Removing inter-subject technical variability in magnetic resonance imaging studies

Magnetic resonance imaging (MRI) intensities are acquired in arbitrary units, making scans non-comparable across sites and between subjects. Intensity normalization is a first step for the improvement of comparability of the images across subjects. However, we show that unwanted inter-scan variabili...

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Published in:NeuroImage (Orlando, Fla.) Vol. 132; pp. 198 - 212
Main Authors: Fortin, Jean-Philippe, Sweeney, Elizabeth M., Muschelli, John, Crainiceanu, Ciprian M., Shinohara, Russell T.
Format: Journal Article
Language:English
Published: United States Elsevier Inc 15.05.2016
Elsevier Limited
Subjects:
ADNI
Algorithms
Alzheimer Disease - diagnostic imaging
Alzheimer Disease - pathology
Alzheimer's disease
Biomarkers
Brain - anatomy & histology
Brain - pathology
Datasets
Female
Gene expression
Humans
Image Processing, Computer-Assisted
Linear Models
Magnetic Resonance Imaging - methods
Male
Medical imaging
Methods
MRI
Neuroimaging - methods
NMR
Normalization
Nuclear magnetic resonance
Reproducibility of Results
ROC Curve
Scan effect
Scanners
Signal Processing, Computer-Assisted
Studies
ADNI
Normalization
MRI
Alzheimer's disease
Scan effect
ISSN:1053-8119, 1095-9572
Online Access:Get full text
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Abstract Magnetic resonance imaging (MRI) intensities are acquired in arbitrary units, making scans non-comparable across sites and between subjects. Intensity normalization is a first step for the improvement of comparability of the images across subjects. However, we show that unwanted inter-scan variability associated with imaging site, scanner effect, and other technical artifacts is still present after standard intensity normalization in large multi-site neuroimaging studies. We propose RAVEL (Removal of Artificial Voxel Effect by Linear regression), a tool to remove residual technical variability after intensity normalization. As proposed by SVA and RUV [Leek and Storey, 2007, 2008, Gagnon-Bartsch and Speed, 2012], two batch effect correction tools largely used in genomics, we decompose the voxel intensities of images registered to a template into a biological component and an unwanted variation component. The unwanted variation component is estimated from a control region obtained from the cerebrospinal fluid (CSF), where intensities are known to be unassociated with disease status and other clinical covariates. We perform a singular value decomposition (SVD) of the control voxels to estimate factors of unwanted variation. We then estimate the unwanted factors using linear regression for every voxel of the brain and take the residuals as the RAVEL-corrected intensities. We assess the performance of RAVEL using T1-weighted (T1-w) images from more than 900 subjects with Alzheimer's disease (AD) and mild cognitive impairment (MCI), as well as healthy controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We compare RAVEL to two intensity-normalization-only methods: histogram matching and White Stripe. We show that RAVEL performs best at improving the replicability of the brain regions that are empirically found to be most associated with AD, and that these regions are significantly more present in structures impacted by AD (hippocampus, amygdala, parahippocampal gyrus, enthorinal area, and fornix stria terminals). In addition, we show that the RAVEL-corrected intensities have the best performance in distinguishing between MCI subjects and healthy subjects using the mean hippocampal intensity (AUC=67%), a marked improvement compared to results from intensity normalization alone (AUC=63% and 59% for histogram matching and White Stripe, respectively). RAVEL is promising for many other imaging modalities. •Between-scan unwanted variation is still present after intensity normalization.•We propose a scan-effect removal tool for removing post-normalization artifacts.•We model the unwanted variability between MRI T1-w scans using CSF region.•We use a large subset of the ADNI database to showcase our method.•We show that our method improves replicability of the voxels associated with AD.•MRI intensities corrected by our method improves prediction of AD and MCI.
AbstractList Magnetic resonance imaging (MRI) intensities are acquired in arbitrary units, making scans non-comparable across sites and between subjects. Intensity normalization is a first step for the improvement of comparability of the images across subjects. However, we show that unwanted inter-scan variability associated with imaging site, scanner effect, and other technical artifacts is still present after standard intensity normalization in large multi-site neuroimaging studies. We propose RAVEL (Removal of Artificial Voxel Effect by Linear regression), a tool to remove residual technical variability after intensity normalization. As proposed by SVA and RUV [Leek and Storey, 2007, 2008, Gagnon-Bartsch and Speed, 2012], two batch effect correction tools largely used in genomics, we decompose the voxel intensities of images registered to a template into a biological component and an unwanted variation component. The unwanted variation component is estimated from a control region obtained from the cerebrospinal fluid (CSF), where intensities are known to be unassociated with disease status and other clinical covariates. We perform a singular value decomposition (SVD) of the control voxels to estimate factors of unwanted variation. We then estimate the unwanted factors using linear regression for every voxel of the brain and take the residuals as the RAVEL-corrected intensities. We assess the performance of RAVEL using T1-weighted (T1-w) images from more than 900 subjects with Alzheimer's disease (AD) and mild cognitive impairment (MCI), as well as healthy controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We compare RAVEL to two intensity-normalization-only methods: histogram matching and White Stripe. We show that RAVEL performs best at improving the replicability of the brain regions that are empirically found to be most associated with AD, and that these regions are significantly more present in structures impacted by AD (hippocampus, amygdala, parahippocampal gyrus, enthorinal area, and fornix stria terminals). In addition, we show that the RAVEL-corrected intensities have the best performance in distinguishing between MCI subjects and healthy subjects using the mean hippocampal intensity (AUC=67%), a marked improvement compared to results from intensity normalization alone (AUC=63% and 59% for histogram matching and White Stripe, respectively). RAVEL is promising for many other imaging modalities.
Magnetic resonance imaging (MRI) intensities are acquired in arbitrary units, making scans non-comparable across sites and between subjects. Intensity normalization is a first step for the improvement of comparability of the images across subjects. However, we show that unwanted inter-scan variability associated with imaging site, scanner effect, and other technical artifacts is still present after standard intensity normalization in large multi-site neuroimaging studies. We propose RAVEL (Removal of Artificial Voxel Effect by Linear regression), a tool to remove residual technical variability after intensity normalization. As proposed by SVA and RUV [Leek and Storey, 2007, 2008, Gagnon-Bartsch and Speed, 2012], two batch effect correction tools largely used in genomics, we decompose the voxel intensities of images registered to a template into a biological component and an unwanted variation component. The unwanted variation component is estimated from a control region obtained from the cerebrospinal fluid (CSF), where intensities are known to be unassociated with disease status and other clinical covariates. We perform a singular value decomposition (SVD) of the control voxels to estimate factors of unwanted variation. We then estimate the unwanted factors using linear regression for every voxel of the brain and take the residuals as the RAVEL-corrected intensities. We assess the performance of RAVEL using T1-weighted (T1-w) images from more than 900 subjects with Alzheimer's disease (AD) and mild cognitive impairment (MCI), as well as healthy controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We compare RAVEL to two intensity-normalization-only methods: histogram matching and White Stripe. We show that RAVEL performs best at improving the replicability of the brain regions that are empirically found to be most associated with AD, and that these regions are significantly more present in structures impacted by AD (hippocampus, amygdala, parahippocampal gyrus, enthorinal area, and fornix stria terminals). In addition, we show that the RAVEL-corrected intensities have the best performance in distinguishing between MCI subjects and healthy subjects using the mean hippocampal intensity (AUC=67%), a marked improvement compared to results from intensity normalization alone (AUC=63% and 59% for histogram matching and White Stripe, respectively). RAVEL is promising for many other imaging modalities. •Between-scan unwanted variation is still present after intensity normalization.•We propose a scan-effect removal tool for removing post-normalization artifacts.•We model the unwanted variability between MRI T1-w scans using CSF region.•We use a large subset of the ADNI database to showcase our method.•We show that our method improves replicability of the voxels associated with AD.•MRI intensities corrected by our method improves prediction of AD and MCI.
Author Crainiceanu, Ciprian M.
Sweeney, Elizabeth M.
Shinohara, Russell T.
Fortin, Jean-Philippe
Muschelli, John
AuthorAffiliation 2 Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania
1 Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, University of Pennsylvania
AuthorAffiliation_xml – name: 1 Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, University of Pennsylvania
– name: 2 Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania
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  givenname: Elizabeth M.
  surname: Sweeney
  fullname: Sweeney, Elizabeth M.
  organization: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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  givenname: John
  surname: Muschelli
  fullname: Muschelli, John
  organization: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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  givenname: Ciprian M.
  surname: Crainiceanu
  fullname: Crainiceanu, Ciprian M.
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/26923370$$D View this record in MEDLINE/PubMed
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Cites_doi 10.1002/mrm.22159
10.1523/JNEUROSCI.16-14-04491.1996
10.1212/WNL.55.4.484
10.1109/TMI.2010.2046908
10.1017/S1041610202008281
10.1002/hbm.10062
10.1016/j.neuroimage.2013.05.007
10.1016/j.neurobiolaging.2003.08.006
10.1016/j.jns.2005.02.009
10.1186/s13059-014-0503-2
10.1038/nrg2825
10.1093/biostatistics/kxr034
10.1006/nimg.2000.0582
10.1136/jnnp.63.2.214
10.1007/s004150050387
10.1177/1352458514551594
10.1016/j.neuroimage.2013.03.066
10.1016/j.neurobiolaging.2009.10.006
10.1136/jnnp.71.4.441
10.1002/ana.410320412
10.1002/jmri.21049
10.1016/j.neuroimage.2011.05.038
10.1002/(SICI)1522-2594(199912)42:6<1072::AID-MRM11>3.0.CO;2-M
10.1038/nn.3606
10.1073/pnas.1421412111
10.32614/RJ-2015-013
10.1016/0197-4580(94)90143-0
10.1016/S0197-4580(03)00084-8
10.1016/j.neuroimage.2009.01.054
10.1002/jmri.20658
10.1038/nmeth756
10.1109/42.836373
10.1016/j.neuroimage.2009.12.059
10.1016/j.pscychresns.2006.12.013
10.1093/nar/gku864
10.1016/j.nurt.2007.05.008
10.1016/j.neuroimage.2007.09.073
10.1016/j.neuroimage.2004.07.051
10.1093/bib/bbt054
10.1212/WNL.52.7.1397
10.1016/0730-725X(93)90474-R
10.1111/jon.12129
10.1212/WNL.54.9.1760
10.1016/j.jalz.2012.01.011
10.3233/JAD-2012-102103
10.1016/j.media.2007.06.004
10.1016/j.nicl.2014.08.008
10.1016/j.neuroimage.2009.01.002
10.1016/j.pscychresns.2011.06.014
10.1097/00001756-200210280-00022
10.1016/j.neuroimage.2005.08.009
10.1109/42.906424
10.1109/ISBI.2004.1398484
10.1111/j.1552-6569.2008.00296.x
10.1073/pnas.0808709105
10.1371/journal.pone.0025446
10.1001/archneur.1992.00530310053012
10.1001/archneur.59.1.62
10.1212/WNL.41.3.351
10.1177/1352458515569098
10.1016/j.media.2010.12.003
10.1191/135248506ms1301oa
10.1016/j.neuroimage.2008.06.037
10.1016/j.neuroimage.2005.05.015
10.1016/S1474-4422(06)70550-6
10.1016/j.nicl.2013.03.002
10.1016/j.nicl.2015.10.013
10.1212/WNL.57.9.1669
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Copyright 2016
Published by Elsevier Inc.
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CorporateAuthor The Alzheimer's Disease Neuroimaging Initiative
Alzheimer's Disease Neuroimaging Initiative
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Keywords ADNI
Normalization
MRI
Alzheimer's disease
Scan effect
Language English
License Published by Elsevier Inc.
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Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.ucla.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
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References Shinohara, Muschelli (bb0330) 2015
Cameron Craddock, Holtzheimer, Hu, Mayberg (bb0050) 2009; 620
Wolz, Julkunen, Koikkalainen, Niskanen, Zhang, Rueckert, Soininen, Lötjönen (bb0415) 2011; 60
Fox, Warrington, Freeborough, Hartikainen, Kennedy, Stevens, Rossor (bb0110) 1996; 119
Sweeney, Shinohara, Dewey, Schindler, Muschelli, Reich, Crainiceanu, Eloyan (bb0360) 2016; 10
Madabhushi, Udupa, Moonis (bb0230) 2006; 240
Jacob, Gagnon-Bartsch, Speed (bb0170) 2013
Leek (bb0190) 2014; 420
Leung, Clarkson, Bartlett, Clegg, Jack, Weiner, Fox, Ourselin, Alzheimer's Disease Neuroimaging Initiative (bb0215) 2010; 500
Leek, Storey (bb0205) 2008; 1050
Chong, Lim (bb0065) 2009
Mori, Yoneda, Yamashita, Hirono, Ikeda, Yamadori (bb0255) 1997; 630
Vereecken, Vogels, Nieuwenhuys (bb0385) 1994; 150
Gagnon-Bartsch, Speed (bb0115) 2012; 130
Hornek, Petrovický, Hort, Krásenský, Brabec, Bojar, Vanecková, Seidl (bb0145) 2006; 1130
Hartung, Prell, Gaser, Turner, Tietz, Ilse, Bokemeyer, Witte, Grosskreutz (bb0140) 2014
Chételat, Desgranges, De La Sayette, Viader, Eustache, Baron (bb0055) 2002; 130
Weisenfeld, Warfield (bb0395) 2004
Pujol, Junqué, Vendrell, Grau, Mart-Vilalta, Olivé, Gili (bb0295) 1992; 490
Oishi, Faria, Jiang, Li, Akhter, Zhang, Hsu, Miller, van Zijl, Albert, Lyketsos, Woods, Toga, Pike, Rosa-Neto, Evans, Mazziotta, Mori (bb0280) 2009; 460
Bakshi, Benedict, Bermel, Caruthers, Puli, Tjoa, Fabiano, Jacobs (bb0020) 2002; 590
Meier, Weiner, Guttmann (bb0235) 2007; 40
Xu, Jack, O′Brien, Kokmen, Smith, Ivnik, Boeve, Tangalos, Petersen (bb0420) 2000; 540
Tjoa, Benedict, Weinstock-Guttman, Fabiano, Bakshi (bb0365) 2005; 2340
Scott, DeKosky, Scheff (bb0315) 1991; 410
Fortin, Labbe, Lemire, Zanke, Hudson, Fertig, Greenwood, Hansen (bb0105) 2014; 150
Dedeurwaerder, Defrance, Bizet, Calonne, Bontempi, Fuks (bb0085) 2014; 150
Whitwell, Przybelski, Weigand, Knopman, Boeve, Petersen, Jack (bb0405) 2007; 1300
Zhang, Brady, Smith (bb0425) 2001; 200
Callen, Black, Gao, Caldwell, Szalai (bb0045) 2001; 570
Nyúl, Udupa (bb0270) 1999; 420
Bourgon (bb0030) 2006
Avants, Epstein, Grossman, Gee (bb0010) 2008; 120
Mielke, Kozauer, Chan, George, Toroney, Zerrate, Bandeen-Roche, Wang, Vanzijl, Pekar, Mori, Lyketsos, Albert (bb0245) 2009; 46
Chételat, Landeau, Eustache, Mézenge, Viader, de la Sayette, Desgranges, Baron (bb0060) 2005; 270
Gaonkar, Davatzikos (bb0120) 2013; 78
Nyúl, Udupa, Zhang (bb0275) 2000; 190
Braak, Del Tredici (bb0035) 2012; 80
Ashburner, Friston (bb0005) 2000; 110
Shah, Xiao, Subbanna, Francis, Arnold, Collins, Arbel (bb0325) 2011; 150
Whitcher, Schmid, Thornton (bb0400) 2011; 440
Khan, Liu, Provenzano, Berman, Profaci, Sloan, Mayeux, Duff, Small (bb0185) 2014; 170
Vardhan, Prastawa, Vachet, Piven, Gerig (bb0375) 2014
Muschelli, Sweeney, Lindquist, Crainiceanu (bb0260) 2015; 70
Neema, Arora, Healy, Guss, Brass, Duan, Buckle, Glanz, Stazzone, Khoury, Weiner, Guttmann, Bakshi (bb0265) 2009; 190
Pennanen, Kivipelto, Tuomainen, Hartikainen, Hänninen, Laakso, Hallikainen, Vanhanen, Nissinen, Helkala, Vainio, Vanninen, Partanen, Soininen (bb0285) 2004; 250
Visser, Scheltens, Verhey, Schmand, Launer, Jolles, Jonker (bb0390) 1999; 2460
Shinohara, Sweeney, Goldsmith, Shiee, Mateen, Calabresi, Jarso, Pham, Reich, Crainiceanu (bb0340) 2014; 6
Delano-Wood, Stricker, Sorg, Nation, Jak, Woods, Libon, Delis, Frank, Bondi (bb0090) 2012; 290
Gómez-Isla, Price, McKeel, Morris, Growdon, Hyman (bb0135) 1996; 160
Bottino, Castro, Gomes, Buchpiguel, Marchetti, Neto (bb0025) 2002; 140
Mejia, Sweeney, Dewey, Nair, Sati, Shea, Reich, Shinohara (bb0240) 2015
Smith, Jenkinson, Woolrich, Beckmann, Behrens, Johansen-Berg, Bannister, De Luca, Drobnjak, Flitney (bb0350) 2004; 23
Scott, DeKosky, Sparks, Knox, Scheff (bb0320) 1992; 320
Irizarry, Warren, Spencer, Kim, Biswal, Frank, Gabrielson, Garcia, Geoghegan, Germino, Griffin, Hilmer, Hoffman, Jedlicka, Kawasaki, Martnez-Murillo, Morsberger, Lee, Petersen, Quackenbush, Scott, Wilson, Yang, Ye, Yu (bb0150) 2005; 20
Liu, Spulber, Lehtimäki, Könönen, Hallikainen, Gröhn, Kivipelto, Hallikainen, Vanninen, Soininen (bb0220) 2011; 320
Miller, Younes, Ratnanather, Brown, Trinh, Lee, Tward, Mahon, Mori, Albert, BIOCARD Research Team (bb0250) 2015; 36
Reich, White, Cortese, Vuolo, Shea, Collins, Petkau (bb0305) 2015
Brass, Benedict, Weinstock-Guttman, Munschauer, Bakshi (bb0040) 2006; 120
Leek, Storey (bb0200) 2007; 30
Davatzikos, Bhatt, Shaw, Batmanghelich, Trojanowski (bb0075) 2011; 320
Davatzikos, Ruparel, Fan, Shen, Acharyya, Loughead, Gur, Langleben (bb0070) 2005; 280
Leek, Scharpf, Bravo, Simcha, Langmead, Johnson, Geman, Baggerly, Irizarry (bb0210) 2010; 110
R Core Team (bb0300) 2014
Jager, Deuerling-Zheng, Frericks, Wacker, Hornegger (bb0175) 2006
Ghassemi, Brown, Narayanan, Banwell, Nakamura, Arnold (bb0130) 2015; 250
Shinohara, Crainiceanu, Caffo, Gaitán, Reich (bb0335) 2011; 570
Leek, Peng (bb0195) 2015; 1120
Poulin, Dautoff, Morris, Barrett, Dickerson, Alzheimer's Disease Neuroimaging Initiative (bb0290) 2011; 1940
Ridha, Barnes, Bartlett, Godbolt, Pepple, Rossor, Fox (bb0310) 2006; 50
Wolf, Hensel, Kruggel, Riedel-Heller, Arendt, Wahlund, Gertz (bb0410) 2004; 250
Tustison, Avants, Cook, Zheng, Egan, Yushkevich, Gee (bb0370) 2010; 290
Jack, Bernstein, Fox, Thompson, Alexander, Harvey, Borowski, Britson, Whitwell, Ward (bb0165) 2008; 270
Sweeney, Shinohara, Shiee, Mateen, Chudgar, Cuzzocreo, Calabresi, Pham, Reich, Crainiceanu (bb0355) 2013; 2
De Martino, Valente, Staeren, Ashburner, Goebel, Formisano (bb0080) 2008; 430
Smith (bb0345) 2002; 170
Vemuri, Gunter, Senjem, Whitwell, Kantarci, Knopman, Boeve, Petersen, Jack (bb0380) 2008; 390
Avants, Kandel, Duda, Cook (bb0015) 2015
Du, Schuff, Amend, Laakso, Hsu, Jagust, Yaffe, Kramer, Reed, Norman, Chui, Weiner (bb0095) 2001; 710
Jack, Petersen, Xu, O′Brien, Smith, Ivnik, Boeve, Waring, Tangalos, Kokmen (bb0155) 1999; 520
Luoma, Raininko, Nummi, Luukkonen (bb0225) 1993; 110
Farrow, Thiyagesh, Wilkinson, Parks, Ingram, Woodruff (bb0100) 2007; 1550
Jack, Petersen, Xu, O′Brien, Smith, Ivnik, Boeve, Tangalos, Kokmen (bb0160) 2000; 550
Jovicich, Marizzoni, Sala-Llonch, Bosch, Bartrés-Faz, Arnold, Benninghoff, Wiltfang, Roccatagliata, Nobili (bb0180) 2013; 83
Ghassemi, Brown, Banwell, Narayanan, Arnold, Canadian Pediatric Demyelinating Disease Study Group (bb0125) 2015; 210
Shinohara (10.1016/j.neuroimage.2016.02.036_bb0340) 2014; 6
Bottino (10.1016/j.neuroimage.2016.02.036_bb0025) 2002; 140
Vardhan (10.1016/j.neuroimage.2016.02.036_bb0375) 2014
Mori (10.1016/j.neuroimage.2016.02.036_bb0255) 1997; 630
Poulin (10.1016/j.neuroimage.2016.02.036_bb0290) 2011; 1940
Miller (10.1016/j.neuroimage.2016.02.036_bb0250) 2015; 36
Khan (10.1016/j.neuroimage.2016.02.036_bb0185) 2014; 170
Oishi (10.1016/j.neuroimage.2016.02.036_bb0280) 2009; 460
Tustison (10.1016/j.neuroimage.2016.02.036_bb0370) 2010; 290
Ghassemi (10.1016/j.neuroimage.2016.02.036_bb0125) 2015; 210
Jack (10.1016/j.neuroimage.2016.02.036_bb0160) 2000; 550
Wolz (10.1016/j.neuroimage.2016.02.036_bb0415) 2011; 60
Jovicich (10.1016/j.neuroimage.2016.02.036_bb0180) 2013; 83
Muschelli (10.1016/j.neuroimage.2016.02.036_bb0260) 2015; 70
Reich (10.1016/j.neuroimage.2016.02.036_bb0305) 2015
Whitcher (10.1016/j.neuroimage.2016.02.036_bb0400) 2011; 440
Hornek (10.1016/j.neuroimage.2016.02.036_bb0145) 2006; 1130
Jack (10.1016/j.neuroimage.2016.02.036_bb0155) 1999; 520
Gómez-Isla (10.1016/j.neuroimage.2016.02.036_bb0135) 1996; 160
Sweeney (10.1016/j.neuroimage.2016.02.036_bb0360) 2016; 10
Shinohara (10.1016/j.neuroimage.2016.02.036_bb0330)
Smith (10.1016/j.neuroimage.2016.02.036_bb0350) 2004; 23
Leung (10.1016/j.neuroimage.2016.02.036_bb0215) 2010; 500
Neema (10.1016/j.neuroimage.2016.02.036_bb0265) 2009; 190
Chételat (10.1016/j.neuroimage.2016.02.036_bb0060) 2005; 270
Du (10.1016/j.neuroimage.2016.02.036_bb0095) 2001; 710
Scott (10.1016/j.neuroimage.2016.02.036_bb0315) 1991; 410
Smith (10.1016/j.neuroimage.2016.02.036_bb0345) 2002; 170
Ghassemi (10.1016/j.neuroimage.2016.02.036_bb0130) 2015; 250
Hartung (10.1016/j.neuroimage.2016.02.036_bb0140) 2014
Irizarry (10.1016/j.neuroimage.2016.02.036_bb0150) 2005; 20
Leek (10.1016/j.neuroimage.2016.02.036_bb0205) 2008; 1050
Wolf (10.1016/j.neuroimage.2016.02.036_bb0410) 2004; 250
Bakshi (10.1016/j.neuroimage.2016.02.036_bb0020) 2002; 590
Cameron Craddock (10.1016/j.neuroimage.2016.02.036_bb0050) 2009; 620
Fortin (10.1016/j.neuroimage.2016.02.036_bb0105) 2014; 150
Ridha (10.1016/j.neuroimage.2016.02.036_bb0310) 2006; 50
Mejia (10.1016/j.neuroimage.2016.02.036_bb0240) 2015
Gagnon-Bartsch (10.1016/j.neuroimage.2016.02.036_bb0115) 2012; 130
Shinohara (10.1016/j.neuroimage.2016.02.036_bb0335) 2011; 570
Davatzikos (10.1016/j.neuroimage.2016.02.036_bb0070) 2005; 280
Leek (10.1016/j.neuroimage.2016.02.036_bb0195) 2015; 1120
Zhang (10.1016/j.neuroimage.2016.02.036_bb0425) 2001; 200
Avants (10.1016/j.neuroimage.2016.02.036_bb0010) 2008; 120
Bourgon (10.1016/j.neuroimage.2016.02.036_bb0030) 2006
Chételat (10.1016/j.neuroimage.2016.02.036_bb0055) 2002; 130
Madabhushi (10.1016/j.neuroimage.2016.02.036_bb0230) 2006; 240
Jager (10.1016/j.neuroimage.2016.02.036_bb0175) 2006
Delano-Wood (10.1016/j.neuroimage.2016.02.036_bb0090) 2012; 290
Braak (10.1016/j.neuroimage.2016.02.036_bb0035) 2012; 80
Jacob (10.1016/j.neuroimage.2016.02.036_bb0170) 2013
Fox (10.1016/j.neuroimage.2016.02.036_bb0110) 1996; 119
Weisenfeld (10.1016/j.neuroimage.2016.02.036_bb0395) 2004
Luoma (10.1016/j.neuroimage.2016.02.036_bb0225) 1993; 110
Nyúl (10.1016/j.neuroimage.2016.02.036_bb0275) 2000; 190
Scott (10.1016/j.neuroimage.2016.02.036_bb0320) 1992; 320
Tjoa (10.1016/j.neuroimage.2016.02.036_bb0365) 2005; 2340
Xu (10.1016/j.neuroimage.2016.02.036_bb0420) 2000; 540
Liu (10.1016/j.neuroimage.2016.02.036_bb0220) 2011; 320
Pennanen (10.1016/j.neuroimage.2016.02.036_bb0285) 2004; 250
Sweeney (10.1016/j.neuroimage.2016.02.036_bb0355) 2013; 2
Davatzikos (10.1016/j.neuroimage.2016.02.036_bb0075) 2011; 320
Ashburner (10.1016/j.neuroimage.2016.02.036_bb0005) 2000; 110
Leek (10.1016/j.neuroimage.2016.02.036_bb0200) 2007; 30
Farrow (10.1016/j.neuroimage.2016.02.036_bb0100) 2007; 1550
Vemuri (10.1016/j.neuroimage.2016.02.036_bb0380) 2008; 390
Nyúl (10.1016/j.neuroimage.2016.02.036_bb0270) 1999; 420
Leek (10.1016/j.neuroimage.2016.02.036_bb0210) 2010; 110
Meier (10.1016/j.neuroimage.2016.02.036_bb0235) 2007; 40
Chong (10.1016/j.neuroimage.2016.02.036_bb0065) 2009
Leek (10.1016/j.neuroimage.2016.02.036_bb0190) 2014; 420
Jack (10.1016/j.neuroimage.2016.02.036_bb0165) 2008; 270
Dedeurwaerder (10.1016/j.neuroimage.2016.02.036_bb0085) 2014; 150
Vereecken (10.1016/j.neuroimage.2016.02.036_bb0385) 1994; 150
Gaonkar (10.1016/j.neuroimage.2016.02.036_bb0120) 2013; 78
Pujol (10.1016/j.neuroimage.2016.02.036_bb0295) 1992; 490
Whitwell (10.1016/j.neuroimage.2016.02.036_bb0405) 2007; 1300
R Core Team (10.1016/j.neuroimage.2016.02.036_bb0300)
Shah (10.1016/j.neuroimage.2016.02.036_bb0325) 2011; 150
Brass (10.1016/j.neuroimage.2016.02.036_bb0040) 2006; 120
Visser (10.1016/j.neuroimage.2016.02.036_bb0390) 1999; 2460
Avants (10.1016/j.neuroimage.2016.02.036_bb0015)
De Martino (10.1016/j.neuroimage.2016.02.036_bb0080) 2008; 430
Callen (10.1016/j.neuroimage.2016.02.036_bb0045) 2001; 570
Mielke (10.1016/j.neuroimage.2016.02.036_bb0245) 2009; 46
References_xml – volume: 420
  year: 1999
  ident: bb0270
  article-title: On standardizing the mr image intensity scale
  publication-title: Magn. Reson. Med.
– volume: 280
  start-page: 663
  year: 2005
  end-page: 668
  ident: bb0070
  article-title: Classifying spatial patterns of brain activity with machine learning methods: application to lie detection
  publication-title: NeuroImage
– volume: 78
  year: 2013
  ident: bb0120
  article-title: Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification
  publication-title: NeuroImage
– volume: 83
  start-page: 472
  year: 2013
  end-page: 484
  ident: bb0180
  article-title: Brain morphometry reproducibility in multi-center 3
  publication-title: NeuroImage
– volume: 46
  start-page: 47
  year: 2009
  end-page: 55
  ident: bb0245
  article-title: Regionally-specific diffusion tensor imaging in mild cognitive impairment and Alzheimer's disease
  publication-title: NeuroImage
– volume: 240
  year: 2006
  ident: bb0230
  article-title: Comparing mr image intensity standardization against tissue characterizability of magnetization transfer ratio imaging
  publication-title: J. Magn. Reson. Imaging
– year: 2014
  ident: bb0140
  article-title: Voxel-based mri intensitometry reveals extent of cerebral white matter pathology in amyotrophic lateral sclerosis
– volume: 190
  start-page: 3
  year: 2009
  end-page: 8
  ident: bb0265
  article-title: Deep gray matter involvement on brain MRI scans is associated with clinical progression in multiple sclerosis
  publication-title: J. Neuroimaging
– volume: 50
  year: 2006
  ident: bb0310
  article-title: Tracking atrophy progression in familial alzheimer's disease: a serial mri study
  publication-title: Lancet Neurol.
– volume: 120
  start-page: 26
  year: 2008
  end-page: 41
  ident: bb0010
  article-title: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain
  publication-title: Med. Image Anal.
– volume: 110
  start-page: 733
  year: 2010
  end-page: 739
  ident: bb0210
  article-title: Tackling the widespread and critical impact of batch effects in high-throughput data
  publication-title: Nat. Rev. Genet.
– volume: 40
  start-page: 485
  year: 2007
  end-page: 498
  ident: bb0235
  article-title: Time-series modeling of multiple sclerosis disease activity: a promising window on disease progression and repair potential?
  publication-title: Neurotherapeutics
– volume: 110
  start-page: 805
  year: 2000
  end-page: 821
  ident: bb0005
  article-title: Voxel-based morphometry—the methods
  publication-title: NeuroImage
– volume: 130
  start-page: 1939
  year: 2002
  end-page: 1943
  ident: bb0055
  article-title: Mapping gray matter loss with voxel-based morphometry in mild cognitive impairment
  publication-title: Neuroreport
– volume: 30
  start-page: 1724
  year: 2007
  end-page: 1735
  ident: bb0200
  article-title: Capturing heterogeneity in gene expression studies by surrogate variable analysis
  publication-title: PLoS Genet.
– volume: 390
  year: 2008
  ident: bb0380
  article-title: Alzheimer's disease diagnosis in individual subjects using structural mr images: validation studies
  publication-title: NeuroImage
– volume: 190
  year: 2000
  ident: bb0275
  article-title: New variants of a method of mri scale standardization
  publication-title: IEEE Trans. Med. Imaging
– volume: 570
  year: 2011
  ident: bb0335
  article-title: Population-wide principal component-based quantification of blood–brain-barrier dynamics in multiple sclerosis
  publication-title: NeuroImage
– volume: 270
  start-page: 934
  year: 2005
  end-page: 946
  ident: bb0060
  article-title: Using voxel-based morphometry to map the structural changes associated with rapid conversion in MCI: a longitudinal mri study
  publication-title: NeuroImage
– volume: 550
  year: 2000
  ident: bb0160
  article-title: Rates of hippocampal atrophy correlate with change in clinical status in aging and ad
  publication-title: Neurology
– year: 2004
  ident: bb0395
  article-title: Normalization of joint image-intensity statistics in MRI using the kullback–leibler divergence
  publication-title: Biomedical Imaging: Nano to Macro, 2004 IEEE International Symposium on (101–104IEEE)
– volume: 170
  year: 2014
  ident: bb0185
  article-title: Molecular drivers and cortical spread of lateral entorhinal cortex dysfunction in preclinical Alzheimer's disease
  publication-title: Nat. Neurosci.
– volume: 250
  year: 2004
  ident: bb0285
  article-title: Hippocampus and entorhinal cortex in mild cognitive impairment and early ad
  publication-title: Neurobiol. Aging
– volume: 410
  year: 1991
  ident: bb0315
  article-title: Volumetric atrophy of the amygdala in alzheimer's disease: quantitative serial reconstruction
  publication-title: Neurology
– volume: 620
  start-page: 1619
  year: 2009
  end-page: 1628
  ident: bb0050
  article-title: Disease state prediction from resting state functional connectivity
  publication-title: Magn. Reson. Med.
– volume: 10
  start-page: 1
  year: 2016
  end-page: 17
  ident: bb0360
  article-title: Relating multi-sequence longitudinal intensity profiles and clinical covariates in incident multiple sclerosis lesions
  publication-title: NeuroImage Clin.
– year: 2015
  ident: bb0305
  article-title: Sample-size calculations for short-term proof-of-concept studies of tissue protection and repair in multiple sclerosis lesions via conventional clinical imaging
  publication-title: Mult. Scler. J.
– volume: 250
  year: 2004
  ident: bb0410
  article-title: Structural correlates of mild cognitive impairment
  publication-title: Neurobiol. Aging
– volume: 120
  start-page: 437
  year: 2006
  end-page: 444
  ident: bb0040
  article-title: Cognitive impairment is associated with subcortical magnetic resonance imaging grey matter t2 hypointensity in multiple sclerosis
  publication-title: Mult. Scler.
– volume: 1550
  year: 2007
  ident: bb0100
  article-title: Fronto-temporal-lobe atrophy in early-stage alzheimer's disease identified using an improved detection methodology
  publication-title: Psychiatry Res.
– volume: 6
  start-page: 9
  year: 2014
  end-page: 19
  ident: bb0340
  article-title: Australian imaging biomarkers lifestyle flagship study of ageing, and Alzheimer's disease neuroimaging initiative. Statistical normalization techniques for magnetic resonance imaging
  publication-title: Neuroimage Clin.
– volume: 290
  start-page: 589
  year: 2012
  ident: bb0090
  article-title: Posterior cingulum white matter disruption and its associations with verbal memory and stroke risk in mild cognitive impairment
  publication-title: J. Alzheimers Dis.
– volume: 570
  year: 2001
  ident: bb0045
  article-title: Beyond the hippocampus: Mri volumetry confirms widespread limbic atrophy in ad
  publication-title: Neurology
– volume: 630
  year: 1997
  ident: bb0255
  article-title: Medial temporal structures relate to memory impairment in alzheimer's disease: an MRI volumetric study
  publication-title: J. Neurol. Neurosurg. Psychiatry
– volume: 150
  start-page: 45
  year: 1994
  end-page: 54
  ident: bb0385
  article-title: Neuron loss and shrinkage in the amygdala in alzheimer's disease
  publication-title: Neurobiol. Aging
– start-page: 37
  year: 2015
  ident: bb0240
  article-title: Statistical estimation of t1 relaxation time using conventional magnetic resonance imaging
  publication-title: UPenn Biostatistics Working Papers, Working Paper
– volume: 590
  start-page: 62
  year: 2002
  end-page: 68
  ident: bb0020
  article-title: T2 hypointensity in the deep gray matter of patients with multiple sclerosis: a quantitative magnetic resonance imaging study
  publication-title: Arch. Neurol.
– start-page: 3
  year: 2009
  end-page: 15
  ident: bb0065
  article-title: Neuroimaging biomarkers in alzheimer's disease
  publication-title: The Handbook of Neuropsychiatric Biomarkers, Endophenotypes and Genes
– volume: 150
  start-page: 503
  year: 2014
  ident: bb0105
  article-title: Functional normalization of 450
  publication-title: Genome Biol.
– volume: 2
  start-page: 402
  year: 2013
  end-page: 413
  ident: bb0355
  article-title: Oasis is automated statistical inference for segmentation, with applications to multiple sclerosis lesion segmentation in mri
  publication-title: Neuroimage Clin.
– year: 2014
  ident: bb0375
  article-title: Characterizing Growth Patterns in Longitudinal Mri Using Image Contrast
  publication-title: SPIE Medical Imaging, pages 90340D–90340D. International Society for Optics and Photonics
– volume: 1940
  start-page: 7
  year: 2011
  end-page: 13
  ident: bb0290
  article-title: Amygdala atrophy is prominent in early alzheimer's disease and relates to symptom severity
  publication-title: Psychiatry Res.
– year: 2006
  ident: bb0030
  article-title: Chromatin Immunoprecipitation and High-Density Tiling Microarrays: A Generative Model, Methods for Analysis, and Methodology Assessment in the Absence of a “Gold Standard”
– volume: 2460
  year: 1999
  ident: bb0390
  article-title: Medial temporal lobe atrophy and memory dysfunction as predictors for dementia in subjects with mild cognitive impairment
  publication-title: J. Neurol.
– volume: 440
  start-page: 1
  year: 2011
  end-page: 28
  ident: bb0400
  article-title: Working with the DICOM and NIfTI data standards in R
  publication-title: J. Stat. Softw.
– volume: 200
  start-page: 45
  year: 2001
  end-page: 57
  ident: bb0425
  article-title: Segmentation of brain mr images through a hidden markov random field model and the expectation–maximization algorithm
  publication-title: IEEE Trans. Med. Imaging
– volume: 520
  year: 1999
  ident: bb0155
  article-title: Prediction of ad with mri-based hippocampal volume in mild cognitive impairment
  publication-title: Neurology
– volume: 420
  year: 2014
  ident: bb0190
  article-title: svaseq: removing batch effects and other unwanted noise from sequencing data
  publication-title: Nucleic Acids Res.
– volume: 290
  year: 2010
  ident: bb0370
  article-title: N4itk: improved n3 bias correction
  publication-title: IEEE Trans. Med. Imaging
– year: 2015
  ident: bb0015
  article-title: Antsr: Ants in r
– volume: 320
  start-page: 1558
  year: 2011
  end-page: 1571
  ident: bb0220
  article-title: Diffusion tensor imaging and tract-based spatial statistics in alzheimer's disease and mild cognitive impairment
  publication-title: Neurobiol. Aging
– volume: 490
  start-page: 711
  year: 1992
  end-page: 717
  ident: bb0295
  article-title: Biological significance of iron-related magnetic resonance imaging changes in the brain
  publication-title: Arch. Neurol.
– volume: 70
  start-page: 163
  year: 2015
  end-page: 175
  ident: bb0260
  article-title: fslr: Connecting the fsl software with r
  publication-title: The R J.
– start-page: 269
  year: 2006
  end-page: 276
  ident: bb0175
  article-title: A new method for mri intensity standardization with application to lesion detection in the brain
  publication-title: Vision Modeling and Visualization
– volume: 130
  start-page: 539
  year: 2012
  end-page: 552
  ident: bb0115
  article-title: Using control genes to correct for unwanted variation in microarray data
  publication-title: Biostatistics
– volume: 2340
  start-page: 17
  year: 2005
  end-page: 24
  ident: bb0365
  article-title: Mri t2 hypointensity of the dentate nucleus is related to ambulatory impairment in multiple sclerosis
  publication-title: J. Neurol. Sci.
– year: 2015
  ident: bb0330
  article-title: Whitestripe: White matter normalization for magnetic resonance images using whitestripe
– volume: 36
  year: 2015
  ident: bb0250
  article-title: Amygdalar atrophy in symptomatic alzheimer's disease based on diffeomorphometry: the biocard cohort
  publication-title: Neurobiol. Aging
– volume: 80
  start-page: 227
  year: 2012
  end-page: 233
  ident: bb0035
  article-title: Alzheimer's disease: pathogenesis and prevention
  publication-title: Alzheimers Dement.
– volume: 60
  year: 2011
  ident: bb0415
  article-title: Multi-method analysis of mri images in early diagnostics of alzheimer's disease
  publication-title: PLoS One
– volume: 1050
  start-page: 18718
  year: 2008
  end-page: 18723
  ident: bb0205
  article-title: A general framework for multiple testing dependence
  publication-title: Proc. Natl. Acad. Sci.
– volume: 160
  year: 1996
  ident: bb0135
  article-title: Profound loss of layer ii entorhinal cortex neurons occurs in very mild alzheimer's disease
  publication-title: J. Neurosci.
– volume: 540
  year: 2000
  ident: bb0420
  article-title: Usefulness of mri measures of entorhinal cortex versus hippocampus in ad
  publication-title: Neurology
– volume: 430
  start-page: 44
  year: 2008
  end-page: 58
  ident: bb0080
  article-title: Combining multivariate voxel selection and support vector machines for mapping and classification of fmri spatial patterns
  publication-title: NeuroImage
– volume: 1130
  year: 2006
  ident: bb0145
  article-title: Amygdalar volume and psychiatric symptoms in alzheimer's disease: an mri analysis
  publication-title: Acta Neurol. Scand.
– year: 2014
  ident: bb0300
  article-title: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria
– volume: 250
  start-page: 184
  year: 2015
  end-page: 190
  ident: bb0130
  article-title: Normalization of white matter intensity on t1-weighted images of patients with acquired central nervous system demyelination
  publication-title: J. Neuroimaging
– volume: 119
  year: 1996
  ident: bb0110
  article-title: Presymptomatic hippocampal atrophy in alzheimer's disease. a longitudinal mri study
  publication-title: Brain
– volume: 170
  year: 2002
  ident: bb0345
  article-title: Fast robust automated brain extraction
  publication-title: Hum. Brain Mapp.
– volume: 23
  start-page: S208
  year: 2004
  end-page: S219
  ident: bb0350
  article-title: Advances in functional and structural mr image analysis and implementation as fsl
  publication-title: NeuroImage
– volume: 320
  start-page: e19
  year: 2011
  end-page: e27
  ident: bb0075
  article-title: Prediction of mci to ad conversion, via MRI, csf biomarkers, and pattern classification
  publication-title: Neurobiol. Aging
– year: 2013
  ident: bb0170
  article-title: Correcting Gene Expression Data When Neither the Unwanted Variation Nor the Factor of Interest Are Observed. Tech. Rep. 818
– volume: 150
  year: 2011
  ident: bb0325
  article-title: Evaluating intensity normalization on mris of human brain with multiple sclerosis
  publication-title: Med. Image Anal.
– volume: 1120
  year: 2015
  ident: bb0195
  article-title: Opinion: reproducible research can still be wrong: adopting a prevention approach
  publication-title: Proc. Natl. Acad. Sci. U. S. A.
– volume: 140
  start-page: 59
  year: 2002
  end-page: 72
  ident: bb0025
  article-title: Volumetric mri measurements can differentiate Alzheimer's disease, mild cognitive impairment, and normal aging
  publication-title: Int. Psychogeriatr.
– volume: 150
  year: 2014
  ident: bb0085
  article-title: A comprehensive overview of infinium humanmethylation450 data processing
  publication-title: Brief. Bioinform.
– volume: 500
  year: 2010
  ident: bb0215
  article-title: Robust atrophy rate measurement in alzheimer's disease using multi-site serial MRI: tissue-specific intensity normalization and parameter selection
  publication-title: NeuroImage
– volume: 460
  year: 2009
  ident: bb0280
  article-title: Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: application to normal elderly and alzheimer's disease participants
  publication-title: NeuroImage
– volume: 270
  start-page: 685
  year: 2008
  end-page: 691
  ident: bb0165
  article-title: The alzheimer's disease neuroimaging initiative (adni): Mri methods
  publication-title: J. Magn. Reson. Imaging
– volume: 110
  start-page: 549
  year: 1993
  end-page: 555
  ident: bb0225
  article-title: Is the signal intensity of cerebrospinal fluid constant? intensity measurements with high and low field magnetic resonance imagers
  publication-title: Magn. Reson. Imaging
– volume: 710
  year: 2001
  ident: bb0095
  article-title: Magnetic resonance imaging of the entorhinal cortex and hippocampus in mild cognitive impairment and Alzheimer's disease
  publication-title: J. Neurol. Neurosurg. Psychiatry
– volume: 210
  start-page: 718
  year: 2015
  end-page: 725
  ident: bb0125
  article-title: Quantitative measurement of tissue damage and recovery within new t2w lesions in pediatric-and adult-onset multiple sclerosis
  publication-title: Mult. Scler. J.
– volume: 320
  year: 1992
  ident: bb0320
  article-title: Amygdala cell loss and atrophy in alzheimer's disease
  publication-title: Ann. Neurol.
– volume: 20
  start-page: 345
  year: 2005
  end-page: 350
  ident: bb0150
  article-title: Multiple-laboratory comparison of microarray platforms
  publication-title: Nat. Methods
– volume: 1300
  year: 2007
  ident: bb0405
  article-title: 3d maps from multiple mri illustrate changing atrophy patterns as subjects progress from mild cognitive impairment to alzheimer's disease
  publication-title: Brain
– year: 2013
  ident: 10.1016/j.neuroimage.2016.02.036_bb0170
– volume: 620
  start-page: 1619
  issue: 6
  year: 2009
  ident: 10.1016/j.neuroimage.2016.02.036_bb0050
  article-title: Disease state prediction from resting state functional connectivity
  publication-title: Magn. Reson. Med.
  doi: 10.1002/mrm.22159
– volume: 160
  issue: 14
  year: 1996
  ident: 10.1016/j.neuroimage.2016.02.036_bb0135
  article-title: Profound loss of layer ii entorhinal cortex neurons occurs in very mild alzheimer's disease
  publication-title: J. Neurosci.
  doi: 10.1523/JNEUROSCI.16-14-04491.1996
– volume: 550
  issue: 4
  year: 2000
  ident: 10.1016/j.neuroimage.2016.02.036_bb0160
  article-title: Rates of hippocampal atrophy correlate with change in clinical status in aging and ad
  publication-title: Neurology
  doi: 10.1212/WNL.55.4.484
– volume: 290
  issue: 6
  year: 2010
  ident: 10.1016/j.neuroimage.2016.02.036_bb0370
  article-title: N4itk: improved n3 bias correction
  publication-title: IEEE Trans. Med. Imaging
  doi: 10.1109/TMI.2010.2046908
– volume: 140
  start-page: 59
  issue: 1
  year: 2002
  ident: 10.1016/j.neuroimage.2016.02.036_bb0025
  article-title: Volumetric mri measurements can differentiate Alzheimer's disease, mild cognitive impairment, and normal aging
  publication-title: Int. Psychogeriatr.
  doi: 10.1017/S1041610202008281
– volume: 170
  issue: 3
  year: 2002
  ident: 10.1016/j.neuroimage.2016.02.036_bb0345
  article-title: Fast robust automated brain extraction
  publication-title: Hum. Brain Mapp.
  doi: 10.1002/hbm.10062
– volume: 83
  start-page: 472
  year: 2013
  ident: 10.1016/j.neuroimage.2016.02.036_bb0180
  article-title: Brain morphometry reproducibility in multi-center 3t mri studies: a comparison of cross-sectional and longitudinal segmentations
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2013.05.007
– ident: 10.1016/j.neuroimage.2016.02.036_bb0300
– volume: 250
  issue: 7
  year: 2004
  ident: 10.1016/j.neuroimage.2016.02.036_bb0410
  article-title: Structural correlates of mild cognitive impairment
  publication-title: Neurobiol. Aging
  doi: 10.1016/j.neurobiolaging.2003.08.006
– volume: 2340
  start-page: 17
  issue: 1–2
  year: 2005
  ident: 10.1016/j.neuroimage.2016.02.036_bb0365
  article-title: Mri t2 hypointensity of the dentate nucleus is related to ambulatory impairment in multiple sclerosis
  publication-title: J. Neurol. Sci.
  doi: 10.1016/j.jns.2005.02.009
– volume: 150
  start-page: 503
  issue: 11
  year: 2014
  ident: 10.1016/j.neuroimage.2016.02.036_bb0105
  article-title: Functional normalization of 450k methylation array data improves replication in large cancer studies
  publication-title: Genome Biol.
  doi: 10.1186/s13059-014-0503-2
– volume: 110
  start-page: 733
  issue: 10
  year: 2010
  ident: 10.1016/j.neuroimage.2016.02.036_bb0210
  article-title: Tackling the widespread and critical impact of batch effects in high-throughput data
  publication-title: Nat. Rev. Genet.
  doi: 10.1038/nrg2825
– volume: 130
  start-page: 539
  issue: 3
  year: 2012
  ident: 10.1016/j.neuroimage.2016.02.036_bb0115
  article-title: Using control genes to correct for unwanted variation in microarray data
  publication-title: Biostatistics
  doi: 10.1093/biostatistics/kxr034
– volume: 110
  start-page: 805
  issue: 6
  year: 2000
  ident: 10.1016/j.neuroimage.2016.02.036_bb0005
  article-title: Voxel-based morphometry—the methods
  publication-title: NeuroImage
  doi: 10.1006/nimg.2000.0582
– volume: 630
  issue: 2
  year: 1997
  ident: 10.1016/j.neuroimage.2016.02.036_bb0255
  article-title: Medial temporal structures relate to memory impairment in alzheimer's disease: an MRI volumetric study
  publication-title: J. Neurol. Neurosurg. Psychiatry
  doi: 10.1136/jnnp.63.2.214
– volume: 2460
  issue: 6
  year: 1999
  ident: 10.1016/j.neuroimage.2016.02.036_bb0390
  article-title: Medial temporal lobe atrophy and memory dysfunction as predictors for dementia in subjects with mild cognitive impairment
  publication-title: J. Neurol.
  doi: 10.1007/s004150050387
– volume: 210
  start-page: 718
  issue: 6
  year: 2015
  ident: 10.1016/j.neuroimage.2016.02.036_bb0125
  article-title: Quantitative measurement of tissue damage and recovery within new t2w lesions in pediatric-and adult-onset multiple sclerosis
  publication-title: Mult. Scler. J.
  doi: 10.1177/1352458514551594
– volume: 78
  year: 2013
  ident: 10.1016/j.neuroimage.2016.02.036_bb0120
  article-title: Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2013.03.066
– volume: 440
  start-page: 1
  issue: 6
  year: 2011
  ident: 10.1016/j.neuroimage.2016.02.036_bb0400
  article-title: Working with the DICOM and NIfTI data standards in R
  publication-title: J. Stat. Softw.
– volume: 320
  start-page: 1558
  issue: 9
  year: 2011
  ident: 10.1016/j.neuroimage.2016.02.036_bb0220
  article-title: Diffusion tensor imaging and tract-based spatial statistics in alzheimer's disease and mild cognitive impairment
  publication-title: Neurobiol. Aging
  doi: 10.1016/j.neurobiolaging.2009.10.006
– volume: 710
  issue: 4
  year: 2001
  ident: 10.1016/j.neuroimage.2016.02.036_bb0095
  article-title: Magnetic resonance imaging of the entorhinal cortex and hippocampus in mild cognitive impairment and Alzheimer's disease
  publication-title: J. Neurol. Neurosurg. Psychiatry
  doi: 10.1136/jnnp.71.4.441
– volume: 320
  issue: 4
  year: 1992
  ident: 10.1016/j.neuroimage.2016.02.036_bb0320
  article-title: Amygdala cell loss and atrophy in alzheimer's disease
  publication-title: Ann. Neurol.
  doi: 10.1002/ana.410320412
– year: 2006
  ident: 10.1016/j.neuroimage.2016.02.036_bb0030
– volume: 270
  start-page: 685
  issue: 4
  year: 2008
  ident: 10.1016/j.neuroimage.2016.02.036_bb0165
  article-title: The alzheimer's disease neuroimaging initiative (adni): Mri methods
  publication-title: J. Magn. Reson. Imaging
  doi: 10.1002/jmri.21049
– ident: 10.1016/j.neuroimage.2016.02.036_bb0015
– volume: 119
  issue: Pt 6
  year: 1996
  ident: 10.1016/j.neuroimage.2016.02.036_bb0110
  article-title: Presymptomatic hippocampal atrophy in alzheimer's disease. a longitudinal mri study
  publication-title: Brain
– volume: 570
  issue: 4
  year: 2011
  ident: 10.1016/j.neuroimage.2016.02.036_bb0335
  article-title: Population-wide principal component-based quantification of blood–brain-barrier dynamics in multiple sclerosis
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2011.05.038
– volume: 420
  issue: 6
  year: 1999
  ident: 10.1016/j.neuroimage.2016.02.036_bb0270
  article-title: On standardizing the mr image intensity scale
  publication-title: Magn. Reson. Med.
  doi: 10.1002/(SICI)1522-2594(199912)42:6<1072::AID-MRM11>3.0.CO;2-M
– volume: 170
  issue: 2
  year: 2014
  ident: 10.1016/j.neuroimage.2016.02.036_bb0185
  article-title: Molecular drivers and cortical spread of lateral entorhinal cortex dysfunction in preclinical Alzheimer's disease
  publication-title: Nat. Neurosci.
  doi: 10.1038/nn.3606
– volume: 1120
  issue: 6
  year: 2015
  ident: 10.1016/j.neuroimage.2016.02.036_bb0195
  article-title: Opinion: reproducible research can still be wrong: adopting a prevention approach
  publication-title: Proc. Natl. Acad. Sci. U. S. A.
  doi: 10.1073/pnas.1421412111
– volume: 70
  start-page: 163
  issue: 1
  year: 2015
  ident: 10.1016/j.neuroimage.2016.02.036_bb0260
  article-title: fslr: Connecting the fsl software with r
  publication-title: The R J.
  doi: 10.32614/RJ-2015-013
– volume: 150
  start-page: 45
  issue: 1
  year: 1994
  ident: 10.1016/j.neuroimage.2016.02.036_bb0385
  article-title: Neuron loss and shrinkage in the amygdala in alzheimer's disease
  publication-title: Neurobiol. Aging
  doi: 10.1016/0197-4580(94)90143-0
– volume: 250
  issue: 3
  year: 2004
  ident: 10.1016/j.neuroimage.2016.02.036_bb0285
  article-title: Hippocampus and entorhinal cortex in mild cognitive impairment and early ad
  publication-title: Neurobiol. Aging
  doi: 10.1016/S0197-4580(03)00084-8
– volume: 46
  start-page: 47
  issue: 1
  year: 2009
  ident: 10.1016/j.neuroimage.2016.02.036_bb0245
  article-title: Regionally-specific diffusion tensor imaging in mild cognitive impairment and Alzheimer's disease
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2009.01.054
– start-page: 3
  year: 2009
  ident: 10.1016/j.neuroimage.2016.02.036_bb0065
  article-title: Neuroimaging biomarkers in alzheimer's disease
– volume: 240
  issue: 3
  year: 2006
  ident: 10.1016/j.neuroimage.2016.02.036_bb0230
  article-title: Comparing mr image intensity standardization against tissue characterizability of magnetization transfer ratio imaging
  publication-title: J. Magn. Reson. Imaging
  doi: 10.1002/jmri.20658
– start-page: 37
  year: 2015
  ident: 10.1016/j.neuroimage.2016.02.036_bb0240
  article-title: Statistical estimation of t1 relaxation time using conventional magnetic resonance imaging
– volume: 20
  start-page: 345
  issue: 5
  year: 2005
  ident: 10.1016/j.neuroimage.2016.02.036_bb0150
  article-title: Multiple-laboratory comparison of microarray platforms
  publication-title: Nat. Methods
  doi: 10.1038/nmeth756
– volume: 190
  issue: 2
  year: 2000
  ident: 10.1016/j.neuroimage.2016.02.036_bb0275
  article-title: New variants of a method of mri scale standardization
  publication-title: IEEE Trans. Med. Imaging
  doi: 10.1109/42.836373
– start-page: 269
  year: 2006
  ident: 10.1016/j.neuroimage.2016.02.036_bb0175
  article-title: A new method for mri intensity standardization with application to lesion detection in the brain
– volume: 500
  issue: 2
  year: 2010
  ident: 10.1016/j.neuroimage.2016.02.036_bb0215
  article-title: Robust atrophy rate measurement in alzheimer's disease using multi-site serial MRI: tissue-specific intensity normalization and parameter selection
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2009.12.059
– volume: 1550
  issue: 1
  year: 2007
  ident: 10.1016/j.neuroimage.2016.02.036_bb0100
  article-title: Fronto-temporal-lobe atrophy in early-stage alzheimer's disease identified using an improved detection methodology
  publication-title: Psychiatry Res.
  doi: 10.1016/j.pscychresns.2006.12.013
– volume: 420
  issue: 21
  year: 2014
  ident: 10.1016/j.neuroimage.2016.02.036_bb0190
  article-title: svaseq: removing batch effects and other unwanted noise from sequencing data
  publication-title: Nucleic Acids Res.
  doi: 10.1093/nar/gku864
– volume: 40
  start-page: 485
  issue: 3
  year: 2007
  ident: 10.1016/j.neuroimage.2016.02.036_bb0235
  article-title: Time-series modeling of multiple sclerosis disease activity: a promising window on disease progression and repair potential?
  publication-title: Neurotherapeutics
  doi: 10.1016/j.nurt.2007.05.008
– volume: 390
  issue: 3
  year: 2008
  ident: 10.1016/j.neuroimage.2016.02.036_bb0380
  article-title: Alzheimer's disease diagnosis in individual subjects using structural mr images: validation studies
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2007.09.073
– volume: 23
  start-page: S208
  year: 2004
  ident: 10.1016/j.neuroimage.2016.02.036_bb0350
  article-title: Advances in functional and structural mr image analysis and implementation as fsl
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2004.07.051
– volume: 150
  issue: 6
  year: 2014
  ident: 10.1016/j.neuroimage.2016.02.036_bb0085
  article-title: A comprehensive overview of infinium humanmethylation450 data processing
  publication-title: Brief. Bioinform.
  doi: 10.1093/bib/bbt054
– volume: 520
  issue: 7
  year: 1999
  ident: 10.1016/j.neuroimage.2016.02.036_bb0155
  article-title: Prediction of ad with mri-based hippocampal volume in mild cognitive impairment
  publication-title: Neurology
  doi: 10.1212/WNL.52.7.1397
– volume: 110
  start-page: 549
  issue: 4
  year: 1993
  ident: 10.1016/j.neuroimage.2016.02.036_bb0225
  article-title: Is the signal intensity of cerebrospinal fluid constant? intensity measurements with high and low field magnetic resonance imagers
  publication-title: Magn. Reson. Imaging
  doi: 10.1016/0730-725X(93)90474-R
– volume: 250
  start-page: 184
  issue: 2
  year: 2015
  ident: 10.1016/j.neuroimage.2016.02.036_bb0130
  article-title: Normalization of white matter intensity on t1-weighted images of patients with acquired central nervous system demyelination
  publication-title: J. Neuroimaging
  doi: 10.1111/jon.12129
– volume: 540
  issue: 9
  year: 2000
  ident: 10.1016/j.neuroimage.2016.02.036_bb0420
  article-title: Usefulness of mri measures of entorhinal cortex versus hippocampus in ad
  publication-title: Neurology
  doi: 10.1212/WNL.54.9.1760
– volume: 80
  start-page: 227
  issue: 3
  year: 2012
  ident: 10.1016/j.neuroimage.2016.02.036_bb0035
  article-title: Alzheimer's disease: pathogenesis and prevention
  publication-title: Alzheimers Dement.
  doi: 10.1016/j.jalz.2012.01.011
– volume: 290
  start-page: 589
  issue: 3
  year: 2012
  ident: 10.1016/j.neuroimage.2016.02.036_bb0090
  article-title: Posterior cingulum white matter disruption and its associations with verbal memory and stroke risk in mild cognitive impairment
  publication-title: J. Alzheimers Dis.
  doi: 10.3233/JAD-2012-102103
– volume: 120
  start-page: 26
  issue: 1
  year: 2008
  ident: 10.1016/j.neuroimage.2016.02.036_bb0010
  article-title: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain
  publication-title: Med. Image Anal.
  doi: 10.1016/j.media.2007.06.004
– volume: 6
  start-page: 9
  year: 2014
  ident: 10.1016/j.neuroimage.2016.02.036_bb0340
  article-title: Australian imaging biomarkers lifestyle flagship study of ageing, and Alzheimer's disease neuroimaging initiative. Statistical normalization techniques for magnetic resonance imaging
  publication-title: Neuroimage Clin.
  doi: 10.1016/j.nicl.2014.08.008
– volume: 460
  issue: 2
  year: 2009
  ident: 10.1016/j.neuroimage.2016.02.036_bb0280
  article-title: Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: application to normal elderly and alzheimer's disease participants
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2009.01.002
– volume: 1940
  start-page: 7
  issue: 1
  year: 2011
  ident: 10.1016/j.neuroimage.2016.02.036_bb0290
  article-title: Amygdala atrophy is prominent in early alzheimer's disease and relates to symptom severity
  publication-title: Psychiatry Res.
  doi: 10.1016/j.pscychresns.2011.06.014
– volume: 1300
  issue: Pt 7
  year: 2007
  ident: 10.1016/j.neuroimage.2016.02.036_bb0405
  article-title: 3d maps from multiple mri illustrate changing atrophy patterns as subjects progress from mild cognitive impairment to alzheimer's disease
  publication-title: Brain
– volume: 130
  start-page: 1939
  issue: 15
  year: 2002
  ident: 10.1016/j.neuroimage.2016.02.036_bb0055
  article-title: Mapping gray matter loss with voxel-based morphometry in mild cognitive impairment
  publication-title: Neuroreport
  doi: 10.1097/00001756-200210280-00022
– volume: 280
  start-page: 663
  issue: 3
  year: 2005
  ident: 10.1016/j.neuroimage.2016.02.036_bb0070
  article-title: Classifying spatial patterns of brain activity with machine learning methods: application to lie detection
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2005.08.009
– year: 2014
  ident: 10.1016/j.neuroimage.2016.02.036_bb0140
– volume: 200
  start-page: 45
  issue: 1
  year: 2001
  ident: 10.1016/j.neuroimage.2016.02.036_bb0425
  article-title: Segmentation of brain mr images through a hidden markov random field model and the expectation–maximization algorithm
  publication-title: IEEE Trans. Med. Imaging
  doi: 10.1109/42.906424
– year: 2004
  ident: 10.1016/j.neuroimage.2016.02.036_bb0395
  article-title: Normalization of joint image-intensity statistics in MRI using the kullback–leibler divergence
  doi: 10.1109/ISBI.2004.1398484
– volume: 190
  start-page: 3
  issue: 1
  year: 2009
  ident: 10.1016/j.neuroimage.2016.02.036_bb0265
  article-title: Deep gray matter involvement on brain MRI scans is associated with clinical progression in multiple sclerosis
  publication-title: J. Neuroimaging
  doi: 10.1111/j.1552-6569.2008.00296.x
– volume: 1050
  start-page: 18718
  issue: 48
  year: 2008
  ident: 10.1016/j.neuroimage.2016.02.036_bb0205
  article-title: A general framework for multiple testing dependence
  publication-title: Proc. Natl. Acad. Sci.
  doi: 10.1073/pnas.0808709105
– volume: 36
  issue: Suppl. 1
  year: 2015
  ident: 10.1016/j.neuroimage.2016.02.036_bb0250
  article-title: Amygdalar atrophy in symptomatic alzheimer's disease based on diffeomorphometry: the biocard cohort
  publication-title: Neurobiol. Aging
– volume: 60
  issue: 10
  year: 2011
  ident: 10.1016/j.neuroimage.2016.02.036_bb0415
  article-title: Multi-method analysis of mri images in early diagnostics of alzheimer's disease
  publication-title: PLoS One
  doi: 10.1371/journal.pone.0025446
– volume: 320
  start-page: e19
  issue: (12):2322
  year: 2011
  ident: 10.1016/j.neuroimage.2016.02.036_bb0075
  article-title: Prediction of mci to ad conversion, via MRI, csf biomarkers, and pattern classification
  publication-title: Neurobiol. Aging
– volume: 490
  start-page: 711
  issue: 7
  year: 1992
  ident: 10.1016/j.neuroimage.2016.02.036_bb0295
  article-title: Biological significance of iron-related magnetic resonance imaging changes in the brain
  publication-title: Arch. Neurol.
  doi: 10.1001/archneur.1992.00530310053012
– volume: 590
  start-page: 62
  issue: 1
  year: 2002
  ident: 10.1016/j.neuroimage.2016.02.036_bb0020
  article-title: T2 hypointensity in the deep gray matter of patients with multiple sclerosis: a quantitative magnetic resonance imaging study
  publication-title: Arch. Neurol.
  doi: 10.1001/archneur.59.1.62
– volume: 410
  issue: 3
  year: 1991
  ident: 10.1016/j.neuroimage.2016.02.036_bb0315
  article-title: Volumetric atrophy of the amygdala in alzheimer's disease: quantitative serial reconstruction
  publication-title: Neurology
  doi: 10.1212/WNL.41.3.351
– year: 2015
  ident: 10.1016/j.neuroimage.2016.02.036_bb0305
  article-title: Sample-size calculations for short-term proof-of-concept studies of tissue protection and repair in multiple sclerosis lesions via conventional clinical imaging
  publication-title: Mult. Scler. J.
  doi: 10.1177/1352458515569098
– volume: 30
  start-page: 1724
  issue: 9
  year: 2007
  ident: 10.1016/j.neuroimage.2016.02.036_bb0200
  article-title: Capturing heterogeneity in gene expression studies by surrogate variable analysis
  publication-title: PLoS Genet.
– volume: 150
  issue: 2
  year: 2011
  ident: 10.1016/j.neuroimage.2016.02.036_bb0325
  article-title: Evaluating intensity normalization on mris of human brain with multiple sclerosis
  publication-title: Med. Image Anal.
  doi: 10.1016/j.media.2010.12.003
– volume: 120
  start-page: 437
  issue: 4
  year: 2006
  ident: 10.1016/j.neuroimage.2016.02.036_bb0040
  article-title: Cognitive impairment is associated with subcortical magnetic resonance imaging grey matter t2 hypointensity in multiple sclerosis
  publication-title: Mult. Scler.
  doi: 10.1191/135248506ms1301oa
– volume: 430
  start-page: 44
  issue: 1
  year: 2008
  ident: 10.1016/j.neuroimage.2016.02.036_bb0080
  article-title: Combining multivariate voxel selection and support vector machines for mapping and classification of fmri spatial patterns
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2008.06.037
– volume: 270
  start-page: 934
  issue: 4
  year: 2005
  ident: 10.1016/j.neuroimage.2016.02.036_bb0060
  article-title: Using voxel-based morphometry to map the structural changes associated with rapid conversion in MCI: a longitudinal mri study
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2005.05.015
– volume: 50
  issue: 10
  year: 2006
  ident: 10.1016/j.neuroimage.2016.02.036_bb0310
  article-title: Tracking atrophy progression in familial alzheimer's disease: a serial mri study
  publication-title: Lancet Neurol.
  doi: 10.1016/S1474-4422(06)70550-6
– volume: 2
  start-page: 402
  year: 2013
  ident: 10.1016/j.neuroimage.2016.02.036_bb0355
  article-title: Oasis is automated statistical inference for segmentation, with applications to multiple sclerosis lesion segmentation in mri
  publication-title: Neuroimage Clin.
  doi: 10.1016/j.nicl.2013.03.002
– volume: 10
  start-page: 1
  year: 2016
  ident: 10.1016/j.neuroimage.2016.02.036_bb0360
  article-title: Relating multi-sequence longitudinal intensity profiles and clinical covariates in incident multiple sclerosis lesions
  publication-title: NeuroImage Clin.
  doi: 10.1016/j.nicl.2015.10.013
– year: 2014
  ident: 10.1016/j.neuroimage.2016.02.036_bb0375
  article-title: Characterizing Growth Patterns in Longitudinal Mri Using Image Contrast
– ident: 10.1016/j.neuroimage.2016.02.036_bb0330
– volume: 570
  issue: 9
  year: 2001
  ident: 10.1016/j.neuroimage.2016.02.036_bb0045
  article-title: Beyond the hippocampus: Mri volumetry confirms widespread limbic atrophy in ad
  publication-title: Neurology
  doi: 10.1212/WNL.57.9.1669
– volume: 1130
  issue: 1
  year: 2006
  ident: 10.1016/j.neuroimage.2016.02.036_bb0145
  article-title: Amygdalar volume and psychiatric symptoms in alzheimer's disease: an mri analysis
  publication-title: Acta Neurol. Scand.
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Snippet Magnetic resonance imaging (MRI) intensities are acquired in arbitrary units, making scans non-comparable across sites and between subjects. Intensity...
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SubjectTerms ADNI
Algorithms
Alzheimer Disease - diagnostic imaging
Alzheimer Disease - pathology
Alzheimer's disease
Biomarkers
Brain - anatomy & histology
Brain - pathology
Datasets
Female
Gene expression
Humans
Image Processing, Computer-Assisted
Linear Models
Magnetic Resonance Imaging - methods
Male
Medical imaging
Methods
MRI
Neuroimaging - methods
NMR
Normalization
Nuclear magnetic resonance
Reproducibility of Results
ROC Curve
Scan effect
Scanners
Signal Processing, Computer-Assisted
Studies
Title Removing inter-subject technical variability in magnetic resonance imaging studies
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https://dx.doi.org/10.1016/j.neuroimage.2016.02.036
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Volume 132
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