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CSF SUPPRESSION METHODS FOR DTI 19

Biomarkers of neurological status in HIV infection: 81. Steel, R. M.; Bastin, M. E.; McConnell, S.; Marshall, I.;
a 3‐year study. Proteomics Clin. Appl. 4(3):295-303; Cunningham-Owens, D. G.; Lawrie, S. M.; Johnstone,
2010. E. C.; Best, J. J. Diffusion tensor imaging (DTI) and
72. Rashid, W.; Hadjiprocopis, A.; Griffin, C. M.; Chard, D. proton magnetic resonance spectroscopy (1 H MRS) in
T.; Davies, G. R.; Barker, G. J.; Tofts, P. S.; Thompson, schizophrenic subjects and normal controls. Psychiat.
A. J.; Miller, D. H. Diffusion tensor imaging of early Research: Neuroimag. 106(3):161-70; 2001.
relapsing-remitting multiple sclerosis with histogram 82. Teipel, S. J.; Meindl, T.; Wagner, M.; Stieltjes, B.;
analysis using automated segmentation and brain Reuter, S.; Hauenstein, K. H.; Filippi, M.; Ernemann,
volume correction. Mult. Scler. 10(1):9-15; 2004. U.; Reiser, M. F.; Hampel, H. Longitudinal changes in
73. Rick-Jackson, A.; Stoffers, D.; Sheldon, S.; Kuperman, fiber tract integrity in healthy aging and mild cognitive
J.; Dale, A.; Goldstein, J.; Corey-Bloom, J.; Poldrack, R. impairment: a DTI follow-up study. J. Alzheimers Dis.
A.; Aron, A. R. Evaluating imaging biomarkers for neu- 22(2):507-522; 2009.
rodegeneration in pre-symptomatic Huntington’s dis- 83. Teipel, S. J.; Reuter, S.; Stieltjes, B.; Acosta-Cabronero,
ease using machine learning techniques. Neuroimage J.; Ernemann, U.; Fellgiebel, A.; Filippi, M.; Frisoni, G.;
56(2):788-96; 2011. Hentschel, F.; Jessen, F.; Klöppel, S. Multicenter stabil-
74. Roosendaal, S. D.; Geurts, J. J.; Vrenken, H.; Hulst, H. ity of diffusion tensor imaging measures: a European
E.; Cover, K. S.; Castelijns, J. A.; Pouwels, P. J.; Barkhof, clinical and physical phantom study. Psychiatry Res.
F. Regional DTI differences in multiple sclerosis pa- Neuroimaging 94(3):363-371; 2011.
tients. Neuroimage 44(4):1397-1403; 2009. 84. Turner, R.; Le Bihan, D.; Maier, J.; Vavrek, R.; Hedges,
75. Salminen, L. E.; Conturo, T. E.; Laidlaw, D. H.; Cabeen, L. K.; Pekar, J. Echo-planar imaging of intravoxel in-
R. P.; Akbudak, E.; Lane, E. M.; Heaps, J. M.; Bolzenius, coherent motion. Radiology 177(2):407-414; 1990.
J. D.; Baker, L. M.; Cooley, S.; Scott, S.; Cagle, L. M.; 85. Vapnik, V. N. The nature of statistical learning theory.
Phillips, S.; Paul, R. H. Regional age differences in gray New York, NY: Springer; 2000.
matter diffusivity among healthy older adults. Brain 86. Vaughan, J. T.; Adriany, G.; Snyder, C. J.; Tian, J.; Thiel,
Imag. Behav. 10(1):203-211; 2016. T.; Bolinger, L.; Liu, H.; DelaBarre, L.; Ugurbil, K.
76. Salminen, L. E.; Schofield, P. R.; Pierce, K. D.; Luo, X.; Efficient high‐frequency body coil for high‐field MRI.
Zhao, Y.; Laidlaw, D. H.; Cabeen, R. P.; Conturo, T. E.; Magn. Reson. Med. 52(4):851-859; 2004.
Lane, E. M.; Heaps, J. M.; Bolzenius, J. D., Baker, L. M.; 87. Vos, S. B.; Viergever, M.A.; Leemans, A. The aniso-
Cooley, S. A.; Scott, S.; Cagle, L. M.; Paul, R. H. Genetic tropic bias of fractional anisotropy in anisotropically
markers of cholesterol transport and gray matter dif- acquired DTI data. Proc. Int. Soc. Mag. Reson. Med.
fusion: a preliminary study of the CETP I405V poly- 19:1945; 2011.
morphism. J. Neural Transm. 122(11):1581-92; 2015. 88. Vos, S. B.; Jones, D. K.; Viergever, M. A.; Leemans,
77. Schrouff, J.; Cremers, J.; Garraux, G.; Baldassarre, A. Partial volume effect as a hidden covariate in DTI
L.; Mourão-Miranda, J.; Phillips, C. Localizing and analyses. Neuroimage 55(4):1566-1576; 2011.
comparing weight maps generated from linear ker- 89. Wang, H.; Nie, F.; Huang, H.; Risacher, S. L.; Saykin, A.
nel machine learning models. In: 3rd Workshop on J.; Shen, L. Identifying disease sensitive and quantita-
Pattern Recognition in Neuroimaging (PRNI 2013). tive trait-relevant biomarkers from multidimensional
Philadelphia, PA: IEEE Computer Society Conference heterogeneous imaging genetics data via sparse multi-
Publishing Services; 2013:124-127. modal multitask learning. Bioinformatics 28(12):i127-
78. Shimony, J. S.; McKinstry, R. C.; Akbudak, E.; 36; 2012.
Aronovitz, J. A.; Snyder, A. Z.; Lori, N. F.; Cull, T. S.; 90. Wedeen, V. J.; Hagmann, P.; Tseng, W. Y.; Reese, T. G.;
Conturo, T. E. Quantitative diffusion-tensor anisot- Weisskoff, R. M. Mapping complex tissue architecture
ropy brain MR imaging: normative human data and with diffusion spectrum magnetic resonance imaging.
anatomic analysis. Radiology 212(3):770-84; 1999. Magn. Reson. Med. 54(6):1377-1386; 2005.
79. Skudlarski, P.; Jagannathan, K.; Anderson, K.; Stevens, 91. Weishaupt, D.; Köchli, V. D.; Marincek, B. How
M. C.; Calhoun, V. D.; Skudlarska, B. A.; Pearlson, G. does MRI work?: an introduction to the physics and
Brain connectivity is not only lower but different in function of magnetic resonance imaging. Heidelberg,
schizophrenia: a combined anatomical and functional Germany: Springer Science & Business Media; 2008.
approach. Biol. Psychiatry 68(1):61-69; 2010. 92. Wesbey, G. E.; Moseley, M. E.; Ehman, R. L.
80. Stanisz, G. J.; Odrobina, E. E.; Pun, J.; Escaravage, M.; Translational molecular self-diffusion in magnetic
Graham, S. J.; Bronskill, M. J.; Henkelman, R. M. T1, resonance imaging: II. measurement of the self-dif-
T2 relaxation and magnetization transfer in tissue at fusion coefficient. Invest. Radiol. 19(6):491-8; 1984.
3T. Magn. Reson. Med. 54(3):507-512; 2005. 93. Yang, H.; Liu, J.; Sui, J.; Pearlson, G.; Calhoun, V. D. A