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PLoS One. 2015 Apr 28;10(4):e0122731. doi: 10.1371/journal.pone.0122731. eCollection 2014.

Gaussian Mixture Models and Model Selection for [18F] Fluorodeoxyglucose Positron Emission Tomography Classification in Alzheimer's Disease.

Collaborators (414)

Weiner MW, Aisen P, Weiner M, Aisen P, Jack CR Jr, Jagust W, Saykin AJ, Khachaturian Z, Sorensen G, Carrillo M, Kuller L, Raichle M, Paul S, Davies P, Fillit H, Hefti F, Holtzman D, Mesulam LM, Potter W, Schwartz A, Green RC, Montine T, Petersen R, Aisen P, Thomas RG, Sather T, Jiminez G, Balasubramanian AB, Mason J, Beckett L, Harvey D, Donohue M, Jack CR Jr, Bernstein M, Fox RN, Thompson P, Schuff N, DeCArli C, Borowski B, Gunter J, Senjem M, Jones D, Jagust W, Toga AW, Koeppe RA, Foster N, Reiman EM, Chen K, Mathis C, Crawford K, Cairns NJ, Shaw LM, Lee V, Korecka M, Figurski M, Neu S, Saykin AJ, Foroud TM, Potkin S, Faber K, Kim S, Weiner MW, Khachaturian Z, Thal L, Weiner MW, Snyder PJ, Potter W, Paul S, Albert M, Frank R, Khachaturian Z, Hsiao J, Kaye J, Quinn J, Silbert L, Lind B, Carter R, Dolen S, Schneider LS, Pawluczyk S, Beccera M, Cerbone B, Spann BM, Brewer J, Vanderswag H, Fleisher A, Heidebrink JL, Lord JL, Petersen R, Mason SS, Albers CS, Knopman D, Johnson K, Doody RS, Villanueva-Meyer J, Chowdhury M, Rountree S, Dang M, Stern Y, Honig LS, Bell KL, Morris JC, Carroll M, Creech ML, Franklin E, Mintun MA, Schneider S, Oliver A, Marson D, Griffith R, Clark D, Geldmacher D, Brockington J, Roberson E, Love MN, Grossman H, Mitsis E, Shah RC, deToledo-Morrell L, Duara R, Varon D, Greig MT, Roberts P, Albert M, Onyike C, D'Agostino D 2nd, Kielb S, Galvin JE, Michel CA, Pogorelec DM, Rusinek H, de Leon MJ, Glodzik L, De Santi S, Doraiswamy P, Petrella JR, Borges-Neto S, Wong TZ, Coleman E, Arnold SE, Karlawish JH, Wolk D, Clark CM, Smith CD, Jicha G, Hardy P, Sinha P, Oates E, Conrad G, Lopez OL, Oakley M, Simpson DM, Porsteinsson AP, Goldstein BS, Martin K, Makino KM, Ismail M, Mulnard RA, Mc-Adams-Ortiz C, Womack K, Mathews D, Quiceno M, Levey AI, Lah JJ, Cellar JS, Burns JM, Swerdlow RH, Brooks WM, Apostolova L, Tingus K, Woo E, Silverman DH, Lu PH, Bartzokis G, Graff-Radford NR, Parfitt F, Kendall T, Johnson H, Farlow MR, Hake AM, Matthews BR, Brosch JR, Hunt C, van Dyck CH, Carson RE, MacAvoy MG, Varma P, Chertkow H, Bergman H, Hosein C, Black S, Stefanovic B, Caldwell C, Hsiung R, Feldman H, Mudge B, Assaly M, Finger E, Pasternack S, Rachisky I, Trost D, Kertesz A, Center LR, Bernick C, Munic D, Mesulam MM, Lipowski K, Weintraub S, Bonakdarpour B, Kerwin D, Wu CK, Johnson N, Sadowsky C, Villena T, Turner RS, Johnson K, Reynolds B, Sperling RA, Johnson KA, Marshall G, Yesavage J, Taylor JL, Lane B, Rosen A, Tinklenberg J, Sabbagh MN, Belden CM, Jacobson SA, Sirrel SA, Kowall N, Killiany R, Budson AE, Norbash A, Johnson PL, Obisesan TO, Allard J, Lerner A, Ogrocki P, Tatsuoka C, Fatica P, Fletcher E, Maillard P, Olichney J, DeCarli C, Carmichael O, Kittur S, Borrie M, Bartha R, Johnson S, Asthana S, Carlsson CM, Potkin SG, Preda A, Nguyen D, Tariot P, Burke A, Trncic N, Fleisher A, Reeder S, Bates V, Capote H, Rainka M, Scharre DW, Kataki M, Adeli A, Zimmerman EA, Celmins D, Brown AD, Pearlson GD, Blank K, Anderson K, Gordineer L, Flashman LA, Seltzer M, Hynes ML, Santulli RB, Sink KM, Williamson JD, Garg P, Watkins F, Ott BR, Querfurth H, Tremont G, Salloway S, Malloy P, Correia S, Rosen HJ, Miller BL, Perry D, Mintzer J, Spicer K, Bachman D, Pasternak S, Rachinsky I, Rogers J, Kertesz A, Drost D, Hernando R, Sarrael A, Schultz SK, Ponto LL, Shim H, Smith KE, Relkin N, Chaing G, Lin M, Ravdin L, Smith A, Raj BA, Fargher K, Weiner MW, Aisen P, Weiner M, Aisen P, Green RR, Harvey D, Jack CR Jr, Jagust RW, Saykin AJ, Shaw LM, Trojanowki JQ, Neylan T, Grafman J, Green RC, Montine T, Weiner M, Petersen R, Aisen P, Thomas RG, Sather T, Morrison R, Jiminez G, Neylan T, Harvey D, Donohue M, Jack CR Jr, Bernstein M, Borowski B, Gunter J, Senjem M, Jagust W, Koeppe RA, Reiman EM, Chen K, Landau S, Crawford K, Cairns NJ, Householder E, Shaw LM, Trojanowki JQ, Lee V, Korecka M, Figurski M, Neu S, Saykin AJ, Foroud TM, Potkin S, Faber K, Kim S, Weiner MW, Schneider LS, Pawluczyk S, Brewer J, Vanderswag H, Stern Y, Honig LS, Bell KL, Fleischman D, Arfanakis K, Shah RC, Duara R, Varon D, Greig MT, Doraiswamy P, Petrella JR, James O, Porsteinsson AP, Goldstein B, Martin KS, Mulnard RA, McAdams-Ortiz C, Mintzer J, Massoglia D, Brawman-Mintzer O, Sadowsky C, Martinez W, Villena T, Jagust W, Landau S, Rosen H, Perry D, Turner RS, Behan K, Reynolds B, Sperling RA, Johnson KA, Marshall G, Sabbagh MN, Jacobson SA, Sirrel SA, Obisesan TO, Allard J, Johnson SC, Fruehling J, Harding MA, Peskind ER, Petrie EC, Li G, Yesavage JA, Taylor JL, Chao S, Relkin N, Chaing G, Ravdin L.

Author information

1
Institut für Informatik/I12, Technische Universität München, Garching bei München, Germany.
2
Klinik und Poliklinik für Psychiatrie und Psychotherapie, Technische Universität München, München, Germany.
3
Nuklearmedizinische Klinik, Technische Universität München, München, Germany.
4
Klinik und Poliklinik für Nuklearmedizin, Universität zu Köln, Köln, Germany.
5
Institut für Informatik, Johannes Gutenberg-Universität Mainz, Mainz, Germany.

Abstract

We present a method to discover discriminative brain metabolism patterns in [18F] fluorodeoxyglucose positron emission tomography (PET) scans, facilitating the clinical diagnosis of Alzheimer's disease. In the work, the term "pattern" stands for a certain brain region that characterizes a target group of patients and can be used for a classification as well as interpretation purposes. Thus, it can be understood as a so-called "region of interest (ROI)". In the literature, an ROI is often found by a given brain atlas that defines a number of brain regions, which corresponds to an anatomical approach. The present work introduces a semi-data-driven approach that is based on learning the characteristics of the given data, given some prior anatomical knowledge. A Gaussian Mixture Model (GMM) and model selection are combined to return a clustering of voxels that may serve for the definition of ROIs. Experiments on both an in-house dataset and data of the Alzheimer's Disease Neuroimaging Initiative (ADNI) suggest that the proposed approach arrives at a better diagnosis than a merely anatomical approach or conventional statistical hypothesis testing.

PMID:
25919662
PMCID:
PMC4412726
DOI:
10.1371/journal.pone.0122731
[Indexed for MEDLINE]
Free PMC Article

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