at 1.5 T in patients with high lesion load, the mean cortical thickness difference with and without in-painting is ∼2%. Based on these results, it appears that in-painting has only a small effect on the estimated regional and global cortical thickness. Hum Brain Mapp 36:3749-3760, 2015. © 2015 Wiley Periodicals, Inc. A comprehensive analysis of the effect of lesion in-painting on the estimation of cortical thickness using magnetic resonance imaging was performed on a large cohort of 918 relapsing-remitting multiple sclerosis patients who participated in a phase III multicenter clinical trial. An automatic lesion in-painting algorithm was developed and implemented. Cortical thickness was measured using the FreeSurfer pipeline with and without in-painting. The effect of in-painting was evaluated using FreeSurfer's paired analysis pipeline. Multivariate regression analysis was also performed with field strength and lesion load as additional factors. Overall, the estimated cortical thickness was different with in-painting than without. The effect of in-painting was observed to be region dependent, more significant in the left hemisphere compared to the right, was more prominent at 1.5 T relative to 3 T, and was greater at higher lesion volumes. Our results show that even for data acquired at 1.5 T in patients with high lesion load, the mean cortical thickness difference with and without in-painting is 2%. Based on these results, it appears that in-painting has only a small effect on the estimated regional and global cortical thickness. Hum Brain Mapp 36:3749-3760, 2015. © 2015 Wiley Periodicals, Inc. A comprehensive analysis of the effect of lesion in-painting on the estimation of cortical thickness using magnetic resonance imaging was performed on a large cohort of 918 relapsing-remitting multiple sclerosis patients who participated in a phase III multicenter clinical trial. An automatic lesion in-painting algorithm was developed and implemented. Cortical thickness was measured using the FreeSurfer pipeline with and without in-painting. The effect of in-painting was evaluated using FreeSurfer's paired analysis pipeline. Multivariate regression analysis was also performed with field strength and lesion load as additional factors. Overall, the estimated cortical thickness was different with in-painting than without. The effect of in-painting was observed to be region dependent, more significant in the left hemisphere compared to the right, was more prominent at 1.5 T relative to 3 T, and was greater at higher lesion volumes. Our results show that even for data acquired at 1.5 T in patients with high lesion load, the mean cortical thickness difference with and without in-painting is 2%. Based on these results, it appears that in-painting has only a small effect on the estimated regional and global cortical thickness. Hum Brain Mapp 36:3749-3760, 2015. copyright 2015 Wiley Periodicals, Inc. |
| Author | Datta, Sushmita Choi, Sangbum Cofield, Stacey S. Rahbar, Mohammad H Cutter, Gary R. Narayana, Ponnada A. Hasan, Khader M. Lublin, Fred D. Wolinsky, Jerry S. Govindarajan, Koushik A. |
| AuthorAffiliation | 5 Department of Neurology University of Texas Medical School at Houston Houston Texas 3 Department of Biostatistics University of Alabama at Birmingham Birmingham Alabama 4 The Corinne Goldsmith Dickinson Center for Multiple Sclerosis Mount Sinai School of Medicine New York New York 1 Department of Diagnostic and Interventional Imaging University of Texas Medical School at Houston Houston Texas 2 Division of Clinical and Translational Sciences Internal Medicine University of Texas Medical School at Houston Houston Texas |
| AuthorAffiliation_xml | – name: 2 Division of Clinical and Translational Sciences Internal Medicine University of Texas Medical School at Houston Houston Texas – name: 3 Department of Biostatistics University of Alabama at Birmingham Birmingham Alabama – name: 4 The Corinne Goldsmith Dickinson Center for Multiple Sclerosis Mount Sinai School of Medicine New York New York – name: 5 Department of Neurology University of Texas Medical School at Houston Houston Texas – name: 1 Department of Diagnostic and Interventional Imaging University of Texas Medical School at Houston Houston Texas |
| Author_xml | – sequence: 1 givenname: Koushik A. surname: Govindarajan fullname: Govindarajan, Koushik A. organization: Department of Diagnostic and Interventional Imaging, University of Texas Medical School at Houston, Texas, Houston – sequence: 2 givenname: Sushmita surname: Datta fullname: Datta, Sushmita organization: Department of Diagnostic and Interventional Imaging, University of Texas Medical School at Houston, Texas, Houston – sequence: 3 givenname: Khader M. surname: Hasan fullname: Hasan, Khader M. organization: Department of Diagnostic and Interventional Imaging, University of Texas Medical School at Houston, Texas, Houston – sequence: 4 givenname: Sangbum surname: Choi fullname: Choi, Sangbum organization: Division of Clinical and Translational Sciences, Internal Medicine, University of Texas Medical School at Houston, Texas, Houston – sequence: 5 givenname: Mohammad H surname: Rahbar fullname: Rahbar, Mohammad H organization: Division of Clinical and Translational Sciences, Internal Medicine, University of Texas Medical School at Houston, Texas, Houston – sequence: 6 givenname: Stacey S. surname: Cofield fullname: Cofield, Stacey S. organization: Department of Biostatistics, University of Alabama at Birmingham, Alabama, Birmingham – sequence: 7 givenname: Gary R. surname: Cutter fullname: Cutter, Gary R. organization: Department of Biostatistics, University of Alabama at Birmingham, Alabama, Birmingham – sequence: 8 givenname: Fred D. surname: Lublin fullname: Lublin, Fred D. organization: The Corinne Goldsmith Dickinson Center for Multiple Sclerosis, Mount Sinai School of Medicine, New York, New York – sequence: 9 givenname: Jerry S. surname: Wolinsky fullname: Wolinsky, Jerry S. organization: Department of Neurology, University of Texas Medical School at Houston, Texas, Houston – sequence: 10 givenname: Ponnada A. surname: Narayana fullname: Narayana, Ponnada A. email: Ponnada.a.narayana@uth.tmc.edu organization: Department of Diagnostic and Interventional Imaging, University of Texas Medical School at Houston, Texas, Houston |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26096844$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1148_radiol_2019191061 crossref_primary_10_1002_acn3_52237 crossref_primary_10_1007_s00234_022_03041_5 crossref_primary_10_1590_0004_282x20170072 crossref_primary_10_1007_s00415_023_11870_4 crossref_primary_10_1007_s00429_022_02498_7 crossref_primary_10_1111_ene_13923 crossref_primary_10_1007_s13311_016_0479_6 crossref_primary_10_1111_jon_12611 crossref_primary_10_1016_j_neuroimage_2016_07_035 crossref_primary_10_1038_s41598_022_08477_6 crossref_primary_10_1097_MD_0000000000003208 crossref_primary_10_1093_braincomms_fcad153 crossref_primary_10_3389_fneur_2020_00021 |
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| Copyright | 2015 Wiley Periodicals, Inc. |
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| Keywords | multiple sclerosis lesions FreeSurfer cortical thickness multiple sclerosis lesion in-painting |
| Language | English |
| License | http://onlinelibrary.wiley.com/termsAndConditions#vor 2015 Wiley Periodicals, Inc. |
| LinkModel | DirectLink |
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| Notes | NINDS/NIH (CombiRx trial) - No. U01 NS045719 istex:3713FF85D9D2C2267A74491A2859E2EA48B5A64C NIBIB/NIH (Image segmentation) - No. 2 R01 EB02095 ArticleID:HBM22875 ark:/67375/WNG-SKX3LXX5-D NINDS/NIH - No. R01NS078244 CombiRx Investigators Group: M. Agius, Sacramento, CA; K. Bashir, Birmingham, AL; R. Baumhefner, Los Angeles, CA; G. Birnbaum, Golden Valley, MN; G. Blevins, Edmonton, AB, Canada; R. Bomprezzi, Phoenix, AZ; A. Boster, Columbus, OH; T. Brown, Kirkland, WA; J. Burkholder, Canton, OH; A. Camac, Lexington, MA; D. Campagnolo, Phoenix, AZ; J. Carter, Scottsdale, AZ; B. Cohen, Chicago, IL; J. Cooper, Berkeley, CA; J. Corboy, Aurora, CO; A. Cross, Saint Louis, MO; L. Dewitt, Salt Lake City, UT; J. Dunn, Kirkland, WA; K. Edwards, Latham, NY; E. Eggenberger, East Lansing, MI; J. English, Atlanta, GA; W. Felton, Richmond, VA; P. Fodor, Colorado Springs, CO; C. Ford, Albuquerque, NM; M. Freedman, Ottawa, Ontario, Canada; S. Galetta, Philadelphia, PA; G. Garmany, Boulder, CO; A. Goodman, Rochester, NY; M. Gottesman, Mineola, NY; C. Gottschalk, New Haven, CT; M. Gruental, Albany, NY; M. Gudesblatt, Patchogue, NY; R. Hamill, Burlington, VT; J. Herbert, New York, NY; R. Holub, Albany, NY; W. Honeycutt, Maitland, FL; B. Hughes, Des Moines, IA; G. Hutton, Houston, TX; D. Jacobs, Philadelphia, PA; K. Johnson, Baltimore, MD; L. Kasper, Lebanon, NH; J. Kattah, Peoria, IL; M. Kaufman, Charlotte, NC; M. Keegan, Rochester, NY; O. Khan, Detroit, MI; B. Khatri, Milwaukee, WI; M. Kita, Seattle, WA; B. Koffman, Toledo, OH; E. Lallana, Lebanon, NH; N. Lava, Albany, NY; J. Lindsey, Houston, TX; P. Loge, Billings, MT; S. Lynch, Kansas City, KS; F. McGee, Richmond, VA; L. Mejico, Syracuse, NY; L. Metz, Calgary, AB, Canada; P. O'Connor, Toronto, ON, Canada; K. Pandey, Albany, NY; H. Panitch, Burlington, VT; J. Preiningerova, New Haven, CT; K. Rammohan, Columbus, OH; C. Riley, New Haven, CT; P. Riskind, Worcester, MA; L. Rolak, Marshfield, WI; W. Royal, Baltimore, MD; S. Scarberry, Fargo, ND; A. Schulman, Richmond, VA; T. Scott, Pittsburgh, PA; C. Sheppard, Uniontown, OH; W. Sheremata, Miami, FL; L. Stone, Cleveland, OH; W. Stuart, Atlanta, GA; S. Subramaniam, Nashville, TN; V. Thadani, Lebanon, NH; F. Thomas, Saint Louis, MO; B. Thrower, Atlanta, GA; M. Tullman, New York, NY; A. Turel, Danville, PA; T. Vollmer, Phoenix, AZ; S. Waldman, La Habra, CA; B. Weinstock‐Guttman, Buffalo, NY; J. Wendt, Tucson, AZ; R. Williams, Billings, MT; D. Wynn, Northbrook, IL; M. Yeung, Calgary, AB Canada. Koushik A. Govindarajan and Sushmita Datta contributed equally to this work. MRI Analysis Center: JS Wolinsky, PA Narayana, F Nelson, I Vainrub, S Datta, R He, B Gates, K Ton. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 ObjectType-Undefined-3 |
| OpenAccessLink | https://proxy.k.utb.cz/login?url=http://doi.org/10.1002/hbm.22875 |
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| PublicationCentury | 2000 |
| PublicationDate | October 2015 |
| PublicationDateYYYYMMDD | 2015-10-01 |
| PublicationDate_xml | – month: 10 year: 2015 text: October 2015 |
| PublicationDecade | 2010 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: San Antonio – name: Hoboken |
| PublicationTitle | Human brain mapping |
| PublicationTitleAlternate | Hum. Brain Mapp |
| PublicationYear | 2015 |
| Publisher | Blackwell Publishing Ltd John Wiley & Sons, Inc John Wiley and Sons Inc |
| Publisher_xml | – name: Blackwell Publishing Ltd – name: John Wiley & Sons, Inc – name: John Wiley and Sons Inc |
| References | Datta S, Sajja BR, He R, Wolinsky JS, Gupta RK, Narayana PA (2006): Segmentation and quantification of black holes in multiple sclerosis. Neuroimage 29:467-474. Calabrese M, Rinaldi F, Mattisi I, Grossi P, Favaretto A, Atzori M, Bernardi V, Barachino L, Romualdi C, Rinaldi L, Perini P, Gallo P (2010): Widespread cortical thinning characterizes patients with MS with mild cognitive impairment. Neurology 74:321-328. Lemaitre H, Goldman AL, Sambataro F, Verchinski BA, Meyer-Lindenberg A, Weinberger DR, Mattay VS (2012): Normal age-related brain morphometric changes: Nonuniformity across cortical thickness, surface area and gray matter volume? Neurobiol Aging 33:617.e1-617.e9. Calabrese M, Grossi P, Favaretto A, Romualdi C, Atzori M, Rinaldi F, Perini P, Saladini M, Gallo P (2012): Cortical pathology in multiple sclerosis patients with epilepsy: A 3 year longitudinal study. J Neurol Neurosurg Psychiatry 83:49-54. Dale AM, Fischl B, Sereno MI (1999): Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9:179-194. Ballester C, Bertalmio M, Caselles V, Sapiro G, Verdera J (2001): Filling-in by joint interpolation of vector fields and gray levels. IEEE Trans Image Process 10:1200-1211. Charil A, Dagher A, Lerch JP, Zijdenbos AP, Worsley K, Evans AC (2007): Focal cortical atrophy in multiple sclerosis: Relation to lesion load and disability. Neuroimage 34:509-517. Lindsey JW, Scott TF, Lynch SG, Cofield SS, Nelson, F, Conwit R, et al. (2012): The CombiRx trial of combined therapy with interferon and glatiramer acetate in relapsing remitting MS: Design and baseline characteristics. Mult Scler Relat Disord 1:81-86. Ségonne F, Dale AM, Busa E, Glessner M, Salat D, Hahn HK, Fischl B (2004): A hybrid approach to the skull stripping problem in MRI. Neuroimage 22:1060-1075. Ceccarelli A, Jackson JS, Tauhid S, Arora A, Gorky J, Dell'Oglio E, Bakshi A, Chitnis T, Khoury SJ, Weiner HL, Guttmann CR, Bakshi R, Neema M (2012): The impact of lesion in-painting and registration methods on voxel-based morphometry in detecting regional cerebral gray matter atrophy in multiple sclerosis. Am J Neuroradiol 33:1579-1585. Holland CM, Charil A, Csapo I, Liptak Z, Ichise M, Khoury SJ, Bakshi R, Weiner HL, Guttmann CR (2012): The relationship between normal cerebral perfusion patterns and white matter lesion distribution in 1249 patients with multiple sclerosis. J Neuroimaging 22:129-136. Dickerson BC, Feczko E, Augustinack JC, Pacheco J, Morris JC, Fischl B, Buckner RL (2009): Differential effects of aging and Alzheimer's disease on medial temporal lobe cortical thickness and surface area. Neurobiol Aging 30:432-440. Fischl B, Dale AM (2000): Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci USA 97:11050-11055. Olson CL (1974): Comparative robustness of six tests in multivariate analysis of variance. J Am Stat Assoc 69:894-908. Collins DL, Neelin P, Peters TM, Evans AC (1994): Automatic 3-D intersubject registration of MR volumetric data in standardized Talairach space. J Comput Assist Tomogr 18:192-205. Geurts JJ, Pouwels PJ, Uitdehaag BM, Polman CH, Barkhof F, Castelijns JA (2005): Intracortical lesions in multiple sclerosis: Improved detection with 3D double inversion-recovery MR imaging. Radiology 236:254-260. Fischl B, Lui A, Dale AM (2001): Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex. IEEE Transactions on Medical Imaging 20:70-80. Lu H, Nagae-Poetscher LM, Golay X, Lin D, van Pomper M, Zijl PCM (2005): Routine clinical brain MRI sequences for use at 3.0 Tesla. J Magn Reson Imaging 22:13-22. Bock NA, Hashim E, Janik R, Konyer NB, Weiss M, Stanisz GJ, Turner R, Geyer S (2013): Optimizing T1-weighted imaging of cortical myelin content at 3.0 T. Neuroimage 65:1-12. Battaglini M, Jenkinson M, De Stefano N (2012): Evaluating and reducing the impact of white matter lesions on brain volume measurements. Hum Brain Mapp 33:2062-2071. Fischl B, Sereno MI, Dale AM (1999): Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage 9:195-207. Sdika M, Pelletier D (2009): Nonrigid registration of multiple sclerosis brain images using lesion inpainting for morphometry or lesion mapping. Hum Brain Mapp 30:1060-1067. Hand DJ, Taylor CC (1987): Multivariate Analysis of Variance and Repeated Measures. London, UK: Chapman and Hall. Wonderlick JS, Ziegler DA, Hosseini-Varnamkhasti P, Locascio JJ, Bakkour A, van der Kouwe A, Triantafyllou C, Corkin S, Dickerson BC (2009): Reliability of MRI-derived cortical and subcortical morphometric measures: Effects of pulse sequence, voxel geometry and parallel imaging. Neuroimage 44:1324-1333. Bottomley PA, Foster TH, Argersinger RE, Pfeifer LH (1984): A review of normal tissue hydrogen NMR relaxation times and relaxation mechanisms from 1-100 MHz: Dependence on tissue type, NMR frequency, temperature, species, excision, and age. Med Phys 11:425-448. Han X, Jovicich J, Salat D, van der Kouwe A, Quinn B, Czanner S, Busa E, Pacheco J, Albert M, Killiany R, Maguire P, Rosas D, Makris N, Dale A, Dickerson B, Fischl B (2006): Reliability of MRI-derived measurements of human cerebral cortical thickness: The effects of field strength, scanner upgrade and manufacturer. Neuroimage 32:180-194. Datta S, Sajja BR, He R, Gupta RK, Wolinsky JS, Narayana PA (2007): Segmentation of gadolinium-enhanced lesions on MRI in multiple sclerosis. J Magn Reson Imaging 25:932-937. Plessan KJ, Hugdahl K, Bansal R, Hao X, Peterson B (2014): Sex, age, and cognitive correlates of asymmetries in thickness of the cortical mantle across the life span. J Neurosci 34:6294-6302. Ramasamy DP, Benedict RH, Cox JL, Fritz D, Abdelrahman N, Hussein S, Minagar A, Dwyer MG, Zivadinov R (2009): Extent of cerebellum, subcortical and cortical atrophy in patients with MS: A case-control study. J Neurol Sci 282:47-54. Sajja BR, Datta S, He R, Mehta M, Gupta RK, Wolinsky JS, Narayana PA (2006): Unified approach for multiple sclerosis lesion segmentation on brain MRI. Ann Biomed Eng 34:142-151. Chard DT, Jackson JS, Miller DH, Wheeler-Kingshott CA (2010): Reducing the impact of white matter lesions on automated measures of brain gray and white matter volumes. J Magn Reson Imaging 32:223-228. Desikan RS, Segonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ (2006): An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31:968-980. Shiee N, Bazin PL, Cuzzocreo JL, Ye C, Kishore B, Carass A, Calabresi PA, Reich DS, Prince JL, Pham DL (2014): Reconstruction of the human cerebral cortex robust to white matter lesions: Method and validation. Hum Brain Mapp 35:3385-3401. Narayana PA, Govindarajan KA, Goel P, Datta S, Lincoln JA, Cofield SS, Cutter GR, Lublin FD, Wolinsky JS, the CombiRX Investigators Group (2013): Regional cortical thickness in relapsing remitting multiple sclerosis: A multi-center study. Neuroimage Clin 2:120-131. Olson CL (1976): On choosing a test statistic in multivariate analysis of variance. Psychol Bull 83:579-586. Govindarajan KA, Freeman L, Cai C, Rahbar MH, Narayana PA (2014): Effect of intrinsic and extrinsic factors on global and regional cortical thickness. PLoS One 9:e96429. Magon S, Gaetano L, Chakravarthy MM, Lerch JP, Naegelin Y, Stippich C, Kappos L, Radue EW, Sprenger T (2014): White matter lesion filling improves the accuracy of cortical thickness measurements in multiple sclerosis patients: A longitudinal study. BMC Neurosci 15:106-115. Smith SM (2002): Fast robust automated brain extraction. Hum Brain Mapp 17:143-155. Nelson F, Datta S, Garcia N, Rozario NL, Perez F, Cutter G, Narayana PA, Wolinsky JS (2011): Intracortical lesions by 3T magnetic resonance imaging and correlation with cognitive impairment in multiple sclerosis. Mult Scler 17:1122-1129. Sailer M, Fischl B, Salat D, Tempelmann C, Schönfeld MA, Busa E, Bodammer N, Heinze HJ, Dale A (2003): Focal thinning of the cerebral cortex in multiple sclerosis. Brain 126:1734-1744. Thompson PM, Toga AW (2003): Mapping brain asymmetry. Nat Rev Neurosci 4:37-48. 2012; 83 2004; 22 2002; 17 2009; 44 2010; 32 2006; 31 2013; 2 2006; 34 2013; 65 2006; 32 1976; 83 2005; 236 2011; 17 2012; 33 2007; 34 2005; 22 2001; 20 1999; 9 1974; 69 2009; 30 2012; 1 1984; 11 2000; 97 1987 2014; 15 2006; 29 2003; 4 2014; 35 2009; 282 1994; 18 2014 2014; 9 2003; 126 2012; 22 2010; 74 2014; 34 2007; 25 2001; 10 1988 e_1_2_5_27_1 e_1_2_5_28_1 e_1_2_5_25_1 e_1_2_5_26_1 e_1_2_5_23_1 e_1_2_5_24_1 e_1_2_5_21_1 e_1_2_5_44_1 e_1_2_5_22_1 e_1_2_5_43_1 e_1_2_5_29_1 e_1_2_5_42_1 e_1_2_5_20_1 e_1_2_5_41_1 e_1_2_5_40_1 e_1_2_5_15_1 e_1_2_5_38_1 e_1_2_5_14_1 e_1_2_5_39_1 e_1_2_5_17_1 e_1_2_5_36_1 e_1_2_5_9_1 e_1_2_5_16_1 e_1_2_5_37_1 e_1_2_5_8_1 e_1_2_5_11_1 e_1_2_5_34_1 e_1_2_5_7_1 e_1_2_5_10_1 e_1_2_5_35_1 e_1_2_5_6_1 e_1_2_5_13_1 e_1_2_5_32_1 e_1_2_5_5_1 e_1_2_5_12_1 e_1_2_5_33_1 e_1_2_5_4_1 e_1_2_5_3_1 e_1_2_5_2_1 e_1_2_5_19_1 e_1_2_5_18_1 e_1_2_5_30_1 e_1_2_5_31_1 21543552 - Mult Scler. 2011 Sep;17(9):1122-9 12511860 - Nat Rev Neurosci. 2003 Jan;4(1):37-48 19038349 - Neuroimage. 2009 Feb 15;44(4):1324-33 15987979 - Radiology. 2005 Jul;236(1):254-60 20575080 - J Magn Reson Imaging. 2010 Jul;32(1):223-8 16530430 - Neuroimage. 2006 Jul 1;31(3):968-80 17869384 - Neurobiol Aging. 2009 Mar;30(3):432-40 16525763 - Ann Biomed Eng. 2006 Jan;34(1):142-51 9931268 - Neuroimage. 1999 Feb;9(2):179-94 19201003 - J Neurol Sci. 2009 Jul 15;282(1-2):47-54 12391568 - Hum Brain Mapp. 2002 Nov;17(3):143-55 20739099 - Neurobiol Aging. 2012 Mar;33(3):617.e1-9 24789100 - PLoS One. 2014;9(5):e96429 11293693 - IEEE Trans Med Imaging. 2001 Jan;20(1):70-80 12805100 - Brain. 2003 Aug;126(Pt 8):1734-44 15971174 - J Magn Reson Imaging. 2005 Jul;22(1):13-22 9931269 - Neuroimage. 1999 Feb;9(2):195-207 16126416 - Neuroimage. 2006 Jan 15;29(2):467-74 24382742 - Hum Brain Mapp. 2014 Jul;35(7):3385-401 8126267 - J Comput Assist Tomogr. 1994 Mar-Apr;18(2):192-205 25200127 - BMC Neurosci. 2014;15:106 23036446 - Neuroimage. 2013 Jan 15;65:1-12 25876935 - Mult Scler Relat Disord. 2012 Apr;1(2):81-6 6482839 - Med Phys. 1984 Jul-Aug;11(4):425-48 15219578 - Neuroimage. 2004 Jul;22(3):1060-75 10984517 - Proc Natl Acad Sci U S A. 2000 Sep 26;97(20):11050-5 18412131 - Hum Brain Mapp. 2009 Apr;30(4):1060-7 21890577 - J Neurol Neurosurg Psychiatry. 2012 Jan;83(1):49-54 16651008 - Neuroimage. 2006 Aug 1;32(1):180-94 21447022 - J Neuroimaging. 2012 Apr;22(2):129-36 24790200 - J Neurosci. 2014 Apr 30;34(18):6294-302 21882300 - Hum Brain Mapp. 2012 Sep;33(9):2062-71 17112743 - Neuroimage. 2007 Jan 15;34(2):509-17 18255537 - IEEE Trans Image Process. 2001;10(8):1200-11 20101038 - Neurology. 2010 Jan 26;74(4):321-8 22460341 - AJNR Am J Neuroradiol. 2012 Sep;33(8):1579-85 17457804 - J Magn Reson Imaging. 2007 May;25(5):932-7 24179765 - Neuroimage Clin. 2012 Nov 30;2:120-31 |
| References_xml | – reference: Bottomley PA, Foster TH, Argersinger RE, Pfeifer LH (1984): A review of normal tissue hydrogen NMR relaxation times and relaxation mechanisms from 1-100 MHz: Dependence on tissue type, NMR frequency, temperature, species, excision, and age. Med Phys 11:425-448. – reference: Chard DT, Jackson JS, Miller DH, Wheeler-Kingshott CA (2010): Reducing the impact of white matter lesions on automated measures of brain gray and white matter volumes. J Magn Reson Imaging 32:223-228. – reference: Fischl B, Dale AM (2000): Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci USA 97:11050-11055. – reference: Bock NA, Hashim E, Janik R, Konyer NB, Weiss M, Stanisz GJ, Turner R, Geyer S (2013): Optimizing T1-weighted imaging of cortical myelin content at 3.0 T. Neuroimage 65:1-12. – reference: Sailer M, Fischl B, Salat D, Tempelmann C, Schönfeld MA, Busa E, Bodammer N, Heinze HJ, Dale A (2003): Focal thinning of the cerebral cortex in multiple sclerosis. Brain 126:1734-1744. – reference: Sajja BR, Datta S, He R, Mehta M, Gupta RK, Wolinsky JS, Narayana PA (2006): Unified approach for multiple sclerosis lesion segmentation on brain MRI. Ann Biomed Eng 34:142-151. – reference: Thompson PM, Toga AW (2003): Mapping brain asymmetry. Nat Rev Neurosci 4:37-48. – reference: Calabrese M, Rinaldi F, Mattisi I, Grossi P, Favaretto A, Atzori M, Bernardi V, Barachino L, Romualdi C, Rinaldi L, Perini P, Gallo P (2010): Widespread cortical thinning characterizes patients with MS with mild cognitive impairment. Neurology 74:321-328. – reference: Wonderlick JS, Ziegler DA, Hosseini-Varnamkhasti P, Locascio JJ, Bakkour A, van der Kouwe A, Triantafyllou C, Corkin S, Dickerson BC (2009): Reliability of MRI-derived cortical and subcortical morphometric measures: Effects of pulse sequence, voxel geometry and parallel imaging. Neuroimage 44:1324-1333. – reference: Lemaitre H, Goldman AL, Sambataro F, Verchinski BA, Meyer-Lindenberg A, Weinberger DR, Mattay VS (2012): Normal age-related brain morphometric changes: Nonuniformity across cortical thickness, surface area and gray matter volume? Neurobiol Aging 33:617.e1-617.e9. – reference: Smith SM (2002): Fast robust automated brain extraction. Hum Brain Mapp 17:143-155. – reference: Calabrese M, Grossi P, Favaretto A, Romualdi C, Atzori M, Rinaldi F, Perini P, Saladini M, Gallo P (2012): Cortical pathology in multiple sclerosis patients with epilepsy: A 3 year longitudinal study. J Neurol Neurosurg Psychiatry 83:49-54. – reference: Nelson F, Datta S, Garcia N, Rozario NL, Perez F, Cutter G, Narayana PA, Wolinsky JS (2011): Intracortical lesions by 3T magnetic resonance imaging and correlation with cognitive impairment in multiple sclerosis. Mult Scler 17:1122-1129. – reference: Han X, Jovicich J, Salat D, van der Kouwe A, Quinn B, Czanner S, Busa E, Pacheco J, Albert M, Killiany R, Maguire P, Rosas D, Makris N, Dale A, Dickerson B, Fischl B (2006): Reliability of MRI-derived measurements of human cerebral cortical thickness: The effects of field strength, scanner upgrade and manufacturer. Neuroimage 32:180-194. – reference: Collins DL, Neelin P, Peters TM, Evans AC (1994): Automatic 3-D intersubject registration of MR volumetric data in standardized Talairach space. J Comput Assist Tomogr 18:192-205. – reference: Ballester C, Bertalmio M, Caselles V, Sapiro G, Verdera J (2001): Filling-in by joint interpolation of vector fields and gray levels. IEEE Trans Image Process 10:1200-1211. – reference: Dickerson BC, Feczko E, Augustinack JC, Pacheco J, Morris JC, Fischl B, Buckner RL (2009): Differential effects of aging and Alzheimer's disease on medial temporal lobe cortical thickness and surface area. Neurobiol Aging 30:432-440. – reference: Olson CL (1974): Comparative robustness of six tests in multivariate analysis of variance. J Am Stat Assoc 69:894-908. – reference: Datta S, Sajja BR, He R, Gupta RK, Wolinsky JS, Narayana PA (2007): Segmentation of gadolinium-enhanced lesions on MRI in multiple sclerosis. J Magn Reson Imaging 25:932-937. – reference: Desikan RS, Segonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ (2006): An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31:968-980. – reference: Olson CL (1976): On choosing a test statistic in multivariate analysis of variance. Psychol Bull 83:579-586. – reference: Geurts JJ, Pouwels PJ, Uitdehaag BM, Polman CH, Barkhof F, Castelijns JA (2005): Intracortical lesions in multiple sclerosis: Improved detection with 3D double inversion-recovery MR imaging. Radiology 236:254-260. – reference: Magon S, Gaetano L, Chakravarthy MM, Lerch JP, Naegelin Y, Stippich C, Kappos L, Radue EW, Sprenger T (2014): White matter lesion filling improves the accuracy of cortical thickness measurements in multiple sclerosis patients: A longitudinal study. BMC Neurosci 15:106-115. – reference: Shiee N, Bazin PL, Cuzzocreo JL, Ye C, Kishore B, Carass A, Calabresi PA, Reich DS, Prince JL, Pham DL (2014): Reconstruction of the human cerebral cortex robust to white matter lesions: Method and validation. Hum Brain Mapp 35:3385-3401. – reference: Govindarajan KA, Freeman L, Cai C, Rahbar MH, Narayana PA (2014): Effect of intrinsic and extrinsic factors on global and regional cortical thickness. PLoS One 9:e96429. – reference: Dale AM, Fischl B, Sereno MI (1999): Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9:179-194. – reference: Lu H, Nagae-Poetscher LM, Golay X, Lin D, van Pomper M, Zijl PCM (2005): Routine clinical brain MRI sequences for use at 3.0 Tesla. J Magn Reson Imaging 22:13-22. – reference: Narayana PA, Govindarajan KA, Goel P, Datta S, Lincoln JA, Cofield SS, Cutter GR, Lublin FD, Wolinsky JS, the CombiRX Investigators Group (2013): Regional cortical thickness in relapsing remitting multiple sclerosis: A multi-center study. Neuroimage Clin 2:120-131. – reference: Ceccarelli A, Jackson JS, Tauhid S, Arora A, Gorky J, Dell'Oglio E, Bakshi A, Chitnis T, Khoury SJ, Weiner HL, Guttmann CR, Bakshi R, Neema M (2012): The impact of lesion in-painting and registration methods on voxel-based morphometry in detecting regional cerebral gray matter atrophy in multiple sclerosis. Am J Neuroradiol 33:1579-1585. – reference: Plessan KJ, Hugdahl K, Bansal R, Hao X, Peterson B (2014): Sex, age, and cognitive correlates of asymmetries in thickness of the cortical mantle across the life span. J Neurosci 34:6294-6302. – reference: Lindsey JW, Scott TF, Lynch SG, Cofield SS, Nelson, F, Conwit R, et al. (2012): The CombiRx trial of combined therapy with interferon and glatiramer acetate in relapsing remitting MS: Design and baseline characteristics. Mult Scler Relat Disord 1:81-86. – reference: Datta S, Sajja BR, He R, Wolinsky JS, Gupta RK, Narayana PA (2006): Segmentation and quantification of black holes in multiple sclerosis. Neuroimage 29:467-474. – reference: Ramasamy DP, Benedict RH, Cox JL, Fritz D, Abdelrahman N, Hussein S, Minagar A, Dwyer MG, Zivadinov R (2009): Extent of cerebellum, subcortical and cortical atrophy in patients with MS: A case-control study. J Neurol Sci 282:47-54. – reference: Ségonne F, Dale AM, Busa E, Glessner M, Salat D, Hahn HK, Fischl B (2004): A hybrid approach to the skull stripping problem in MRI. Neuroimage 22:1060-1075. – reference: Battaglini M, Jenkinson M, De Stefano N (2012): Evaluating and reducing the impact of white matter lesions on brain volume measurements. Hum Brain Mapp 33:2062-2071. – reference: Sdika M, Pelletier D (2009): Nonrigid registration of multiple sclerosis brain images using lesion inpainting for morphometry or lesion mapping. 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| Title | Effect of in-painting on cortical thickness measurements in multiple sclerosis: A large cohort study |
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