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PLoS One. 2012;7(10):e44877. doi: 10.1371/journal.pone.0044877. Epub 2012 Oct 10.

Classification of structural MRI images in Alzheimer's disease from the perspective of ill-posed problems.

Collaborators (255)

Weiner M, Aisen P, Weiner M, Aisen P, Petersen R, Jack CR Jr, Jagust W, Trojanowki JQ, Toga AW, Beckett L, Green RC, Saykin AJ, Morris J, Liu E, Green RC, Montine T, Petersen R, Aisen P, Gamst A, Thomas RG, Donohue M, Walter S, Gessert D, Sather T, Beckett L, Harvey D, Gamst A, Donohue M, Kornak J, Jack CR Jr, Dale A, Bernstein M, Felmlee J, Fox N, Thompson P, Schuff N, Alexander G, DeCarli C, Jagust W, Bandy D, Koeppe RA, Foster N, Reiman EM, Chen K, Mathis C, Morris J, Cairns NJ, Taylor-Reinwald L, Trojanowki JQ, Shaw L, Lee VM, Korecka M, Toga AW, Crawford K, Neu S, Saykin AJ, Foroud TM, Potkin S, Shen L, Kachaturian Z, Frank R, Snyder PJ, Molchan S, Kaye J, Quinn J, Lind B, Dolen S, Schneider LS, Pawluczyk S, Spann BM, Brewer J, Vanderswag H, Heidebrink JL, Lord JL, Petersen R, Johnson K, Doody RS, Villanueva-Meyer J, Chowdhury M, Stern Y, Honig LS, Bell KL, Morris JC, Ances B, Carroll M, Leon S, Mintun MA, Schneider S, Marson D, Griffith R, Clark D, Grossman H, Mitsis E, Romirowsky A, deToledo-Morrell L, Shah RC, Duara R, Varon D, Roberts P, Albert M, Onyike C, Kielb S, Rusinek H, de Leon MJ, Glodzik L, De Santi S, Doraiswamy P, Petrella JR, Coleman R, Arnold SE, Karlawish JH, Wolk D, Smith CD, Jicha G, Hardy P, Lopez OL, Oakley M, Simpson DM, Porsteinsson AP, Goldstein BS, Martin K, Makino KM, Ismail M, Brand C, Mulnard RA, Thai G, Mc-Adams-Ortiz C, Womack K, Mathews D, Quiceno M, Diaz-Arrastia R, King R, Weiner M, Martin-Cook K, DeVous M, Levey AI, Lah JJ, Cellar JS, Burns JM, Anderson HS, Swerdlow RH, Apostolova L, Lu PH, Bartzokis G, Silverman DH, Graff-Radford NR, Parfitt F, Johnson H, Farlow MR, Hake AM, Matthews BR, Herring S, van Dyck CH, Carson RE, MacAvoy MG, Chertkow H, Bergman H, Hosein C, Black S, Stefanovic B, Caldwell C, Hsiung GY, Feldman H, Mudge B, Assaly M, Kertesz A, Rogers J, Trost D, Bernick C, Munic D, Kerwin D, Mesulam MM, Lipowski K, Wu CK, Johnson N, Sadowsky C, Martinez W, Villena T, Turner RS, Johnson K, Reynolds B, Sperling RA, Johnson KA, Marshall G, Frey M, Yesavage J, Taylor JL, Lane B, Rosen A, Tinklenberg J, Sabbagh M, Belden C, Jacobson S, Kowall N, Killiany R, Budson AE, Norbash A, Johnson PL, Obisesan TO, Wolday S, Bwayo SK, Lerner A, Hudson L, Ogrocki P, Fletcher E, Carmichael O, Olichney J, DeCarli C, Kittur S, Borrie M, Lee TY, Bartha R, Johnson S, Asthana S, Carlsson CM, Potkin SG, Preda A, Nguyen D, Tariot P, Fleisher A, Reeder S, Bates V, Capote H, Rainka M, Scharre DW, Kataki M, Zimmerman EA, Celmins D, Brown AD, Pearlson GD, Blank K, Anderson K, Saykin AJ, Santulli RB, Schwartz ES, Sink KM, Williamson JD, Garg P, Watkins F, Ott BR, Querfurth H, Tremont G, Salloway S, Malloy P, Correia S, Rosen HJ, Miller BL, Mintzer J, Longmire CF, Spicer K, Finger E, Rachinsky I, Rogers J, Kertesz A, Drost D.

Author information

1
Department of Biostatistical Sciences, Wake Forest School of Medicine, North Carolina, United States of America. Casanova@wakehealth.edu

Abstract

BACKGROUND:

Machine learning neuroimaging researchers have often relied on regularization techniques when classifying MRI images. Although these were originally introduced to deal with "ill-posed" problems it is rare to find studies that evaluate the ill-posedness of MRI image classification problems. In addition, to avoid the effects of the "curse of dimensionality" very often dimension reduction is applied to the data.

METHODOLOGY:

Baseline structural MRI data from cognitively normal and Alzheimer's disease (AD) patients from the AD Neuroimaging Initiative database were used in this study. We evaluated here the ill-posedness of this classification problem across different dimensions and sample sizes and its relationship to the performance of regularized logistic regression (RLR), linear support vector machine (SVM) and linear regression classifier (LRC). In addition, these methods were compared with their principal components space counterparts.

PRINCIPAL FINDINGS:

In voxel space the prediction performance of all methods increased as sample sizes increased. They were not only relatively robust to the increase of dimension, but they often showed improvements in accuracy. We linked this behavior to improvements in conditioning of the linear kernels matrices. In general the RLR and SVM performed similarly. Surprisingly, the LRC was often very competitive when the linear kernel matrices were best conditioned. Finally, when comparing these methods in voxel and principal component spaces, we did not find large differences in prediction performance.

CONCLUSIONS AND SIGNIFICANCE:

We analyzed the problem of classifying AD MRI images from the perspective of linear ill-posed problems. We demonstrate empirically the impact of the linear kernel matrix conditioning on different classifiers' performance. This dependence is characterized across sample sizes and dimensions. In this context we also show that increased dimensionality does not necessarily degrade performance of machine learning methods. In general, this depends on the nature of the problem and the type of machine learning method.

PMID:
23071501
PMCID:
PMC3468621
DOI:
10.1371/journal.pone.0044877
[Indexed for MEDLINE]
Free PMC Article
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